Improving Simulated Soil Moisture Fields Through Assimilation of AMSR-E Soil Moisture Retrievals with an Ensemble Kalman Filter and a Mass Conservation Constraint

NASA Technical Reports Server (NTRS)

Li, Bailing; Toll, David; Zhan, Xiwu; Cosgrove, Brian

2011-01-01

Model simulated soil moisture fields are often biased due to errors in input parameters and deficiencies in model physics. Satellite derived soil moisture estimates, if retrieved appropriately, represent the spatial mean of soil moisture in a footprint area, and can be used to reduce model bias (at locations near the surface) through data assimilation techniques. While assimilating the retrievals can reduce model bias, it can also destroy the mass balance enforced by the model governing equation because water is removed from or added to the soil by the assimilation algorithm. In addition, studies have shown that assimilation of surface observations can adversely impact soil moisture estimates in the lower soil layers due to imperfect model physics, even though the bias near the surface is decreased. In this study, an ensemble Kalman filter (EnKF) with a mass conservation updating scheme was developed to assimilate the actual value of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture retrievals to improve the mean of simulated soil moisture fields by the Noah land surface model. Assimilation results using the conventional and the mass conservation updating scheme in the Little Washita watershed of Oklahoma showed that, while both updating schemes reduced the bias in the shallow root zone, the mass conservation scheme provided better estimates in the deeper profile. The mass conservation scheme also yielded physically consistent estimates of fluxes and maintained the water budget. Impacts of model physics on the assimilation results are discussed.

Satellite Estimation of Daily Land Surface Water Vapor Pressure Deficit from AMSR- E

NASA Astrophysics Data System (ADS)

Jones, L. A.; Kimball, J. S.; McDonald, K. C.; Chan, S. K.; Njoku, E. G.; Oechel, W. C.

2007-12-01

Vapor pressure deficit (VPD) is a key variable for monitoring land surface water and energy exchanges, and estimating plant water stress. Multi-frequency day/night brightness temperatures from the Advanced Microwave Scanning Radiometer on EOS Aqua (AMSR-E) were used to estimate daily minimum and average near surface (2 m) air temperatures across a North American boreal-Arctic transect. A simple method for determining daily mean VPD (Pa) from AMSR-E air temperature retrievals was developed and validated against observations across a regional network of eight study sites ranging from boreal grassland and forest to arctic tundra. The method assumes that the dew point and minimum daily air temperatures tend to equilibrate in areas with low night time temperatures and relatively moist conditions. This assumption was tested by comparing the VPD algorithm results derived from site daily temperature observations against results derived from AMSR-E retrieved temperatures alone. An error analysis was conducted to determine the amount of error introduced in VPD estimates given known levels of error in satellite retrieved temperatures. Results indicate that the assumption generally holds for the high latitude study sites except for arid locations in mid-summer. VPD estimates using the method with AMSR-E retrieved temperatures compare favorably with site observations. The method can be applied to land surface temperature retrievals from any sensor with day and night surface or near-surface thermal measurements and shows potential for inferring near-surface wetness conditions where dense vegetation may hinder surface soil moisture retrievals from low-frequency microwave sensors. This work was carried out at The University of Montana, at San Diego State University, and at the Jet Propulsion Laboratory, California Institute of Technology, under contract to the National Aeronautics and Space Administration.

Evaluating the Utility of Remotely-Sensed Soil Moisture Retrievals for Operational Agricultural Drought Monitoring

NASA Technical Reports Server (NTRS)

Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt

2010-01-01

Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.

On the potential of a multi-temporal AMSR-E data analysis for soil wetness monitoring

NASA Astrophysics Data System (ADS)

Lacava, T.; Coviello, I.; Calice, G.; Mazzeo, G.; Pergola, N.; Tramutoli, V.

2009-12-01

Soil moisture is a critical element for both global water and energy budget. The use of satellite remote sensing data for a characterizations of soil moisture fields at different spatial and temporal scales has more and more increased during last years, thanks also to the new generation of microwave sensors (both active and passive) orbiting around the Earth. Among microwave radiometers which could be used for soil moisture retrieval, the Advanced Microwave Scanning Radiometer on Earth Observing System (AMSR-E), is the one that, for its spectral characteristics, should give more reliable results. The possibility of collect information in five observational bands in the range 6.9 - 89 GHz (with dual polarization), make it currently, waiting for the next ESA Soil Moisture and Ocean Salinity Mission (SMOS - scheduled for September 2009) and the NASA Soil Moisture Active Passive Mission (SMAP - scheduled for 2013), the best radiometer for soil moisture retrieval. Unfortunately, after its launch (AMSR-E is flying aboard EOS-AQUA satellite since 2002) diffuse C-band Radio-Frequency Interferences (RFI) were discovered contaminating AMSR-E radiances over many areas in the world. For this reason, often X-band (less RFI affected) based soil moisture retrieval algorithms, instead of the original based on C-band, have been preferred. As a consequence, the sensitivity of such measurements is decreased, because of the lower penetrating capability of the X band wavelengths than C-band, as well as for their greater noisiness, due to their high sensitivity to the presence of vegetation in the sensor field of view. In order to face all these problems, in this work a general methodology for multi-temporal satellite data analysis (Robust Satellite Techniques, RST) will be used. RST approach, already successfully applied in the framework of hydro-meteorological risk mitigation, should help us in managing AMSR-E data for several purposes. In this paper, in particular, we have looked into the possible improvement, both in terms of quality and reliability, of AMSR-E C-band soil moisture retrieval which, a differential approach like RST, may produce. To reach this aim, a multi-temporal analysis of long-term historical series of AMSR-E C-band data has been performed. Preliminary results of such an analysis will be shown in this work and discussed also by a comparison with the standard AMSR-E soil moisture products, daily provided by NASA. In detail, achievements obtained investigating several flooding events happened in the past over different areas of the world will be presented.

Impact of SMOS soil moisture data assimilation on NCEP-GFS forecasts

NASA Astrophysics Data System (ADS)

Zhan, X.; Zheng, W.; Meng, J.; Dong, J.; Ek, M.

2012-04-01

Soil moisture is one of the few critical land surface state variables that have long memory to impact the exchanges of water, energy and carbon between the land surface and atmosphere. Accurate information about soil moisture status is thus required for numerical weather, seasonal climate and hydrological forecast as well as for agricultural production forecasts, water management and many other water related economic or social activities. Since the successful launch of ESA's soil moisture ocean salinity (SMOS) mission in November 2009, about 2 years of soil moisture retrievals has been collected. SMOS is believed to be the currently best satellite sensors for soil moisture remote sensing. Therefore, it becomes interesting to examine how the collected SMOS soil moisture data are compared with other satellite-sensed soil moisture retrievals (such as NASA's Advanced Microwave Scanning Radiometer -AMSR-E and EUMETSAT's Advanced Scatterometer - ASCAT)), in situ soil moisture measurements, and how these data sets impact numerical weather prediction models such as the Global Forecast System of NOAA-NCEP. This study implements the Ensemble Kalman filter in GFS to assimilate the AMSR-E, ASCAT and SMOS soil moisture observations after a quantitative assessment of their error rate based on in situ measurements from ground networks around contiguous United States. in situ soil moisture measurements from ground networks (such as USDA Soil Climate Analysis network - SCAN and NOAA's U.S. Climate Reference Network -USCRN) are used to evaluate the GFS soil moisture simulations (analysis). The benefits and uncertainties of assimilating the satellite data products in GFS are examined by comparing the GFS forecasts of surface temperature and rainfall with and without the assimilations. From these examinations, the advantages of SMOS soil moisture data products over other satellite soil moisture data sets will be evaluated. The next step toward operationally assimilating soil moisture and other land observations into GFS will also be discussed.

Analytical Retrieval of Global Land Surface Emissivity Maps at AMSR-E passive microwave frequencies

NASA Astrophysics Data System (ADS)

Norouzi, H.; Temimi, M.; Khanbilvardi, R.

2009-12-01

Land emissivity is a crucial boundary condition in Numerical Weather Prediction (NWP) modeling. Land emissivity is also a key indicator of land surface and subsurface properties. The objective of this study, supported by NOAA-NESDIS, is to develop global land emissivity maps using AMSR-E passive microwave measurements along with several ancillary data. The International Satellite Cloud Climatology Project (ISCCP) database has been used to obtain several inputs for the proposed approach such as land surface temperature, cloud mask and atmosphere profile. The Community Radiative Transfer Model (CRTM) has been used to estimate upwelling and downwelling atmospheric contributions. Although it is well known that correction of the atmospheric effect on brightness temperature is required at higher frequencies (over 19 GHz), our preliminary results have shown that a correction at 10.7 GHz is also necessary over specific areas. The proposed approach is based on three main steps. First, all necessary data have been collected and processed. Second, a global cloud free composite of AMSR-E data and corresponding ancillary images is created. Finally, monthly composting of emissivity maps has been performed. AMSR-E frequencies at 6.9, 10.7, 18.7, 36.5 and 89.0 GHz have been used to retrieve the emissivity. Water vapor information obtained from ISCCP (TOVS data) was used to calculate upwelling, downwelling temperatures and atmospheric transmission in order to assess the consistency of those derived from the CRTM model. The frequent land surface temperature (LST) determination (8 times a day) in the ISCCP database has allowed us to assess the diurnal cycle effect on emissivity retrieval. Differences in magnitude and phase between thermal temperature and low frequencies microwave brightness temperature have been noticed. These differences seem to vary in space and time. They also depend on soil texture and thermal inertia. The proposed methodology accounts for these factors and resultant differences in phase and magnitude between LST and microwave brightness temperature. Additional factors such as topography and vegetation cover are under investigation. In addition, the potential of extrapolating the obtained land emissivity maps to different window and sounding channels has been also investigated in this study. The extrapolation of obtained emissivities to different incident angles is also under investigation. Land emissivity maps have been developed at different AMSR-E frequencies. Obtained product has been validated and compared to global land use distribution. Moreover, global soil moisture AMSR-E product maps have been also used to assess to the spatial distribution of the emissivity. Moreover, obtained emissivity maps seem to be consistent with landuse/land cover maps. They also agree well with land emissivity maps obtained from the ISCCP database and developed using SSM/I observations (for frequencies over 19 GHz).

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

NASA Technical Reports Server (NTRS)

Parinussa, Robert M.; de Jeu, Richard A. M.; van Der Schalie, Robin; Crow, Wade T.; Lei, Fangni; Holmes, Thomas R. H.

2016-01-01

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E's day- and night-time observations.

Towards improving the NASA standard soil moisture retrieval algorithm and product

NASA Astrophysics Data System (ADS)

Mladenova, I. E.; Jackson, T. J.; Njoku, E. G.; Bindlish, R.; Cosh, M. H.; Chan, S.

2013-12-01

Soil moisture mapping using passive-based microwave remote sensing techniques has proven to be one of the most effective ways of acquiring reliable global soil moisture information on a routine basis. An important step in this direction was made by the launch of the Advanced Microwave Scanning Radiometer on the NASA's Earth Observing System Aqua satellite (AMSR-E). Along with the standard NASA algorithm and operational AMSR-E product, the easy access and availability of the AMSR-E data promoted the development and distribution of alternative retrieval algorithms and products. Several evaluation studies have demonstrated issues with the standard NASA AMSR-E product such as dampened temporal response and limited range of the final retrievals and noted that the available global passive-based algorithms, even though based on the same electromagnetic principles, produce different results in terms of accuracy and temporal dynamics. Our goal is to identify the theoretical causes that determine the reduced sensitivity of the NASA AMSR-E product and outline ways to improve the operational NASA algorithm, if possible. Properly identifying the underlying reasons that cause the above mentioned features of the NASA AMSR-E product and differences between the alternative algorithms requires a careful examination of the theoretical basis of each approach. Specifically, the simplifying assumptions and parametrization approaches adopted by each algorithm to reduce the dimensionality of unknowns and characterize the observing system. Statistically-based error analyses, which are useful and necessary, provide information on the relative accuracy of each product but give very little information on the theoretical causes, knowledge that is essential for algorithm improvement. Thus, we are currently examining the possibility of improving the standard NASA AMSR-E global soil moisture product by conducting a thorough theoretically-based review of and inter-comparisons between several well established global retrieval techniques. A detailed discussion focused on the theoretical basis of each approach and algorithms sensitivity to assumptions and parametrization approaches will be presented. USDA is an equal opportunity provider and employer.

A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations

NASA Astrophysics Data System (ADS)

Du, Jinyang; Kimball, John S.; Jones, Lucas A.; Kim, Youngwook; Glassy, Joseph; Watts, Jennifer D.

2017-11-01

Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002-December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (Tmx and Tmn), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003-2010) and AMSR2 (2013-2015) observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE ≤ 0.06), PWV (R ≥ 0.91; RMSE ≤ 4.94 mm), Tmx and Tmn (R ≥ 0.90; RMSE ≤ 3.48 °C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE ≤ 0.06 cm3 cm-3). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at http://files.ntsg.umt.edu/data/LPDR_v2/.<

Satellite Microwave Remote Sensing for Environmental Modeling of Mosquito Population Dynamics

PubMed Central

Chuang, Ting-Wu; Henebry, Geoffrey M.; Kimball, John S.; VanRoekel-Patton, Denise L.; Hildreth, Michael B.; Wimberly, Michael C.

2012-01-01

Environmental variability has important influences on mosquito life cycles and understanding the spatial and temporal patterns of mosquito populations is critical for mosquito control and vector-borne disease prevention. Meteorological data used for model-based predictions of mosquito abundance and life cycle dynamics are typically acquired from ground-based weather stations; however, data availability and completeness are often limited by sparse networks and resource availability. In contrast, environmental measurements from satellite remote sensing are more spatially continuous and can be retrieved automatically. This study compared environmental measurements from the NASA Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and in situ weather station data to examine their ability to predict the abundance of two important mosquito species (Aedes vexans and Culex tarsalis) in Sioux Falls, South Dakota, USA from 2005 to 2010. The AMSR-E land parameters included daily surface water inundation fraction, surface air temperature, soil moisture, and microwave vegetation opacity. The AMSR-E derived models had better fits and higher forecasting accuracy than models based on weather station data despite the relatively coarse (25-km) spatial resolution of the satellite data. In the AMSR-E models, air temperature and surface water fraction were the best predictors of Aedes vexans, whereas air temperature and vegetation opacity were the best predictors of Cx. tarsalis abundance. The models were used to extrapolate spatial, seasonal, and interannual patterns of climatic suitability for mosquitoes across eastern South Dakota. Our findings demonstrate that environmental metrics derived from satellite passive microwave radiometry are suitable for predicting mosquito population dynamics and can potentially improve the effectiveness of mosquito-borne disease early warning systems. PMID:23049143

Utilization of downscaled microwave satellite data and GRACE Total Water Storage anomalies for improving streamflow prediction in the Lower Mekong Basin

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Gupta, M.; Bolten, J. D.

2016-12-01

The Mekong river is the world's eighth largest in discharge with draining an area of 795,000 km² from the Eastern watershed of the Tibetan Plateau to the Mekong Delta including, Myanmar, Laos PDR, Thailand, Cambodia, Vietnam and three provinces of China. The populations in these countries are highly dependent on the Mekong River and they are vulnerable to the availability and quality of the water resources within the Mekong River Basin. Soil moisture is one of the most important hydrological cycle variables and is available from passive microwave satellite sensors (such as AMSR-E, SMOS and SMAP), but their spatial resolution is frequently too coarse for effective use by land managers and decision makers. The merging of satellite observations with numerical models has led to improved land surface predictions. Although performance of the models have been continuously improving, the laboratory methods for determining key hydraulic parameters are time consuming and expensive. The present study assesses a method to determine the effective soil hydraulic parameters using a downscaled microwave remote sensing soil moisture product based on the NASA Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture downscaling algorithm is based on a regression relationship between 1-km MODIS land surface temperature and 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) to produce an enhanced spatial resolution ASMR-E-based soil moisture product. Since the optimized parameters are based on the near surface soil moisture information, further constraints are applied during the numerical simulation through the assimilation of GRACE Total Water Storage (TWS) within the land surface model. This work improves the hydrological fluxes and the state variables are optimized and the optimal parameter values are then transferred for retrieving hydrological fluxes. To evaluate the performance of the system in helping improve simulation accuracy and whether they can be used to obtain soil moisture profiles at poorly gauged catchments the root mean square error (RMSE) and Mean Bias error (MBE) are used to measure the performance of the simulations.

Satellite Soil Moisture and Water Storage Observations Identify Early and Late Season Water Supply Influencing Plant Growth in the Missouri Watershed

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Colliander, A.; Njoku, E. G.

2017-12-01

We employ an array of continuously overlapping global satellite sensor observations including combined surface soil moisture (SM) estimates from SMAP, AMSR-E and AMSR-2, GRACE terrestrial water storage (TWS), and satellite precipitation measurements, to characterize seasonal timing and inter-annual variations of the regional water supply pattern and its associated influence on vegetation growth estimates from MODIS enhanced vegetation index (EVI), AMSR-E/2 vegetation optical depth (VOD) and GOME-2 solar-induced florescence (SIF). Satellite SM is used as a proxy of plant-available water supply sensitive to relatively rapid changes in surface condition, GRACE TWS measures seasonal and inter-annual variations in regional water storage, while precipitation measurements represent the direct water input to the analyzed ecosystem. In the Missouri watershed, we find surface SM variations are the dominant factor controlling vegetation growth following the peak of the growing season. Water supply to growth responds to both direct precipitation inputs and groundwater storage carry-over from prior seasons (winter and spring), depending on land cover distribution and regional climatic condition. For the natural grassland in the more arid central and northwest watershed areas, an early season anomaly in precipitation or surface temperature can have a lagged impact on summer vegetation growth by affecting the surface SM and the underlying TWS supplies. For the croplands in the more humid eastern portions of the watershed, the correspondence between surface SM and plant growth weakens. The combination of these complementary remote-sensing observations provides an effective means for evaluating regional variations in the timing and availability of water supply influencing vegetation growth.

Muiti-Sensor Historical Climatology of Satellite-Derived Global Land Surface Moisture

NASA Technical Reports Server (NTRS)

Owe, Manfred; deJeu, Richard; Holmes, Thomas

2007-01-01

A historical climatology of continuous satellite derived global land surface soil moisture is being developed. The data set consists of surface soil moisture retrievals from observations of both historical and currently active satellite microwave sensors, including Nimbus-7 SMMR, DMSP SSM/I, TRMM TMI, and AQUA AMSR-E. The data sets span the period from November 1978 through the end of 2006. The soil moisture retrievals are made with the Land Parameter Retrieval Model, a physically-based model which was developed jointly by researchers from the above institutions. These data are significant in that they are the longest continuous data record of observational surface soil moisture at a global scale. Furthermore, while previous reports have intimated that higher frequency sensors such as on SSM/I are unable to provide meaningful information on soil moisture, our results indicate that these sensors do provide highly useful soil moisture data over significant parts of the globe, and especially in critical areas located within the Earth's many arid and semi-arid regions.

Simultaneous Assimilation of AMSR-E Brightness Temperature and MODIS LST to Improve Soil Moisture with Dual Ensemble Kalman Smoother

NASA Astrophysics Data System (ADS)

Huang, Chunlin; Chen, Weijin; Wang, Weizhen; Gu, Juan

2017-04-01

Uncertainties in model parameters can easily cause systematic differences between model states and observations from ground or satellites, which significantly affect the accuracy of soil moisture estimation in data assimilation systems. In this paper, a novel soil moisture assimilation scheme is developed to simultaneously assimilate AMSR-E brightness temperature (TB) and MODIS Land Surface Temperature (LST), which can correct model bias by simultaneously updating model states and parameters with dual ensemble Kalman filter (DEnKS). The Common Land Model (CoLM) and a Q-h Radiative Transfer Model (RTM) are adopted as model operator and observation operator, respectively. The assimilation experiment is conducted in Naqu, Tibet Plateau, from May 31 to September 27, 2011. Compared with in-situ measurements, the accuracy of soil moisture estimation is tremendously improved in terms of a variety of scales. The updated soil temperature by assimilating MODIS LST as input of RTM can reduce the differences between the simulated and observed brightness temperatures to a certain degree, which helps to improve the estimation of soil moisture and model parameters. The updated parameters show large discrepancy with the default ones and the former effectively reduces the states bias of CoLM. Results demonstrate the potential of assimilating both microwave TB and MODIS LST to improve the estimation of soil moisture and related parameters. Furthermore, this study also indicates that the developed scheme is an effective soil moisture downscaling approach for coarse-scale microwave TB.

Statistical Analysis of the Correlation between Microwave Emission Anomalies and Seismic Activity Based on AMSR-E Satellite Data

NASA Astrophysics Data System (ADS)

qin, kai; Wu, Lixin; De Santis, Angelo; Zhang, Bin

2016-04-01

Pre-seismic thermal IR anomalies and ionosphere disturbances have been widely reported by using the Earth observation system (EOS). To investigate the possible physical mechanisms, a series of detecting experiments on rock loaded to fracturing were conducted. Some experiments studies have demonstrated that microwave radiation energy will increase under the loaded rock in specific frequency and the feature of radiation property can reflect the deformation process of rock fracture. This experimental result indicates the possibility that microwaves are emitted before earthquakes. Such microwaves signals are recently found to be detectable before some earthquake cases from the brightness temperature data obtained by the microwave-radiometer Advanced Microwave-Scanning Radiometer for the EOS (AMSR-E) aboard the satellite Aqua. This suggested that AMSR-E with vertical- and horizontal-polarization capability for six frequency bands (6.925, 10.65, 18.7, 23.8, 36.5, and 89.0 GHz) would be feasible to detect an earthquake which is associated with rock crash or plate slip. However, the statistical analysis of the correlation between satellite-observed microwave emission anomalies and seismic activity are firstly required. Here, we focus on the Kamchatka peninsula to carry out a statistical study, considering its high seismicity activity and the dense orbits covering of AMSR-E in high latitudes. 8-years (2003-2010) AMSR-E microwave brightness temperature data were used to reveal the spatio-temporal association between microwave emission anomalies and 17 earthquake events (M>5). Firstly, obvious spatial difference of microwave brightness temperatures between the seismic zone at the eastern side and the non-seismic zone the western side within the Kamchatka peninsula are found. Secondly, using both vertical- and horizontal-polarization to extract the temporal association, it is found that abnormal changes of microwave brightness temperatures appear generally 2 months before the M>6 earthquakes. Since the microwave emissions observed by AMSR-E are affected by various factors (e.g., emission of the earth's surface and emission, absorption and scattering of the atmosphere), further study together with the surface temperature, soil moisture and atmospheric water vapor will remove the weather and climate influences.

DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia

NASA Astrophysics Data System (ADS)

Malbéteau, Yoann; Merlin, Olivier; Molero, Beatriz; Rüdiger, Christoph; Bacon, Stephan

2016-03-01

Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10 km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40-50 km resolution SMOS and AMSR-E data are disaggregated at 1 km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010-2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and AMSR-E in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and AMSR-E in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability.

Using high-resolution soil moisture modelling to assess the uncertainty of microwave remotely sensed soil moisture products at the correct spatial and temporal support

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; Bierkens, M. F. P.; Van Dam, J. C.; De Jong, S. M.

2012-04-01

Soil moisture is a key variable in the hydrological cycle and important in hydrological modelling. When assimilating soil moisture into flood forecasting models, the improvement of forecasting skills depends on the ability to accurately estimate the spatial and temporal patterns of soil moisture content throughout the river basin. Space-borne remote sensing may provide this information with a high temporal and spatial resolution and with a global coverage. Currently three microwave soil moisture products are available: AMSR-E, ASCAT and SMOS. The quality of these satellite-based products is often assessed by comparing them with in-situ observations of soil moisture. This comparison is however hampered by the difference in spatial and temporal support (i.e., resolution, scale), because the spatial resolution of microwave satellites is rather low compared to in-situ field measurements. Thus, the aim of this study is to derive a method to assess the uncertainty of microwave satellite soil moisture products at the correct spatial support. To overcome the difference in support size between in-situ soil moisture observations and remote sensed soil moisture, we used a stochastic, distributed unsaturated zone model (SWAP, van Dam (2000)) that is upscaled to the support of different satellite products. A detailed assessment of the SWAP model uncertainty is included to ensure that the uncertainty in satellite soil moisture is not overestimated due to an underestimation of the model uncertainty. We simulated unsaturated water flow up to a depth of 1.5m with a vertical resolution of 1 to 10 cm and on a horizontal grid of 1 km2 for the period Jan 2010 - Jun 2011. The SWAP model was first calibrated and validated on in-situ data of the REMEDHUS soil moisture network (Spain). Next, to evaluate the satellite products, the model was run for areas in the proximity of 79 meteorological stations in Spain, where model results were aggregated to the correct support of the satellite product by averaging model results from the 1 km2 grid within the remote sensing footprint. Overall 440 (AMSR-E, SMOS) to 680 (ASCAT) timeseries were compared to the aggregated SWAP model results, providing valuable information on the uncertainty of satellite soil moisture at the proper support. Our results show that temporal dynamics are best captured by ASCAT resulting in an average correlation of 0.72 with the model, while ASMR-E (0.41) and SMOS (0.42) are less capable of representing these dynamics. Standard deviations found for ASCAT and SMOS are low, 0.049 and 0.051m3m-3 respectively, while AMSR-E has a higher value of 0.062m3m-3. All standard deviations are higher than the average model uncertainty of 0.017m3m-3. All satellite products show a negative bias compared to the model results, with the largest value for SMOS. Satellite uncertainty is not found to be significantly related to topography, but is found to increase in densely vegetated areas. In general AMSR-E has most difficulties capturing soil moisture dynamics in Spain, while SMOS and mainly ASCAT have a fair to good performance. However, all products contain valuable information about the near-surface soil moisture over Spain. Van Dam, J.C., 2000, Field scale water flow and solute transport. SWAP model concepts, parameter estimation and case studies. Ph.D. thesis, Wageningen University

NASA Giovanni: A Tool for Visualizing, Analyzing, and Inter-Comparing Soil Moisture Data

NASA Technical Reports Server (NTRS)

Teng, William; Rui, Hualan; Vollmer, Bruce; deJeu, Richard; Fang, Fan; Lei, Guang-Dih

2012-01-01

There are many existing satellite soil moisture algorithms and their derived data products, but there is no simple way for a user to inter-compare the products or analyze them together with other related data (e.g., precipitation). An environment that facilitates such inter-comparison and analysis would be useful for validation of satellite soil moisture retrievals against in situ data and for determining the relationships between different soil moisture products. The latter relationships are particularly important for applications users, for whom the continuity of soil moisture data, from whatever source, is critical. A recent example was provided by the sudden demise of EOS Aqua AMSR-E and the end of its soil moisture data production, as well as the end of other soil moisture products that had used the AMSR-E brightness temperature data. The purpose of the current effort is to create an environment, as part of the NASA Giovanni family of portals, that facilitates inter-comparisons of soil moisture algorithms and their derived data products.

Evaluation of AMSR2 soil moisture products over the contiguous United States using in situ data from the International Soil Moisture Network

NASA Astrophysics Data System (ADS)

Wu, Qiusheng; Liu, Hongxing; Wang, Lei; Deng, Chengbin

2016-03-01

High quality soil moisture datasets are required for various environmental applications. The launch of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1-Water (GCOM-W1) in May 2012 has provided global near-surface soil moisture data, with an average revisit frequency of two days. Since AMSR2 is a new passive microwave system in operation, it is very important to evaluate the quality of AMSR2 products before widespread utilization of the data for scientific research. In this paper, we provide a comprehensive evaluation of the AMSR2 soil moisture products retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm. The evaluation was performed for a three-year period (July 2012-June 2015) over the contiguous United States. The AMSR2 soil moisture products were evaluated by comparing ascending and descending overpass products to each other as well as comparing them to in situ soil moisture observations of 598 monitoring stations obtained from the International Soil Moisture Network (ISMN). The accuracy of AMSR2 soil moisture product was evaluated against several types of monitoring networks, and for different land cover types and ecoregions. Three performance metrics, including mean difference (MD), root mean squared difference (RMSD), and correlation coefficient (R), were used in our accuracy assessment. Our evaluation results revealed that AMSR2 soil moisture retrievals are generally lower than in situ measurements. The AMSR2 soil moisture retrievals showed the best agreement with in situ measurements over the Great Plains and the worst agreement over forested areas. This study offers insights into the suitability and reliability of AMSR2 soil moisture products for different ecoregions. Although AMSR2 soil moisture retrievals represent useful and effective measurements for some regions, further studies are required to improve the data accuracy.

Four Decades of Microwave Satellite Soil Moisture Observations: Product validation and inter-satellite comparisons

NASA Astrophysics Data System (ADS)

Lanka, K.; Pan, M.; Wanders, N.; Kumar, D. N.; Wood, E. F.

2017-12-01

The satellite based passive and active microwave sensors enhanced our ability to retrieve soil moisture at global scales. It has been almost four decades since the first passive microwave satellite sensor was launched in 1978. Since then soil moisture has gained considerable attention in hydro-meteorological, climate, and agricultural research resulting in the deployment of two dedicated missions in the last decade, SMOS and SMAP. Signifying the four decades of microwave remote sensing of soil moisture, this work aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy of retrieving soil moisture. We considered daily coverage, temporal performance, and spatial performance to assess the accuracy of products corresponding to eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged soil moisture product (ESA-CCI combined product), using 1058 ISMN in-situ stations and the VIC LSM soil moisture simulations (VICSM) over the CONUS. Our analysis indicated that the daily coverage has increased from 30 % during 1980s to 85 % (during non-winter months) with the launch of dedicated soil moisture missions SMOS and SMAP. The temporal validation of passive and active soil moisture products with the ISMN data place the range of median RMSE as 0.06-0.10 m3/m3 and median correlation as 0.20-0.68. When TMI, AMSR-E and WindSAT are evaluated, the AMSR-E sensor is found to have produced the brightness temperatures with better quality, given that these sensors are paired with same retrieval algorithm (LPRM). The ASCAT product shows a significant improvement during the temporal validation of retrievals compared to its predecessor ERS, thanks to enhanced sensor configuration. The SMAP mission, through its improved sensor design and RFI handling, shows a high retrieval accuracy under all-topography conditions. Although the retrievals from the SMOS mission are affected by issues such as RFI, the accuracy is still comparable to or better than that of AMSR-E and ASCAT sensors. All soil moisture products have indicated better agreement with the ISMN data than the VICSM, which indicate that they produce soil moisture with better accuracy than the VICSM over the CONUS.

Satellite microwave detection of contrasting changes in surface inundation across pan-Arctic permafrost zones

NASA Astrophysics Data System (ADS)

Watts, J.; Kimball, J. S.; Jones, L. A.; Schroeder, R.; McDonald, K. C.

2012-12-01

Surface water inundation in the Arctic is concomitant with soil permafrost and strongly influences land-atmosphere water, energy and carbon (CO2, CH4) exchange, and plant community structure. We examine recent (2003-2010) surface water inundation patterns across the pan-Arctic (≥ 50 deg.N) and within major permafrost zones using satellite passive microwave remote sensing retrievals of fractional open water extent (Fw) derived from Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 18.7 and 23.8 GHz brightness temperatures. The AMSR-E Fw retrievals are insensitive to atmosphere contamination and solar illumination effects, enabling daily Fw monitoring across the Arctic. The Fw retrievals are sensitive to sub-grid scale open water inundation area, including lakes and wetlands, within the relatively coarse (~25-km resolution) satellite footprint. A forward model error sensitivity analysis indicates that total Fw retrieval uncertainty is within ±4.1% (RMSE), and AMSR-E Fw compares favorably (0.71 < R2 < 0.84) with alternative static open water maps derived from finer scale (30-m to 250-m resolution) Landsat, MODIS and SRTM radar-based products. The Fw retrievals also show dynamic seasonal and annual variability in surface inundation that corresponds (0.71 < R < 0.87) with regional wet/dry cycles inferred from basin discharge records, including Yukon, Mackenzie, Ob, Yenisei, and Lena basins. A regional change analysis of the 8-yr AMSR-E record shows no significant trend in pan-Arctic wide Fw, and instead reveals contrasting inundation changes within permafrost zones. Widespread Fw wetting is observed within continuous (92% of grid cells with significant trend show wetting; p < 0.1) and discontinuous (82%) permafrost zones, while areas with sporadic/isolated permafrost show widespread (71%) Fw drying. These results are consistent with previous studies showing evidence of changes in regional surface hydrology influenced by permafrost degradation under recent climate warming. Changes in Fw may also be linked to shifts in regional precipitation patterns and a lengthening non-frozen season. Regional changes observed in the AMSR-E Fw record compliment finer-scale permafrost monitoring efforts and documented variability in surface inundation extent may help constrain pan-Arctic lake and wetland CO2, CH4 emission estimates. This work was supported under the Jet Propulsion Laboratory, California Institute of Technology under contract to the National Aeronautics and Space Administration, NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) programs.

AMSR2 soil moisture product validation

USDA-ARS?s Scientific Manuscript database

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W) mission. AMSR2 fills the void left by the loss of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) after almost 10 years. Both missions provide brightness te...

A comparison of Argo nominal surface and near-surface temperature for validation of AMSR-E SST

NASA Astrophysics Data System (ADS)

Liu, Zenghong; Chen, Xingrong; Sun, Chaohui; Wu, Xiaofen; Lu, Shaolei

2017-05-01

Satellite SST (sea surface temperature) from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) is compared with in situ temperature observations from Argo profiling floats over the global oceans to evaluate the advantages of Argo NST (near-surface temperature: water temperature less than 1 m from the surface). By comparing Argo nominal surface temperature ( 5 m) with its NST, a diurnal cycle caused by daytime warming and nighttime cooling was found, along with a maximum warming of 0.08±0.36°C during 14:00-15:00 local time. Further comparisons between Argo 5-m temperature/Argo NST and AMSR-E SST retrievals related to wind speed, columnar water vapor, and columnar cloud water indicate warming biases at low wind speed (<5 m/s) and columnar water vapor >28 mm during daytime. The warming tendency is more remarkable for AMSR-E SST/Argo 5-m temperature compared with AMSR-E SST/Argo NST, owing to the effect of diurnal warming. This effect of diurnal warming events should be excluded before validation for microwave SST retrievals. Both AMSR-E nighttime SST/Argo 5-m temperature and nighttime SST/Argo NST show generally good agreement, independent of wind speed and columnar water vapor. From our analysis, Argo NST data demonstrated their advantages for validation of satellite-retrieved SST.

AN ACTIVE-PASSIVE COMBINED ALGORITHM FOR HIGH SPATIAL RESOLUTION RETRIEVAL OF SOIL MOISTURE FROM SATELLITE SENSORS (Invited)

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Mladenova, I. E.; Narayan, U.

2009-12-01

Soil moisture is known to be an essential factor in controlling the partitioning of rainfall into surface runoff and infiltration and solar energy into latent and sensible heat fluxes. Remote sensing has long proven its capability to obtain soil moisture in near real-time. However, at the present time we have the Advanced Scanning Microwave Radiometer (AMSR-E) on board NASA’s AQUA platform is the only satellite sensor that supplies a soil moisture product. AMSR-E coarse spatial resolution (~ 50 km at 6.9 GHz) strongly limits its applicability for small scale studies. A very promising technique for spatial disaggregation by combining radar and radiometer observations has been demonstrated by the authors using a methodology is based on the assumption that any change in measured brightness temperature and backscatter from one to the next time step is due primarily to change in soil wetness. The approach uses radiometric estimates of soil moisture at a lower resolution to compute the sensitivity of radar to soil moisture at the lower resolution. This estimate of sensitivity is then disaggregated using vegetation water content, vegetation type and soil texture information, which are the variables on which determine the radar sensitivity to soil moisture and are generally available at a scale of radar observation. This change detection algorithm is applied to several locations. We have used aircraft observed active and passive data over Walnut Creek watershed in Central Iowa in 2002; the Little Washita Watershed in Oklahoma in 2003 and the Murrumbidgee Catchment in southeastern Australia for 2006. All of these locations have different soils and land cover conditions which leads to a rigorous test of the disaggregation algorithm. Furthermore, we compare the derived high spatial resolution soil moisture to in-situ sampling and ground observation networks

Estimating Root Mean Square Errors in Remotely Sensed Soil Moisture over Continental Scale Domains

NASA Technical Reports Server (NTRS)

Draper, Clara S.; Reichle, Rolf; de Jeu, Richard; Naeimi, Vahid; Parinussa, Robert; Wagner, Wolfgang

2013-01-01

Root Mean Square Errors (RMSE) in the soil moisture anomaly time series obtained from the Advanced Scatterometer (ASCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E; using the Land Parameter Retrieval Model) are estimated over a continental scale domain centered on North America, using two methods: triple colocation (RMSETC ) and error propagation through the soil moisture retrieval models (RMSEEP ). In the absence of an established consensus for the climatology of soil moisture over large domains, presenting a RMSE in soil moisture units requires that it be specified relative to a selected reference data set. To avoid the complications that arise from the use of a reference, the RMSE is presented as a fraction of the time series standard deviation (fRMSE). For both sensors, the fRMSETC and fRMSEEP show similar spatial patterns of relatively highlow errors, and the mean fRMSE for each land cover class is consistent with expectations. Triple colocation is also shown to be surprisingly robust to representativity differences between the soil moisture data sets used, and it is believed to accurately estimate the fRMSE in the remotely sensed soil moisture anomaly time series. Comparing the ASCAT and AMSR-E fRMSETC shows that both data sets have very similar accuracy across a range of land cover classes, although the AMSR-E accuracy is more directly related to vegetation cover. In general, both data sets have good skill up to moderate vegetation conditions.

GCOM-W AMSR2 soil moisture product validation using core validation sites

USDA-ARS?s Scientific Manuscript database

The Advanced Microwave Scanning Radiometer 2 (AMSR2) is part of the Global Change Observation Mission-Water (GCOM-W). AMSR2 has filled the gap in passive microwave observations left by the loss of the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) after almost 10 years of obs...

Downscaled soil moisture from SMAP evaluated using high density observations

USDA-ARS?s Scientific Manuscript database

Recently, a soil moisture downscaling algorithm based on a regression relationship between daily temperature changes and daily average soil moisture was developed to produce an enhanced spatial resolution on soil moisture product for the Advanced Microwave Scanning Radiometer–EOS (AMSR-E) satellite ...

SMAP Soil Moisture Disaggregation using Land Surface Temperature and Vegetation Data

NASA Astrophysics Data System (ADS)

Fang, B.; Lakshmi, V.

2016-12-01

Soil moisture (SM) is a key parameter in agriculture, hydrology and ecology studies. The global SM retrievals have been providing by microwave remote sensing technology since late 1970s and many SM retrieval algorithms have been developed, calibrated and applied on satellite sensors such as AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System), AMSR-2 (Advanced Microwave Scanning Radiometer 2) and SMOS (Soil Moisture and Ocean Salinity). Particularly, SMAP (Soil Moisture Active/Passive) satellite, which was developed by NASA, was launched in January 2015. SMAP provides soil moisture products of 9 km and 36 km spatial resolutions which are not capable for research and applications of finer scale. Toward this issue, this study applied a SM disaggregation algorithm to disaggregate SMAP passive microwave soil moisture 36 km product. This algorithm was developed based on the thermal inertial relationship between daily surface temperature variation and daily average soil moisture which is modulated by vegetation condition, by using remote sensing retrievals from AVHRR (Advanced Very High Resolution Radiometer, MODIS (Moderate Resolution Imaging Spectroradiometer), SPOT (Satellite Pour l'Observation de la Terre), as well as Land Surface Model (LSM) output from NLDAS (North American Land Data Assimilation System). The disaggregation model was built at 1/8o spatial resolution on monthly basis and was implemented to calculate and disaggregate SMAP 36 km SM retrievals to 1 km resolution in Oklahoma. The SM disaggregation results were also validated using MESONET (Mesoscale Network) and MICRONET (Microscale Network) ground SM measurements.

Snowmelt and Surface Freeze/Thaw Timings over Alaska derived from Passive Microwave Observations using a Wavelet Classifier

NASA Astrophysics Data System (ADS)

Steiner, N.; McDonald, K. C.; Dinardo, S. J.; Miller, C. E.

2015-12-01

Arctic permafrost soils contain a vast amount of organic carbon that will be released into the atmosphere as carbon dioxide or methane when thawed. Surface to air greenhouse gas fluxes are largely dependent on such surface controls as the frozen/thawed state of the snow and soil. Satellite remote sensing is an important means to create continuous mapping of surface properties. Advances in the ability to determine soil and snow freeze/thaw timings from microwave frequency observations improves upon our ability to predict the response of carbon gas emission to warming through synthesis with in-situ observation, such as the 2012-2015 Carbon in Arctic Reservoir Vulnerability Experiment (CARVE). Surface freeze/thaw or snowmelt timings are often derived using a constant or spatially/temporally variable threshold applied to time-series observations. Alternately, time-series singularity classifiers aim to detect discontinuous changes, or "edges", in time-series data similar to those that occur from the large contrast in dielectric constant during the freezing or thaw of soil or snow. We use multi-scale analysis of continuous wavelet transform spectral gradient brightness temperatures from various channel combinations of passive microwave radiometers, Advanced Microwave Scanning Radiometer (AMSR-E, AMSR2) and Special Sensor Microwave Imager (SSM/I F17) gridded at a 10 km posting with resolution proportional to the observational footprint. Channel combinations presented here aim to illustrate and differentiate timings of "edges" from transitions in surface water related to various landscape components (e.g. snow-melt, soil-thaw). To support an understanding of the physical basis of observed "edges" we compare satellite measurements with simple radiative transfer microwave-emission modeling of the snow, soil and vegetation using in-situ observations from the SNOw TELemetry (SNOTEL) automated weather stations. Results of freeze/thaw and snow-melt timings and trends are reported for Alaska and the North-West Canadian Arctic for the period 2002 to 2015.

An approach to constructing a homogeneous time series of soil mositure using SMOS

USDA-ARS?s Scientific Manuscript database

Overlapping soil moisture time series derived from two satellite microwave radiometers (SMOS, Soil Moisture and Ocean Salinity; AMSR-E, Advanced Microwave Scanning Radiometer - Earth Observing System) are used to generate a soil moisture time series from 2003 to 2010. Two statistical methodologies f...

Value of Available Global Soil Moisture Products for Agricultural Monitoring

NASA Astrophysics Data System (ADS)

Mladenova, Iliana; Bolten, John; Crow, Wade; de Jeu, Richard

2016-04-01

The first operationally derived and publicly distributed global soil moil moisture product was initiated with the launch of the Advanced Scanning Microwave Mission on the NASA's Earth Observing System Aqua satellite (AMSR-E). AMSR-E failed in late 2011, but its legacy is continued by AMSR2, launched in 2012 on the JAXA Global Change Observation Mission-Water (GCOM-W) mission. AMSR is a multi-frequency dual-polarization instrument, where the lowest two frequencies (C- and X-band) were used for soil moisture retrieval. Theoretical research and small-/field-scale airborne campaigns, however, have demonstrated that soil moisture would be best monitored using L-band-based observations. This consequently led to the development and launch of the first L-band-based mission-the ESA's Soil Moisture Ocean Salinity (SMOS) mission (2009). In early 2015 NASA launched the second L-band-based mission, the Soil Moisture Active Passive (SMAP). These satellite-based soil moisture products have been demonstrated to be invaluable sources of information for mapping water stress areas, crop monitoring and yield forecasting. Thus, a number of agricultural agencies routinely utilize and rely on global soil moisture products for improving their decision making activities, determining global crop production and crop prices, identifying food restricted areas, etc. The basic premise of applying soil moisture observations for vegetation monitoring is that the change in soil moisture conditions will precede the change in vegetation status, suggesting that soil moisture can be used as an early indicator of expected crop condition change. Here this relationship was evaluated across multiple microwave frequencies by examining the lag rank cross-correlation coefficient between the soil moisture observations and the Normalized Difference Vegetation Index (NDVI). A main goal of our analysis is to evaluate and inter-compare the value of the different soil moisture products derived using L-band (SMOS) versus C-/X-band (AMSR2) observations. The soil moisture products analyzed here were derived using the Land Parameter Retrieval Model.

Evaluating the Utility of Satellite Soil Moisture Retrievals over Irrigated Areas and the Ability of Land Data Assimilation Methods to Correct for Unmodeled Processes

NASA Technical Reports Server (NTRS)

Kumar, S. V.; Peters-Lidard, C. D.; Santanello, J. A.; Reichle, R. H.; Draper, C. S.; Koster, R. D.; Nearing, G.; Jasinski, M. F.

2015-01-01

Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

Soil moisture retrieval at regional scale from AMSR2 data (Conference Presentation)

NASA Astrophysics Data System (ADS)

Paloscia, Simonetta; Santi, Emanuele; Pettinato, Simone; Brocca, Luca; Ciabatta, Luca

2016-10-01

The aim of this work is to exploit the potential of AMSR2 for hydrological applications on a regional scale and in heterogeneous environments characterised by different surface covers at subpixel resolution. The soil moisture content (SMC) estimated from Advanced Microwave Scanning Radiometer 2 (AMSR2) through the ANN-based "HydroAlgo" algorithm is firstly compared with the outputs of the Soil Water Balance hydrological model (SWBM). The comparison is performed over Italy, by considering all the available overpasses of AMSR2, since July 2012. The SMC generated by HydroAlgo is then considered as input for generating a rainfall product through the SM2RAIN algorithm. The comparison between observed and estimated rainfall in central Italy provided satisfactory results with a substantial room for improvement. In this work, the ANN "HydroAlgo" algorithm [1], which was originally developed for AMSR-E, was adapted and re-trained for AMSR2, accounting for the two C band channels provided by this new sensor. The disaggregation technique implemented in HydroAlgo [2], devoted to the improvement of ground resolution, made this algorithm particularly suitable for the application to such a heterogeneous environment. The algorithm allows obtaining a SMC product with enhanced spatial resolution (0.1°), which is more suitable for hydrological applications. The AMSR2 derived SMC is compared with simulated data obtained from the application of a well-established soil water balance model [3]. The training and test of the algorithm are carried out on a test area in central Italy, while the entire Italy is considered for the validation. The last step of the activity is the use of the HydroAlgo SMC into the SM2RAIN algorithm [4], in order to exploit the potential contribution of this product at enhanced resolution for rainfall estimation. [1] E. Santi, S. Pettinato, S. Paloscia, P. Pampaloni, G. Macelloni, and M. Brogioni (2012), "An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo", Hydrology and Earth System Sciences, 16, pp. 3659-3676, doi:10.5194/hess-16-3659-2012. [2] E. Santi (2010), "An application of SFIM technique to enhance the spatial resolution of microwave radiometers", Intern. J. Remote Sens., vol. 31, 9, pp. 2419-2428. [3] L. Brocca, S. Camici, F. Melone, T. Moramarco, J. Martinez-Fernandez, J.-F. Didon-Lescot, R. Morbidelli (2014), "Improving the representation of soil moisture by using a semi-analytical infiltration model", Hydrological Processes, 28(4), pp. 2103-2115, doi:10.1002/hyp.9766. [4] Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141, doi:10.1002/2014JD021489.

Land Surface Phenologies and Seasonalities of Croplands and Grasslands in the US Prairie Pothole Region Using Passive Microwave Data (2003-2015)

NASA Astrophysics Data System (ADS)

Alemu, W. G.; Henebry, G. M.

2017-12-01

Grasslands and wetlands in the Prairie Pothole Region (PPR) have been converted to croplands in recent years. Crops cultivated in the PPR are also changing: spring wheat and alfalfa/hay are being switched to corn and soybean due to biofuel demand. According to the USDA Cropland Data Layer (CDL) from 2003 to 2015, spring wheat significantly decreased (r2 = 0.74), while corn and soybeans significantly increased (r2 = 0.86). We characterized land surface phenologies and land surface seasonalities across the PPR using the finer temporal (twice daily) but much lower spatial (25 km) resolution Advanced Microwave Scanning Radiometer (AMSR: blended from AMSR-E and AMSR2) enhanced land surface parameters for 2003-2015 (DOY 91-330 annual cycles). We tracked the temporal development of these land surface parameters as a function of accumulated growing degree-days (AGDD) based on the AMSR retrieved air temperature data. Growing degree-days (GDD) revealed distinct seasonality typical to temperate grasslands and croplands. GDD peaks were 23°C and it peaks at 1700°C AGDD. Precipitable water vapor (V) displayed seasonality comparable to GDD. Vegetation optical depth (VOD) revealed distinct land surface phenologies for grasslands versus croplands. We explored the changing crop fractions within the 25 km AMSR pixels using the CDL. Crop-dominated sites VOD time series caught the early spring growth, ploughing, and crop growth dynamics. In contrast, the VOD time series at grass-dominated sites exhibited a lower but more extended amplitude throughout the non-frozen season. VODs peaked at 1.6 and 1.3 for croplands and grasslands, respectively. Croplands peaked about a month later than grasslands (2200 °C AGDD vs. 1600 °C AGDD). The other parameters available from the AMSR dataset—soil moisture (sm), and fractional open water (fw)—together with the AGDD time series constructed from the AMSR air temperature data revealed the passage of storm systems during the growing season. Soil moisture and fractional water were slightly higher in early spring compared to the main growing season; however, neither parameter exhibited distinct seasonality. VOD and fw time series curves displayed significant interannual variation that can enable to augment drought-monitoring efforts.

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

USDA-ARS?s Scientific Manuscript database

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote sensing methods have the spatial and temporal resolution required to...

NCA-LDAS land analysis: Development and performance of a multisensory, multivariate land data assimilation for the National Climate Assessment

NASA Astrophysics Data System (ADS)

Kumar, S.; Jasinski, M. F.; Mocko, D. M.; Rodell, M.; Borak, J.; Li, B.; Beaudoing, H. K.; Peters-Lidard, C. D.

2017-12-01

This presentation will describe one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover and irrigation intensity environmental data records (EDRs) from Scanning Multi-channel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), the Advanced Scatterometer (ASCAT), the Moderate-Resolution Imaging Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission and the Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamflow and ET. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g. SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g. SMMR, SSM/I, AMSR-E). The evaluation also indicates high skill of NCA-LDAS when compared with other land analysis products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of Western U.S. during 1979-2015, particularly in the Southwestern U.S.

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

NASA Astrophysics Data System (ADS)

Li, Nana; Jia, Li; Lu, Jing; Menenti, Massimo; Zhou, Jie

2017-01-01

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as "HM model") and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from -7 to -0.5 K in LST amplitude and from -300 to 300 J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Comparing AMSR-E soil moisture estimates to the extended record of the U.S. Climate Reference Network (USCRN)

USDA-ARS?s Scientific Manuscript database

Soil moisture plays an integral role in various aspects ranging from multi-scale hydrologic modeling to agricultural decision analysis to multi-scale hydrologic modeling, from climate change assessments to drought prediction and prevention. The broad availability of soil moisture estimates has only...

Land Data Assimilation of Satellite-Based Soil Moisture Products Using the Land Information System Over the NLDAS Domain

NASA Technical Reports Server (NTRS)

Mocko, David M.; Kumar, S. V.; Peters-Lidard, C. D.; Tian, Y.

2011-01-01

This presentation will include results from data assimilation simulations using the NASA-developed Land Information System (LIS). Using the ensemble Kalman filter in LIS, two satellite-based soil moisture products from the AMSR-E instrument were assimilated, one a NASA-based product and the other from the Land Parameter Retrieval Model (LPRM). The domain and land-surface forcing data from these simulations were from the North American Land Data Assimilation System Phase-2, over the period 2002-2008. The Noah land-surface model, version 3.2, was used during the simulations. Changes to estimates of land surface states, such as soil moisture, as well as changes to simulated runoff/streamflow will be presented. Comparisons over the NLDAS domain will also be made to two global reference evapotranspiration (ET) products, one an interpolated product based on FLUXNET tower data and the other a satellite- based algorithm from the MODIS instrument. Results of an improvement metric show that assimilating the LPRM product improved simulated ET estimates while the NASA-based soil moisture product did not.

Past, Current and Future of the Advanced Microwave Scanning Radiometer (AMSR) Series

NASA Astrophysics Data System (ADS)

Kachi, M.; Maeda, T.; Ono, N.; Tomii, N.; Kasahara, M.; Mokuno, M.; Sobue, S.

2017-12-01

Due to its penetrating capability, passive microwave remote sensing provides all-weather observation of the Earth's surface through clouds, and has bulk sensitivity to atmospheric column and some land surface layers such as snow. The first AMSR series instrument on orbit was the AMSR for EOS (AMSR-E) provided to NASA's Aqua satellite launched in May 2002. AMSR-E had 1.6-m diameter antenna and 14 channels with V and H polarizations including surface-sensitive C-band (6.9-GHz) channels those were not available in previous passive microwave imagers. Instant Field Of View (IFOV) of AMSR-E is largely improved due to antenna size. This IFOV improvement mainly contribute to C-band channel since its IFOV is larger (75x43-km) even though bigger antenna size. The latest AMSR series instrument on orbit, AMSR-2, was launched in May 2012 on board the Global Change Observation Mission - Water (GCOM-W) satellite. The GCOM-W satellite was injected to the A-train orbit to keep observation continuities to AMSR-E and seek synergies with the other A-train constellation satellites. Antenna size of AMSR-2 is 2.0-m diameter with 16 channels. Channel set is almost identical to that of AMSR-E, but new 7.3-GHz channels are added along with previous 6.9-GHz channels to mitigate influence of Radio Frequency Interferences (RFIs) in brightness temperature. IFOV of AMSR-2 is also improved from AMSR-E due to larger antenna size. AMSR-2 has completed its 5-year designed mission life in May 2017, and continues scientific observations without any serious problem. Besides the 10-month gaps between AMSR-E and AMSR2, the AMSR series provide long-term high-resolution and highly-frequent global observation of water-related parameters over 15-year. Upon the success of AMSR series, we have started discussion of possible follow-on mission with various user communities as well as expansion of application of AMSR-2 and follow-on data in new fields. Highest priority from users is gap-less, in terms of both time and quality, transition from AMSR-2 to follow-on mission to continue operational applications and keep long-term data record for climate studies. Currently, JAXA is studying possible payload capability of AMSR-2 follow-on onto the Greenhouse Gases Observation Satellite-3 (GOSAT-3) scheduled to be launched in Japanese Fiscal Year 2022.

Land, Atmosphere Near Real-time Capability for EOS (LANCE) AMSR2 Data System

NASA Astrophysics Data System (ADS)

Smith, D. K.; Harrison, S.; Lin, H.; Flynn, S.; Nair, M.; Conover, H.; Graves, S. J.

2016-12-01

The Land, Atmosphere Near real-time Capability for EOS (LANCE) system was initiated to ensure the availability of NASA satellite data products to those partners who have grown to rely upon near real-time (NRT) data for their decision support systems. The LANCE Advanced Microwave Scanning Radiometer-EOS (AMSR-E) system was able to address the needs of the NRT community in areas such as weather prediction and forecasting, monitoring of natural hazards, disaster relief, agriculture, and homeland security for nearly one year before the instrument failed in 2011. The timely launch of Global Change Observation Mission -Water 1 (GCOM-W1) and the AMSR2 instrument by the Japanese Aerospace Exploration Agency (JAXA) in 2012 was very important to continue the time series of AMSR instruments. The LANCE element for AMSR2 was able to leverage the LANCE AMSR-E system architecture, using modified AMSR-E standard product algorithms in order to make preliminary data products available to NRT users before US AMSR2 standard product algorithms were available. This presentation will describe the five AMSR2 NRT product suites available from LANCE - Sea Ice, Snow, Rain/Ocean, and Soil Moisture. We will also discuss future plans for LANCE AMSR2.

Relating Satellite Gravimetry Data To Global Snow Water Equivalent Data

NASA Astrophysics Data System (ADS)

Baumann, Sabine

2017-04-01

In 04/2002, the gravimetric satellites GRACE were launched. They measure Earth's gravity via a precise microwave system. These satellites assess changes of Earth's mass. Main contributions of these changes originate from hydrological compartments as e.g. surface water, groundwater, soil moisture, or snow water equivalent (SWE). The benefit of GRACE data is to receive a direct measured signal. The data are not calibrated with other data (as e.g. done in models) or unusable due to particular Earth's surface conditions (e.g. AMSR-e for thick and wet snow surfaces). GRACE data show changes in total water storage (TWS) but cannot distinguish between different sources. Therefore, other data, models, and methods are necessary to extract the different compartments. Due to the spatial resolution of 200,000 km2 and an accuracy of 2.5 cm w.e., mostly other global products are compared with GRACE. In this study, the hydrological model WGHM (TWS and SWE), the land surface model GLDAS (TWS and SWE), and the passive microwave sensor AMSR-E (SWE) are compared with the GRACE data. All data have to be pre-processed in the same way as the GRACE data to be comparable. A correlation analysis was performed between the different products assuming that changes in TWS can be linked to changes in SWE if either SWE is the dominant compartment of TWS or if SWE changes proportionally with TWS. To focus on the SWE product a second correlation was performed only for the winter season. Spatial extent was focused on the large permafrost areas in North America and Russia. By this method, those areas were detected in which GRACE data can be integrated for SWE data assessment to, for example, improve the models.

Disaggregation Of Passive Microwave Soil Moisture For Use In Watershed Hydrology Applications

NASA Astrophysics Data System (ADS)

Fang, Bin

In recent years the passive microwave remote sensing has been providing soil moisture products using instruments on board satellite/airborne platforms. Spatial resolution has been restricted by the diameter of antenna which is inversely proportional to resolution. As a result, typical products have a spatial resolution of tens of kilometers, which is not compatible for some hydrological research applications. For this reason, the dissertation explores three disaggregation algorithms that estimate L-band passive microwave soil moisture at the subpixel level by using high spatial resolution remote sensing products from other optical and radar instruments were proposed and implemented in this investigation. The first technique utilized a thermal inertia theory to establish a relationship between daily temperature change and average soil moisture modulated by the vegetation condition was developed by using NLDAS, AVHRR, SPOT and MODIS data were applied to disaggregate the 25 km AMSR-E soil moisture to 1 km in Oklahoma. The second algorithm was built on semi empirical physical models (NP89 and LP92) derived from numerical experiments between soil evaporation efficiency and soil moisture over the surface skin sensing depth (a few millimeters) by using simulated soil temperature derived from MODIS and NLDAS as well as AMSR-E soil moisture at 25 km to disaggregate the coarse resolution soil moisture to 1 km in Oklahoma. The third algorithm modeled the relationship between the change in co-polarized radar backscatter and the remotely sensed microwave change in soil moisture retrievals and assumed that change in soil moisture was a function of only the canopy opacity. The change detection algorithm was implemented using aircraft based the remote sensing data from PALS and UAVSAR that were collected in SMPAVEX12 in southern Manitoba, Canada. The PALS L-band h-polarization radiometer soil moisture retrievals were disaggregated by combining them with the PALS and UAVSAR L-band hh-polarization radar spatial resolutions of 1500 m and 5 m/800 m, respectively. All three algorithms were validated using ground measurements from network in situ stations or handheld hydra probes. The validation results demonstrate the practicability on coarse resolution passive microwave soil moisture products.

Clarifications on the "Comparison Between SMOS, VUA, ASCAT, and ECMWF Soil Moisture Products Over Four Watersheds in U.S."

NASA Technical Reports Server (NTRS)

Wagner, Wolfgang; Luca, Brocca; Naeimi, Vahid; Reichle, Rolf; Draper, Clara; de Jeu, Richard; Ryu, Dongryeol; Su, Chun-Hsu; Western, Andrew; Calvet, Jean-Christophe;

Calibration to improve forward model simulation of microwave emissivity at GPM frequencies over the U.S. Southern Great Plains

PubMed Central

Harrison, Kenneth W.; Tian, Yudong; Peters-Lidard, Christa D.; Ringerud, Sarah; Kumar, Sujay V.

2018-01-01

Better estimation of land surface microwave emissivity promises to improve over-land precipitation retrievals in the GPM era. Forward models of land microwave emissivity are available but have suffered from poor parameter specification and limited testing. Here, forward models are calibrated and the accompanying change in predictive power is evaluated. With inputs (e.g., soil moisture) from the Noah land surface model and applying MODIS LAI data, two microwave emissivity models are tested, the Community Radiative Transfer Model (CRTM) and Community Microwave Emission Model (CMEM). The calibration is conducted with the NASA Land Information System (LIS) parameter estimation subsystem using AMSR-E based emissivity retrievals for the calibration dataset. The extent of agreement between the modeled and retrieved estimates is evaluated using the AMSR-E retrievals for a separate 7-year validation period. Results indicate that calibration can significantly improve the agreement, simulating emissivity with an across-channel average root-mean-square-difference (RMSD) of about 0.013, or about 20% lower than if relying on daily estimates based on climatology. The results also indicate that calibration of the microwave emissivity model alone, as was done in prior studies, results in as much as 12% higher across-channel average RMSD, as compared to joint calibration of the land surface and microwave emissivity models. It remains as future work to assess the extent to which the improvements in emissivity estimation translate into improvements in precipitation retrieval accuracy. PMID:29795962

A comparison between two algorithms for the retrieval of soil moisture using AMSR-E data

USDA-ARS?s Scientific Manuscript database

A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground condit...

Impact of Surface Roughness on AMSR-E Sea Ice Products

NASA Technical Reports Server (NTRS)

Stroeve, Julienne C.; Markus, Thorsten; Maslanik, James A.; Cavalieri, Donald J.; Gasiewski, Albin J.; Heinrichs, John F.; Holmgren, Jon; Perovich, Donald K.; Sturm, Matthew

2006-01-01

This paper examines the sensitivity of Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to surface roughness by a using radiative transfer model to simulate AMSR-E Tbs as a function of incidence angle at which the surface is viewed. The simulated Tbs are then used to examine the influence that surface roughness has on two operational sea ice algorithms, namely: 1) the National Aeronautics and Space Administration Team (NT) algorithm and 2) the enhanced NT algorithm, as well as the impact of roughness on the AMSR-E snow depth algorithm. Surface snow and ice data collected during the AMSR-Ice03 field campaign held in March 2003 near Barrow, AK, were used to force the radiative transfer model, and resultant modeled Tbs are compared with airborne passive microwave observations from the Polarimetric Scanning Radiometer. Results indicate that passive microwave Tbs are very sensitive even to small variations in incidence angle, which can cause either an over or underestimation of the true amount of sea ice in the pixel area viewed. For example, this paper showed that if the sea ice areas modeled in this paper mere assumed to be completely smooth, sea ice concentrations were underestimated by nearly 14% using the NT sea ice algorithm and by 7% using the enhanced NT algorithm. A comparison of polarization ratios (PRs) at 10.7,18.7, and 37 GHz indicates that each channel responds to different degrees of surface roughness and suggests that the PR at 10.7 GHz can be useful for identifying locations of heavily ridged or rubbled ice. Using the PR at 10.7 GHz to derive an "effective" viewing angle, which is used as a proxy for surface roughness, resulted in more accurate retrievals of sea ice concentration for both algorithms. The AMSR-E snow depth algorithm was found to be extremely sensitive to instrument calibration and sensor viewing angle, and it is concluded that more work is needed to investigate the sensitivity of the gradient ratio at 37 and 18.7 GHz to these factors to improve snow depth retrievals from spaceborne passive microwave sensors.

Monitoring and Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and In-situ Observations.

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Kim, Y.; Colliander, A.; Njoku, E. G.

2015-12-01

We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE, in-situ groundwater measurements and atmospheric moisture data to delineate and characterize the evolution of drought and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply. We use these data to investigate the supply changes from water components at different depth in relation to satellite based vegetation metrics, including vegetation greenness (NDVI) measures from MODIS and related higher order productivity (GPP) before, during and following the major drought events observed in the continental US for the past 14 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, NDVI and GPP. We study how changes in atmosphere moisture stress and coupling of water storage components at different depth impact on the spatial and temporal correlation between TWS, SM and vegetation metrics. In Texas, we find that surface SM and GRACE TWS agree with each other in general, and both capture the underlying water supply constraints to vegetation growth. Triggered by a transit increase in precipitation following the 2011 hydrological drought, vegetation productivity in Texas shows more sensitivity to surface SM than TWS. In the Great Plains, the correspondence between TWS and vegetation productivity is modulated by temperature-induced atmosphere moisture stress and by the coupling between surface soil moisture and groundwater through irrigation.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

All-weather Land Surface Temperature Estimation from Satellite Data

NASA Astrophysics Data System (ADS)

Zhou, J.; Zhang, X.

2017-12-01

Satellite remote sensing, including the thermal infrared (TIR) and passive microwave (MW), provides the possibility to observe LST at large scales. For better modeling the land surface processes with high temporal resolutions, all-weather LST from satellite data is desirable. However, estimation of all-weather LST faces great challenges. On the one hand, TIR remote sensing is limited to clear-sky situations; this drawback reduces its usefulness under cloudy conditions considerably, especially in regions with frequent and/or permanent clouds. On the other hand, MW remote sensing suffers from much greater thermal sampling depth (TSD) and coarser spatial resolution than TIR; thus, MW LST is generally lower than TIR LST, especially at daytime. Two case studies addressing the challenges mentioned previously are presented here. The first study is for the development of a novel thermal sampling depth correction method (TSDC) to estimate the MW LST over barren land; this second study is for the development of a feasible method to merge the TIR and MW LSTs by addressing the coarse resolution of the latter one. In the first study, the core of the TSDC method is a new formulation of the passive microwave radiation balance equation, which allows linking bulk MW radiation to the soil temperature at a specific depth, i.e. the representative temperature: this temperature is then converted to LST through an adapted soil heat conduction equation. The TSDC method is applied to the 6.9 GHz channel in vertical polarization of AMSR-E. Evaluation shows that LST estimated by the TSDC method agrees well with the MODIS LST. Validation is based on in-situ LSTs measured at the Gobabeb site in western Namibia. The results demonstrate the high accuracy of the TSDC method: it yields a root-mean squared error (RMSE) of 2 K and ignorable systematic error over barren land. In the second study, the method consists of two core processes: (1) estimation of MW LST from MW brightness temperature and (2) three-time-scale decomposition of LST. The method is applied to two MW sensors (i.e. AMSR-E and AMSR2) and MODIS in northeast China and its surrounding area, with dominating land covers of forest and cropland. By comparing against the in-situ LST and surface air temperature, we find the merged LST has similar accuracy to the MODIS LST in version 6 and good image quality.

Improving terrestrial evaporation estimates over continental Australia through assimilation of SMOS soil moisture

NASA Astrophysics Data System (ADS)

Martens, B.; Miralles, D.; Lievens, H.; Fernández-Prieto, D.; Verhoest, N. E. C.

2016-06-01

Terrestrial evaporation is an essential variable in the climate system that links the water, energy and carbon cycles over land. Despite this crucial importance, it remains one of the most uncertain components of the hydrological cycle, mainly due to known difficulties to model the constraints imposed by land water availability on terrestrial evaporation. The main objective of this study is to assimilate satellite soil moisture observations from the Soil Moisture and Ocean Salinity (SMOS) mission into an existing evaporation model. Our over-arching goal is to find an optimal use of satellite soil moisture that can help to improve our understanding of evaporation at continental scales. To this end, the Global Land Evaporation Amsterdam Model (GLEAM) is used to simulate evaporation fields over continental Australia for the period September 2010-December 2013. SMOS soil moisture observations are assimilated using a Newtonian Nudging algorithm in a series of experiments. Model estimates of surface soil moisture and evaporation are validated against soil moisture probe and eddy-covariance measurements, respectively. Finally, an analogous experiment in which Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture is assimilated (instead of SMOS) allows to perform a relative assessment of the quality of both satellite soil moisture products. Results indicate that the modelled soil moisture from GLEAM can be improved through the assimilation of SMOS soil moisture: the average correlation coefficient between in situ measurements and the modelled soil moisture over the complete sample of stations increased from 0.68 to 0.71 and a statistical significant increase in the correlations is achieved for 17 out of the 25 individual stations. Our results also suggest a higher accuracy of the ascending SMOS data compared to the descending data, and overall higher quality of SMOS compared to AMSR-E retrievals over Australia. On the other hand, the effect of soil moisture data assimilation on the evaporation fields is very mild, and difficult to assess due to the limited availability of eddy-covariance data. Nonetheless, our continental-scale simulations indicate that the assimilation of soil moisture can have a substantial impact on the estimated dynamics of evaporation in water-limited regimes. Progressing towards our goal of using satellite soil moisture to increase understanding of global land evaporation, future research will focus on the global application of this methodology and the consideration of multiple evaporation models.

Rapid prototyping of soil moisture estimates using the NASA Land Information System

NASA Astrophysics Data System (ADS)

Anantharaj, V.; Mostovoy, G.; Li, B.; Peters-Lidard, C.; Houser, P.; Moorhead, R.; Kumar, S.

2007-12-01

The Land Information System (LIS), developed at the NASA Goddard Space Flight Center, is a functional Land Data Assimilation System (LDAS) that incorporates a suite of land models in an interoperable computational framework. LIS has been integrated into a computational Rapid Prototyping Capabilities (RPC) infrastructure. LIS consists of a core, a number of community land models, data servers, and visualization systems - integrated in a high-performance computing environment. The land surface models (LSM) in LIS incorporate surface and atmospheric parameters of temperature, snow/water, vegetation, albedo, soil conditions, topography, and radiation. Many of these parameters are available from in-situ observations, numerical model analysis, and from NASA, NOAA, and other remote sensing satellite platforms at various spatial and temporal resolutions. The computational resources, available to LIS via the RPC infrastructure, support e- Science experiments involving the global modeling of land-atmosphere studies at 1km spatial resolutions as well as regional studies at finer resolutions. The Noah Land Surface Model, available with-in the LIS is being used to rapidly prototype soil moisture estimates in order to evaluate the viability of other science applications for decision making purposes. For example, LIS has been used to further extend the utility of the USDA Soil Climate Analysis Network of in-situ soil moisture observations. In addition, LIS also supports data assimilation capabilities that are used to assimilate remotely sensed soil moisture retrievals from the AMSR-E instrument onboard the Aqua satellite. The rapid prototyping of soil moisture estimates using LIS and their applications will be illustrated during the presentation.

A New Approach in Downscaling Microwave Soil Moisture Product using Machine Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, Peyman; Yan, Hongxiang; Moradkhani, Hamid

2016-04-01

Understating the soil moisture pattern has significant impact on flood modeling, drought monitoring, and irrigation management. Although satellite retrievals can provide an unprecedented spatial and temporal resolution of soil moisture at a global-scale, their soil moisture products (with a spatial resolution of 25-50 km) are inadequate for regional study, where a resolution of 1-10 km is needed. In this study, a downscaling approach using Genetic Programming (GP), a specialized version of Genetic Algorithm (GA), is proposed to improve the spatial resolution of satellite soil moisture products. The GP approach was applied over a test watershed in United States using the coarse resolution satellite data (25 km) from Advanced Microwave Scanning Radiometer - EOS (AMSR-E) soil moisture products, the fine resolution data (1 km) from Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index, and ground based data including land surface temperature, vegetation and other potential physical variables. The results indicated the great potential of this approach to derive the fine resolution soil moisture information applicable for data assimilation and other regional studies.

Validation of satellite-based operational flood monitoring in Southern Queensland, Australia

NASA Astrophysics Data System (ADS)

Gouweleeuw, Ben; Ticehurst, Catherine; Lerat, Julien; Thew, Peter

2010-05-01

The integration of remote sensing observations with stage data and flood modeling has the potential to provide improved support to a number of disciplines, such as flood warning emergency response and operational water resources management. The ability of remote sensing technology to monitor the dynamics of hydrological events lies in its capacity to map surface water. For flood monitoring, remote sensing imagery needs to be available sufficiently frequently to capture subsequent inundation stages. MODIS optical data are available at a moderately high spatial and temporal resolution (250m-1km, twice daily), but are affected by cloud cover. AMSR-E passive microwave observations are available at comparable temporal resolution, but coarse spatial resolution (5-70km), where the smaller footprints corresponds with the higher frequency bands, which are affected by precipitating clouds. A novel operational technique to monitor flood extent combines MODIS reflectance and AMSR-E passive microwave imagery to optimize data continuity. Flood extent is subsequently combined with a DEM to obtain total flood water volume. The flood extent and volume product is operational for the lower-Balonne floodplain in Southern Queensland, Australia. For validation purposes, two moderate flood events coinciding with the MODIS and AMSR-E sensor lifetime are evaluated. The flood volume estimated from MODIS/AMSR-E images gives an accurate indication of both the timing and the magnitude of the flood peak compared to the net volume from recorded flow. In the flood recession, however, satellite-derived water volume declines rapidly, while the net flow volume remains level. This may be explained by a combination of ungauged outflows, soil infiltration, evaporation and diversion of flood water into many large open reservoirs for irrigation purposes. The open water storage extent unchanged, the water volume product is not sensitive enough to capture the change in storage water level. Additional information on the latter, e.g. via telemetered buoys, may circumvent this limitation.

Land Surface Microwave Emissivities Derived from AMSR-E and MODIS Measurements with Advanced Quality Control

NASA Technical Reports Server (NTRS)

Moncet, Jean-Luc; Liang, Pan; Galantowicz, John F.; Lipton, Alan E.; Uymin, Gennady; Prigent, Catherine; Grassotti, Christopher

2011-01-01

A microwave emissivity database has been developed with data from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and with ancillary land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same Aqua spacecraft. The primary intended application of the database is to provide surface emissivity constraints in atmospheric and surface property retrieval or assimilation. An additional application is to serve as a dynamic indicator of land surface properties relevant to climate change monitoring. The precision of the emissivity data is estimated to be significantly better than in prior databases from other sensors due to the precise collocation with high-quality MODIS LST data and due to the quality control features of our data analysis system. The accuracy of the emissivities in deserts and semi-arid regions is enhanced by applying, in those regions, a version of the emissivity retrieval algorithm that accounts for the penetration of microwave radiation through dry soil with diurnally varying vertical temperature gradients. These results suggest that this penetration effect is more widespread and more significant to interpretation of passive microwave measurements than had been previously established. Emissivity coverage in areas where persistent cloudiness interferes with the availability of MODIS LST data is achieved using a classification-based method to spread emissivity data from less-cloudy areas that have similar microwave surface properties. Evaluations and analyses of the emissivity products over homogeneous snow-free areas are presented, including application to retrieval of soil temperature profiles. Spatial inhomogeneities are the largest in the vicinity of large water bodies due to the large water/land emissivity contrast and give rise to large apparent temporal variability in the retrieved emissivities when satellite footprint locations vary over time. This issue will be dealt with in the future by including a water fraction correction. Also note that current reliance on the MODIS day-night algorithm as a source of LST limits the coverage of the database in the Polar Regions. We will consider relaxing the current restriction as part of future development.

Inversion of AMSR-E observations for land surface temperature estimation: 2. Global comparison with infrared satellite temperature

NASA Astrophysics Data System (ADS)

Ermida, S. L.; Jiménez, C.; Prigent, C.; Trigo, I. F.; DaCamara, C. C.

2017-03-01

A comparison of land surface temperature (Ts) derived from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) with infrared Ts is presented. The infrared Ts include clear-sky estimates from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Spinning Enhanced Visible and Infrared Imager, the Geostationary Operational Environmental Satellite (GOES) Imager, and the Japanese Meteorological Imager. The higher discrepancies between AMSR-E and MODIS are observed over deserts and snow-covered areas. The former seems to be associated with Ts underestimation by MODIS, whereas the latter is mostly related to uncertainties in microwave emissivity over snow/ice. Ts differences between AMSR-E and MODIS are significantly reduced after masking out snow and deserts, with a bias change from 2.6/4.6 K to 3.0/1.4 K for daytime/nighttime and a standard deviation (STD) decrease from 7.3/7.9 K to 5.1/3.9 K. When comparing with all infrared sensors, the STD of the differences between microwave and infrared Ts is generally higher than between IR retrievals. However, the biases between microwave and infrared Ts are, in some cases, of the same order as the ones observed between infrared products. This is the case for GOES, with daytime biases with respect to AMSR-E and MODIS of 0.45 K and 0.60 K, respectively. While the infrared Ts are clear-sky estimates, AMSR-E also provides Ts under cloudy conditions. For frequently cloudy regions, this results in a large increase of available Ts estimates (>250%), making the microwave Ts a very powerful complement of the infrared estimates.

Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons

NASA Astrophysics Data System (ADS)

Karthikeyan, L.; Pan, Ming; Wanders, Niko; Kumar, D. Nagesh; Wood, Eric F.

2017-11-01

Soil moisture is widely recognized as an important land surface variable that provides a deeper knowledge of land-atmosphere interactions and climate change. Space-borne passive and active microwave sensors have become valuable and essential sources of soil moisture observations at global scales. Over the past four decades, several active and passive microwave sensors have been deployed, along with the recent launch of two fully dedicated missions (SMOS and SMAP). Signifying the four decades of microwave remote sensing of soil moisture, this Part 2 of the two-part review series aims to present an overview of how our knowledge in this field has improved in terms of the design of sensors and their accuracy for retrieving soil moisture. The first part discusses the developments made in active and passive microwave soil moisture retrieval algorithms. We assess the evolution of the products of various sensors over the last four decades, in terms of daily coverage, temporal performance, and spatial performance, by comparing the products of eight passive sensors (SMMR, SSM/I, TMI, AMSR-E, WindSAT, AMSR2, SMOS and SMAP), two active sensors (ERS-Scatterometer, MetOp-ASCAT), and one active/passive merged soil moisture product (ESA-CCI combined product) with the International Soil Moisture Network (ISMN) in-situ stations and the Variable Infiltration Capacity (VIC) land surface model simulations over the Contiguous United States (CONUS). In the process, the regional impacts of vegetation conditions on the spatial and temporal performance of soil moisture products are investigated. We also carried out inter-satellite comparisons to study the roles of sensor design and algorithms on the retrieval accuracy. We find that substantial improvements have been made over recent years in this field in terms of daily coverage, retrieval accuracy, and temporal dynamics. We conclude that the microwave soil moisture products have significantly evolved in the last four decades and will continue to make key contributions to the progress of hydro-meteorological and climate sciences.

Implementation of a global-scale operational data assimilation system for satellite-based soil moisture retrievals

NASA Astrophysics Data System (ADS)

Bolten, J.; Crow, W.; Zhan, X.; Reynolds, C.

2008-08-01

Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuations provided by the US Department of Agriculture (USDA) Production Estimation and Crop Assessment Division (PECAD). The current system utilized by PECAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by PECAD. This study incorporates NASA's soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) into the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE a daily product of integrated AMSR-E soil moisture observations with the PECAD two-layer soil moisture model forecasts. A methodology of the system design and a brief evaluation of the system performance over the Conterminous United States (CONUS) is presented.

Importance of Vertical Coupling in Agricultural Models on Assimilation of Satellite-derived Soil Moisture

NASA Astrophysics Data System (ADS)

Mladenova, I. E.; Crow, W. T.; Teng, W. L.; Doraiswamy, P.

2010-12-01

Crop yield in crop production models is simulated as a function of weather, ground conditions and management practices and it is driven by the amount of nutrients, heat and water availability in the root-zone. It has been demonstrated that assimilation of satellite-derived soil moisture data has the potential to improve the model root-zone soil water (RZSW) information. However, the satellite estimates represent the moisture conditions of the top 3 cm to 5 cm of the soil profile depending on system configuration and surface conditions (i.e. soil wetness, density of the canopy cover, etc). The propagation of this superficial information throughout the profile will depend on the model physics. In an Ensemble Kalman Filter (EnKF) data assimilation system, as the one examined here, the update of each soil layer is done through the Kalman Gain, K. K is a weighing factor that determines how much correction will be performed on the forecasts. Furthermore, K depends on the strength of the correlation between the surface and the root-zone soil moisture; the stronger this correlation is, the more observations will impact the analysis. This means that even if the satellite-derived product has higher sensitivity and accuracy as compared to the model estimates, the improvement of the RZSW will be negligible if the surface-root zone coupling is weak, where the later is determined by the model subsurface physics. This research examines: (1) the strength of the vertical coupling in the Environmental Policy Integrated Climate (EPIC) model over corn and soybeans covered fields in Iowa, US, (2) the potential to improve EPIC RZSW information through assimilation of satellite soil moisture data derived from the Advanced Microwave Scanning Radiometer (AMSR-E) and (3) the impact of the vertical coupling on the EnKF performance.

Using Large-Scale Precipitation to Validate AMSR-E Satellite Soil Moisture Estimates by Means of Mutual Information

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Salvucci, G.

2013-12-01

Validation of remotely sensed soil moisture is complicated by the difference in scale between remote sensing footprints and traditional ground-based soil moisture measurements. To address this issue, a new method was developed to evaluate the useful information content of remotely sensed soil moisture data using only large-scale precipitation (i.e. without modeling). Under statistically stationary conditions [Salvucci, 2001], precipitation conditionally averaged according to soil moisture (denoted E[P|S]) results in a sigmoidal shape in a manner that reflects the dependence of drainage, runoff, and evapotranspiration on soil moisture. However, errors in satellite measurement and algorithmic conversion of satellite data to soil moisture can degrade this relationship. Thus, remotely sensed soil moisture products can be assessed by the degree to which the natural sigmoidal relationship is preserved. The metric of mutual information was used as an error-dependent measure of the strength of the sigmoidal relationship, calculated from a two-dimensional histogram of soil moisture versus precipitation estimated using Gaussian mixture models. Three AMSR-E algorithms (VUA-NASA [Owe et al., 2001], NASA [Njoku et al., 2003], and U. Montana [Jones & Kimball, 2010]) were evaluated with the method for a nine-year period (2002-2011) over the contiguous United States at ¼° latitude-longitude resolution, using precipitation from the North American Land Data Assimilation System (NLDAS). The U. Montana product resulted in the highest mutual information for 57% of the region, followed by VUA-NASA and NASA at 40% and 3%, respectively. Areas where the U. Montana product yielded the maximum mutual information generally coincided with low vegetation biomass and flatter terrain, while the VUA-NASA product contained more useful information in more rugged and highly vegetated areas. Additionally, E[P|S] curves resulting from the Gaussian mixture method can potentially be decomposed into their conditional evapotranspiration and drainage plus runoff components using matrix factorization methods, allowing for time-averaged mapping of these fluxes over the study area.

AMSR Validation Program

NASA Astrophysics Data System (ADS)

Lobl, E. S.

2003-12-01

AMSR and AMSR-E are passive microwave radiometers built by NASDA in Japan. AMSR flies on ADEOS II, launched December 14 2001, and AMSR-E flies on NASA's Aqua satellite, launched May 4 2001. The Science teams in both countries have developed algorithms to retrieve different atmospheric parameters from the data obtained by these radiometers. The US Science team has developed a Validation plan that involved several campaigns. In fact most of these campaign have taken place this year: 2003, nicknamed the "Golden Year" for AMSR Validation. The first campaign started in January 2003 with the Extra-tropical precipitation campaign, followed by IOP3 for Cold Lands Processes Experiment (CLPX) in Colorado. After the change out of some of the instruments, the Validation program continued with the Arctic Sea Ice campaign based in Alaska, and followed by CLPX IOP 4, back in Colorado. Soil Moisture EXperiment 03 (SMEX03) started in late June in Alabama and Georgia, and then completed in Oklahoma mid-July. The last campaign in this series is AMSR Antarctic Sea Ice (AASI)/SMEX in Brazil. The major goals of each campaign, and very preliminary data will be shown. Most of these campaigns were in collaboration with the Japanese AMSR scientists.

Satellite Based Soil Moisture Product Validation Using NOAA-CREST Ground and L-Band Observations

NASA Astrophysics Data System (ADS)

Norouzi, H.; Campo, C.; Temimi, M.; Lakhankar, T.; Khanbilvardi, R.

2015-12-01

Soil moisture content is among most important physical parameters in hydrology, climate, and environmental studies. Many microwave-based satellite observations have been utilized to estimate this parameter. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is one of many remotely sensors that collects daily information of land surface soil moisture. However, many factors such as ancillary data and vegetation scattering can affect the signal and the estimation. Therefore, this information needs to be validated against some "ground-truth" observations. NOAA - Cooperative Remote Sensing and Technology (CREST) center at the City University of New York has a site located at Millbrook, NY with several insitu soil moisture probes and an L-Band radiometer similar to Soil Moisture Passive and Active (SMAP) one. This site is among SMAP Cal/Val sites. Soil moisture information was measured at seven different locations from 2012 to 2015. Hydra probes are used to measure six of these locations. This study utilizes the observations from insitu data and the L-Band radiometer close to ground (at 3 meters height) to validate and to compare soil moisture estimates from AMSR2. Analysis of the measurements and AMSR2 indicated a weak correlation with the hydra probes and a moderate correlation with Cosmic-ray Soil Moisture Observing System (COSMOS probes). Several differences including the differences between pixel size and point measurements can cause these discrepancies. Some interpolation techniques are used to expand point measurements from 6 locations to AMSR2 footprint. Finally, the effect of penetration depth in microwave signal and inconsistencies with other ancillary data such as skin temperature is investigated to provide a better understanding in the analysis. The results show that the retrieval algorithm of AMSR2 is appropriate under certain circumstances. This validation algorithm and similar study will be conducted for SMAP mission. Keywords: Remote Sensing, Soil Moisture, AMSR2, SMAP, L-Band.

Crop effect to soil moisture retrieval at different microwave frequencies

NASA Astrophysics Data System (ADS)

Zhang, Zhongjun; Luan, Jinzhe

2006-12-01

In soil moisture retrieval by microwave remote sensing technology, vegetation effect is important, due to its emission upward as well as masking the soil surface contribution. Because of good penetration characteristics through crop at low frequencies, L-band is often used, where crop is treated as a uniform layer, and 0 th-order Brightness Temperature model is used. Higher frequencies upper than L-band, the frequencies both on NASA AQUA AMSR-E and FY-3 to be launched next year in CHINA, may be more informative in SM retrieval. The multiple-scattering effects inside crop and that between crop layer and soil surface will be increasing when frequencies go higher from L-band. In this paper, a Matrix-Doubling model that account for multiple-scattering based on ray tracing technique is used to simulate the microwave emission of vegetated-surface at C- and X-band. The orientation and size of crop element such as leaves and cylinders are accounted for in crop layer, and AIEM is used for calculation of ground surface scattering. Simulation results from this model for corn and SGP99 experiment data are in good agreement. Since complicated theoretical model as used in this paper involves too many parameters, to make SM retrieval more directly, corresponding terms from the developed model are matched with 0 th-order,so as to derive effective single scattering albedo and vegetation opacity at C- and X-band.

An End-to-End Description of the Data Flow of AMSR-E and GLAS Data Products: Product Generation Through Product Delivery to Users

NASA Astrophysics Data System (ADS)

Lutz, B. J.; Marquis, M.

2001-12-01

The Aqua and ICESat missions are components of the Earth Observing System (EOS). The Advanced Microwave Scanning Radiometer (AMSR-E) instrument will fly on the Aqua satellite planned for launch in Spring 2002. AMSR-E is a passive microwave instrument, modified from the AMSR instrument, which will be deployed on the Japanese Advanced Earth Observing Satellite-II (ADEOS-II). AMSR-E will observe the atmosphere, land, oceans, and cryosphere, yielding measurements of precipitation, cloud water, water vapor, surface wetness, sea surface temperatures, oceanic wind speed, sea ice concentrations, snow depth, and snow water content. The Geoscience Laser Altimeter System (GLAS) instrument will fly aboard the ICESat satellite scheduled for launch in Summer 2002. This instrument will measure ice-sheet topography and temporal changes in topography; cloud heights, planetary boundary heights and aerosol vertical structure; and land and water topography. The GLAS and AMSR-E teams have both chosen to utilize Science Investigator-led Processing Systems (SIPS) to process their respective EOS data products. The SIPS facilities are funded by the Earth Science Data and Information System (ESDIS) Project at NASA's Goddard Space Flight Center and operated under the direction of a science team leader. The SIPS capitalize upon the scientific expertise of the science teams and the distributed processing capabilities of their institutions. The SIPS are charged with routine production of their respective EOS data products for archival at a Distributed Active Archive Center (DAAC). The National Snow and Ice Data Center (NSIDC) DAAC in Boulder, Colorado will archive all AMSR-E and GLAS data products. The NSIDC DAAC will distribute these data products to users throughout the world. The SIPS processing flows of both teams are rather complex. The AMSR-E SIPS is composed of three separate processing facilities (Japan, California, and Alabama). The ICESat SIPS is composed of one main processing center (Maryland) and an important secondary data set processing center (Texas) that generates required auxiliary data products. The EOSDIS Core System (ECS) has developed extensive protocols and procedures to ensure timeliness and completeness of delivery of the data from the SIPS to the DAACs. The NSIDC DAAC, in addition to being the repository of AMSR-E and GLAS data products, provides enhanced services, documentation and guides for these data. NSIDC is a liaison between the science teams and the user community. This poster will display flow diagrams showing: a) the AMSR-E and the ICESat SIPS, and the process of how their Level 1, 2, and 3 data products are generated; b) the staging and delivery of these sets of data to the NSIDC DAAC for archival, and the ECS protocols required to ensure delivery; and c) the services and "value-added" products that the NSIDC DAAC provides to the user community in support of the Aqua (AMSR-E) and ICESat missions.

Sea Ice Surface Temperature Product from the Moderate Resolution Imaging Spectroradiometer (MODIS)

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Key, Jeffrey R.; Casey, Kimberly A.; Riggs, George A.; Cavalieri, Donald J.

2003-01-01

Global sea ice products are produced from the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) on board both the Terra and Aqua satellites. Daily sea ice extent and ice-surface temperature (IST) products are available at 1- and 4-km resolution. Validation activities have been undertaken to assess the accuracy of the MODIS IST product at the South Pole station in Antarctica and in the Arctic Ocean using near-surface air-temperature data from a meteorological station and drifting buoys. Results from the study areas show that under clear skies, the MODIS ISTs are very close to those of the near-surface air temperatures with a bias of -1.1 and -1.2 K, and an uncertainty of 1.6 and 1.7 K, respectively. It is shown that the uncertainties would be reduced if the actual temperature of the ice surface were reported instead of the near-surface air temperature. It is not possible to get an accurate IST from MODIS in the presence of even very thin clouds or fog, however using both the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and the MODIS on the Aqua satellite, it may be possible to develop a relationship between MODIS-derived IST and ice temperature derived from the AMSR-E. Since the AMSR-E measurements are generally unaffected by cloud cover, they may be used to complement the MODIS IST measurements.

A New Approach for Validating Satellite Estimates of Soil Moisture Using Large-Scale Precipitation: Comparing AMSR-E Products

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Salvucci, G.

2012-12-01

Soil moisture influences many hydrological processes in the water and energy cycles, such as runoff generation, groundwater recharge, and evapotranspiration, and thus is important for climate modeling, water resources management, agriculture, and civil engineering. Large-scale estimates of soil moisture are produced almost exclusively from remote sensing, while validation of remotely sensed soil moisture has relied heavily on ground truthing, which is at an inherently smaller scale. Here we present a complementary method to determine the information content in different soil moisture products using only large-scale precipitation data (i.e. without modeling). This study builds on the work of Salvucci [2001], Saleem and Salvucci [2002], and Sun et al. [2011], in which precipitation was conditionally averaged according to soil moisture level, resulting in moisture-outflow curves that estimate the dependence of drainage, runoff, and evapotranspiration on soil moisture (i.e. sigmoidal relations that reflect stressed evapotranspiration for dry soils, roughly constant flux equal to potential evaporation minus capillary rise for moderately dry soils, and rapid drainage for very wet soils). We postulate that high quality satellite estimates of soil moisture, using large-scale precipitation data, will yield similar sigmoidal moisture-outflow curves to those that have been observed at field sites, while poor quality estimates will yield flatter, less informative curves that explain less of the precipitation variability. Following this logic, gridded ¼ degree NLDAS precipitation data were compared to three AMSR-E derived soil moisture products (VUA-NASA, or LPRM [Owe et al., 2001], NSIDC [Njoku et al., 2003], and NSIDC-LSP [Jones & Kimball, 2011]) for a period of nine years (2001-2010) across the contiguous United States. Gaps in the daily soil moisture data were filled using a multiple regression model reliant on past and future soil moisture and precipitation, and soil moisture was then converted to a ranked wetness index, in order to reconcile the wide range and magnitude of the soil moisture products. Generalized linear models were employed to fit a polynomial model to precipitation, given wetness index. Various measures of fit (e.g. log likelihood) were used to judge the amount of information in each soil moisture product, as indicated by the amount of precipitation variability explained by the fitted model. Using these methods, regional patterns appear in soil moisture product performance.

Toward all weather, long record, and real-time land surface temperature retrievals from microwave satellite observations

NASA Astrophysics Data System (ADS)

Jimenez, Carlos; Prigent, Catherine; Aires, Filipe; Ermida, Sofia

2017-04-01

The land surface temperature can be estimated from satellite passive microwave observations, with limited contamination from the clouds as compared to the infrared satellite retrievals. With ˜60% cloud cover in average over the globe, there is a need for "all weather," long record, and real-time estimates of land surface temperature (Ts) from microwaves. A simple yet accurate methodology is developed to derive the land surface temperature from microwave conical scanner observations, with the help of pre-calculated land surface microwave emissivities. The method is applied to the Special Sensor Microwave/Imagers (SSM/I) and the Earth observation satellite (EOS) Advanced Microwave Scanning Radiometer (AMSR-E) observations?, regardless of the cloud cover. The SSM/I results are compared to infrared estimates from International Satellite Cloud Climatology Project (ISCCP) and from Advanced Along Track Scanning Radiometer (AATSR), under clear-sky conditions. Limited biases are observed (˜0.5 K for both comparisons) with a root-mean-square difference (RMSD) of ˜5 K, to be compared to the RMSE of ˜3.5 K between ISCCP et AATSR. AMSR-E results are compared with the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky estimates. As both instruments are on board the same satellite, this reduces the uncertainty associated to the observations match-up, resulting in a lower RMSD of ˜ 4K. The microwave Ts is compared to in situ Ts time series from a collection of ground stations over a large range of environments. For 22 stations available in the 2003-2004 period, SSM/I Ts agrees very well for stations in vegetated environments (down to RMSD of ˜2.5 K for several stations), but the retrieval methodology encounters difficulties under cold conditions due to the large variability of snow and ice surface emissivities. For 10 stations in the year 2010, AMSR-E presents an all-station mean RMSD of ˜4.0 K with respect tom the ground Ts. Over the same stations, MODIS agrees better (RMSD of 2.4 K), ?but AMSR-E provides a larger number of Ts estimates by being able to measure under cloudy conditions, with an approximated ratio of 3 to 1 over the analysed stations. At many stations the RMSD of the AMSR-E clear and cloudy-sky are comparable, highlighting the ability of the microwave inversions to provide Ts under most atmospheric and surface conditions.

An Integrated Retrieval Framework for AMSR2: Implications for Light Precipitation and Sea Ice Edge Detectability

NASA Astrophysics Data System (ADS)

Duncan, D.; Kummerow, C. D.; Meier, W.

2016-12-01

Over the lifetime of AMSR-E, operational retrieval algorithms were developed and run for precipitation, ocean suite (SST, wind speed, cloud liquid water path, and column water vapor over ocean), sea ice, snow water equivalent, and soil moisture. With a separate algorithm for each group, the retrievals were never interactive or integrated in any way despite many co-sensitivities. AMSR2, the follow-on mission to AMSR-E, retrieves the same parameters at a slightly higher spatial resolution. We have combined the operational algorithms for AMSR2 in a way that facilitates sharing information between the retrievals. Difficulties that arose were mainly related to calibration, spatial resolution, coastlines, and order of processing. The integration of all algorithms for AMSR2 has numerous benefits, including better detection of light precipitation and sea ice, fewer screened out pixels, and better quality flags. Integrating the algorithms opens up avenues for investigating the limits of detectability for precipitation from a passive microwave radiometer and the impact of spatial resolution on sea ice edge detection; these are investigated using CloudSat and MODIS coincident observations from the A-Train constellation.

Characterizing Seasonal Drought, Water Supply Pattern and Their Impact on Vegetation Growth Using Satellite Soil Moisture Data, GRACE Water Storage and Precipitation Observations

NASA Astrophysics Data System (ADS)

A, G.; Velicogna, I.; Kimball, J. S.; Du, J.; Kim, Y.; Njoku, E. G.; Colliander, A.

2016-12-01

We combine soil moisture (SM) data from AMSR-E, AMSR-2 and SMAP, terrestrial water storage (TWS) changes from GRACE and precipitation measurements from GPCP to delineate and characterize drought and water supply pattern and its impact on vegetation growth. GRACE TWS provides spatially continuous observations of total terrestrial water storage changes and regional drought extent, persistence and severity, while satellite derived soil moisture estimates provide enhanced delineation of plant-available soil moisture. Together these data provide complementary metrics quantifying available plant water supply and have important implications for water resource management. We use these data to investigate the supply changes from different water components in relation to satellite based vegetation productivity metrics from MODIS, before, during and following the major drought events observed in the continental US during the past 13 years. We observe consistent trends and significant correlations between monthly time series of TWS, SM, and vegetation productivity. In Texas and surrounding semi-arid areas, we find that the spatial pattern of the vegetation-moisture relation follows the gradient in mean annual precipitation. In Texas, GRACE TWS and surface SM show strong coupling and similar characteristic time scale in relatively normal years, while during the 2011 onward hydrological drought, GRACE TWS manifests a longer time scale than that of surface SM, implying stronger drought persistence in deeper water storage. In the Missouri watershed, we find a spatially varying vegetation-moisture relationship where in the drier northwestern portion of the basin, the inter-annual variability in summer vegetation productivity is closely associated with changes in carry-on GRACE TWS from spring, whereas in the moist southeastern portion of the basin, summer precipitation is the dominant controlling factor on vegetation growth.

EOS Aqua AMSR-E Arctic Sea Ice Validation Program: Arctic2003 Aircraft Campaign Flight Report

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.; Markus,T.

2003-01-01

In March 2003 a coordinated Arctic sea ice validation field campaign using the NASA Wallops P-3B aircraft was successfully completed. This campaign was part of the program for validating the Earth Observing System (EOS) Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea ice products. The AMSR-E, designed and built by the Japanese National Space Development Agency for NASA, was launched May 4, 2002 on the EOS Aqua spacecraft. The AMSR-E sea ice products to be validated include sea ice concentration, sea ice temperature, and snow depth on sea ice. This flight report describes the suite of instruments flown on the P-3, the objectives of each of the seven flights, the Arctic regions overflown, and the coordination among satellite, aircraft, and surface-based measurements. Two of the seven aircraft flights were coordinated with scientists making surface measurements of snow and ice properties including sea ice temperature and snow depth on sea ice at a study area near Barrow, AK and at a Navy ice camp located in the Beaufort Sea. Two additional flights were dedicated to making heat and moisture flux measurements over the St. Lawrence Island polynya to support ongoing air-sea-ice processes studies of Arctic coastal polynyas. The remaining flights covered portions of the Bering Sea ice edge, the Chukchi Sea, and Norton Sound.

Land Surface Microwave Emissivity Dynamics: Observations, Analysis and Modeling

NASA Technical Reports Server (NTRS)

Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Kumar, Sujay; Ringerud, Sarah

2014-01-01

Land surface microwave emissivity affects remote sensing of both the atmosphere and the land surface. The dynamical behavior of microwave emissivity over a very diverse sample of land surface types is studied. With seven years of satellite measurements from AMSR-E, we identified various dynamical regimes of the land surface emission. In addition, we used two radiative transfer models (RTMs), the Community Radiative Transfer Model (CRTM) and the Community Microwave Emission Modeling Platform (CMEM), to simulate land surface emissivity dynamics. With both CRTM and CMEM coupled to NASA's Land Information System, global-scale land surface microwave emissivities were simulated for five years, and evaluated against AMSR-E observations. It is found that both models have successes and failures over various types of land surfaces. Among them, the desert shows the most consistent underestimates (by approx. 70-80%), due to limitations of the physical models used, and requires a revision in both systems. Other snow-free surface types exhibit various degrees of success and it is expected that parameter tuning can improve their performances.

Research on Applicability Analysis of Drought Index in Liaoning Area

NASA Astrophysics Data System (ADS)

Wang, Xin; Ding, Hua; Shuang Sun, Li; Li, Ru Ren; Liu, Yu Mei

2018-05-01

Based on brightness temperature data of AMSR-E (advanced microwave scanning radiometer — earth observing system) in 2009 and 2011, the inversion on 8 brightness temperature ratios is performed as alternative drought indexes in this paper. The correlation analysis is made through the soil moisture extracted from inversion drought index and data itself, and 3 kinds of alternative drought that relatively coincide with soil moisture of AMSR-E data itself are selected. And then on this basis, the analysis on the change situation of 3 kinds of microwave moisture indexes in 10 pixel × 10 pixel rectangular region of Shenyang and Chaoyang is made, and the evaluation on the monitoring advantages and disadvantages of 3 kinds of indexes on soil moisture is performed, so as to obtain the optimal index PIv6.9 for drought monitoring. In the end, in order to further study PIv6.9 on soil moisture monitoring situation within the range of Liaoning province, four days with relatively large precipitation are selected according to meteorological station data in 2009, the precipitation data of 51 meteorological stations in Liaoning province are interpolated within the range of the whole province by utilizing Kriging method, and the contrastive analysis on the spatial distribution of precipitation and PIv6.9 index is made. The results show that PIv6.9 can best reflect the spatial distribution characteristics of drought status in Liaoning province.

The Integration of SMOS Soil Moisture in a Consistent Soil Moisture Climate Record

NASA Astrophysics Data System (ADS)

de Jeu, Richard; Kerr, Yann; Wigneron, Jean Pierre; Rodriguez-Fernandez, Nemesio; Al-Yaari, Amen; van der Schalie, Robin; Dolman, Han; Drusch, Matthias; Mecklenburg, Susanne

2015-04-01

Recently, a study funded by the European Space Agency (ESA) was set up to provide guidelines for the development of a global soil moisture climate record with a special emphasis on the integration of SMOS. Three different data fusion approaches were designed and implemented on 10 year passive microwave data (2003-2013) from two different satellite sensors; the ESA Soil Moisture Ocean Salinity Mission (SMOS) and the NASA/JAXA Advanced Scanning Microwave Radiometer (AMSR-E). The AMSR-E data covered the period from January 2003 until Oct 2011 and SMOS data covered the period from June 2010 until the end of 2013. The fusion approaches included a neural network approach (Rodriguez-Fernandez et al., this conference session HS6.4), a regression approach (Wigneron et al., 2004), and an approach based on the baseline algorithm of ESAs current Climate Change Initiative soil moisture program, the Land Parameter Retrieval Model (Van der Schalie et al., this conference session HS6.4). With this presentation we will show the first results from this study including a description of the different approaches and the validation activities using both globally covered modeled datasets and ground observations from the international soil moisture network. The statistical validation analyses will give us information on the temporal and spatial performance of the three different approaches. Based on these results we will then discuss the next steps towards a seamless integration of SMOS in a consistent soil moisture climate record. References Wigneron J.-P., J.-C. Calvet, P. de Rosnay, Y. Kerr, P. Waldteufel, K. Saleh, M. J. Escorihuela, A. Kruszewski, 'Soil Moisture Retrievals from Bi-Angular L-band Passive Microwave Observations', IEEE Trans. Geosc. Remote Sens. Let., vol 1, no. 4, 277-281, 2004.

Current status of the global change observation mission - water SHIZUKU (GCOM-W) and the advanced microwave scanning radiometer 2 (AMSR2) (Conference Presentation)

NASA Astrophysics Data System (ADS)

Maeda, Takashi; Kachi, Misako; Kasahara, Marehito

2016-10-01

Japan Aerospace Exploration Agency (JAXA) launched the Global Change Observation Mission - Water (GCOM-W) or "SHIZUKU" in 18 May 2012 (JST) from JAXA's Tanegashima Space Center. The GCOM-W satellite joins to NASA's A-train orbit since June 2012, and its observation is ongoing. The GCOM-W satellite carries the Advanced Microwave Scanning Radiometer 2 (AMSR2). The AMSR2 is a multi-frequency, total-power microwave radiometer system with dual polarization channels for all frequency bands, and successor microwave radiometer to the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) loaded on the NASA's Aqua satellite. The AMSR-E kept observation in the slower rotation speed (2 rotations per minute) for cross-calibration with AMSR2 since December 2012, its operation ended in December 2015. The AMSR2 is designed almost similarly as the AMSR-E. The AMSR2 has a conical scanning system with large-size offset parabolic antenna, a feed horn cluster to realize multi-frequency observation, and an external calibration system with two temperature standards. However, some important improvements are made. For example, the main reflector size of the AMSR2 is expanded to 2.0 m to observe the Earth's surface in higher spatial resolution, and 7.3-GHz channel is newly added to detect radio frequency interferences at 6.9 GHz. In this paper, we present a recent topic for the AMSR2 (i.e., RFI detection performances) and the current operation status of the AMSR2.

Soil Moisture and Temperature Measuring Networks in the Tibetan Plateau and Their Hydrological Applications

NASA Astrophysics Data System (ADS)

Yang, Kun; Chen, Yingying; Qin, Jun; Lu, Hui

2017-04-01

Multi-sphere interactions over the Tibetan Plateau directly impact its surrounding climate and environment at a variety of spatiotemporal scales. Remote sensing and modeling are expected to provide hydro-meteorological data needed for these process studies, but in situ observations are required to support their calibration and validation. For this purpose, we have established two networks on the Tibetan Plateau to measure densely two state variables (soil moisture and temperature) and four soil depths (0 5, 10, 20, and 40 cm). The experimental area is characterized by low biomass, high soil moisture dynamic range, and typical freeze-thaw cycle. As auxiliary parameters of these networks, soil texture and soil organic carbon content are measured at each station to support further studies. In order to guarantee continuous and high-quality data, tremendous efforts have been made to protect the data logger from soil water intrusion, to calibrate soil moisture sensors, and to upscale the point measurements. One soil moisture network is located in a semi-humid area in central Tibetan Plateau (Naqu), which consists of 56 stations with their elevation varying over 4470 4950 m and covers three spatial scales (1.0, 0.3, 0.1 degree). The other is located in a semi-arid area in southern Tibetan Plateau (Pali), which consists of 25 stations and covers an area of 0.25 degree. The spatiotemporal characteristics of the former network were analyzed, and a new spatial upscaling method was developed to obtain the regional mean soil moisture truth from the point measurements. Our networks meet the requirement for evaluating a variety of soil moisture products, developing new algorithms, and analyzing soil moisture scaling. Three applications with the network data are presented in this paper. 1. Evaluation of Current remote sensing and LSM products. The in situ data have been used to evaluate AMSR-E, AMSR2, SMOS and SMAP products and four modeled outputs by the Global Land Data Assimilation System (GLDAS). 2. Development of New Products. We developed a dual-pass land data assimilation system. The essential idea of the system is to calibrate a land data assimilation system before a normal data assimilation. The calibration is based on satellite data rather than in situ data. Through this way, we may alleviate the impact of uncertainties in determining the error covariance of both observation operator and model operation, as it is always tough to determine the covariance. The performance of the data assimilation system is presented through comparison against the Tibetan Plateau soil moisture measuring networks. And the results are encouraging. 3. Estimation of Soil Parameter Values in a Land Surface Model. We explored the possibility to estimate soil parameter values by assimilating AMSR-E brightness temperature (TB) data. In the assimilation system, the TB is simulated by the coupled system of a land surface model (LSM) and a radiative transfer model (RTM), and the simulation errors highly depend on parameters in both the LSM and the RTM. Thus, sensitive soil parameters may be inversely estimated through minimizing the TB errors. The effectiveness of the estimated parameter values is evaluated against intensive measurements of soil parameters and soil moisture in three grasslands of the Tibetan Plateau and the Mongolian Plateau. The results indicate that this satellite data-based approach can improve the data quality of soil porosity, a key parameter for soil moisture modeling, and LSM simulations with the estimated parameter values reasonably reproduce the measured soil moisture. This demonstrates it is feasible to calibrate LSMs for soil moisture simulations at grid scale by assimilating microwave satellite data, although more efforts are expected to improve the robustness of the model calibration.

Land and atmosphere interactions using satellite remote sensing and a coupled mesoscale/land surface model

NASA Astrophysics Data System (ADS)

Hong, Seungbum

Land and atmosphere interactions have long been recognized for playing a key role in climate and weather modeling. However their quantification has been challenging due to the complex nature of the land surface amongst various other reasons. One of the difficult parts in the quantification is the effect of vegetation which are related to land surface processes such soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examined the effects of vegetation and its relationship with soil moisture on the simulated land-atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Then, this study evaluates the model improvements for each simulation method.

A Combined Soil Moisture Product of the Tibetan Plateau using Different Sensors Simultaneously

NASA Astrophysics Data System (ADS)

Zeng, Y.; Dente, L.; Su, B.; Wang, L.

2012-12-01

It is always challenging to find a single satellite-derived soil moisture product that has complete coverage of the Tibetan Plateau for a long time period and is suitable for climate change studies at sub-continental scale. Meanwhile, having a number of independent satellite-derived soil moisture data sets does not mean that it is straightforward to create long-term consistent time series, due to the differences among the data sets related to the different retrieval approaches. Therefore, this study is focused on the development and validation of a simple Bayesian based method to merge/blend different satellite-derived soil moisture data. The merging method was firstly tested over the Maqu region (north-eastern fringe of the Tibetan Plateau), where in situ soil moisture data were collected, for the period from May 2008 to December 2010. The in situ data provided by the 20 monitoring stations in the Maqu region were compared to the AMSR-E soil moisture products by VUA-NASA and the ASCAT soil moisture products by TU Wien, in order to determine bias and standard deviation. It was found that the bias between the satellite and the in situ data varies with seasons. The satellite-derived products were first corrected for the bias and then merged. This is generally caused by notable differences in the represented depth, spatial extent and so on. The systematic bias is affected by the spatial variability and the temporal stability (Dente et al. 2012). The dependence of the bias on season was investigated and identified as the monsoon season only (May-September), in winter only (December - February), and in the period between the monsoon season and winter (March-April, October-November, called the transition season) (Dente et al. 2012, Su et al. 2011). After the date merging procedure, the standard deviations between the satellite and the in situ data reduced from 0.0839 to 0.0622 for ASCAT data, and from 0.0682 to 0.0593 for AMSR-E data. The developed merging method is therefore suitable to provide a more accurate soil moisture product than the AMSR-E and ASCAT products. As the merging method was shown to be promising over the Maqu region, it will be extended to the entire Tibetan Plateau. Then, the combined soil moisture product will be validated over the monitored sites located in Ngari and Naqu regions. References: Dente, L.; Vekerdy, Z.; Wen, J.; Su, Z., (2012) Maqu network for validation of satellite-derived soil moisture products. International Journal of Applied Earth Observation and Geoinformation, 17, 55-65. Su, Z., Wen, J., Dente, L., van der Velde, R. and ... [et al.] (2011) The Tibetan plateau observatory of plateau scale soil moisture and soil temperature, Tibet - Obs, for quantifying uncertainties in coarse resolution satellite and model products. In: Hydrology and earth system sciences (HESS): open access, 15 (2011)7 pp. 2303-2016.

Impact of Soil Moisture Assimilation on Land Surface Model Spin-Up and Coupled LandAtmosphere Prediction

NASA Technical Reports Server (NTRS)

Santanello, Joseph A., Jr.; Kumar, Sujay V.; Peters-Lidard, Christa D.; Lawston, P.

2016-01-01

Advances in satellite monitoring of the terrestrial water cycle have led to a concerted effort to assimilate soil moisture observations from various platforms into offline land surface models (LSMs). One principal but still open question is that of the ability of land data assimilation (LDA) to improve LSM initial conditions for coupled short-term weather prediction. In this study, the impact of assimilating Advanced Microwave Scanning Radiometer for EOS (AMSR-E) soil moisture retrievals on coupled WRF Model forecasts is examined during the summers of dry (2006) and wet (2007) surface conditions in the southern Great Plains. LDA is carried out using NASAs Land Information System (LIS) and the Noah LSM through an ensemble Kalman filter (EnKF) approach. The impacts of LDA on the 1) soil moisture and soil temperature initial conditions for WRF, 2) land-atmosphere coupling characteristics, and 3) ambient weather of the coupled LIS-WRF simulations are then assessed. Results show that impacts of soil moisture LDA during the spin-up can significantly modify LSM states and fluxes, depending on regime and season. Results also indicate that the use of seasonal cumulative distribution functions (CDFs) is more advantageous compared to the traditional annual CDF bias correction strategies. LDA performs consistently regardless of atmospheric forcing applied, with greater improvements seen when using coarser, global forcing products. Downstream impacts on coupled simulations vary according to the strength of the LDA impact at the initialization, where significant modifications to the soil moisture flux- PBL-ambient weather process chain are observed. Overall, this study demonstrates potential for future, higher-resolution soil moisture assimilation applications in weather and climate research.

Topographic Effects on the Surface Emissivity of a Mountainous Area Observed by a Spaceborne Microwave Radiometer

PubMed Central

Pulvirenti, Luca; Pierdicca, Nazzareno; Marzano, Frank S.

2008-01-01

A simulation study to understand the influence of topography on the surface emissivity observed by a satellite microwave radiometer is carried out. We analyze the effects due to changes in observation angle, including the rotation of the polarization plane. A mountainous area in the Alps (Northern Italy) is considered and the information on the relief extracted from a digital elevation model is exploited. The numerical simulation refers to a radiometric image, acquired by a conically-scanning radiometer similar to AMSR-E, i.e., flying at 705 km of altitude with an observation angle of 55°. To single out the impact on surface emissivity, scattering of the radiation due to the atmosphere or neighboring elevated surfaces is not considered. C and X bands, for which atmospheric effects are negligible, and Ka band are analyzed. The results indicate that the changes in the local observation angle tend to lower the apparent emissivity of a radiometric pixel with respect to the corresponding flat surface characteristics. The effect of the rotation of the polarization plane enlarges (vertical polarization), or attenuates (horizontal polarization) this decrease. By doing some simplifying assumptions for the radiometer antenna, the conclusion is that the microwave emissivity at vertical polarization is underestimated, whilst the opposite occurs for horizontal polarization, except for Ka band, for which both under- and overprediction may occur. A quantification of the differences with respect to a flat soil and an approximate evaluation of their impact on soil moisture retrieval are yielded. PMID:27879773

Comparing soil moisture memory in satellite observations and models

NASA Astrophysics Data System (ADS)

Stacke, Tobias; Hagemann, Stefan; Loew, Alexander

2013-04-01

A major obstacle to a correct parametrization of soil processes in large scale global land surface models is the lack of long term soil moisture observations for large parts of the globe. Currently, a compilation of soil moisture data derived from a range of satellites is released by the ESA Climate Change Initiative (ECV_SM). Comprising the period from 1978 until 2010, it provides the opportunity to compute climatological relevant statistics on a quasi-global scale and to compare these to the output of climate models. Our study is focused on the investigation of soil moisture memory in satellite observations and models. As a proxy for memory we compute the autocorrelation length (ACL) of the available satellite data and the uppermost soil layer of the models. Additional to the ECV_SM data, AMSR-E soil moisture is used as observational estimate. Simulated soil moisture fields are taken from ERA-Interim reanalysis and generated with the land surface model JSBACH, which was driven with quasi-observational meteorological forcing data. The satellite data show ACLs between one week and one month for the greater part of the land surface while the models simulate a longer memory of up to two months. Some pattern are similar in models and observations, e.g. a longer memory in the Sahel Zone and the Arabian Peninsula, but the models are not able to reproduce regions with a very short ACL of just a few days. If the long term seasonality is subtracted from the data the memory is strongly shortened, indicating the importance of seasonal variations for the memory in most regions. Furthermore, we analyze the change of soil moisture memory in the different soil layers of the models to investigate to which extent the surface soil moisture includes information about the whole soil column. A first analysis reveals that the ACL is increasing for deeper layers. However, its increase is stronger in the soil moisture anomaly than in its absolute values and the first even exceeds the latter in the deepest layer. From this we conclude that the seasonal soil moisture variations dominate the memory close to the surface but these are dampened in lower layers where the memory is mainly affected by longer term variations.

High resolution change estimation of soil moisture and its assimilation into a land surface model

NASA Astrophysics Data System (ADS)

Narayan, Ujjwal

Near surface soil moisture plays an important role in hydrological processes including infiltration, evapotranspiration and runoff. These processes depend non-linearly on soil moisture and hence sub-pixel scale soil moisture variability characterization is important for accurate modeling of water and energy fluxes at the pixel scale. Microwave remote sensing has evolved as an attractive technique for global monitoring of near surface soil moisture. A radiative transfer model has been tested and validated for soil moisture retrieval from passive microwave remote sensing data under a full range of vegetation water content conditions. It was demonstrated that soil moisture retrieval errors of approximately 0.04 g/g gravimetric soil moisture are attainable with vegetation water content as high as 5 kg/m2. Recognizing the limitation of low spatial resolution associated with passive sensors, an algorithm that uses low resolution passive microwave (radiometer) and high resolution active microwave (radar) data to estimate soil moisture change at the spatial resolution of radar operation has been developed and applied to coincident Passive and Active L and S band (PALS) and Airborne Synthetic Aperture Radar (AIRSAR) datasets acquired during the Soil Moisture Experiments in 2002 (SMEX02) campaign with root mean square error of 10% and a 4 times enhancement in spatial resolution. The change estimation algorithm has also been used to estimate soil moisture change at 5 km resolution using AMSR-E soil moisture product (50 km) in conjunction with the TRMM-PR data (5 km) for a 3 month period demonstrating the possibility of high resolution soil moisture change estimation using satellite based data. Soil moisture change is closely related to precipitation and soil hydraulic properties. A simple assimilation framework has been implemented to investigate whether assimilation of surface layer soil moisture change observations into a hydrologic model will potentially improve it performance. Results indicate an improvement in model prediction of near surface and deep layer soil moisture content when the update is performed to the model state as compared to free model runs. It is also seen that soil moisture change assimilation is able to mitigate the effect of erroneous precipitation input data.

Satellite-observed changes in vegetation sensitivities to surface soil moisture and total water storage variations since the 2011 Texas drought

NASA Astrophysics Data System (ADS)

A, Geruo; Velicogna, Isabella; Kimball, John S.; Du, Jinyang; Kim, Youngwook; Colliander, Andreas; Njoku, Eni

2017-05-01

We combine soil moisture (SM) data from AMSR-E and AMSR-2, and changes in terrestrial water storage (TWS) from time-variable gravity data from GRACE to delineate and characterize the evolution of drought and its impact on vegetation growth. GRACE-derived TWS provides spatially continuous observations of changes in overall water supply and regional drought extent, persistence and severity, while satellite-derived SM provides enhanced delineation of shallow-depth soil water supply. Together these data provide complementary metrics quantifying available plant water supply. We use these data to investigate the supply changes from water components at different depths in relation to satellite-based enhanced vegetation index (EVI) and gross primary productivity (GPP) from MODIS and solar-induced fluorescence (SIF) from GOME-2, during and following major drought events observed in the state of Texas, USA and its surrounding semiarid area for the past decade. We find that in normal years the spatial pattern of the vegetation-moisture relationship follows the gradient in mean annual precipitation. However since the 2011 hydrological drought, vegetation growth shows enhanced sensitivity to surface SM variations in the grassland area located in central Texas, implying that the grassland, although susceptible to drought, has the capacity for a speedy recovery. Vegetation dependency on TWS weakens in the shrub-dominated west and strengthens in the grassland and forest area spanning from central to eastern Texas, consistent with changes in water supply pattern. We find that in normal years GRACE TWS shows strong coupling and similar characteristic time scale to surface SM, while in drier years GRACE TWS manifests stronger persistence, implying longer recovery time and prolonged water supply constraint on vegetation growth. The synergistic combination of GRACE TWS and surface SM, along with remote-sensing vegetation observations provides new insights into drought impact on vegetation-moisture relationship, and unique information regarding vegetation resilience and the recovery of hydrological drought.

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

NASA Technical Reports Server (NTRS)

Cavalieri, D. J.; Marksu, T.; Ivanoff, A.; Miller, J. A.; Brucker, L.; Sturm, M.; Maslanik, J. A.; Heinrichs, J. F.; Gasiewski, A.; Leuschen, C.;

Global inter-comparison of microwave and infrared LST from multiple sensors (AMSR-E, MODIS, SEVIRI, GOES, and MTSAT-2)

NASA Astrophysics Data System (ADS)

Ermida, Sofia L.; Jiménez, Carlos; Prigent, Catherine; Trigo, Isabel F.; DaCamara, Carlos C.

2017-04-01

Land Surface Temperature (LST) is an important diagnostic parameter of land surface conditions. Satellite LST products generally rely on measurements in the thermal infrared (IR) atmospheric window, which only allows clear sky estimates. Microwave (MW) observations can alternatively be used to derive an all-weather LST. Here we present an inter-comparison between LST derived from the Advanced Microwave Scanning Radiometer - Earth observation system (AMSR-E), the MODerate resolution Imaging Spectroradiometer (MODIS) on-board Aqua, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on-board Meteosat Second Generation (MSG) satellites, the Geostationary Operational Environmental Satellite (GOES) and the Japanese Meteorological Imager (JAMI) on-board the Multifunction Transport SATellite (MTSAT-2). The higher discrepancies between MW and IR products are observed over snow covered areas. MW emissivity is highly variable for snow-covered ground and not always properly accounted for by the climatological emissivity used in the retrieval. There is a conspicuous bias between MODIS and AMSR-E over desert areas, which is most likely related to the underestimation of LST by MODIS as previously reported in other studies. Inter-comparison between all IR and MW retrievals shows that the STD of the differences between MW and IR LST is generally higher than between IR retrievals. However, the biases between MW and IR LST are, in some cases, of the same order as the ones observed among infrared products. In particular, GOES presents a daytime bias with respect to AMSR-E of 0.45 K whereas the bias with respect to MODIS is 0.60 K. Given that AMSR-E can provide LST under cloudy conditions, the use of microwaves, considering simultaneous overpasses with IR, represents an increase of more than 250% of the number of available LST estimates over equatorial regions. With the MW products of a comparable quality to the IR ones, the MW LST is a very powerful complement of the IR estimates.

Multi-Sensor Approach to Mapping Snow Cover Using Data From NASA's EOS Aqua and Terra Spacecraft

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.

2003-12-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Over the past several decades both optical and passive microwave satellite data have been utilized for snow mapping at the regional to global scale. For the period 1978 to 2002, we have shown earlier that both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are, depending on season, less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Snow mapping using optical data is based on the magnitude of the surface reflectance while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Our previous work has defined the respective advantages and disadvantages of these two types of satellite data for snow cover mapping and it is clear that a blended product is optimal. We present a multi-sensor approach to snow mapping based both on historical data as well as data from current NASA EOS sensors. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets. An example of validation at the brightness temperature level is provided through the comparison of AMSR-E with data from the well-calibrated heritage SSM/I sensor over a large homogeneous snow-covered surface (Dome C, Antarctica). Prototype snow cover maps from AMSR-E compare well with maps derived from SSM/I. Our current blended product is being developed in the 25 km EASE-Grid while the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) allowing the blended product to indicate percent snow cover over the larger grid cell. Relationships between the percent area covered by snow as indicated by the MODIS data and the threshold for the appearance of snow as indicated by the passive microwave data are presented. Both MODIS and AMSR-E data have enhanced spatial resolution compared to the earlier data sources and examples of how this increased spatial resolution results in more accurate snow cover maps are presented. A wide range of validation data sets are being employed in this study including the NASA Cold Lands Processes Field Experiment undertaken in Colorado during 2002 and 2003.

ARISE (Antarctic Remote Ice Sensing Experiment) in the East 2003: Validation of Satellite-derived Sea-ice Data Product

NASA Technical Reports Server (NTRS)

Massom, Robert A.; Worby, Anthony; Lytle, Victoria; Markus, Thorsten; Allison, Ian; Scambos, Theodore; Enomoto, Hiroyuki; Tateyama, Kazutaka; Haran, Terence; Comiso, Josefino C.;

Remote Sensing of Climatic Anomalies and West Nile Virus Risk in the United States

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Chuang, T.; Henebry, G. M.; Kimball, J. S.

2012-12-01

West Nile virus (WNV) is the most widespread and important mosquito-borne pathogen in North America, and the national resurgence of human WNV cases during the summer of 2012 has highlighted the persistent threat posed by this potentially fatal disease. Advance warning of the timing and locations of WNV outbreaks can help public health officials to more effectively target WNV prevention and control efforts. To this end, we used environmental monitoring data from earth observing satellites to develop environmental indices of WNV risk and applied these indices to model seasonal and interannual patterns of mosquito populations and human disease cases. Our overarching hypothesis is that anomalies of cumulative temperature and moisture throughout the mosquito season affect the risk of WNV transmission to humans through their influences on mosquito populations, bird communities, and the extrinsic incubation period of the virus itself. In a preliminary study, we developed a model of WNV in the northern Great Plains using satellite optical-IR remote sensing products from MODIS, including land surface temperature, vegetation indices, and actual evapotranspiration computed using the simplified surface energy balance method. This model was applied in 2011 and 2012 to forecast spatial patterns of WNV relative risk prior to the main transmission season in July-September. We expanded this modeling approach to a national level using a daily global land surface parameter database developed from the NASA Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E). This dataset provides several novel environmental variables that are potentially relevant to mosquito ecology, including near-surface air temperature, surface soil moisture, fractional open water cover, and estimates of vegetation canopy opacity to microwave emissions at three microwave frequencies. Preliminary analyses demonstrated that higher temperatures during the amplification season are consistently associated with increased risk of WNV outbreaks, but moisture effects are more variable and are contingent upon regional differences in landscape hydrology and vector and host species. Although the AMSR-E sensor on Aqua ceased effective operations in October 2011 due to a sensor malfunction, similar satellite microwave observations from WindSat and AMSR2 sensors will enable the continuation of global land parameter retrievals and support future applications for modeling and forecasting WNV and other mosquito-borne diseases.

SMOS and AMSR-2 soil moisture evaluation using representative monitoring sites in southern Australia

NASA Astrophysics Data System (ADS)

Walker, J. P.; Mei Sun, M. S.; Rudiger, C.; Parinussa, R.; Koike, T.; Kerr, Y. H.

2016-12-01

The performance of soil moisture products from AMSR-2 and SMOS were evaluated against representative surface soil moisture stations within the Yanco study area in the Murrumbidgee Catchment, in southeast Australia. AMSR-2 Level 3 (L3) soil moisture products retrieved from two sets of brightness temperatures using the Japanese Aerospace exploration Agency (JAXA) and the Land Parameter Retrieval Model (LPRM) algorithms were included. For the LPRM algorithm, two different parameterization methods were applied. In the case of SMOS, two versions of the SMOS L3 soil moisture product were assessed. Results based on using "random" and representative stations to evaluate the products were contrasted. The latest versions of the JAXA (JX2) and LPRM (LP3) products were found to perform better than the earlier versions (JX1, LP1 and LP2). Moreover, soil moisture retrieval based on the latter version of brightness temperature and parameterization scheme improved when C-band observations were used, as opposed to the X-band data. Yet, X-band retrievals were found to perform better than C-band. Inter-comparing AMSR-2 X-band products from different acquisition times showed a better performance for 1:30 pm overpasses whereas SMOS 6:00 am retrievals were found to perform the best. The mean average error (MAE) goal accuracy of the AMSR-2 mission (MAE < 0.08 m3/m3) was met by both versions of the JAXA products, the LPRM X-band products retrieved from the reprocessed version of brightness temperatures, and both versions of SMOS products. Nevertheless, none of the products achieved the SMOS target accuracy of 0.04 m3/m3. Finally, the product performance depended on the statistics used in their evaluation; based on temporal and absolute accuracy JX2 is recommended, whereas LP3 X-band 1:30 pm and SMOS2 6:00 am are recommended based on temporal accuracy alone.

The Impact of AMSR-E Soil Moisture Assimilation on Evapotranspiration Estimation

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Kumar, Sujay; Mocko, David; Tian, Yudong

2012-01-01

An assessment ofETestimates for current LDAS systems is provided along with current research that demonstrates improvement in LSM ET estimates due to assimilating satellite-based soil moisture products. Using the Ensemble Kalman Filter in the Land Information System, we assimilate both NASA and Land Parameter Retrieval Model (LPRM) soil moisture products into the Noah LSM Version 3.2 with the North American LDAS phase 2 CNLDAS-2) forcing to mimic the NLDAS-2 configuration. Through comparisons with two global reference ET products, one based on interpolated flux tower data and one from a new satellite ET algorithm, over the NLDAS2 domain, we demonstrate improvement in ET estimates only when assimilating the LPRM soil moisture product.

EOS Aqua AMSR-E Sea Ice Validation Program: Meltpond2000 Flight Report

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J.

2000-01-01

This flight report describes the field component of Meltpond2000, the first in a series of Arctic and Antarctic aircraft campaigns planned as part of NASA's Earth Observing System Aqua sea ice validation program for the Advanced Microwave Scanning Radiometer (AMSR-E). This prelaunch Arctic field campaign was carried out between June 25 and July 6, 2000 from Thule, Greenland, with the objective of quantifying the errors incurred by the AMSR-E sea ice algorithms resulting from the presence of melt ponds. A secondary objective of the mission was to develop a microwave capability to discriminate between melt ponds and seawater using low-frequency microwave radiometers. Meltpond2000 was a multiagency effort involving personnel from the Navy, NOAA, and NASA. The field component of the mission consisted of making five 8-hour flights from Thule Air Base with a Naval Air Warfare Center P-3 aircraft over portions of Baffin Bay and the Canadian Arctic. The aircraft sensors were provided and operated by the Microwave Radiometry Group of NOAA's Environmental TechnologyLaboratory. A Navy ice observer from the National Ice Center provided visual documentation of surface ice conditions during each of the flights. Two of the five flights were coordinated with Canadian scientists making surface measurements of melt ponds at an ice camp located near Resolute Bay, Canada. Coordination with the Canadians will provide additional information on surface characteristics and will be of great value in the interpretation of the aircraft and high-resolution satellite data sets.

Evaluation of Crop-Water Consumption Simulation to Support Agricultural Water Resource Management using Satellite-based Water Cycle Observations

NASA Astrophysics Data System (ADS)

Gupta, M.; Bolten, J. D.; Lakshmi, V.

2016-12-01

Water scarcity is one of the main factors limiting agricultural development. Numerical models integrated with remote sensing datasets are increasingly being used operationally as inputs for crop water balance models and agricultural forecasting due to increasing availability of high temporal and spatial resolution datasets. However, the model accuracy in simulating soil water content is affected by the accuracy of the soil hydraulic parameters used in the model, which are used in the governing equations. However, soil databases are known to have a high uncertainty across scales. Also, for agricultural sites, the in-situ measurements of soil moisture are currently limited to discrete measurements at specific locations, and such point-based measurements do not represent the spatial distribution at a larger scale accurately, as soil moisture is highly variable both spatially and temporally. The present study utilizes effective soil hydraulic parameters obtained using a 1-km downscaled microwave remote sensing soil moisture product based on the NASA Advanced Microwave Scanning Radiometer (AMSR-E) using the genetic algorithm inverse method within the Catchment Land Surface Model (CLSM). Secondly, to provide realistic irrigation estimates for agricultural sites, an irrigation scheme within the land surface model is triggered when the root-zone soil moisture deficit reaches the threshold, 50% with respect to the maximum available water capacity obtained from the effective soil hydraulic parameters. An additional important criterion utilized is the estimation of crop water consumption based on dynamic root growth and uptake in root zone layer. Model performance is evaluated using MODIS land surface temperature (LST) product. The soil moisture estimates for the root zone are also validated with the in situ field data, for three sites (2- irrigated and 1- rainfed) located at the University of Nebraska Agricultural Research and Development Center near Mead, NE and monitored by three AmeriFlux installations (Verma et al., 2005).

Drought Prediction for Socio-Cultural Stability Project

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa; Eylander, John B.; Koster, Randall; Narapusetty, Balachandrudu; Kumar, Sujay; Rodell, Matt; Bolten, John; Mocko, David; Walker, Gregory; Arsenault, Kristi;

Anatomy of a local-scale drought: Application of assimilated remote sensing products, crop model, and statistical methods to an agricultural drought study

NASA Astrophysics Data System (ADS)

Mishra, Ashok K.; Ines, Amor V. M.; Das, Narendra N.; Prakash Khedun, C.; Singh, Vijay P.; Sivakumar, Bellie; Hansen, James W.

2015-07-01

Drought is of global concern for society but it originates as a local problem. It has a significant impact on water quantity and quality and influences food, water, and energy security. The consequences of drought vary in space and time, from the local scale (e.g. county level) to regional scale (e.g. state or country level) to global scale. Within the regional scale, there are multiple socio-economic impacts (i.e., agriculture, drinking water supply, and stream health) occurring individually or in combination at local scales, either in clusters or scattered. Even though the application of aggregated drought information at the regional level has been useful in drought management, the latter can be further improved by evaluating the structure and evolution of a drought at the local scale. This study addresses a local-scale agricultural drought anatomy in Story County in Iowa, USA. This complex problem was evaluated using assimilated AMSR-E soil moisture and MODIS-LAI data into a crop model to generate surface and sub-surface drought indices to explore the anatomy of an agricultural drought. Quantification of moisture supply in the root zone remains a gray area in research community, this challenge can be partly overcome by incorporating assimilation of soil moisture and leaf area index into crop modeling framework for agricultural drought quantification, as it performs better in simulating crop yield. It was noted that the persistence of subsurface droughts is in general higher than surface droughts, which can potentially improve forecast accuracy. It was found that both surface and subsurface droughts have an impact on crop yields, albeit with different magnitudes, however, the total water available in the soil profile seemed to have a greater impact on the yield. Further, agricultural drought should not be treated equal for all crops, and it should be calculated based on the root zone depth rather than a fixed soil layer depth. We envisaged that the results of this study will enhance our understanding of agricultural droughts in different parts of the world.

An Evaluation of Soil Moisture Retrievals Using Aircraft and Satellite Passive Microwave Observations during SMEX02

NASA Technical Reports Server (NTRS)

Bolten, John D.; Lakshmi, Venkat

2009-01-01

The Soil Moisture Experiments conducted in Iowa in the summer of 2002 (SMEX02) had many remote sensing instruments that were used to study the spatial and temporal variability of soil moisture. The sensors used in this paper (a subset of the suite of sensors) are the AQUA satellite-based AMSR-E (Advanced Microwave Scanning Radiometer- Earth Observing System) and the aircraft-based PSR (Polarimetric Scanning Radiometer). The SMEX02 design focused on the collection of near simultaneous brightness temperature observations from each of these instruments and in situ soil moisture measurements at field- and domain- scale. This methodology provided a basis for a quantitative analysis of the soil moisture remote sensing potential of each instrument using in situ comparisons and retrieved soil moisture estimates through the application of a radiative transfer model. To this end, the two sensors are compared with respect to their estimation of soil moisture.

Spatio-temporal Root Zone Soil Moisture Estimation for Indo - Gangetic Basin from Satellite Derived (AMSR-2 and SMOS) Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Sure, A.; Dikshit, O.

2017-12-01

Root zone soil moisture (RZSM) is an important element in hydrology and agriculture. The estimation of RZSM provides insight in selecting the appropriate crops for specific soil conditions (soil type, bulk density, etc.). RZSM governs various vadose zone phenomena and subsequently affects the groundwater processes. With various satellite sensors dedicated to estimating surface soil moisture at different spatial and temporal resolutions, estimation of soil moisture at root zone level for Indo - Gangetic basin which inherits complex heterogeneous environment, is quite challenging. This study aims at estimating RZSM and understand its variation at the level of Indo - Gangetic basin with changing land use/land cover, topography, crop cycles, soil properties, temperature and precipitation patterns using two satellite derived soil moisture datasets operating at distinct frequencies with different principles of acquisition. Two surface soil moisture datasets are derived from AMSR-2 (6.9 GHz - `C' Band) and SMOS (1.4 GHz - `L' band) passive microwave sensors with coarse spatial resolution. The Soil Water Index (SWI), accounting for soil moisture from the surface, is derived by considering a theoretical two-layered water balance model and contributes in ascertaining soil moisture at the vadose zone. This index is evaluated against the widely used modelled soil moisture dataset of GLDAS - NOAH, version 2.1. This research enhances the domain of utilising the modelled soil moisture dataset, wherever the ground dataset is unavailable. The coupling between the surface soil moisture and RZSM is analysed for two years (2015-16), by defining a parameter T, the characteristic time length. The study demonstrates that deriving an optimal value of T for estimating SWI at a certain location is a function of various factors such as land, meteorological, and agricultural characteristics.

Snow Cover Mapping at the Continental to Global Scale Using Combined Visible and Passive Microwave Satellite Data

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M.; Savoie, M. H.

2007-12-01

Over the past several decades both visible and passive microwave satellite data have been utilized for snow mapping at the continental to global scale. Snow mapping using visible data has been based primarily on the magnitude of the surface reflectance, and in more recent cases on specific spectral signatures, while microwave data can be used to identify snow cover because the microwave energy emitted by the underlying soil is scattered by the snow grains resulting in a sharp decrease in brightness temperature and a characteristic negative spectral gradient. Both passive microwave and visible data sets indicate a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. We describe the respective problems as well as the advantages and disadvantages of these two types of satellite data for snow cover mapping and demonstrate how a multi-sensor approach is optimal. For the period 1978 to present we combine data from the NOAA weekly snow charts with snow cover derived from the SMMR and SSM/I brightness temperature data. For the period since 2002 we blend NASA EOS MODIS and AMSR-E data sets. Our current product incorporates MODIS data from the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) with microwave-derived snow water equivalent (SWE) at 25 km, resulting in a blended product that includes percent snow cover in the larger grid cell whenever the microwave SWE signal is absent. Validation of AMSR-E at the brightness temperature level is provided through the comparison with data from the well-calibrated heritage SSM/I sensor over large homogeneous snow-covered surfaces (e.g. Dome C region, Antarctica). We also describe how the application of the higher frequency microwave channels (85 and 89 GHz)enhances accurate mapping of shallow and intermittent snow cover.

Biases in Total Precipitable Water Vapor Climatologies from Atmospheric Infrared Sounder and Advanced Microwave Scanning Radiometer

NASA Technical Reports Server (NTRS)

Fetzer, Eric J.; Lambrigtsen, Bjorn H.; Eldering, Annmarie; Aumann, Hartmut H.; Chahine, Moustafa T.

2006-01-01

We examine differences in total precipitable water vapor (PWV) from the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Scanning Radiometer (AMSR-E) experiments sharing the Aqua spacecraft platform. Both systems provide estimates of PWV over water surfaces. We compare AIRS and AMSR-E PWV to constrain AIRS retrieval uncertainties as functions of AIRS retrieved infrared cloud fraction. PWV differences between the two instruments vary only weakly with infrared cloud fraction up to about 70%. Maps of AIRS-AMSR-E PWV differences vary with location and season. Observational biases, when both instruments observe identical scenes, are generally less than 5%. Exceptions are in cold air outbreaks where AIRS is biased moist by 10-20% or 10-60% (depending on retrieval processing) and at high latitudes in winter where AIRS is dry by 5-10%. Sampling biases, from different sampling characteristics of AIRS and AMSR-E, vary in sign and magnitude. AIRS sampling is dry by up to 30% in most high-latitude regions but moist by 5-15% in subtropical stratus cloud belts. Over the northwest Pacific, AIRS samples conditions more moist than AMSR-E by a much as 60%. We hypothesize that both wet and dry sampling biases are due to the effects of clouds on the AIRS retrieval methodology. The sign and magnitude of these biases depend upon the types of cloud present and on the relationship between clouds and PWV. These results for PWV imply that climatologies of height-resolved water vapor from AIRS must take into consideration local meteorological processes affecting AIRS sampling.

Merging thermal and microwave satellite observations for a high-resolution soil moisture data product

USDA-ARS?s Scientific Manuscript database

Many societal applications of soil moisture data products require high spatial resolution and numerical accuracy. Current thermal geostationary satellite sensors (GOES Imager and GOES-R ABI) could produce 2-16km resolution soil moisture proxy data. Passive microwave satellite radiometers (e.g. AMSR...

EOS Aqua AMSR-E Sea Ice Validation Program: Meltpond 2000 Flight Report

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J.

2000-01-01

This flight report describes the field component of Meltpond2000, the first in a series of Arctic and Antarctic aircraft campaigns planned as part of NASA's Earth Observing System Aqua sea ice validation program for the Advanced Microwave Scanning Radiometer (AMSR-E). This prelaunch Arctic field campaign was carried out between June 25 and July 6, 2000 from Thule, Greenland, with the objective of quantifying the errors incurred by the AMSR-E sea ice algorithms resulting from the presence of melt ponds. A secondary objective of the mission was to develop a microwave capability to discriminate between melt ponds and seawater using low-frequency microwave radiometers. Meltpond2000 was a multiagency effort involving personnel from the Navy, National Oceanic and Atmospheric Administration (NOAA), and NASA. The field component of the mission consisted of making five eight-hour flights from Thule Air Base with a Naval Air Warfare Center P-3 aircraft over portions of Baffin Bay and the Canadian Arctic. The aircraft sensors were provided and operated by the Microwave Radiometry Group of NOAA's Environmental Technology Laboratory. A Navy ice observer from the National Ice Center provided visual documentation of surface ice conditions during each of the flights. Two of the five flights were coordinated with Canadian scientists making surface measurements of melt ponds at an ice camp located near Resolute Bay, Canada. Coordination with the Canadians will provide additional information on surface characteristics and will be of great value in the interpretation of the aircraft and high-resolution satellite data sets.

Global Climate Monitoring with the Eos Pm-Platform's Advanced Microwave Scanning Radiometer (AMSR-E)

NASA Technical Reports Server (NTRS)

Spencer, Roy W.

2000-01-01

The Advanced Microwave Scanning Radiometer (AMSR-E) is being built by NASDA to fly on NASA's PM Platform (now called "Aqua") in December 2000. This is in addition to a copy of AMSR that will be launched on Japan's ADEOS-11 satellite in 2001. The AMSRs improve upon the window frequency radiometer heritage of the SSM[l and SMMR instruments. Major improvements over those instruments include channels spanning the 6.9 GHz to 89 GHz frequency range, and higher spatial resolution from a 1.6 m reflector (AMSR-E) and 2.0 m reflector (ADEOS-11 AMSR). The ADEOS-11 AMSR also will have 50.3 and 52.8 GHz channels, providing sensitivity to lower tropospheric temperature. NASA funds an AMSR-E Science Team to provide algorithms for the routine production of a number of standard geophysical products. These products will be generated by the AMSR-E Science Investigator-led Processing System (SIPS) at the Global Hydrology Resource Center (GHRC) in Huntsville, Alabama. While there is a separate NASDA-sponsored activity to develop algorithms and produce products from AMSR, as well as a Joint (NASDA-NASA) AMSR Science Team activity, here I will review only the AMSR-E Team's algorithms and how they benefit from the new capabilities that AMSR-E will provide. The U.S. Team's products will be archived at the National Snow and Ice Data Center (NSIDC). Further information about AMSR-E can be obtained at http://www.jzhcc.msfc.nasa.Vov/AMSR.

Estimation of improved resolution soil moisture in vegetated areas using passive AMSR-E data

NASA Astrophysics Data System (ADS)

Moradizadeh, Mina; Saradjian, Mohammad R.

2018-03-01

Microwave remote sensing provides a unique capability for soil parameter retrievals. Therefore, various soil parameters estimation models have been developed using brightness temperature (BT) measured by passive microwave sensors. Due to the low resolution of satellite microwave radiometer data, the main goal of this study is to develop a downscaling approach to improve the spatial resolution of soil moisture estimates with the use of higher resolution visible/infrared sensor data. Accordingly, after the soil parameters have been obtained using Simultaneous Land Parameters Retrieval Model algorithm, the downscaling method has been applied to the soil moisture estimations that have been validated against in situ soil moisture data. Advance Microwave Scanning Radiometer-EOS BT data in Soil Moisture Experiment 2003 region in the south and north of Oklahoma have been used to this end. Results illustrated that the soil moisture variability is effectively captured at 5 km spatial scales without a significant degradation of the accuracy.

Global-scale assessment and combination of SMAP with ASCAT (Active) and AMSR2 (Passive) soil moisture products

USDA-ARS?s Scientific Manuscript database

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and comb...

A comparison of all-weather land surface temperature products

NASA Astrophysics Data System (ADS)

Martins, Joao; Trigo, Isabel F.; Ghilain, Nicolas; Goettche, Frank-M.; Ermida, Sofia; Olesen, Folke-S.; Gellens-Meulenberghs, Françoise; Arboleda, Alirio

2017-04-01

The Satellite Application Facility on Land Surface Analysis (LSA-SAF, http://landsaf.ipma.pt) has been providing land surface temperature (LST) estimates using SEVIRI/MSG on an operational basis since 2006. The LSA-SAF service has since been extended to provide a wide range of satellite-based quantities over land surfaces, such as emissivity, albedo, radiative fluxes, vegetation state, evapotranspiration, and fire-related variables. Being based on infra-red measurements, the SEVIRI/MSG LST product is limited to clear-sky pixels only. Several all-weather LST products have been proposed by the scientific community either based on microwave observations or using Soil-Vegetation-Atmosphere Transfer models to fill the gaps caused by clouds. The goal of this work is to provide a nearly gap-free operational all-weather LST product and compare these approaches. In order to estimate evapotranspiration and turbulent energy fluxes, the LSA-SAF solves the surface energy budget for each SEVIRI pixel, taking into account the physical and physiological processes occurring in vegetation canopies. This task is accomplished with an adapted SVAT model, which adopts some formulations and parameters of the Tiled ECMWF Scheme for Surface Exchanges over Land (TESSEL) model operated at the European Center for Medium-range Weather Forecasts (ECMWF), and using: 1) radiative inputs also derived by LSA-SAF, which includes surface albedo, down-welling fluxes and fire radiative power; 2) a land-surface characterization obtained by combining the ECOCLIMAP database with both LSA-SAF vegetation products and the H(ydrology)-SAF snow mask; 3) meteorological fields from ECMWF forecasts interpolated to SEVIRI pixels, and 4) soil moisture derived by the H-SAF and LST from LSA-SAF. A byproduct of the SVAT model is surface skin temperature, which is needed to close the surface energy balance. The model skin temperature corresponds to the radiative temperature of the interface between soil and atmosphere, which is assumed to have no heat storage. The modelled skin temperatures are in fair agreement with LST directly estimated from SEVIRI observations. However, in contrast to LST retrievals from SEVIRI/MSG (or other infrared sensors) the SVAT model solves the energy budget equation under all-sky conditions. The SVAT surface skin temperature is then used to fill gaps in LST fields caused by clouds. Since under cloudy conditions the direct incoming solar radiation is greatly reduced, thermal balance at the surface is more easily achieved and directional effects are also less important. Therefore, a better performance of the model skin temperature may be expected. In contrast, under clear skies the satellite LST showed to be more reliable, since the SVAT model shows biases in the daily amplitude of the skin temperature. In the context of the GlobTemperature project (http://www.globtemperature.info/), all-weather LST datasets using AMSR-E microwave radiances were produced, which are compared here to the SVAT-based LST. Both products were validated against in situ data - particularly from Gobabeb & Farm Heimat (Namibia), and Évora (Portugal) - to show that under cloudy conditions the agreement between in-situ LST and modelled skin temperature is acceptable. Compared to the SVAT-based LST, AMSR-E LST is closer to satellite observations (level 2 product); the complementarity of the two approaches is assessed.

[Study of the microwave emissivity characteristics over different land cover types].

PubMed

Zhang, Yong-Pan; Jiang, Ling-Mei; Qiu, Yu-Bao; Wu, Sheng-Li; Shi, Jian-Cheng; Zhang, Li-Xin

2010-06-01

The microwave emissivity over land is very important for describing the characteristics of the lands, and it is also a key factor for retrieving the parameters of land and atmosphere. Different land covers have their emission behavior as a function of structure, water content, and surface roughness. In the present study the global land surface emissivities were calculated using six month (June, 2003-August, 2003, Dec, 2003-Feb, 2004) AMSR-E L2A brightness temperature, MODIS land surface temperature and the layered atmosphere temperature, and humidity and pressure profiles data retrieved from MODIS/Aqua under clear sky conditions. With the information of IGBP land cover types, "pure" pixels were used, which are defined when the fraction cover of each land type is larger than 85%. Then, the emissivity of sixteen land covers at different frequencies, polarization and their seasonal variation were analyzed respectively. The results show that the emissivity of vegetation including forests, grasslands and croplands is higher than that over bare soil, and the polarization difference of vegetation is smaller than that of bare soil. In summer, the emissivity of vegetation is relatively stable because it is in bloom, therefore the authors can use it as its emissivity in our microwave emissivity database over different land cover types. Furthermore, snow cover can heavily impact the change in land cover emissivity, especially in winter.

Global Climate Monitoring with the EOS PM-Platform's Advanced Microwave Scanning Radiometer (AMSR-E)

NASA Technical Reports Server (NTRS)

Spencer, Roy W.

2002-01-01

The Advanced Microwave Scanning 2 Radiometer (AMSR-E) is being built by NASDA to fly on NASA's PM Platform (now called Aqua) in December 2000. This is in addition to a copy of AMSR that will be launched on Japan's ADEOS-II satellite in 2001. The AMSRs improve upon the window frequency radiometer heritage of the SSM/I and SMMR instruments. Major improvements over those instruments include channels spanning the 6.9 GHz to 89 GHz frequency range, and higher spatial resolution from a 1.6 m reflector (AMSR-E) and 2.0 m reflector (ADEOS-II AMSR). The ADEOS-II AMSR also will have 50.3 and 52.8 GHz channels, providing sensitivity to lower tropospheric temperature. NASA funds an AMSR-E Science Team to provide algorithms for the routine production of a number of standard geophysical products. These products will be generated by the AMSR-E Science Investigator-led Processing System (SIPS) at the Global Hydrology Resource Center (GHRC) in Huntsville, Alabama. While there is a separate NASDA-sponsored activity to develop algorithms and produce products from AMSR, as well as a Joint (NASDA-NASA) AMSR Science Team 3 activity, here I will review only the AMSR-E Team's algorithms and how they benefit from the new capabilities that AMSR-E will provide. The US Team's products will be archived at the National Snow and Ice Data Center (NSIDC).

New Technique for Retrieving Liquid Water Path over Land using Satellite Microwave Observations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deeter, M.N.; Vivekanandan, J.

2005-03-18

We present a new methodology for retrieving liquid water path over land using satellite microwave observations. As input, the technique exploits the Advanced Microwave Scanning Radiometer for earth observing plan (EOS) (AMSR-E) polarization-difference signals at 37 and 89 GHz. Regression analysis performed on model simulations indicates that over variable atmospheric and surface conditions the polarization-difference signals can be simply parameterized in terms of the surface emissivity polarization difference ({Delta}{var_epsilon}), surface temperature, liquid water path (LWP), and precipitable water vapor (PWV). The resulting polarization-difference parameterization (PDP) enables fast and direct (noniterative) retrievals of LWP with minimal requirements for ancillary data. Single-more » and dual-channel retrieval methods are described and demonstrated. Data gridding is used to reduce the effects of instrumental noise. The methodology is demonstrated using AMSR-E observations over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site during a six day period in November and December, 2003. Single- and dual-channel retrieval results mostly agree with ground-based microwave retrievals of LWP to within approximately 0.04 mm.« less

Estimation de la superficie du couvert nival a partir d'une combinaison des donnees de teledetection MODIS et AMSR-E dans un contexte de prevision des crues printanieres au Quebec

NASA Astrophysics Data System (ADS)

Bergeron, Jean

Snow cover estimation is a principal source of error for spring streamflow simulations in Québec, Canada. Optical and near infrared remote sensing can improve snow cover area (SCA) estimation due to high spatial resolution but is limited by cloud cover and incoming solar radiation. Passive microwave remote sensing is complementary by its near-transparence to cloud cover and independence to incoming solar radiation, but is limited by its coarse spatial resolution. The study aims to create an improved SCA product from blended passive microwave (AMSR-E daily L3 Brightness Temperature) and optical (MODIS Terra and Aqua daily snow cover L3) remote sensing data in order to improve estimation of river streamflow caused by snowmelt with Québec's operational MOHYSE hydrological model through direct-insertion of the blended SCA product in a coupled snowmelt module (SPH-AV). SCA estimated from AMSR-E data is first compared with SCA estimated with MODIS, as well as with in situ snow depth measurements. Results show good agreement (+95%) between AMSR-E-derived and MODIS-derived SCA products in spring but comparisons with Environment Canada ground stations and SCA derived from Advanced Very High Resolution Radiometer (AVHRR) data show lesser agreements (83 % and 74% respectively). Results also show that AMSR-E generally underestimates SCA. Assimilating the blended snow product in SPH-AV coupled with MOHYSE yields significant improvement of simulated streamflow for the aux Écorces et au Saumon rivers overall when compared with simulations with no update during thaw events, These improvements are similar to results driven by biweekly ground data. Assimilation of remotely-sensed passive microwave data was also found to have little positive impact on springflood forecast due to the difficulty in differentiating melting snow from snow-free surfaces. Considering the direct-insertion and Newtonian nudging assimilation methods, the study also shows the latter method to be superior to the former, notably when assimilating noisy data. Keywords: Snow cover, spring streamflow, MODIS, AMSR-E, hydrological model.

Parametrization of Land Surface Temperature Fields with Optical and Microwave Remote Sensing in Brazil's Atlantic Forest

NASA Astrophysics Data System (ADS)

McDonald, K. C.; Khan, A.; Carnaval, A. C.

2016-12-01

Brazil is home to two of the largest and most biodiverse ecosystems in the world, primarily encompassed in forests and wetlands. A main region of interest in this project is Brazil's Atlantic Forest (AF). Although this forest is only a fraction of the size of the Amazon rainforest, it harbors significant biological richness, making it one of the world's major hotspots for biodiversity. The AF is located on the East to Southeast region of Brazil, bordering the Atlantic Ocean. As luscious and biologically rich as this region is, the area covered by the Atlantic Forest has been diminishing over past decades, mainly due to human influences and effects of climate change. We examine 1 km resolution Land Surface Temperature (LST) data from NASA's Moderate-resolution Imaging Spectroradiometer (MODIS) combined with 25 km resolution radiometric temperature derived from NASA's Advanced Microwave Scanning Radiometer on EOS (AMSR-E) to develop a capability employing both in combination to assess LST. Since AMSR-E is a microwave remote sensing instrument, products derived from its measurements are minimally effected by cloud cover. On the other hand, MODIS data are heavily influenced by cloud cover. We employ a statistical downscaling technique to the coarse-resolution AMSR-E datasets to enhance its spatial resolution to match that of MODIS. Our approach employs 16-day composite MODIS LST data in combination with synergistic ASMR-E radiometric brightness temperature data to develop a combined, downscaled dataset. Our goal is to use this integrated LST retrieval with complementary in situ station data to examine associated influences on regional biodiversity

Parameterization of L-, C- and X-band Radiometer-based Soil Moisture Retrieval Algorithm Using In-situ Validation Sites

NASA Astrophysics Data System (ADS)

Gao, Y.; Colliander, A.; Burgin, M. S.; Walker, J. P.; Chae, C. S.; Dinnat, E.; Cosh, M. H.; Caldwell, T. G.

2017-12-01

Passive microwave remote sensing has become an important technique for global soil moisture estimation over the past three decades. A number of missions carrying sensors at different frequencies that are capable for soil moisture retrieval have been launched. Among them, there are Japan Aerospace Exploration Agency's (JAXA's) Advanced Microwave Scanning Radiometer-EOS (AMSR-E) launched in May 2002 on the National Aeronautics and Space Administration (NASA) Aqua satellite (ceased operation in October 2011), European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) mission launched in November 2009, JAXA's Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the GCOM-W satellite launched in May 2012, and NASA's Soil Moisture Active Passive (SMAP) mission launched in January 2015. Therefore, there is an opportunity to develop a consistent inter-calibrated long-term soil moisture data record based on the availability of these four missions. This study focuses on the parametrization of the tau-omega model at L-, C- and X-band using the brightness temperature (TB) observations from the four missions and the in-situ soil moisture and soil temperature data from core validation sites across various landcover types. The same ancillary data sets as the SMAP baseline algorithm are applied for retrieval at different frequencies. Preliminary comparison of SMAP and AMSR2 TB observations against forward-simulated TB at the Yanco site in Australia showed a generally good agreement with each other and higher correlation for the vertical polarization (R=0.96 for L-band and 0.93 for C- and X-band). Simultaneous calibrations of the vegetation parameter b and roughness parameter h at both horizontal and vertical polarizations are also performed. Finally, a set of model parameters for successfully retrieving soil moisture at different validation sites at L-, C- and X-band respectively are presented. The research described in this paper is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with NASA. Copyright 2017. All rights reserved.

Soil moisture observations using L-, C-, and X-band microwave radiometers

NASA Astrophysics Data System (ADS)

Bolten, John Dennis

The purpose of this thesis is to further the current understanding of soil moisture remote sensing under varying conditions using L-, C-, and X-band. Aircraft and satellite instruments are used to investigate the effects of frequency and spatial resolution on soil moisture sensitivity. The specific objectives of the research are to examine multi-scale observed and modeled microwave radiobrightness, evaluate new EOS Aqua Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperature and soil moisture retrievals, and examine future satellite-based technologies for soil moisture sensing. The cycling of Earth's water, energy and carbon is vital to understanding global climate. Over land, these processes are largely dependent on the amount of moisture within the top few centimeters of the soil. However, there are currently no methods available that can accurately characterize Earth's soil moisture layer at the spatial scales or temporal resolutions appropriate for climate modeling. The current work uses ground truth, satellite and aircraft remote sensing data from three large-scale field experiments having different land surface, topographic and climate conditions. A physically-based radiative transfer model is used to simulate the observed aircraft and satellite measurements using spatially and temporally co-located surface parameters. A robust analysis of surface heterogeneity and scaling is possible due to the combination of multiple datasets from a range of microwave frequencies and field conditions. Accurate characterization of spatial and temporal variability of soil moisture during the three field experiments is achieved through sensor calibration and algorithm validation. Comparisons of satellite observations and resampled aircraft observations are made using soil moisture from a Numerical Weather Prediction (NWP) model in order to further demonstrate a soil moisture correlation where point data was unavailable. The influence of vegetation, spatial scaling, and surface heterogeneity on multi-scale soil moisture prediction is presented. This work demonstrates that derived soil moisture using remote sensing provides a better coverage of soil moisture spatial variability than traditional in-situ sensors. Effects of spatial scale were shown to be less significant than frequency on soil moisture sensitivity. Retrievals of soil moisture using the current methods proved inadequate under some conditions; however, this study demonstrates the need for concurrent spaceborne frequencies including L-, C, and X-band.

Global retrieval of soil moisture and vegetation properties using data-driven methods

NASA Astrophysics Data System (ADS)

Rodriguez-Fernandez, Nemesio; Richaume, Philippe; Kerr, Yann

2017-04-01

Data-driven methods such as neural networks (NNs) are a powerful tool to retrieve soil moisture from multi-wavelength remote sensing observations at global scale. In this presentation we will review a number of recent results regarding the retrieval of soil moisture with the Soil Moisture and Ocean Salinity (SMOS) satellite, either using SMOS brightness temperatures as input data for the retrieval or using SMOS soil moisture retrievals as reference dataset for the training. The presentation will discuss several possibilities for both the input datasets and the datasets to be used as reference for the supervised learning phase. Regarding the input datasets, it will be shown that NNs take advantage of the synergy of SMOS data and data from other sensors such as the Advanced Scatterometer (ASCAT, active microwaves) and MODIS (visible and infra red). NNs have also been successfully used to construct long time series of soil moisture from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) and SMOS. A NN with input data from ASMR-E observations and SMOS soil moisture as reference for the training was used to construct a dataset sharing a similar climatology and without a significant bias with respect to SMOS soil moisture. Regarding the reference data to train the data-driven retrievals, we will show different possibilities depending on the application. Using actual in situ measurements is challenging at global scale due to the scarce distribution of sensors. In contrast, in situ measurements have been successfully used to retrieve SM at continental scale in North America, where the density of in situ measurement stations is high. Using global land surface models to train the NN constitute an interesting alternative to implement new remote sensing surface datasets. In addition, these datasets can be used to perform data assimilation into the model used as reference for the training. This approach has recently been tested at the European Centre for Medium-Range Weather Forecasts (ECMWF). Finally, retrievals using radiative transfer models can also be used as a reference SM dataset for the training phase. This approach was used to retrieve soil moisture from ASMR-E, as mentioned above, and also to implement the official European Space Agency (ESA) SMOS soil moisture product in Near-Real-Time. We will finish with a discussion of the retrieval of vegetation parameters from SMOS observations using data-driven methods.

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Wanders, N.; Bierkens, M. F. P.; de Jong, S. M.; de Roo, A.; Karssenberg, D.

2014-08-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land-surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas. This article was corrected on 15 SEP 2014. See the end of the full text for details.

Rainfall estimation over-land using SMOS soil moisture observations: SM2RAIN, LMAA and SMART algorithms

NASA Astrophysics Data System (ADS)

Massari, Christian; Brocca, Luca; Pellarin, Thierry; Kerr, Yann; Crow, Wade; Cascon, Carlos; Ciabatta, Luca

2016-04-01

Recent advancements in the measurement of precipitation from space have provided estimates at scales that are commensurate with the needs of the hydrological and land-surface model communities. However, as demonstrated in a number of studies (Ebert et al. 2007, Tian et al. 2007, Stampoulis et al. 2012) satellite rainfall estimates are characterized by low accuracy in certain conditions and still suffer from a number of issues (e.g., bias) that may limit their utility in over-land applications (Serrat-Capdevila et al. 2014). In recent years many studies have demonstrated that soil moisture observations from ground and satellite sensors can be used for correcting satellite precipitation estimates (e.g. Crow et al., 2011; Pellarin et al., 2013), or directly estimating rainfall (SM2RAIN, Brocca et al., 2014). In this study, we carried out a detailed scientific analysis in which these three different methods are used for: i) estimating rainfall through satellite soil moisture observations (SM2RAIN, Brocca et al., 2014); ii) correcting rainfall through a Land surface Model Assimilation Algorithm (LMAA) (an improvement of a previous work of Crow et al. 2011 and Pellarin et al. 2013) and through the Soil Moisture Analysis Rainfall Tool (SMART, Crow et al. 2011). The analysis is carried within the ESA project "SMOS plus Rainfall" and involves 9 sites in Europe, Australia, Africa and USA containing high-quality hydrometeorological and soil moisture observations. Satellite soil moisture data from Soil Moisture and Ocean Salinity (SMOS) mission are employed for testing their potential in deriving a cumulated rainfall product at different temporal resolutions. The applicability and accuracy of the three algorithms is investigated also as a function of climatic and soil/land use conditions. A particular attention is paid to assess the expected limitations soil moisture based rainfall estimates such as soil saturation, freezing/snow conditions, SMOS RFI, irrigated areas, contribution of surface runoff and evapotranspiration, vegetation coverage, temporal sampling, and the assimilation/modelling approach. The 9 selected sites gather such potential problems which are shown and discussed at the conference. REFERENCES Ebert, E. E.; Janowiak, J. E.; Kidd, C. Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical Models. Bull. Am. Meteorol. Soc. 2007, 88, 47-64. Tian, Y.; Peters-Lidard, C. D.; Choudhury, B. J.; Garcia, M. Multitemporal Analysis of TRMM-Based Satellite Precipitation Products for Land Data Assimilation Applications. J. Hydrometeorol. 2007, 8, 1165-1183. Stampoulis, D.; Anagnostou, E. N. Evaluation of Global Satellite Rainfall Products over Continental Europe. J. Hydrometeorol. 2012, 13, 588-603. Serrat-Capdevila, A.; Valdes, J. B.; Stakhiv, E. Z. Water Management Applications for Satellite Precipitation Products: Synthesis and Recommendations. JAWRA J. Am. Water Resour. Assoc. 2014, 50, 509-525. Crow, W. T.; van den Berg, M. J.; Huffman, G. J.; Pellarin, T. Correcting rainfall using satellite-based surface soil moisture retrievals: The Soil Moisture Analysis Rainfall Tool (SMART). Water Resour. Res. 2011, 47, W08521. Pellarin, T.; Louvet, S.; Gruhier, C.; Quantin, G.; Legout, C. A simple and effective method for correcting soil moisture and precipitation estimates using AMSR-E measurements. Remote Sens. Environ. 2013, 136, 28-36. Brocca, L.; Ciabatta, L.; Massari, C.; Moramarco, T.; Hahn, S.; Hasenauer, S.; Kidd, R.; Dorigo, W.; Wagner, W.; Levizzani, V. Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data. J. Geophys. Res. Atmos. 2014, 119, 5128-5141.

Developing Soil Moisture Profiles Utilizing Remotely Sensed MW and TIR Based SM Estimates Through Principle of Maximum Entropy

NASA Astrophysics Data System (ADS)

Mishra, V.; Cruise, J. F.; Mecikalski, J. R.

2015-12-01

Developing accurate vertical soil moisture profiles with minimum input requirements is important to agricultural as well as land surface modeling. Earlier studies show that the principle of maximum entropy (POME) can be utilized to develop vertical soil moisture profiles with accuracy (MAE of about 1% for a monotonically dry profile; nearly 2% for monotonically wet profiles and 3.8% for mixed profiles) with minimum constraints (surface, mean and bottom soil moisture contents). In this study, the constraints for the vertical soil moisture profiles were obtained from remotely sensed data. Low resolution (25 km) MW soil moisture estimates (AMSR-E) were downscaled to 4 km using a soil evaporation efficiency index based disaggregation approach. The downscaled MW soil moisture estimates served as a surface boundary condition, while 4 km resolution TIR based Atmospheric Land Exchange Inverse (ALEXI) estimates provided the required mean root-zone soil moisture content. Bottom soil moisture content is assumed to be a soil dependent constant. Mulit-year (2002-2011) gridded profiles were developed for the southeastern United States using the POME method. The soil moisture profiles were compared to those generated in land surface models (Land Information System (LIS) and an agricultural model DSSAT) along with available NRCS SCAN sites in the study region. The end product, spatial soil moisture profiles, can be assimilated into agricultural and hydrologic models in lieu of precipitation for data scarce regions.Developing accurate vertical soil moisture profiles with minimum input requirements is important to agricultural as well as land surface modeling. Previous studies have shown that the principle of maximum entropy (POME) can be utilized with minimal constraints to develop vertical soil moisture profiles with accuracy (MAE = 1% for monotonically dry profiles; MAE = 2% for monotonically wet profiles and MAE = 3.8% for mixed profiles) when compared to laboratory and field data. In this study, vertical soil moisture profiles were developed using the POME model to evaluate an irrigation schedule over a maze field in north central Alabama (USA). The model was validated using both field data and a physically based mathematical model. The results demonstrate that a simple two-constraint entropy model under the assumption of a uniform initial soil moisture distribution can simulate most soil moisture profiles within the field area for 6 different soil types. The results of the irrigation simulation demonstrated that the POME model produced a very efficient irrigation strategy with loss of about 1.9% of the total applied irrigation water. However, areas of fine-textured soil (i.e. silty clay) resulted in plant stress of nearly 30% of the available moisture content due to insufficient water supply on the last day of the drying phase of the irrigation cycle. Overall, the POME approach showed promise as a general strategy to guide irrigation in humid environments, with minimum input requirements.

Comparison of AMSR-E and SSM/I snow parameter retrievals over the Ob river basin

USGS Publications Warehouse

Mognard, N.M.; Grippa, M.; LeToan, T.; Kelly, R.E.J.; Chang, A.T.C.; Josberger, E.G.

2004-01-01

Passive microwave observations from the Advanced Microwave Scanning Radiometer - EOS (AMSR-E) and from the Special Sensor Microwave Imager (SSM/I) are used to analyse the evolution of the snow pack in the Ob river basin during the snow season of 2002-03. The Ob river is the biggest Russian river with respect to its watershed area (2 975 000 km2). The Ob originates in the Altai mountains and flows northward across the vast West Siberian lowland towards the Arctic Ocean. The majority of snow cover is contained in the lowlands rather than in mountainous regions and persists for six months or more. During the snow season, surface air temperatures are very cold. Therefore, the combination of cold dry snow and large areas of uniform topography is ideal for snowpack extent and water equivalent retrievals from passive microwave observations. The thermal gradient through the snow pack is estimated and used to model the growth of the snow grain size and to compute the evolution of the passive microwave derived snow depth over the region. A comparison between the AMSR-E and SSM/I estimates is performed and the differences between the snow parameters from the two satellite instruments are analysed.

Arctic Sea Ice Parameters from AMSR-E Data using Two Techniques, and Comparisons with Sea Ice from SSM

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Parkinson, Claire L.

2007-01-01

We use two algorithms to process AMSR-E data in order to determine algorithm dependence, if any, on the estimates of sea ice concentration, ice extent and area, and trends and to evaluate how AMSR-E data compare with historical SSM/I data. The monthly ice concentrations derived from the two algorithms from AMSR-E data (the AMSR-E Bootstrap Algorithm, or ABA, and the enhanced NASA Team algorithm, or NT2) differ on average by about 1 to 3%, with data from the consolidated ice region being generally comparable for ABA and NT2 retrievals while data in the marginal ice zones and thin ice regions show higher values when the NT2 algorithm is used. The ice extents and areas derived separately from AMSR-E using these two algorithms are, however, in good agreement, with the differences (ABA-NT2) being about 6.6 x 10(exp 4) square kilometers on average for ice extents and -6.6 x 10(exp 4) square kilometers for ice area which are small compared to mean seasonal values of 10.5 x 10(exp 6) and 9.8 x 10(exp 6) for ice extent and area: respectively. Likewise, extents and areas derived from the same algorithm but from AMSR-E and SSM/I data are consistent but differ by about -24.4 x 10(exp 4) square kilometers and -13.9 x 10(exp 4) square kilometers, respectively. The discrepancies are larger with the estimates of extents than area mainly because of differences in channel selection and sensor resolutions. Trends in extent during the AMSR-E era were also estimated and results from all three data sets are shown to be in good agreement (within errors).

Evapotranspiration Controls Imposed by Soil Moisture: A Spatial Analysis across the United States

NASA Astrophysics Data System (ADS)

Rigden, A. J.; Tuttle, S. E.; Salvucci, G.

2014-12-01

We spatially analyze the control over evapotranspiration (ET) imposed by soil moisture across the United States using daily estimates of satellite-derived soil moisture and data-driven ET over a nine-year period (June 2002-June 2011) at 305 locations. The soil moisture data are developed using 0.25-degree resolution satellite observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), where the 9-year time series for each 0.25-degree pixel was selected from three potential algorithms (VUA-NASA, U. Montana, & NASA) based on the maximum mutual information between soil moisture and precipitation (Tuttle & Salvucci (2014), Remote Sens Environ, 114: 207-222). The ET data are developed independent of soil moisture using an emergent relationship between the diurnal cycle of the relative humidity profile and ET. The emergent relation is that the vertical variance of the relative humidity profile is less than what would occur for increased or decreased ET rates, suggesting that land-atmosphere feedback processes minimize this variance (Salvucci and Gentine (2013), PNAS, 110(16): 6287-6291). The key advantage of using this approach to estimate ET is that no measurements of surface limiting factors (soil moisture, leaf area, canopy conductance) are required; instead, ET is estimated from meteorological data measured at 305 common weather stations that are approximately uniformly distributed across the United States. The combination of these two independent datasets allows for a unique spatial analysis of the control on ET imposed by the availability of soil moisture. We fit evaporation efficiency curves across the United States at each of the 305 sites during the summertime (May-June-July-August-September). Spatial patterns are visualized by mapping optimal curve fitting coefficients across the Unites States. An analysis of efficiency curves and their spatial patterns will be presented.

Improving soil moisture simulation to support Agricultural Water Resource Management using Satellite-based water cycle observations

NASA Astrophysics Data System (ADS)

Gupta, Manika; Bolten, John; Lakshmi, Venkat

2016-04-01

Efficient and sustainable irrigation systems require optimization of operational parameters such as irrigation amount which are dependent on the soil hydraulic parameters that affect the model's accuracy in simulating soil water content. However, it is a scientific challenge to provide reliable estimates of soil hydraulic parameters and irrigation estimates, given the absence of continuously operating soil moisture and rain gauge network. For agricultural water resource management, the in-situ measurements of soil moisture are currently limited to discrete measurements at specific locations, and such point-based measurements do not represent the spatial distribution at a larger scale accurately, as soil moisture is highly variable both spatially and temporally (Wang and Qu 2009). In the current study, flood irrigation scheme within the land surface model is triggered when the root-zone soil moisture deficit reaches below a threshold of 25%, 50% and 75% with respect to the maximum available water capacity (difference between field capacity and wilting point) and applied until the top layer is saturated. An additional important criterion needed to activate the irrigation scheme is to ensure that it is irrigation season by assuming that the greenness vegetation fraction (GVF) of the pixel exceed 0.40 of the climatological annual range of GVF (Ozdogan et al. 2010). The main hypothesis used in this study is that near-surface remote sensing soil moisture data contain useful information that can describe the effective hydrological conditions of the basin such that when appropriately inverted, it would provide field capacity and wilting point soil moisture, which may be representative of that basin. Thus, genetic algorithm inverse method is employed to derive the effective parameters and derive the soil moisture deficit for the root zone by coupling of AMSR-E soil moisture with the physically based hydrological model. Model performance is evaluated using MODIS-evapotranspiration (ET) and MODIS land surface temperature (LST) products. The soil moisture estimates for the root zone are also validated with the in-situ field data, for three sites (2- irrigated and 1- rainfed) located at the University of Nebraska Agricultural Research and Development Center near Mead, NE and monitored by three AmeriFlux installations (Verma et al., 2005) by evaluating the root mean square error (RMSE) and Mean Bias error (MBE).

Assessment of Surface Water Storage trends for increasing groundwater areas in India

NASA Astrophysics Data System (ADS)

Banerjee, Chandan; Kumar, D. Nagesh

2018-07-01

Recent studies based on Gravity Recovery and Climate Experiment (GRACE) satellite mission suggested that groundwater has increased in central and southern parts of India. However, surface water, which is an equally important source of water in these semi-arid areas has not been studied yet. In the present study, the study areas were outlined based on trends in GRACE data followed by trend identification in surface water storages and checking the hypothesis of causality. Surface Water Extent (SWE) and Surface Soil Moisture (SSM) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) respectively, are selected as proxies of surface water storage (SWS). Besides SWE and SSM, trend test was performed for GRACE derived terrestrial water storage (TWS) for the study areas named as R1, R2, GOR1 and KOR1. Granger-causality test is used to test the hypothesis that rainfall is a causal factor of the inter-annual variability of SWE, SSM and TWS. Positive trends were observed in TWS for R1, R2 and GOR1 whereas SWE and SSM show increasing trends for all the study regions. Results suggest that rainfall is the granger-causal of all the storage variables for R1 and R2, the regions exhibiting the most significant positive trends in TWS.

Discrimination of the earthquake-origin microwave emission from the data of the spaceborne radiometer

NASA Astrophysics Data System (ADS)

Maeda, T.; Takano, T.

2007-12-01

Formerly, we found the microwave emission during rock crash in a laboratory for the first time in the world, and calibrated the emitted power. The detected signal is a sequence of pulses which include microwaves at the selected frequency bands of 300MHz, 2GHz and 22GHz. This fact suggested another means to detect an earthquake which is associated with rock crash or plate slip. For this purpose, we have analyzed the data obtained by the microwave radiometer, AMSR-E loaded on the satellite Aqua. Generally, since a microwave emission observed by AMSR-E is affected by various factors (e.g., emission of the earth's surface and emission, absorption and scattering of the atmosphere), we developed some analysis techniques first. Then, we have successfully extracted features observed only at the earthquake occurrence by these techniques. This earthquake was occurred at Morocco in 2004. Since the depth of the seismic center was shallower and the magnitude was larger, we have specifically focused on analysis of this earthquake. This presentation first presents the estimation of the received power by a receiver aboard a satellite. Then, the data obtained by AMSR-E are described including the disturbances or ambiguity of the data. The techniques to extract microwave signatures out of disturbances are given. Finally, an example of the data analysis is explained in the case of Morocco earthquake to show distinct emission of microwaves in relation with geological features.

Trends in the Sea Ice Cover Using Enhanced and Compatible AMSR-E, SSM/I and SMMR Data

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.; Nishio, Fumihiko

2007-01-01

Arguably, the most remarkable manifestation of change in the polar regions is the rapid decline (of about -10 %/decade) in the Arctic perennial ice cover. Changes in the global sea ice cover, however, are more modest, being slightly positive in the Southern Hemisphere and slightly negative in the Northern Hemisphere, the significance of which has not been adequately assessed because of unknown errors in the satellite historical data. We take advantage of the recent and more accurate AMSR-E data to evaluate the true seasonal and interannual variability of the sea ice cover, assess the accuracy of historical data, and determine the real trend. Consistently derived ice concentrations from AMSR-E, SSM/I, and SMMR data were analyzed and a slight bias is observed between AMSR-E and SSM/I data mainly because of differences in resolution. Analysis of the combine SMMR, SSM/I and AMSR-E data set, with the bias corrected, shows that the trends in extent and area of sea ice in the Arctic region is -3.4 +/- 0.2 and -4.0 +/- 0.2 % per decade, respectively, while the corresponding values for the Antarctic region is 0.9 +/- 0.2 and 1.7 .+/- 0.3 % per decade. The higher resolution of the AMSR-E provides an improved determination of the location of the ice edge while the SSM/I data show an ice edge about 6 to 12 km further away from the ice pack. Although the current record of AMSR-E is less than 5 years, the data can be utilized in combination with historical data for more accurate determination of the variability and trends in the ice cover.

Status of calibration and data evaluation of AMSR on board ADEOS-II

NASA Astrophysics Data System (ADS)

Imaoka, Keiji; Fujimoto, Yasuhiro; Kachi, Misako; Takeshima, Toshiaki; Igarashi, Tamotsu; Kawanishi, Toneo; Shibata, Akira

2004-02-01

The Advanced Microwave Scanning Radiometer (AMSR) is the multi-frequency, passive microwave radiometer on board the Advanced Earth Observing Satellite-II (ADEOS-II), currently called Midori-II. The instrument has eight-frequency channels with dual polarization (except 50-GHz band) covering frequencies between 6.925 and 89.0 GHz. Measurement of 50-GHz channels is the first attempt by this kind of conically scanning microwave radiometers. Basic concept of the instrument including hardware configuration and calibration method is almost the same as that of ASMR for EOS (AMSR-E), the modified version of AMSR. Its swath width of 1,600 km is wider than that of AMSR-E. In parallel with the calibration and data evaluation of AMSR-E instrument, almost identical calibration activities have been made for AMSR instrument. After finished the initial checkout phase, the instrument has been continuously obtaining the data in global basis. Time series of radiometer sensitivities and automatic gain control telemetry indicate the stable instrument performance. For the radiometric calibration, we are now trying to apply the same procedure that is being used for AMSR-E. This paper provides an overview of the instrument characteristics, instrument status, and preliminary results of calibration and data evaluation activities.

Satellite Gravimetry Applied to Drought Monitoring

NASA Technical Reports Server (NTRS)

Rodell, Matthew

2010-01-01

Near-surface wetness conditions change rapidly with the weather, which limits their usefulness as drought indicators. Deeper stores of water, including root-zone soil wetness and groundwater, portend longer-term weather trends and climate variations, thus they are well suited for quantifying droughts. However, the existing in situ networks for monitoring these variables suffer from significant discontinuities (short records and spatial undersampling), as well as the inherent human and mechanical errors associated with the soil moisture and groundwater observation. Remote sensing is a promising alternative, but standard remote sensors, which measure various wavelengths of light emitted or reflected from Earth's surface and atmosphere, can only directly detect wetness conditions within the first few centimeters of the land s surface. Such sensors include the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) C-band passive microwave measurement system on the National Aeronautic and Space Administration's (NASA) Aqua satellite, and the combined active and passive L-band microwave system currently under development for NASA's planned Soil Moisture Active Passive (SMAP) satellite mission. These instruments are sensitive to water as deep as the top 2 cm and 5 cm of the soil column, respectively, with the specific depth depending on vegetation cover. Thermal infrared (TIR) imaging has been used to infer water stored in the full root zone, with limitations: auxiliary information including soil grain size is required, the TIR temperature versus soil water content curve becomes flat as wetness increases, and dense vegetation and cloud cover impede measurement. Numerical models of land surface hydrology are another potential solution, but the quality of output from such models is limited by errors in the input data and tradeoffs between model realism and computational efficiency. This chapter is divided into eight sections, the next of which describes the theory behind satellite gravimetry. Following that is a summary of the GRACE mission and how hydrological information is gleaned from its gravity products. The fourth section provides examples of hydrological science enabled by GRACE. The fifth and sixth sections list the challenging aspects of GRACE derived hydrology data and how they are being overcome, including the use of data assimilation. The seventh section describes recent progress in applying GRACE for drought monitoring, including the development of new soil moisture and drought indicator products, and that is followed by a discussion of future prospects in satellite gravimetry based drought monitoring.

Using a Support Vector Machine and a Land Surface Model to Estimate Large-Scale Passive Microwave Temperatures over Snow-Covered Land in North America

NASA Technical Reports Server (NTRS)

Forman, Barton A.; Reichle, Rolf Helmut

2014-01-01

A support vector machine (SVM), a machine learning technique developed from statistical learning theory, is employed for the purpose of estimating passive microwave (PMW) brightness temperatures over snow-covered land in North America as observed by the Advanced Microwave Scanning Radiometer (AMSR-E) satellite sensor. The capability of the trained SVM is compared relative to the artificial neural network (ANN) estimates originally presented in [14]. The results suggest the SVM outperforms the ANN at 10.65 GHz, 18.7 GHz, and 36.5 GHz for both vertically and horizontally-polarized PMW radiation. When compared against daily AMSR-E measurements not used during the training procedure and subsequently averaged across the North American domain over the 9-year study period, the root mean squared error in the SVM output is 8 K or less while the anomaly correlation coefficient is 0.7 or greater. When compared relative to the results from the ANN at any of the six frequency and polarization combinations tested, the root mean squared error was reduced by more than 18 percent while the anomaly correlation coefficient was increased by more than 52 percent. Further, the temporal and spatial variability in the modeled brightness temperatures via the SVM more closely agrees with that found in the original AMSR-E measurements. These findings suggest the SVM is a superior alternative to the ANN for eventual use as a measurement operator within a data assimilation framework.

Validation of EOS Aqua AMSR Sea Ice Products for East Antarctica

NASA Technical Reports Server (NTRS)

Massom, Rob; Lytle, Vicky; Allison, Ian; Worby, Tony; Markus, Thorsten; Scambos, Ted; Haran, Terry; Enomoto, Hiro; Tateyama, Kazu; Pfaffling, Andi

2004-01-01

This paper presents results from AMSR-E validation activities during a collaborative international cruise onboard the RV Aurora Australis to the East Antarctic sea ice zone (64-65 deg.S, 110-120 deg.E) in the early Austral spring of 2003. The validation strategy entailed an IS-day survey of the statistical characteristics of sea ice and snowcover over a Lagrangian grid 100 x 50 km in size (demarcated by 9 drifting ice beacons) i.e. at a scale representative of Ah4SR pixels. Ice conditions ranged h m consolidated first-year ice to a large polynya offshore from Casey Base. Data sets collected include: snow depth and snow-ice interface temperatures on 24 (?) randomly-selected floes in grid cells within a 10 x 50 km area (using helicopters); detailed snow and ice measurements at 13 dedicated ice stations, one of which lasted for 4 days; time-series measurements of snow temperature and thickness at selected sites; 8 aerial photography and thermal-IR radiometer flights; other satellite products (SAR, AVHRR, MODIS, MISR, ASTER and Envisat MERIS); ice drift data; and ancillary meteorological (ship-based, meteorological buoys, twice-daily radiosondes). These data are applied to a validation of standard AMSR-E ice concentration, snowcover thickness and ice-temperature products. In addition, a validation is carried out of ice-surface skin temperature products h m the NOAA AVHRR and EOS MODIS datasets.

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

NASA Astrophysics Data System (ADS)

Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

Reprocessing the Historical Satellite Passive Microwave Record at Enhanced Spatial Resolutions using Image Reconstruction

NASA Astrophysics Data System (ADS)

Hardman, M.; Brodzik, M. J.; Long, D. G.; Paget, A. C.; Armstrong, R. L.

2015-12-01

Beginning in 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, near-global spatial coverage for monitoring changes in hydrologic and cryospheric parameters that include precipitation, soil moisture, surface water, vegetation, snow water equivalent, sea ice concentration and sea ice motion. Currently available global gridded passive microwave data sets serve a diverse community of hundreds of data users, but do not meet many requirements of modern Earth System Data Records (ESDRs) or Climate Data Records (CDRs), most notably in the areas of intersensor calibration, quality-control, provenance and consistent processing methods. The original gridding techniques were relatively primitive and were produced on 25 km grids using the original EASE-Grid definition that is not easily accommodated in modern software packages. Further, since the first Level 3 data sets were produced, the Level 2 passive microwave data on which they were based have been reprocessed as Fundamental CDRs (FCDRs) with improved calibration and documentation. We are funded by NASA MEaSUREs to reprocess the historical gridded data sets as EASE-Grid 2.0 ESDRs, using the most mature available Level 2 satellite passive microwave (SMMR, SSM/I-SSMIS, AMSR-E) records from 1978 to the present. We have produced prototype data from SSM/I and AMSR-E for the year 2003, for review and feedback from our Early Adopter user community. The prototype data set includes conventional, low-resolution ("drop-in-the-bucket" 25 km) grids and enhanced-resolution grids derived from the two candidate image reconstruction techniques we are evaluating: 1) Backus-Gilbert (BG) interpolation and 2) a radiometer version of Scatterometer Image Reconstruction (SIR). We summarize our temporal subsetting technique, algorithm tuning parameters and computational costs, and include sample SSM/I images at enhanced resolutions of up to 3 km. We are actively working with our Early Adopters to finalize content and format of this new, consistently-processed high-quality satellite passive microwave ESDR.

Integrating Enhanced Grace Terrestrial Water Storage Data Into the U.S. and North American Drought Monitors

NASA Technical Reports Server (NTRS)

Housborg, Rasmus; Rodell, Matthew

2010-01-01

NASA's Gravity Recovery and Climate Experiment (GRACE) satellites measure time variations nf the Earth's gravity field enabling reliable detection of spatio-temporal variations in total terrestrial water storage (TWS), including ground water. The U.S. and North American Drought Monitors are two of the premier drought monitoring products available to decision-makers for assessing and minimizing drought impacts, but they rely heavily on precipitation indices and do not currently incorporate systematic observations of deep soil moisture and groundwater storage conditions. Thus GRACE has great potential to improve the Drought Monitors hy filling this observational gap. Horizontal, vertical and temporal disaggregation of the coarse-resolution GRACE TWS data has been accomplished by assimilating GRACE TWS anomalies into the Catchment Land Surface Model using ensemble Kalman smoother. The Drought Monitors combine several short-term and long-term drought indices and indicators expressed in percentiles as a reference to their historical frequency of occurrence for the location and time of year in question. To be consistent, we are in the process of generating a climatology of estimated soil moisture and ground water based on m 60-year Catchment model simulation which will subsequently be used to convert seven years of GRACE assimilated fields into soil moisture and groundwater percentiles. for systematic incorporation into the objective blends that constitute Drought Monitor baselines. At this stage we provide a preliminary evaluation of GRACE assimilated Catchment model output against independent datasets including soil moisture observations from Aqua AMSR-E and groundwater level observations from the U.S. Geological Survey's Groundwater Climate Response Network.

Improving Evapotranspiration Estimates Using Multi-Platform Remote Sensing

NASA Astrophysics Data System (ADS)

Knipper, Kyle; Hogue, Terri; Franz, Kristie; Scott, Russell

2016-04-01

Understanding the linkages between energy and water cycles through evapotranspiration (ET) is uniquely challenging given its dependence on a range of climatological parameters and surface/atmospheric heterogeneity. A number of methods have been developed to estimate ET either from primarily remote-sensing observations, in-situ measurements, or a combination of the two. However, the scale of many of these methods may be too large to provide needed information about the spatial and temporal variability of ET that can occur over regions with acute or chronic land cover change and precipitation driven fluxes. The current study aims to improve the spatial and temporal variability of ET utilizing only satellite-based observations by incorporating a potential evapotranspiration (PET) methodology with satellite-based down-scaled soil moisture estimates in southern Arizona, USA. Initially, soil moisture estimates from AMSR2 and SMOS are downscaled to 1km through a triangular relationship between MODIS land surface temperature (MYD11A1), vegetation indices (MOD13Q1/MYD13Q1), and brightness temperature. Downscaled soil moisture values are then used to scale PET to actual ET (AET) at a daily, 1km resolution. Derived AET estimates are compared to observed flux tower estimates, the North American Land Data Assimilation System (NLDAS) model output (i.e. Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model, Mosiac Model, and Noah Model simulations), the Operational Simplified Surface Energy Balance Model (SSEBop), and a calibrated empirical ET model created specifically for the region. Preliminary results indicate a strong increase in correlation when incorporating the downscaling technique to original AMSR2 and SMOS soil moisture values, with the added benefit of being able to decipher small scale heterogeneity in soil moisture (riparian versus desert grassland). AET results show strong correlations with relatively low error and bias when compared to flux tower estimates. In addition, AET results show improved bias to those reported by SSEBop, with similar correlations and errors when compared to the empirical ET model. Spatial patterns of estimated AET display patterns representative of the basin's elevation and vegetation characteristics, with improved spatial resolution and temporal heterogeneity when compared to previous models.

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Soil Moisture Retrieval Using Convolutional Neural Networks: Application to Passive Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Hu, Z.; Xu, L.; Yu, B.

2018-04-01

A empirical model is established to analyse the daily retrieval of soil moisture from passive microwave remote sensing using convolutional neural networks (CNN). Soil moisture plays an important role in the water cycle. However, with the rapidly increasing of the acquiring technology for remotely sensed data, it's a hard task for remote sensing practitioners to find a fast and convenient model to deal with the massive data. In this paper, the AMSR-E brightness temperatures are used to train CNN for the prediction of the European centre for medium-range weather forecasts (ECMWF) model. Compared with the classical inversion methods, the deep learning-based method is more suitable for global soil moisture retrieval. It is very well supported by graphics processing unit (GPU) acceleration, which can meet the demand of massive data inversion. Once the model trained, a global soil moisture map can be predicted in less than 10 seconds. What's more, the method of soil moisture retrieval based on deep learning can learn the complex texture features from the big remote sensing data. In this experiment, the results demonstrates that the CNN deployed to retrieve global soil moisture can achieve a better performance than the support vector regression (SVR) for soil moisture retrieval.

Global-scale assessment and combination of SMAP with ASCAT (active) and AMSR2 (passive) soil moisture products

NASA Astrophysics Data System (ADS)

Kim, Hyunglok; Parinussa, Robert; Konings, Alexandra G.; Wagner, Wolfgang; Cosh, Michael H.; Lakshmi, Venkat; Zohaib, Muhammad; Choi, Minha

2018-01-01

Global-scale surface soil moisture (SSM) products retrieved from active and passive microwave remote sensing provide an effective method for monitoring near-real-time SSM content with nearly daily temporal resolution. In the present study, we first inter-compared global-scale error patterns and combined the Soil Moisture Active Passive (SMAP), Advanced Scatterometer (ASCAT), and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSM products using a triple collocation (TC) analysis and the maximized Pearson correlation coefficient (R) method from April 2015 to December 2016. The Global Land Data Assimilation System (GLDAS) and global in situ observations were utilized to investigate and to compare the quality of satellite-based SSM products. The average R-values of SMAP, ASCAT, and AMSR2 were 0.74, 0.64, and 0.65 when they compared with in situ networks, respectively. The ubRMSD values were (0.0411, 0.0625, and 0.0708) m3 m- 3; and the bias values were (- 0.0460, 0.0010, and 0.0418) m3 m- 3 for SMAP, ASCAT, and AMSR2, respectively. The highest average R-values from SMAP against the in situ results are very encouraging; only SMAP showed higher R-values than GLDAS in several in situ networks with low ubRMSD (0.0438 m3 m- 3). Overall, SMAP showed a dry bias (- 0.0460 m3 m- 3) and AMSR2 had a wet bias (0.0418 m3 m- 3); while ASCAT showed the least bias (0.0010 m3 m- 3) among all the products. Each product was evaluated using TC metrics with respect to the different ranges of vegetation optical depth (VOD). Under vegetation scarce conditions (VOD < 0.10), such as desert and semi-desert regions, all products have difficulty obtaining SSM information. In regions with moderately vegetated areas (0.10 < VOD < 0.40), SMAP showed the highest Signal-to-Noise Ratio. Over highly vegetated regions (VOD > 0.40) ASCAT showed comparatively better performance than did the other products. Using the maximized R method, SMAP, ASCAT, and AMSR2 products were combined one by one using the GLDAS dataset for reference SSM values. When the satellite products were combined, R-values of the combined products were improved or degraded depending on the VOD ranges produced, when compared with the results from the original products alone. The results of this study provide an overview of SMAP, ASCAT, and AMSR2 reliability and the performance of their combined products on a global scale. This study is the first to show the advantages of the recently available SMAP dataset for effective merging of different satellite products and of their application to various hydro-meteorological problems.

Temporal Stability of Soil Moisture and Radar Backscatter Observed by the Advanced Synthetic Aperture Radar (ASAR)

PubMed Central

Wagner, Wolfgang; Pathe, Carsten; Doubkova, Marcela; Sabel, Daniel; Bartsch, Annett; Hasenauer, Stefan; Blöschl, Günter; Scipal, Klaus; Martínez-Fernández, José; Löw, Alexander

2008-01-01

The high spatio-temporal variability of soil moisture is the result of atmospheric forcing and redistribution processes related to terrain, soil, and vegetation characteristics. Despite this high variability, many field studies have shown that in the temporal domain soil moisture measured at specific locations is correlated to the mean soil moisture content over an area. Since the measurements taken by Synthetic Aperture Radar (SAR) instruments are very sensitive to soil moisture it is hypothesized that the temporally stable soil moisture patterns are reflected in the radar backscatter measurements. To verify this hypothesis 73 Wide Swath (WS) images have been acquired by the ENVISAT Advanced Synthetic Aperture Radar (ASAR) over the REMEDHUS soil moisture network located in the Duero basin, Spain. It is found that a time-invariant linear relationship is well suited for relating local scale (pixel) and regional scale (50 km) backscatter. The observed linear model coefficients can be estimated by considering the scattering properties of the terrain and vegetation and the soil moisture scaling properties. For both linear model coefficients, the relative error between observed and modelled values is less than 5 % and the coefficient of determination (R2) is 86 %. The results are of relevance for interpreting and downscaling coarse resolution soil moisture data retrieved from active (METOP ASCAT) and passive (SMOS, AMSR-E) instruments. PMID:27879759

Rainfall estimation from soil moisture data: crash test for SM2RAIN algorithm

NASA Astrophysics Data System (ADS)

Brocca, Luca; Albergel, Clement; Massari, Christian; Ciabatta, Luca; Moramarco, Tommaso; de Rosnay, Patricia

2015-04-01

Soil moisture governs the partitioning of mass and energy fluxes between the land surface and the atmosphere and, hence, it represents a key variable for many applications in hydrology and earth science. In recent years, it was demonstrated that soil moisture observations from ground and satellite sensors contain important information useful for improving rainfall estimation. Indeed, soil moisture data have been used for correcting rainfall estimates from state-of-the-art satellite sensors (e.g. Crow et al., 2011), and also for improving flood prediction through a dual data assimilation approach (e.g. Massari et al., 2014; Chen et al., 2014). Brocca et al. (2013; 2014) developed a simple algorithm, called SM2RAIN, which allows estimating rainfall directly from soil moisture data. SM2RAIN has been applied successfully to in situ and satellite observations. Specifically, by using three satellite soil moisture products from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observation) and SMOS (Soil Moisture and Ocean Salinity); it was found that the SM2RAIN-derived rainfall products are as accurate as state-of-the-art products, e.g., the real-time version of the TRMM (Tropical Rainfall Measuring Mission) product. Notwithstanding these promising results, a detailed study investigating the physical basis of the SM2RAIN algorithm, its range of applicability and its limitations on a global scale has still to be carried out. In this study, we carried out a crash test for SM2RAIN algorithm on a global scale by performing a synthetic experiment. Specifically, modelled soil moisture data are obtained from HTESSEL model (Hydrology Tiled ECMWF Scheme for Surface Exchanges over Land) forced by ERA-Interim near-surface meteorology. Afterwards, the modelled soil moisture data are used as input into SM2RAIN algorithm for testing weather or not the resulting rainfall estimates are able to reproduce ERA-Interim rainfall data. Correlation, root mean square differences and categorical scores were used to evaluate the goodness of the results. This analysis wants to draw global picture of the performance of SM2RAIN algorithm in absence of errors in soil moisture and rainfall data. First preliminary results over Europe have shown that SM2RAIN performs particularly well over southern Europe (e.g., Spain, Italy and Greece) while its performances diminish by moving towards Northern latitudes (Scandinavia) and over Alps. The results on a global scale will be shown and discussed at the conference session. REFERENCES Brocca, L., Melone, F., Moramarco, T., Wagner, W. (2013). A new method for rainfall estimation through soil moisture observations. Geophysical Research Letters, 40(5), 853-858. Brocca, L., Ciabatta, L., Massari, C., Moramarco, T., Hahn, S., Hasenauer, S., Kidd, R., Dorigo, W., Wagner, W., Levizzani, V. (2014). Soil as a natural rain gauge: estimating global rainfall from satellite soil moisture data. Journal of Geophysical Research, 119(9), 5128-5141. Chen F, Crow WT, Ryu D. (2014) Dual forcing and state correction via soil moisture assimilation for improved rainfall-runoff modeling. J Hydrometeor, 15, 1832-1848. Crow, W.T., van den Berg, M.J., Huffman, G.J., Pellarin, T. (2011). Correcting rainfall using satellite-based surface soil moisture retrievals: the soil moisture analysis rainfall tool (SMART). Water Resour Res, 47, W08521. Dee, D. P.,et al. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. Roy. Meteorol. Soc., 137, 553-597 Massari, C., Brocca, L., Moramarco, T., Tramblay, Y., Didon Lescot, J.-F. (2014). Potential of soil moisture observations in flood modelling: estimating initial conditions and correcting rainfall. Advances in Water Resources, 74, 44-53.

Towards Improved Snow Water Equivalent Estimation via GRACE Assimilation

NASA Technical Reports Server (NTRS)

Forman, Bart; Reichle, Rofl; Rodell, Matt

2011-01-01

Passive microwave (e.g. AMSR-E) and visible spectrum (e.g. MODIS) measurements of snow states have been used in conjunction with land surface models to better characterize snow pack states, most notably snow water equivalent (SWE). However, both types of measurements have limitations. AMSR-E, for example, suffers a loss of information in deep/wet snow packs. Similarly, MODIS suffers a loss of temporal correlation information beyond the initial accumulation and final ablation phases of the snow season. Gravimetric measurements, on the other hand, do not suffer from these limitations. In this study, gravimetric measurements from the Gravity Recovery and Climate Experiment (GRACE) mission are used in a land surface model data assimilation (DA) framework to better characterize SWE in the Mackenzie River basin located in northern Canada. Comparisons are made against independent, ground-based SWE observations, state-of-the-art modeled SWE estimates, and independent, ground-based river discharge observations. Preliminary results suggest improved SWE estimates, including improved timing of the subsequent ablation and runoff of the snow pack. Additionally, use of the DA procedure can add vertical and horizontal resolution to the coarse-scale GRACE measurements as well as effectively downscale the measurements in time. Such findings offer the potential for better understanding of the hydrologic cycle in snow-dominated basins located in remote regions of the globe where ground-based observation collection if difficult, if not impossible. This information could ultimately lead to improved freshwater resource management in communities dependent on snow melt as well as a reduction in the uncertainty of river discharge into the Arctic Ocean.

Characterization of AIRS temperature and water vapor measurement capability using correlative observations

NASA Technical Reports Server (NTRS)

Fetzer, Eric J.; Eldering, Annmarie; Lee, Sung-Yung

2005-01-01

In this presentation we address several fundamental issues in the measurement of temperature and water vapor by AIRS: accuracy, precision, vertical resolution and biases as a function of cloud amount. We use two correlative data sources. First we compare AIRS total water vapor with that from the Advanced microwave Sounding Radiometer for EOS (AMSR-E) instrument, also onboard the Aqua spacecraft. AMSRE uses a mature methodology with a heritage including the operational Special Sensor Microwave Imager (SSM/I) instruments. AIRS and AMSR-E observations are collocated and simultaneous, providing a very large data set for comparison: about 200,000 over-ocean matches daily. We show small cloud-dependent biases between AIRS and AMSR-E total water vapor for several oceanic regions. Our second correlative data source is several hundred dedicated radiosondes launched during AIRS overpasses.

A New Operational Snow Retrieval Algorithm Applied to Historical AMSR-E Brightness Temperatures

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Jeyaratnam, Jeyavinoth

2016-01-01

Snow is a key element of the water and energy cycles and the knowledge of spatio-temporal distribution of snow depth and snow water equivalent (SWE) is fundamental for hydrological and climatological applications. SWE and snow depth estimates can be obtained from spaceborne microwave brightness temperatures at global scale and high temporal resolution (daily). In this regard, the data recorded by the Advanced Microwave Scanning Radiometer-Earth Orbiting System (EOS) (AMSR-E) onboard the National Aeronautics and Space Administration's (NASA) AQUA spacecraft have been used to generate operational estimates of SWE and snow depth, complementing estimates generated with other microwave sensors flying on other platforms. In this study, we report the results concerning the development and assessment of a new operational algorithm applied to historical AMSR-E data. The new algorithm here proposed makes use of climatological data, electromagnetic modeling and artificial neural networks for estimating snow depth as well as a spatio-temporal dynamic density scheme to convert snow depth to SWE. The outputs of the new algorithm are compared with those of the current AMSR-E operational algorithm as well as in-situ measurements and other operational snow products, specifically the Canadian Meteorological Center (CMC) and GlobSnow datasets. Our results show that the AMSR-E algorithm here proposed generally performs better than the operational one and addresses some major issues identified in the spatial distribution of snow depth fields associated with the evolution of effective grain size.

Enhancements to NASA's Land Atmosphere Near real-time Capability for EOS (LANCE)

NASA Astrophysics Data System (ADS)

Michael, K.; Davies, D. K.; Schmaltz, J. E.; Boller, R. A.; Mauoka, E.; Ye, G.; Vermote, E.; Harrison, S.; Rinsland, P. L.; Protack, S.; Durbin, P. B.; Justice, C. O.

2016-12-01

NASA's Land, Atmosphere Near real-time Capability for EOS (LANCE) supports application users interested in monitoring a wide variety of natural and man-made phenomena. Near Real-Time (NRT) data and imagery from the AIRS, AMSR2, MISR, MLS, MODIS, OMI and VIIRS instruments are available much quicker than routine processing allows. Most data products are available within 3 hours from satellite observation. NRT imagery are generally available 3-5 hours after observation. This article describes LANCE and enhancements made to LANCE over the last year. These enhancements include: the addition of MISR L1 Georeferenced Radiance and L2 Cloud Motion Vector products, AMSR2 Unified L2B Half-Orbit 25 km EASE-Grid Surface Soil Moisture products and VIIRS VIIRS Day/Night Band, Land Surface Reflectance and Corrected Surface reflectance products. In addition, the selection of LANCE NRT imagery that can be interactively viewed through Worldview and the Global Imagery Browse Services (GIBS) has been expanded. LANCE is also working to ingest and process data from OMPS.

A Prototype Hail Detection Algorithm and Hail Climatology Developed with the Advanced Microwave Sounding Unit (AMSU)

NASA Technical Reports Server (NTRS)

Ferraro, Ralph; Beauchamp, James; Cecil, Dan; Heymsfeld, Gerald

2015-01-01

In previous studies published in the open literature, a strong relationship between the occurrence of hail and the microwave brightness temperatures (primarily at 37 and 85 GHz) was documented. These studies were performed with the Nimbus-7 SMMR, the TRMM Microwave Imager (TMI) and most recently, the Aqua AMSR-E sensor. This lead to climatologies of hail frequency from TMI and AMSR-E, however, limitations include geographical domain of the TMI sensor (35 S to 35 N) and the overpass time of the Aqua satellite (130 am/pm local time), both of which reduce an accurate mapping of hail events over the global domain and the full diurnal cycle. Nonetheless, these studies presented exciting, new applications for passive microwave sensors. Since 1998, NOAA and EUMETSAT have been operating the AMSU-A/B and the MHS on several operational satellites: NOAA-15 through NOAA-19; MetOp-A and -B. With multiple satellites in operation since 2000, the AMSU/MHS sensors provide near global coverage every 4 hours, thus, offering a much larger time and temporal sampling than TRMM or AMSR-E. With similar observation frequencies near 30 and 85 GHz and additionally three at the 183 GHz water vapor band, the potential to detect strong convection associated with severe storms on a more comprehensive time and space scale exists. In this study, we develop a prototype AMSU-based hail detection algorithm through the use of collocated satellite and surface hail reports over the continental U.S. for a 12-year period (2000-2011). Compared with the surface observations, the algorithm detects approximately 40 percent of hail occurrences. The simple threshold algorithm is then used to generate a hail climatology that is based on all available AMSU observations during 2000-11 that is stratified in several ways, including total hail occurrence by month (March through September), total annual, and over the diurnal cycle. Independent comparisons are made compared to similar data sets derived from other satellite, ground radar and surface reports. The algorithm was also applied to global land measurements for a single year and showed close agreement with other satellite based hail climatologies. Such a product could serve as a prototype for use with a future geostationary based microwave sensor such as NASA's proposed PATH mission.

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2011-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25deg terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSR-E over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

SOIL moisture data intercomparison

NASA Astrophysics Data System (ADS)

Kerr, Yann; Rodriguez-Frenandez, Nemesio; Al-Yaari, Amen; Parens, Marie; Molero, Beatriz; Mahmoodi, Ali; Mialon, Arnaud; Richaume, Philippe; Bindlish, Rajat; Mecklenburg, Susanne; Wigneron, Jean-Pierre

2016-04-01

The Soil Moisture and Ocean Salinity satellite (SMOS) was launched in November 2009 and started delivering data in January 2010. Subsequently, the satellite has been in operation for over 6 years while the retrieval algorithms from Level 1 to Level 2 underwent significant evolutions as knowledge improved. Other approaches for retrieval at Level 2 over land were also investigated while Level 3 and 4 were initiated. In this présentation these improvements are assessed by inter-comparisons of the current Level 2 (V620) against the previous version (V551) and new products either using neural networks or Level 3. In addition a global evaluation of different SMOS soil moisture (SM) products is performed comparing products with those of model simulations and other satellites (AMSR E/ AMSR2 and ASCAT). Finally, all products were evaluated against in situ measurements of soil moisture (SM). The study demonstrated that the V620 shows a significant improvement (including those at level1 improving level2)) with respect to the earlier version V551. Results also show that neural network based approaches can yield excellent results over areas where other products are poor. Finally, global comparison indicates that SMOS behaves very well when compared to other sensors/approaches and gives consistent results over all surfaces from very dry (African Sahel, Arizona), to wet (tropical rain forests). RFI (Radio Frequency Interference) is still an issue even though detection has been greatly improved while RFI sources in several areas of the world are significantly reduced. When compared to other satellite products, the analysis shows that SMOS achieves its expected goals and is globally consistent over different eco climate regions from low to high latitudes and throughout the seasons.

Advances in Land Data Assimilation at the NASA Goddard Space Flight Center

NASA Technical Reports Server (NTRS)

Reichle, Rolf

2009-01-01

Research in land surface data assimilation has grown rapidly over the last decade. In this presentation we provide a brief overview of key research contributions by the NASA Goddard Space Flight Center (GSFC). The GSFC contributions to land assimilation primarily include the continued development and application of the Land Information System (US) and the ensemble Kalman filter (EnKF). In particular, we have developed a method to generate perturbation fields that are correlated in space, time, and across variables and that permit the flexible modeling of errors in land surface models and observations, along with an adaptive filtering approach that estimates observation and model error input parameters. A percentile-based scaling method that addresses soil moisture biases in model and observational estimates opened the path to the successful application of land data assimilation to satellite retrievals of surface soil moisture. Assimilation of AMSR-E surface soil moisture retrievals into the NASA Catchment model provided superior surface and root zone assimilation products (when validated against in situ measurements and compared to the model estimates or satellite observations alone). The multi-model capabilities of US were used to investigate the role of subsurface physics in the assimilation of surface soil moisture observations. Results indicate that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Building on this experience, GSFC leads the development of the Level 4 Surface and Root-Zone Soil Moisture (L4_SM) product for the planned NASA Soil-Moisture-Active-Passive (SMAP) mission. A key milestone was the design and execution of an Observing System Simulation Experiment that quantified the contribution of soil moisture retrievals to land data assimilation products as a function of retrieval and land model skill and yielded an estimate of the error budget for the SMAP L4_SM product. Terrestrial water storage observations from GRACE satellite system were also successfully assimilated into the NASA Catchment model and provided improved estimates of groundwater variability when compared to the model estimates alone. Moreover, satellite-based land surface temperature (LST) observations from the ISCCP archive were assimilated using a bias estimation module that was specifically designed for LST assimilation. As with soil moisture, LST assimilation provides modest yet statistically significant improvements when compared to the model or satellite observations alone. To achieve the improvement, however, the LST assimilation algorithm must be adapted to the specific formulation of LST in the land model. An improved method for the assimilation of snow cover observations was also developed. Finally, the coupling of LIS to the mesoscale Weather Research and Forecasting (WRF) model enabled investigations into how the sensitivity of land-atmosphere interactions to the specific choice of planetary boundary layer scheme and land surface model varies across surface moisture regimes, and how it can be quantified and evaluated against observations. The on-going development and integration of land assimilation modules into the Land Information System will enable the use of GSFC software with a variety of land models and make it accessible to the research community.

Collaboration on Development and Validation of the AMSR-E Snow Water Equivalent Algorithm

NASA Technical Reports Server (NTRS)

Armstrong, Richard L.

2000-01-01

The National Snow and Ice Data Center (NSIDC) has produced a global SMMR and SSM/I Level 3 Brightness Temperature data set in the Equal Area Scalable Earth (EASE) Grid for the period 1978 to 2000. Processing of current data is-ongoing. The EASE-Grid passive microwave data sets are appropriate for algorithm development and validation prior to the launch of AMSR-E. Having the lower frequency channels of SMMR (6.6 and 10.7 GHz) and the higher frequency channels of SSM/I (85.5 GHz) in the same format will facilitate the preliminary development of applications which could potentially make use of similar frequencies from AMSR-E (6.9, 10.7, 89.0 GHz).

Snow Radiance Data Assimilation over High Mountain Asia Using the NASA Land Information System and a Well-Trained Support Vector Machine

NASA Astrophysics Data System (ADS)

Kwon, Y.; Forman, B. A.; Yoon, Y.; Kumar, S.

2017-12-01

High Mountain Asia (HMA) has been progressively losing ice and snow in recent decades, which could negatively impact regional water supply and native ecosystems. One goal of this study is to characterize the spatiotemporal variability of snow (and ice) across the HMA region. In addition, modeled snow water equivalent (SWE) estimates will be enhanced through the assimilation of passive microwave brightness temperatures (TB) collected by the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) as part of a radiance assimilation system. The radiance assimilation framework includes the NASA Land Information System (LIS) in conjunction with a well-trained support vector machine (SVM) that acts as the observation operator. The Noah Land Surface Model with multi-parameterization options (Noah-MP) is used as the prior model for simulating snow dynamics. Noah-MP is forced by meteorological fields from the NASA Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis for the periods 01 Sep. 2002 to 01 Sep. 2011. The radiance assimilation system requires two separate phases: 1) training and 2) assimilation. During the training phase, a nonlinear SVM is generated for three different AMSR-E frequencies - 10.65, 18.7, and 36.5 GHz - at both vertical and horizontal polarization. The trained SVM is then used to predict TB during the assimilation phase. An ensemble Kalman filter will be used to condition the model on AMSR-E brightness temperatures not used during SVM training. The performance of the Noah-MP (with and without radiance assimilation) will be assessed via comparison to in-situ measurements, remotely-sensing geophysical retrievals, and other reanalysis products.

[Atmospheric Influences Analysis on the Satellite Passive Microwave Remote Sensing].

PubMed

Qiu, Yu-bao; Shi, Li-juan; Shi, Jian-cheng; Zhao, Shao-jie

2016-02-01

Passive microwave remote sensing offers its all-weather work capabilities, but atmospheric influences on satellite microwave brightness temperature were different under different atmospheric conditions and environments. In order to clarify atmospheric influences on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), atmospheric radiation were simulated based on AMSR-E configuration under clear sky and cloudy conditions, by using radiative transfer model and atmospheric conditions data. Results showed that atmospheric water vapor was the major factor for atmospheric radiation under clear sky condition. Atmospheric transmittances were almost above 0.98 at AMSR-E's low frequencies (< 18.7 GHz) and the microwave brightness temperature changes caused by atmosphere can be ignored in clear sky condition. Atmospheric transmittances at 36.5 and 89 GHz were 0.896 and 0.756 respectively. The effects of atmospheric water vapor needed to be corrected when using microwave high-frequency channels to inverse land surface parameters in clear sky condition. But under cloud cover or cloudy conditions, cloud liquid water was the key factor to cause atmospheric radiation. When sky was covered by typical stratus cloud, atmospheric transmittances at 10.7, 18.7 and 36.5 GHz were 0.942, 0.828 and 0.605 respectively. Comparing with the clear sky condition, the down-welling atmospheric radiation caused by cloud liquid water increased up to 75.365 K at 36.5 GHz. It showed that the atmospheric correction under different clouds covered condition was the primary work to improve the accuracy of land surface parameters inversion of passive microwave remote sensing. The results also provided the basis for microwave atmospheric correction algorithm development. Finally, the atmospheric sounding data was utilized to calculate the atmospheric transmittance of Hailaer Region, Inner Mongolia province, in July 2013. The results indicated that atmospheric transmittances were close to 1 at C-band and X-band. 89 GHz was greatly influenced by water vapor and its atmospheric transmittance was not more than 0.7. Atmospheric transmittances in Hailaer Region had a relatively stable value in summer, but had about 0.1 fluctuations with the local water vapor changes.

Satellite based assessment of recent permafrost extent and active layer trends over Alaska and Northwest Canada

NASA Astrophysics Data System (ADS)

Kim, Y.; Kimball, J. S.; PARK, H.; Yi, Y.

2017-12-01

Climate change in the Boreal-Arctic region has experienced greater surface air temperature (SAT) warming than the global average in recent decades, which is promoting permafrost thawing and active layer deepening. Permafrost extent (PE) and active layer thickness (ALT) are key environmental indicators of recent climate change, and strongly impact other eco-hydrological processes including land-atmosphere carbon exchange. We developed a new approach for regional estimation and monitoring of PE using daily landscape freeze-thaw (FT) records derived from satellite microwave (37 GHz) brightness temperature (Tb) observations. ALT was estimated within the PE domain using empirical modeling of land cover dependent edaphic factors and an annual thawing index derived from MODIS land surface temperature (LST) observations and reanalysis based surface air temperatures (SAT). The PE and ALT estimates were derived over the 1980-2016 satellite record and NASA ABoVE (Arctic Boreal Vulnerability Experiment) domain encompassing Alaska and Northwest Canada. The baseline model estimates were derived at 25-km resolution consistent with the satellite FT global record. Our results show recent widespread PE decline and deepening ALT trends, with larger spatial variability and model uncertainty along the southern PE boundary. Larger PE and ALT variability occurs over heterogeneous permafrost subzones characterized by dense vegetation, and variable snow cover and organic layer conditions. We also tested alternative PE and ALT estimates derived using finer (6-km) scale satellite Tb (36.5 GHz) and FT retrievals from a calibrated AMSR-E and AMSR2 sensor record. The PE and ALT results were compared against other independent observations, including process model simulations, in situ measurements, and permafrost inventory records. A model sensitivity analysis was conducted to evaluate snow cover, soil organic layer, and vegetation composition impacts to ALT. The finer delineation of permafrost and active layer conditions provides enhanced regional monitoring of PE and ALT changes over the ABoVE domain, including heterogeneous permafrost subzones.

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.; Foster, James L.

2009-01-01

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

Comparison of Envisat ASAR GM, AMSR-E Passive Microwave, and MODIS Optical Remote Sensing for Flood Monitoring in Australia

NASA Astrophysics Data System (ADS)

Ticehurst, C. J.; Bartsch, A.; Doubkova, M.; van Dijk, A. I. J. M.

2009-11-01

Continuous flood monitoring can support emergency response, water management and environmental monitoring. Optical sensors such as MODIS allow inundation mapping with high spatial and temporal resolution (250-1000 m, twice daily) but are affected by cloud cover. Passive microwave sensors also acquire observations at high temporal resolution, but coarser spatial resolution (e.g. ca. 5-70 km for AMSR-E) and smaller footprints are also affected by cloud and/or rain. ScanSAR systems allow all-weather monitoring but require spatial resolution to be traded off against coverage and/or temporal resolution; e.g. the ENVISAT ASAR Global Mode observes at ca. 1 km over large regions about twice a week. The complementary role of the AMSR-E and ASAR GM data to that of MODIS is here introduced for three flood events and locations across Australia. Additional improvements can be made by integrating digital elevation models and stream flow gauging data.

Remote sensing solutions for estimating runoff and recharge in arid environments.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Milewski, A.; Sultan, M.; Yan, E.

2009-06-30

Efforts to understand and to quantify precipitation and its partitioning into runoff evapo-transpiration, and recharge are often hampered by the absence or paucity of appropriate monitoring systems. We applied methodologies for rainfall-runoff and groundwater recharge computations that heavily rely on observations extracted from a wide-range of global remote sensing data sets (TRMM, SSM/I, Landsat TM, AVHRR, AMSR-E, and ASTER) using the arid Sinai Peninsula (SP; area: 61,000 km{sup 2}) and the Eastern Desert (ED; area: 220,000 km{sup 2}) of Egypt as our test sites. A two-fold exercise was conducted. Spatiotemporal remote sensing data (TRMM, AVHRR and AMSR-E) were extracted frommore » global data sets over the test sites using RESDEM, the Remote Sensing Data Extraction Model, and were then used to identify and to verify precipitation events throughout the past 10 years (1998-2007). This was accomplished by using an automated cloud detection technique to identify clouds and to monitor their propagation prior to and throughout the identified precipitation events, and by examining changes in soil moisture (extracted from AMSR-E data) following the identification of clouds. For the investigated period, 246 of 327 events were verified in the SP, and 179 of 304 in the ED. A catchment-based, continuous, semi-distributed hydrologic model (Soil Water and Assessment Tool model; SWAT) was calibrated against observed runoff values from Wadi Girafi Watershed (area: 3350 km{sup 2}) and then used to provide a continuous simulation (1998-2007) of the overland flow, channel flow, transmission losses, evaporation on bare soils and evapo-transpiration, and groundwater recharge for the major (area: 2014-22,030 km{sup 2}) watersheds in the SP (Watir, El-Arish, Dahab, and Awag) and the ED (Qena, Hammamat, Asyuti, Tarfa, El-Quffa, El-Batur, Kharit, Hodein, and Allaqi) covering 48% and 51% of the total areas of the SP and the ED, respectively. For the investigated watersheds in the SP, the average annual precipitation, average annual runoff, and average annual recharge through transmission losses were found to be: 2955 x 10{sup 6}m{sup 3}, 508 x 10{sup 6}m{sup 3} (17.1% total precipitation (TP)), and 463 x 10{sup 6}m{sup 3} (15.7% TP), respectively, whereas in the ED these values are: 807 x 10{sup 6}m{sup 3}, 77.8 x 10{sup 6}m{sup 3} (9.6% TP), and 171 x 10{sup 6}m{sup 3} (21.2% TP), respectively. Results demonstrate the enhanced opportunities for groundwater development in the SP (compared to the ED) and highlight the potential for similar applications in arid areas elsewhere. The adopted remote sensing-based, regionalization approach is not a substitute for traditional methodologies that rely on extensive field datasets from rain gauge and stream flow networks, yet could provide first-order estimates for rainfall, runoff, and recharge over large sectors of the arid world lacking adequate coverage with spatial and temporal precipitation and field data.« less

Source analysis of spaceborne microwave radiometer interference over land

NASA Astrophysics Data System (ADS)

Guan, Li; Zhang, Sibo

2016-03-01

Satellite microwave thermal emissions mixed with signals from active sensors are referred to as radiofrequency interference (RFI). Based on Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) observations from June 1 to 16, 2011, RFI over Europe was identified and analyzed using the modified principal component analysis algorithm in this paper. The X band AMSR-E measurements in England and Italy are mostly affected by the stable, persistent, active microwave transmitters on the surface, while the RFI source of other European countries is the interference of the reflected geostationary TV satellite downlink signals to the measurements of spaceborne microwave radiometers. The locations and intensities of the RFI induced by the geostationary TV and communication satellites changed with time within the observed period. The observations of spaceborne microwave radiometers in ascending portions of orbits are usually interfered with over European land, while no RFI was detected in descending passes. The RFI locations and intensities from the reflection of downlink radiation are highly dependent upon the relative geometry between the geostationary satellite and the measuring passive sensor. Only these fields of view of a spaceborne instrument whose scan azimuths are close to the azimuth relative to the geostationary satellite are likely to be affected by RFI.

Surface Soil Moisture Estimates Across China Based on Multi-satellite Observations and A Soil Moisture Model

NASA Astrophysics Data System (ADS)

Zhang, Ke; Yang, Tao; Ye, Jinyin; Li, Zhijia; Yu, Zhongbo

2017-04-01

Soil moisture is a key variable that regulates exchanges of water and energy between land surface and atmosphere. Soil moisture retrievals based on microwave satellite remote sensing have made it possible to estimate global surface (up to about 10 cm in depth) soil moisture routinely. Although there are many satellites operating, including NASA's Soil Moisture Acitive Passive mission (SMAP), ESA's Soil Moisture and Ocean Salinity mission (SMOS), JAXA's Advanced Microwave Scanning Radiometer 2 mission (AMSR2), and China's Fengyun (FY) missions, key differences exist between different satellite-based soil moisture products. In this study, we applied a single-channel soil moisture retrieval model forced by multiple sources of satellite brightness temperature observations to estimate consistent daily surface soil moisture across China at a spatial resolution of 25 km. By utilizing observations from multiple satellites, we are able to estimate daily soil moisture across the whole domain of China. We further developed a daily soil moisture accounting model and applied it to downscale the 25-km satellite-based soil moisture to 5 km. By comparing our estimated soil moisture with observations from a dense observation network implemented in Anhui Province, China, our estimated soil moisture results show a reasonably good agreement with the observations (RMSE < 0.1 and r > 0.8).

An integrated approach for estimation of methane emissions from wetlands and lakes in high latitude regions

NASA Astrophysics Data System (ADS)

Chiu, C.; Bowling, L. C.; Podest, E.; Bohn, T. J.; Lettenmaier, D. P.; Schroeder, R.; McDonald, K. C.

2009-04-01

In recent years, there has been increasing evidence of significant alteration in the extent of lakes and wetlands in high latitude regions due in part to thawing permafrost, as well as other changes governing surface and subsurface hydrology. Methane is a 23 times more efficient greenhouse gas than carbon dioxide; changes in surface water extent, and the associated subsurface anaerobic conditions, are important controls on methane emissions in high latitude regions. Methane emissions from wetlands vary substantially in both time and space, and are influenced by plant growth, soil organic matter decomposition, methanogenesis, and methane oxidation controlled by soil temperature, water table level and net primary productivity (NPP). The understanding of spatial and temporal heterogeneity of surface saturation, thermal regime and carbon substrate in northern Eurasian wetlands from point measurements are limited. In order to better estimate the magnitude and variability of methane emissions from northern lakes and wetlands, we present an integrated assessment approach based on remote sensing image classification, land surface modeling and process-based ecosystem modeling. Wetlands classifications based on L-band JERS-1 SAR (100m) and ALOS PALSAR (~30m) are used together with topographic information to parameterize a lake and wetland algorithm in the Variable Infiltration Capacity (VIC) land surface model at 25 km resolution. The enhanced VIC algorithm allows subsurface moisture exchange between surface water and wetlands and includes a sub-grid parameterization of water table position within the wetland area using a generalized topographic index. Average methane emissions are simulated by using the Walter and Heimann methane emission model based on temporally and spatially varying soil temperature, net primary productivity and water table generated from the modified VIC model. Our five preliminary study areas include the Z. Dvina, Upper Volga, Yeloguy, Syum, and Chaya river basins. The temporally-variable inundation extent simulated by the VIC model is compared to 25 km resolution inundation products developed from combined QuikSCAT, AMSR-E and MODIS data sets covering the time period from 2002 onward. The seasonal variation in methane emissions associated with sub-grid variability in water table extent is explored between 1948 and 2006. This work was carried out at Purdue University, at the University of Washington, and at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the NASA.

High resolution sea ice modeling for the region of Baffin Bay and the Labrador Sea

NASA Astrophysics Data System (ADS)

Zakharov, I.; Prasad, S.; McGuire, P.

2016-12-01

A multi-category numerical sea ice model (CICE) with a data assimilation module was implemented to derive sea ice parameters in the region of Baffin Bay and the Labrador Sea with resolution higher than 10 km. The model derived ice parameters include concentration, ridge keel measurement, thickness and freeboard. The module for assimilation of ice concentration uses data from the Advance Microwave Scanning Radiometer (AMSR-E) and OSI SAF data. The sea surface temperature (SST) data from AMSRE-AVHRR and Operational SST and Sea Ice Analysis (OSTIA) system were used to correct the SST computed by a mixed layer slab ocean model that is used to determine the growth and melt of sea ice. The ice thickness parameter from the model was compared with the measurements from Soil Moisture Ocean Salinity - Microwave Imaging Radiometer using Aperture Synthesis (SMOS-MIRAS). The freeboard measures where compared with the Cryosat-2 measurements. A spatial root mean square error computed for freeboard measures was found to be within the uncertainty limits of the observation. The model was also used to estimate the correlation parameter between the ridge and the ridge keel measurements in the region of Makkovik Bank. Also, the level ice draft estimated from the model was in good agreement with the ice draft derived from the upward looking sonar (ULS) instrument deployed in the Makkovik bank. The model corrected with ice concentration and SST from remote sensing data demonstrated significant improvements in accuracy of the estimated ice parameters. The model can be used for operational forecast and climate research.

Antarctic Iceberg Tracking Based on Time Series of Aqua AMSRE Microwave Brightness Temperature Measurements

NASA Technical Reports Server (NTRS)

Blonski, Slawomir; Peterson, Craig

2006-01-01

Observations of icebergs are identified as one of the requirements for the GEOSS (Global Earth Observation System of Systems) in the area of reducing loss of life and property from natural and human-induced disasters. However, iceberg observations are not included among targets in the GEOSS 10-Year Implementation Plan, and thus there is an unfulfilled need for iceberg detection and tracking in the near future. Large Antarctic icebergs have been tracked by the National Ice Center and by the academic community using a variety of satellite sensors including both passive and active microwave imagers, such as SSM/I (Special Sensor Microwave/Imager) deployed on the DMSP (Defense Meteorological Satellite Program) spacecraft. Improvements provided in recent years by NASA and non-NASA satellite radars, scatterometers, and radiometers resulted in an increased number of observed icebergs and even prompted a question: Is The Number of Antarctic Icebergs Really Increasing? [D.G. Long, J. Ballantyne, and C. Bertoia, Eos, Transactions of the American Geophysical Union 83 (42): 469 & 474, 15 October 2002]. AMSR-E (Advanced Microwave Scanning Radiometer for the Earth Observing System) represents an improvement over SSM/I, its predecessor. AMSR-E has more measurement channels and higher spatial resolution than SSM/I. For example, the instantaneous field of view of the AMSR-E s 89-GHz channels is 6 km by 4 km versus 16 km by 14 km for SSM/I s comparable 85-GHz channels. AMSR-E, deployed on the Aqua satellite, scans across a 1450-km swath and provides brightness temperature measurements with nearglobal coverage every one or two days. In polar regions, overlapping swaths generate coverage up to multiple times per day and allow for creation of image time series with high temporal resolution. Despite these advantages, only incidental usage of AMSR-E data for iceberg tracking has been reported so far, none in an operational environment. Therefore, an experiment was undertaken in the RPC (Rapid Prototyping Capability) of NASA s (National Aeronautics and Space Administration) Applied Science Program to demonstrate that passive microwave brightness temperature measurements acquired by AMSR-E can be effectively used to track iceberg movement around Antarctica. The RPC s robust computation environment enabled processing of terabytes of data products available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. Iceberg tracking based on the AMSR-E Level 2A data product was compared with records from the National Ice Center for currently existing icebergs. Some icebergs as small as roughly 10 km in size were easily observed, but tracking of many others, even larger ones, was obscured by presence of sea ice surrounding the icebergs. The best results, such as for the large iceberg A22A, were achieved when an iceberg was in open ocean.

Daily High-Resolution Flood Maps of Africa: 1992-present with Near Real Time Updates

NASA Astrophysics Data System (ADS)

Picton, J.; Galantowicz, J. F.; Root, B.

2016-12-01

The ability to characterize past and current flood extents frequently, accurately, and at high resolution is needed for many applications including risk assessment, wetlands monitoring, and emergency management. However, remote sensing methods have not been capable of meeting all of these requirements simultaneously. Cloud cover too often obscures the surface for visual and infrared sensors and observations from radar sensors are too infrequent to create consistent historical databases or monitor evolving events. Lower-resolution (10-50 km) passive microwave sensors, such as SSM/I, AMSR-E, and AMSR2, are sensitive to water cover, acquire useful data during clear and cloudy conditions, have revisit periods of up to twice daily, and provide a continuous record of data from 1992 to the present. What they lack most is the resolution needed to map flood extent. We will present results from a flood mapping system capable of producing high-resolution (90-m) flood extent depictions from lower resolution microwave data. The system uses the strong sensitivity of microwave data to surface water coverage combined with land surface and atmospheric data to derive daily flooded fraction estimates on a sensor-footprint basis. The system downscales flooded fraction to make high-resolution Boolean flood extent depictions that are spatially continuous and consistent with the lower resolution data. The downscaling step is based on a relative floodability (RF) index derived from higher-resolution topographic and hydrological data. We process RF to create a flooded fraction threshold map that relates each 90-m grid point to the surrounding terrain at the microwave scale. We have derived daily, 90-m resolution flood maps for Africa covering 1992-present using SSM/I, AMSR-E, and AMSR2 data and we are now producing new daily maps in near real time. The flood maps are being used by the African Risk Capacity (ARC) Agency to underpin an intergovernmental river flood insurance program in Africa. We will present results showing daily flood extents during major events and discuss: validation of the flood maps against MODIS-derived maps; analyses of minimum detectable flood size; aggregate analyses of flood extent over time; flood map use in ARC's insurance model; and results applying the system to the Americas.

Mapping global surface water inundation dynamics using synergistic information from SMAP, AMSR2 and Landsat

NASA Astrophysics Data System (ADS)

Du, J.; Kimball, J. S.; Galantowicz, J. F.; Kim, S.; Chan, S.; Reichle, R. H.; Jones, L. A.; Watts, J. D.

2017-12-01

A method to monitor global land surface water (fw) inundation dynamics was developed by exploiting the enhanced fw sensitivity of L-band (1.4 GHz) passive microwave observations from the Soil Moisture Active Passive (SMAP) mission. The L-band fw (fwLBand) retrievals were derived using SMAP H-polarization brightness temperature (Tb) observations and predefined L-band reference microwave emissivities for water and land endmembers. Potential soil moisture and vegetation contributions to the microwave signal were represented from overlapping higher frequency Tb observations from AMSR2. The resulting fwLBand global record has high temporal sampling (1-3 days) and 36-km spatial resolution. The fwLBand annual averages corresponded favourably (R=0.84, p<0.001) with a 250-m resolution static global water map (MOD44W) aggregated at the same spatial scale, while capturing significant inundation variations worldwide. The monthly fwLBand averages also showed seasonal inundation changes consistent with river discharge records within six major US river basins. An uncertainty analysis indicated generally reliable fwLBand performance for major land cover areas and under low to moderate vegetation cover, but with lower accuracy for detecting water bodies covered by dense vegetation. Finer resolution (30-m) fwLBand results were obtained for three sub-regions in North America using an empirical downscaling approach and ancillary global Water Occurrence Dataset (WOD) derived from the historical Landsat record. The resulting 30-m fwLBand retrievals showed favourable classification accuracy for water (commission error 31.84%; omission error 28.08%) and land (commission error 0.82%; omission error 0.99%) and seasonal wet and dry periods when compared to independent water maps derived from Landsat-8 imagery. The new fwLBand algorithms and continuing SMAP and AMSR2 operations provide for near real-time, multi-scale monitoring of global surface water inundation dynamics, potentially benefiting hydrological monitoring, flood assessments, and global climate and carbon modeling.

Bayesian Approaches for Model and Multi-mission Satellites Data Fusion

NASA Astrophysics Data System (ADS)

Khaki, M., , Dr; Forootan, E.; Awange, J.; Kuhn, M.

2017-12-01

Traditionally, data assimilation is formulated as a Bayesian approach that allows one to update model simulations using new incoming observations. This integration is necessary due to the uncertainty in model outputs, which mainly is the result of several drawbacks, e.g., limitations in accounting for the complexity of real-world processes, uncertainties of (unknown) empirical model parameters, and the absence of high resolution (both spatially and temporally) data. Data assimilation, however, requires knowledge of the physical process of a model, which may be either poorly described or entirely unavailable. Therefore, an alternative method is required to avoid this dependency. In this study we present a novel approach which can be used in hydrological applications. A non-parametric framework based on Kalman filtering technique is proposed to improve hydrological model estimates without using a model dynamics. Particularly, we assesse Kalman-Taken formulations that take advantage of the delay coordinate method to reconstruct nonlinear dynamics in the absence of the physical process. This empirical relationship is then used instead of model equations to integrate satellite products with model outputs. We use water storage variables from World-Wide Water Resources Assessment (W3RA) simulations and update them using data known as the Gravity Recovery And Climate Experiment (GRACE) terrestrial water storage (TWS) and also surface soil moisture data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) over Australia for the period of 2003 to 2011. The performance of the proposed integration method is compared with data obtained from the more traditional assimilation scheme using the Ensemble Square-Root Filter (EnSRF) filtering technique (Khaki et al., 2017), as well as by evaluating them against ground-based soil moisture and groundwater observations within the Murray-Darling Basin.

Glacier Melt Detection in Complex Terrain Using New AMSR-E Calibrated Enhanced Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record

NASA Astrophysics Data System (ADS)

Ramage, J. M.; Brodzik, M. J.; Hardman, M.

2016-12-01

Passive microwave (PM) 18 GHz and 36 GHz horizontally- and vertically-polarized brightness temperatures (Tb) channels from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) have been important sources of information about snow melt status in glacial environments, particularly at high latitudes. PM data are sensitive to the changes in near-surface liquid water that accompany melt onset, melt intensification, and refreezing. Overpasses are frequent enough that in most areas multiple (2-8) observations per day are possible, yielding the potential for determining the dynamic state of the snow pack during transition seasons. AMSR-E Tb data have been used effectively to determine melt onset and melt intensification using daily Tb and diurnal amplitude variation (DAV) thresholds. Due to mixed pixels in historically coarse spatial resolution Tb data, melt analysis has been impractical in ice-marginal zones where pixels may be only fractionally snow/ice covered, and in areas where the glacier is near large bodies of water: even small regions of open water in a pixel severely impact the microwave signal. We use the new enhanced-resolution Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record product's twice daily obserations to test and update existing snow melt algorithms by determining appropriate melt thresholds for both Tb and DAV for the CETB 18 and 36 GHz channels. We use the enhanced resolution data to evaluate melt characteristics along glacier margins and melt transition zones during the melt seasons in locations spanning a wide range of melt scenarios, including the Patagonian Andes, the Alaskan Coast Range, and the Russian High Arctic icecaps. We quantify how improvement of spatial resolution from the original 12.5 - 25 km-scale pixels to the enhanced resolution of 3.125 - 6.25 km improves the ability to evaluate melt timing across boundaries and transition zones in diverse glacial environments.

On the synergy of SMAP, AMSR2 AND SENTINEL-1 for retrieving soil moisture

NASA Astrophysics Data System (ADS)

Santi, E.; Paloscia, S.; Pettinato, S.; Brocca, L.; Ciabatta, L.; Entekhabi, D.

2018-03-01

An algorithm for retrieving soil moisture content (SMC) from synergic use of both active and passive microwave acquisitions is presented. The algorithm takes advantage of the integration of microwave data from SMAP, Sentinel-1 and AMSR2 for overcoming the SMAP radar failure and obtaining a SMC product at enhanced resolution (0.1° × 0.1°) and improved accuracy with respect to the original SMAP radiometric SMC product. A disaggregation technique based on the Smoothing filter based intensity modulation (SFIM) allows combining the radiometric and SAR data. Disaggregated microwave data are used as inputs of an Artificial Neural Networks (ANN) based algorithm, which is able to exploit the synergy between active and passive acquisitions. The algorithm is defined, trained and tested using the SMEX02 experimental dataset and data simulated by forward electromagnetic models based on the Radiative Transfer Theory. Then the algorithm is adapted to satellite data and tested using one year of SMAP, AMSR2 and Sentinel-1 co-located data on a flat agricultural area located in the Po Valley, in northern Italy. Spatially distributed SMC values at 0.1° × 0.1° resolution generated by the Soil Water Balance Model (SWBM) are considered as reference for this purpose. The synergy of SMAP, Sentinel-1 and AMSR2 allowed increasing the correlation between estimated and reference SMC from R ≅ 0.68 of the SMAP based retrieval up to R ≅ 0.86 of the combination SMAP + Sentinel-1 + AMSR2. The corresponding Root Mean Square Error (RMSE) decreased from RMSE ≅ 0.04 m3/m3 to RMSE ≅ 0.024 m3/m3.

Triple collocation-based estimation of spatially correlated observation error covariance in remote sensing soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wu, Kai; Shu, Hong; Nie, Lei; Jiao, Zhenhang

2018-01-01

Spatially correlated errors are typically ignored in data assimilation, thus degenerating the observation error covariance R to a diagonal matrix. We argue that a nondiagonal R carries more observation information making assimilation results more accurate. A method, denoted TC_Cov, was proposed for soil moisture data assimilation to estimate spatially correlated observation error covariance based on triple collocation (TC). Assimilation experiments were carried out to test the performance of TC_Cov. AMSR-E soil moisture was assimilated with a diagonal R matrix computed using the TC and assimilated using a nondiagonal R matrix, as estimated by proposed TC_Cov. The ensemble Kalman filter was considered as the assimilation method. Our assimilation results were validated against climate change initiative data and ground-based soil moisture measurements using the Pearson correlation coefficient and unbiased root mean square difference metrics. These experiments confirmed that deterioration of diagonal R assimilation results occurred when model simulation is more accurate than observation data. Furthermore, nondiagonal R achieved higher correlation coefficient and lower ubRMSD values over diagonal R in experiments and demonstrated the effectiveness of TC_Cov to estimate richly structuralized R in data assimilation. In sum, compared with diagonal R, nondiagonal R may relieve the detrimental effects of assimilation when simulated model results outperform observation data.

Determination of Ice Water Path in Ice-over-Water Cloud Systems Using Combined MODIS and AMSR-E Measurements

NASA Technical Reports Server (NTRS)

Huang, Jianping; Minnis, Patrick; Lin, Bing; Yi, Yuhong; Fan, T.-F.; Sun-Mack, Sunny; Ayers, J. K.

2006-01-01

To provide more accurate ice cloud properties for evaluating climate models, the updated version of multi-layered cloud retrieval system (MCRS) is used to retrieve ice water path (IWP) in ice-over-water cloud systems over global ocean using combined instrument data from the Aqua satellite. The liquid water path (LWP) of lower layer water clouds is estimated from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements. With the lower layer LWP known, the properties of the upper-level ice clouds are then derived from Moderate Resolution Imaging Spectroradiometer measurements by matching simulated radiances from a two-cloud layer radiative transfer model. Comparisons with single-layer cirrus systems and surface-based radar retrievals show that the MCRS can significantly improve the accuracy and reduce the over-estimation of optical depth and ice water path retrievals for ice over-water cloud systems. During the period from December 2004 through February 2005, the mean daytime ice cloud optical depth and IWP for overlapped ice-over-water clouds over ocean from Aqua are 7.6 and 146.4 gm(sup -2), respectively, significantly less than the initial single layer retrievals of 17.3 and 322.3 gm(sup -2). The mean IWP for actual single-layer clouds was 128.2 gm(sup -2).

Downscaling soil moisture over East Asia through multi-sensor data fusion and optimization of regression trees

NASA Astrophysics Data System (ADS)

Park, Seonyoung; Im, Jungho; Park, Sumin; Rhee, Jinyoung

2017-04-01

Soil moisture is one of the most important keys for understanding regional and global climate systems. Soil moisture is directly related to agricultural processes as well as hydrological processes because soil moisture highly influences vegetation growth and determines water supply in the agroecosystem. Accurate monitoring of the spatiotemporal pattern of soil moisture is important. Soil moisture has been generally provided through in situ measurements at stations. Although field survey from in situ measurements provides accurate soil moisture with high temporal resolution, it requires high cost and does not provide the spatial distribution of soil moisture over large areas. Microwave satellite (e.g., advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR2), the Advanced Scatterometer (ASCAT), and Soil Moisture Active Passive (SMAP)) -based approaches and numerical models such as Global Land Data Assimilation System (GLDAS) and Modern- Era Retrospective Analysis for Research and Applications (MERRA) provide spatial-temporalspatiotemporally continuous soil moisture products at global scale. However, since those global soil moisture products have coarse spatial resolution ( 25-40 km), their applications for agriculture and water resources at local and regional scales are very limited. Thus, soil moisture downscaling is needed to overcome the limitation of the spatial resolution of soil moisture products. In this study, GLDAS soil moisture data were downscaled up to 1 km spatial resolution through the integration of AMSR2 and ASCAT soil moisture data, Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and Moderate Resolution Imaging Spectroradiometer (MODIS) data—Land Surface Temperature, Normalized Difference Vegetation Index, and Land cover—using modified regression trees over East Asia from 2013 to 2015. Modified regression trees were implemented using Cubist, a commercial software tool based on machine learning. An optimization based on pruning of rules derived from the modified regression trees was conducted. Root Mean Square Error (RMSE) and Correlation coefficients (r) were used to optimize the rules, and finally 59 rules from modified regression trees were produced. The results show high validation r (0.79) and low validation RMSE (0.0556m3/m3). The 1 km downscaled soil moisture was evaluated using ground soil moisture data at 14 stations, and both soil moisture data showed similar temporal patterns (average r=0.51 and average RMSE=0.041). The spatial distribution of the 1 km downscaled soil moisture well corresponded with GLDAS soil moisture that caught both extremely dry and wet regions. Correlation between GLDAS and the 1 km downscaled soil moisture during growing season was positive (mean r=0.35) in most regions.

The benefits of using remotely sensed soil moisture in parameter identification of large-scale hydrological models

NASA Astrophysics Data System (ADS)

Karssenberg, D.; Wanders, N.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Large-scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system that is not directly linked to discharge, in particular the unsaturated zone, remains uncalibrated, or might be modified unrealistically. Soil moisture observations from satellites have the potential to fill this gap, as these provide the closest thing to a direct measurement of the state of the unsaturated zone, and thus are potentially useful in calibrating unsaturated zone model parameters. This is expected to result in a better identification of the complete hydrological system, potentially leading to improved forecasts of the hydrograph as well. Here we evaluate this added value of remotely sensed soil moisture in calibration of large-scale hydrological models by addressing two research questions: 1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? 2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to approaches that calibrate only with discharge, such that this leads to improved forecasts of soil moisture content and discharge as well? To answer these questions we use a dual state and parameter ensemble Kalman filter to calibrate the hydrological model LISFLOOD for the Upper Danube area. Calibration is done with discharge and remotely sensed soil moisture acquired by AMSR-E, SMOS and ASCAT. Four scenarios are studied: no calibration (expert knowledge), calibration on discharge, calibration on remote sensing data (three satellites) and calibration on both discharge and remote sensing data. Using a split-sample approach, the model is calibrated for a period of 2 years and validated for the calibrated model parameters on a validation period of 10 years. Results show that calibration with discharge data improves the estimation of groundwater parameters (e.g., groundwater reservoir constant) and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate calibration of parameters related to land surface process (e.g., the saturated conductivity of the soil), which is not possible when calibrating on discharge alone. For the upstream area up to 40000 km2, calibration on both discharge and soil moisture results in a reduction by 10-30 % in the RMSE for discharge simulations, compared to calibration on discharge alone. For discharge in the downstream area, the model performance due to assimilation of remotely sensed soil moisture is not increased or slightly decreased, most probably due to the longer relative importance of the routing and contribution of groundwater in downstream areas. When microwave soil moisture is used for calibration the RMSE of soil moisture simulations decreases from 0.072 m3m-3 to 0.062 m3m-3. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models leading to a better simulation of soil moisture content throughout and a better simulation of discharge in upstream areas, particularly if discharge observations are sparse.

Canadian Experiment for Soil Moisture in 2010 (CanEX-SM10): Overview and Preliminary Results

NASA Technical Reports Server (NTRS)

Magagi, Ramata; Berg, Aaron; Goita, Kalifa; Belair, Stephane; Jackson, Tom; Toth, B.; Walker, A.; McNairn, H.; O'Neill, P.; Moghdam. M;

Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

NASA Astrophysics Data System (ADS)

Tobin, Kenneth J.; Torres, Roberto; Crow, Wade T.; Bennett, Marvin E.

2017-09-01

This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25° and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997-2002; 2002-2005; 2005-2008; 2008-2011; 2011-2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0. 5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash-Sutcliffe coefficients (NSs) for ARM ranged from -0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2-0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1-0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02-0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05-0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

Monitoring Land Surface Soil Moisture from Space with in-Situ Sensors Validation: The Huntsville Example

NASA Technical Reports Server (NTRS)

Wu, Steve Shih-Tseng

1997-01-01

Based on recent advances in microwave remote sensing of soil moisture and in pursuit of research interests in areas of hydrology, soil climatology, and remote sensing, the Center for Hydrology, Soil Climatology, and Remote Sensing (HSCARS) conducted the Huntsville '96 field experiment in Huntsville, Alabama from July 1-14, 1996. We, researchers at the Global Hydrology and Climate Center's MSFC/ES41, are interested in using ground-based microwave sensors, to simulate land surface brightness signatures of those spaceborne sensors that were in operation or to be launched in the near future. The analyses of data collected by the Advanced Microwave Precipitation Radiometer (AMPR) and the C-band radiometer, which together contained five frequencies (6.925,10.7,19.35, 37.1, and 85.5 GHz), and with concurrent in-situ collection of surface cover conditions (surface temperature, surface roughness, vegetation, and surface topology) and soil moisture content, would result in a better understanding of the data acquired over land surfaces by the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR), because these spaceborne sensors contained these five frequencies. This paper described the approach taken and the specific objective to be accomplished in the Huntsville '97 field experiment.

Improving River Flow Predictions from the NOAA NCRFC Forecasting Model by Incorporating Satellite Observations

NASA Astrophysics Data System (ADS)

Tuttle, S. E.; Jacobs, J. M.; Restrepo, P. J.; Deweese, M. M.; Connelly, B.; Buan, S.

2016-12-01

The NOAA National Weather Service North Central River Forecast Center (NCRFC) is responsible for issuing river flow forecasts for parts of the Upper Mississippi, Great Lakes, and Hudson Bay drainages, including the Red River of the North basin (RRB). The NCRFC uses an operational hydrologic modeling infrastructure called the Community Hydrologic Prediction System (CHPS) for its operational forecasts, which currently links the SNOW-17 snow accumulation and ablation model, to the Sacramento-Soil Moisture Accounting (SAC-SMA) rainfall-runoff model, to a number of hydrologic and hydraulic flow routing models. The operational model is lumped and requires only area-averaged precipitation and air temperature as inputs. NCRFC forecasters use observational data of hydrological state variables as a source of supplemental information during forecasting, and can use professional judgment to modify the model states in real time. In a few recent years (e.g. 2009, 2013), the RRB exhibited unexpected anomalous hydrologic behavior, resulting in overestimation of peak flood discharge by up to 70% and highlighting the need for observations with high temporal and spatial coverage. Unfortunately, observations of hydrological states (e.g. soil moisture, snow water equivalent (SWE)) are relatively scarce in the RRB. Satellite remote sensing can fill this need. We use Minnesota's Buffalo River watershed within the RRB as a test case and update the operational CHPS model using modifications based on satellite observations, including AMSR-E SWE and SMOS soil moisture estimates. We evaluate the added forecasting skill of the satellite-enhanced model compared to measured streamflow using hindcasts from 2010-2013.

A Regional, Integrated Monitoring System for the Hydrology of the Pan-Arctic Land Mass

NASA Technical Reports Server (NTRS)

Serreze, Mark; Barry, Roger; Nolin, Anne; Armstrong, Richard; Zhang, Ting-Jung; Vorosmarty, Charles; Lammers, Richard; Frolking, Steven; Bromwich, David; McDonald, Kyle

2005-01-01

Work under this NASA contract developed a system for monitoring and historical analysis of the major components of the pan-Arctic terrestrial water cycle. It is known as Arctic-RIMS (Regional Integrated Hydrological Monitoring System for the Pan-Arctic Landmass). The system uses products from EOS-era satellites, numerical weather prediction models, station records and other data sets in conjunction with an atmosphere-land surface water budgeting scheme. The intent was to compile operational (at 1-2 month time lags) gridded fields of precipitation (P), evapotranspiration (ET), P-ET, soil moisture, soil freeze/thaw state, active layer thickness, snow extent and its water equivalent, soil water storage, runoff and simulated discharge along with estimates of non-closure in the water budget. Using "baseline" water budgeting schemes in conjunction with atmospheric reanalyses and pre-EOS satellite data, water budget fields were conjunction with atmospheric reanalyses and pre-EOS satellite data, water budget fields were compiled to provide historical time series. The goals as outlined in the original proposal can be summarized as follows: 1) Use EOS data to compile hydrologic products for the pan-Arctic terrestrial regions including snowcover/snow water equivalent (SSM/A MODIS, AMSR) and near-surface freeze/thaw dynamics (Sea Winds on QuikSCAT and ADEOS I4 SSMI and AMSR). 2) Implement Arctic-RIMS to use EOS data streams, allied fields and hydrologic models to produce allied outputs that fully characterize pan-Arctic terrestrial and aerological water budgets. 3) Compile hydrologically-based historical products providing a long-term baseline of spatial and temporal variability in the water cycle.

Freeboard, Snow Depth and Sea-Ice Roughness in East Antarctica from In Situ and Multiple Satellite Data

NASA Technical Reports Server (NTRS)

Markus, Thorsten; Masson, Robert; Worby, Anthony; Lytle, Victoria; Kurtz, Nathan; Maksym, Ted

2011-01-01

In October 2003 a campaign on board the Australian icebreaker Aurora Australis had the objective to validate standard Aqua Advanced Microwave Scanning Radiometer (AMSR-E) sea-ice products. Additionally, the satellite laser altimeter on the Ice, Cloud and land Elevation Satellite (ICESat) was in operation. To capture the large-scale information on the sea-ice conditions necessary for satellite validation, the measurement strategy was to obtain large-scale sea-ice statistics using extensive sea-ice measurements in a Lagrangian approach. A drifting buoy array, spanning initially 50 km 100 km, was surveyed during the campaign. In situ measurements consisted of 12 transects, 50 500 m, with detailed snow and ice measurements as well as random snow depth sampling of floes within the buoy array using helicopters. In order to increase the amount of coincident in situ and satellite data an approach has been developed to extrapolate measurements in time and in space. Assuming no change in snow depth and freeboard occurred during the period of the campaign on the floes surveyed, we use buoy ice-drift information as well as daily estimates of thin-ice fraction and rough-ice vs smooth-ice fractions from AMSR-E and QuikSCAT, respectively, to estimate kilometer-scale snow depth and freeboard for other days. The results show that ICESat freeboard estimates have a mean difference of 1.8 cm when compared with the in situ data and a correlation coefficient of 0.6. Furthermore, incorporating ICESat roughness information into the AMSR-E snow depth algorithm significantly improves snow depth retrievals. Snow depth retrievals using a combination of AMSR-E and ICESat data agree with in situ data with a mean difference of 2.3 cm and a correlation coefficient of 0.84 with a negligible bias.

Tile Drainage Expansion Detection using Satellite Soil Moisture Dynamics

NASA Astrophysics Data System (ADS)

Jacobs, J. M.; Cho, E.; Jia, X.

2017-12-01

In the past two decades, tile drainage installation has accelerated throughout the Red River of the North Basin (RRB) in parts of western Minnesota, eastern North Dakota, and a small area of northeastern South Dakota, because the flat topography and low-permeability soils in this region necessitated the removal of excess water to improve crop production. Interestingly, streamflow in the Red River has markedly increased and six of 13 major floods during the past century have occurred since the late 1990s. It has been suggested that the increase in RRB flooding could be due to change in agricultural practices, including extensive tile drainage installation. Reliable information on existing and future tile drainage installation is greatly needed to capture the rapid extension of tile drainage systems and to locate tile drainage systems in the north central U.S. including the RRB region. However, there are few reliable data of tile drainage installation records, except tile drainage permit records in the Bois de Sioux watershed (a sub-basin in southern part of the RRB where permits are required for tile drainage installation). This study presents a tile drainage expansion detection method based on a physical principle that the soil-drying rate may increase with increasing tile drainage for a given area. In order to capture the rate of change in soil drying rate with time over entire RRB (101,500 km2), two satellite-based microwave soil moisture records from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and AMSR2 were used during 2002 to 2016. In this study, a sub-watershed level (HUC10) potential tile drainage growth map was developed and the results show good agreement with tile drainage permit records of six sub-watersheds in the Bois de Sioux watershed. Future analyses will include improvement of the potential tile drainage map through additional information using optical- and thermal-based sensor products and evaluation of its hydrological impacts on intensity, duration, and frequency of extreme streamflow from watershed to basin scale.

Assessing the Relative Performance of Microwave-Based Satellite Rain Rate Retrievals Using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2010-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecasts of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (Aqua) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparisons with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellite estimates is examined via comparisons with space- and time-coincident GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm/hr. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products. 3

Assessing the Relative Performance of Microwave-based Satellite Rain Rate Retrievals using TRMM Ground Validation Data

NASA Technical Reports Server (NTRS)

Wolff, David B.; Fisher, Brad L.

2008-01-01

Space-borne microwave sensors provide critical rain information used in several global multi-satellite rain products, which in turn are used for a variety of important studies, including landslide forecasting, flash flood warning, data assimilation, climate studies, and validation of model forecast of precipitation. This study employs four years (2003-2006) of satellite data to assess the relative performance and skill of SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), AMSR-E (AQUA) and the TRMM Microwave Imager (TMI) in estimating surface rainfall based on direct instantaneous comparison with ground-based rain estimates from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The relative performance of each of these satellites is examined via comparisons with GV radar-based rain rate estimates. Because underlying surface terrain is known to affect the relative performance of the satellite algorithms, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Of all the satellite estimates compared in this study, TMI and AMSR-E exhibited considerably higher correlations and skills in estimating/observing surface precipitation. While SSM/I and AMSU-B exhibited lower correlations and skills for each of the different terrain categories, the SSM/I absolute biases trended slightly lower than AMSRE over ocean, where the observations from both emission and scattering channels were used in the retrievals. AMSU-B exhibited the least skill relative to GV in all of the relevant statistical categories, and an anomalous spike was observed in the probability distribution functions near 1.0 mm hr-1. This statistical artifact appears to be related to attempts by algorithm developers to include some lighter rain rates, not easily detectable by its scatter-only frequencies. AMSU-B, however, agreed well with GV when the matching data was analyzed on monthly scales. These results signal developers of global rainfall products, such as the TRMM Multi-Satellite Precipitation Analysis (TMPA), and the Climate Data Center s Morphing (CMORPH) technique, that care must be taken when incorporating data from these input satellite estimates in order to provide the highest quality estimates in their products.

A Sample of What We Have Learned from A-Train Cloud Measurements

NASA Technical Reports Server (NTRS)

Joiner, Joanna; Vasilkov, Alexander; Ziemke, Jerry; Chandra, Sushil; Spurr, Robert; Bhartia, P. K.; Krotkov, Nick; Sneep, Maarten; Menzel, Paul; Platnick, Steve;

Change in Water Cycle- Important Issue on Climate Earth System

NASA Astrophysics Data System (ADS)

Singh, Pratik

Change in Water Cycle- Important Issue on Climate Earth System PRATIK KUMAR SINGH1 1BALDEVRAM MIRDHA INSTITUTE OF TECHNOLOGY,JAIPUR (RAJASTHAN) ,INDIA Water is everywhere on Earth and is the only known substance that can naturally exist as a gas, liquid, and solid within the relatively small range of air temperatures and pressures found at the Earth's surface.Changes in the hydrological cycle as a consequence of climate and land use drivers are expected to play a central role in governing a vast range of environmental impacts.Earth's climate will undergo changes in response to natural variability, including solar variability, and to increasing concentrations of green house gases and aerosols.Further more, agreement is widespread that these changes may profoundly affect atmospheric water vapor concentrations, clouds and precipitation patterns.As we know that ,a warmer climate, directly leading to increased evaporation, may well accelerate the hydrological cycle, resulting in an increase in the amount of moisture circulating through the atmosphere.The Changing Water Cycle programmer will develop an integrated, quantitative understanding of the changes taking place in the global water cycle, involving all components of the earth system, improving predictions for the next few decades of regional precipitation, evapotranspiration, soil moisture, hydrological storage and fluxes.The hydrological cycle involves evaporation, transpiration, condensation, precipitation, and runoff. NASA's Aqua satellite will monitor many aspects of the role of water in the Earth's systems, and will do so at spatial and temporal scales appropriate to foster a more detailed understanding of each of the processes that contribute to the hydrological cycle. These data and the analyses of them will nurture the development and refinement of hydrological process models and a corresponding improvement in regional and global climate models, with a direct anticipated benefit of more accurate weather and climate forecasts. Aqua is a major mission of the Earth Observing System (EOS), an international program centered in NASA's Earth Science Enterprise to study the Earth in detail from the unique vantage point of space. Focused on key measurements identified by a consensus of U.S. and international scientists, EOS is further enabling studies of the complex interactions amongst the Earth's land, ocean, air, ice and biological systems. Aqua's contributions to monitoring water in the Earth's environment will involve all six of Aqua's instruments: the Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), the Humidity Sounder for Brazil (HSB), the Advanced Microwave Scanning Radiometer- Earth Observing System (AMSR-E), the Moderate Resolution Imaging Spectroradiometer (MODIS), and Clouds and the Earth's Radiant Energy System (CERES). Frozen water in the oceans, in the form of sea ice, will be examined with both AMSR-E and MODIS data, the former allowing routine monitoring of sea ice at a coarse resolution and the latter providing greater spatial resolution but only under cloud-free conditions. Sea ice can insulate the underlying liquid water against heat loss to the often frigid overlying polar atmosphere and also reflects sunlight that would otherwise be available to warm the ocean. AMSR-E measurements will allow the routine derivation of sea ice concentrations in both polar regions, through taking advantage of the marked contrast in microwave emissions of sea ice and liquid water. This will continue, with improved resolution and accuracy, a 22-year satellite record of changes in the extent of polar ice. MODIS, with its finer resolution, will permit the identification of individual ice flows, when unobscured by clouds. AMSR-E and MODIS will also provide monitoring, the AIRS/AMSU/HSB combination will provide more-accurate space-based measurements of atmospheric temperature and water vapor than have ever been obtained before, with the highest vertical resolution to date as well. Since water vapor is the Earth's primary greenhouse gas and contributes significantly to uncertainties in projections of future global warming, it is critical to understand how it varies in the Earth system. We should concern for these drastic changes and should protect it. Keywords-Hydrological cycle,Climate models,Aqua’s instruments

Estimation of Land Surface Temperature from GCOM-W1 AMSR2 Data over the Chinese Landmass

NASA Astrophysics Data System (ADS)

Zhou, Ji; Dai, Fengnan; Zhang, Xiaodong

2016-04-01

As one of the most important parameter at the interface between the earth's surface and atmosphere, land surface temperature (LST) plays a crucial role in many fields, such as climate change monitoring and hydrological modeling. Satellite remote sensing provides the unique possibility to observe LST of large regions at diverse spatial and temporal scales. Compared with thermal infrared (TIR) remote sensing, passive microwave (PW) remote sensing has a better ability in overcoming the influences of clouds; thus, it can be used to improve the temporal resolution of current satellite TIR LST. However, most of current methods for estimation LST from PW remote sensing are empirical and have unsatisfied generalization. In this study, a semi-empirical method is proposed to estimate LST from the observation of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1st-WATER "SHIZUKU" satellite (GCOM-W1). The method is based on the PW radiation transfer equation, which is simplified based on (1) the linear relationship between the emissivities of horizontal and vertical polarization channels at the same frequency and (2) the significant relationship between atmospheric parameters and the atmospheric water vapor content. An iteration approach is used to best fit the pixel-based coefficients in the simplified radiation transfer equation of the horizontal and vertical polarization channels at each frequency. Then an integration approach is proposed to generate the ensemble estimation from estimations of multiple frequencies for different land cover types. This method is trained with the AMSR2 brightness temperature and MODIS LST in 2013 over the entire Chinese landmass and then it is tested with the data in 2014. Validation based on in situ LSTs measured in northwestern China demonstrates that the proposed method has a better accuracy than the polarization radiation method, with a root-mean squared error of 3 K. Although the proposal method is applied to AMSR2 data, it has good ability to extend to other satellite PW sensors, such as the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on board the Aqua satellite and the Special Sensor Microwave/Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellite. It would be beneficial in providing LST to applications at continental and global scales.

The Transition of High-Resolution NASA MODIS Sea Surface Temperatures into the WRF Environmental Modeling System

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Jedlove, Gary J.; Santos, Pablo; Medlin, Jeffrey M.; Rozumalski, Robert A.

2009-01-01

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) composite at 2-km resolution that has been implemented in version 3 of the National Weather Service (NWS) Weather Research and Forecasting (WRF) Environmental Modeling System (EMS). The WRF EMS is a complete, full physics numerical weather prediction package that incorporates dynamical cores from both the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM). The installation, configuration, and execution of either the ARW or NMM models is greatly simplified by the WRF EMS to encourage its use by NWS Weather Forecast Offices (WFOs) and the university community. The WRF EMS is easy to run on most Linux workstations and clusters without the need for compilers. Version 3 of the WRF EMS contains the most recent public release of the WRF-NMM and ARW modeling system (version 3 of the ARW is described in Skamarock et al. 2008), the WRF Pre-processing System (WPS) utilities, and the WRF Post-Processing program. The system is developed and maintained by the NWS National Science Operations Officer Science and Training Resource Coordinator. To initialize the WRF EMS with high-resolution MODIS SSTs, SPoRT developed the composite product consisting of MODIS SSTs over oceans and large lakes with the NCEP Real-Time Global (RTG) filling data over land points. Filling the land points is required due to minor inconsistencies between the WRF land-sea mask and that used to generate the MODIS SST composites. This methodology ensures a continuous field that adequately initializes all appropriate arrays in WRF. MODIS composites covering the Gulf of Mexico, western Atlantic Ocean and the Caribbean are generated daily at 0400, 0700, 1600, and 1900 UTC corresponding to overpass times of the NASA Aqua and Terra polar orbiting satellites. The MODIS SST product is output in gridded binary-1 (GRIB-1) data format for a seamless incorporation into WRF via the WPS utilities. The full-resolution, 1-km MODIS product is sub-sampled to 2-km grid spacing due to limitations in handling very large dimensions in the GRIB-1 data format. The GRIB-1 files are posted online at ftp://ftp.nsstc.org/sstcomp/WRF/, which is directly accessed by the WRF EMS scripts. The MODIS SST composites are also downloaded to the EMS data server, which is accessible by the WRF EMS users and NWS WFOs. The SPoRT MODIS SST composite provides the model with superior detail of the ocean gradients around Florida and surrounding waters, whereas the operational RTG SST typically depicts a relatively smooth field and is not able to capture sharp horizontal gradients in SST. Differences of 2-3 C are common over small horizontal distances, leading to enhanced SST gradients on either side of the Gulf Stream and along the edges of the cooler shelf waters. These sharper gradients can in turn produce atmospheric responses in simulated temperature and wind fields as depicted in LaCasse et al. Differences in atmospheric verification statistics over a several month study were generally small in the vicinity of south Florida; however, the validation of SSTs at specific buoy locations revealed important improvements in the biases and RMS errors, especially in the vicinity of the cooler shelf waters off the east-central Florida coast. A current weakness in the MODIS SST product is the occurrence of occasional discontinuities caused by high latency in SST coverage due to persistent cloud cover. An enhanced method developed by Jedlovec et al. (2009, GHRSST User Symposium) reduces the occurrence of these problems by adding Advanced Microwave Scanning Radiometer -- EOS (AMSR-E) SST data to the compositing process. Enhanced SST composites are produced over the ocean regions surrounding the Continental U.S. at four times each day corresponding to Terra and Aqua equator crossing times. For a given day and overpass time, both MODInd AMSR-E data from the previous seven days form a collection used in the compositing. At each MODIS pixel, cloud-free SST values from the collection are used to form a weighted average based on their latency (number of days from the current day). In this way, recent SST data are given more weight than older data. One of the primary issues involved in incorporating the AMSR-E microwave data in the composites is the tradeoff between the decreased spatial resolution of the AMSR-E data (25 km) and the increased coverage due to its near all-weather capability. Currently, the AMSR-E is given a weight of 20% compared to MODIS data, thereby preserving the spatial structure observed in the MODIS data. Day-time (night-time) AMSR-E SST data from Aqua are used with both Terra and Aqua MODIS day-time (night-time) SST data sets.

A new multi-sensor integrated index for drought monitoring

NASA Astrophysics Data System (ADS)

Jiao, W.; Wang, L.; Tian, C.

2017-12-01

Drought is perceived as one of the most expensive and least understood natural disasters. The remote-sensing-based integrated drought indices, which integrate multiple variables, could reflect the drought conditions more comprehensively than single drought indices. However, most of current remote-sensing-based integrated drought indices focus on agricultural drought (i.e., deficit in soil moisture), their application in monitoring meteorological drought (i.e., deficit in precipitation) was limited. More importantly, most of the remote-sensing-based integrated drought indices did not take into consideration of the spatially non-stationary nature of the related variables, so such indices may lose essential local details when integrating multiple variables. In this regard, we proposed a new mathematical framework for generating integrated drought index for meteorological drought monitoring. The geographically weighted regression (GWR) model and principal component analysis were used to composite Moderate-resolution Imaging Spectroradiometer (MODIS) based temperature condition index (TCI), the Vegetation Index based on the Universal Pattern Decomposition method (VIUPD) based vegetation condition index (VCI), tropical rainfall measuring mission (TRMM) based Precipitation Condition Index (PCI) and Advanced Microwave Scanning Radiometer-EOS (AMSR-E) based soil moisture condition index (SMCI). We called the new remote-sensing-based integrated drought index geographical-location-based integrated drought index (GLIDI). We examined the utility of the GLIDI for drought monitoring in various climate divisions across the continental United States (CONUS). GLIDI showed high correlations with in-situ drought indices and outperformed most other existing drought indices. The results also indicate that the performance of GLIDI is not affected by environmental factors such as land cover, precipitation, temperature and soil conditions. As such, the GLIDI has considerable potential for drought monitoring across various environmental conditions.

Comparison of snow depth retrieval algorithm in Northeastern China based on AMSR2 and FY3B-MWRI data

NASA Astrophysics Data System (ADS)

Fan, Xintong; Gu, Lingjia; Ren, Ruizhi; Zhou, Tingting

2017-09-01

Snow accumulation has a very important influence on the natural environment and human activities. Meanwhile, improving the estimation accuracy of passive microwave snow depth (SD) retrieval is a hotspot currently. Northeastern China is a typical snow study area including many different land cover types, such as forest, grassland and farmland. Especially, there is relatively stable snow accumulation in January every year. The brightness temperatures which are observed by the Advanced Microwave Scanning Radiometer 2 (AMSR2) on GCOM-W1 and FengYun3B Microwave Radiation Imager (FY3B-MWRI) in the same period in 2013 are selected as the study data in the research. The results of snow depth retrieval using AMSR2 standard algorithm and Jiang's FY operational algorithm are compared in the research. Moreover, to validate the accuracy of the two algorithms, the retrieval results are compared with the SD data observed at the national meteorological stations in Northeastern China. Furthermore, the retrieval SD is also compared with AMSR2 and FY standard SD products, respectively. The root mean square errors (RMSE) results using AMSR2 standard algorithms and FY operational algorithm are close in the forest surface, which are 6.33cm and 6.28cm, respectively. However, The FY operational algorithm shows a better result than the AMSR2 standard algorithms in the grassland and farmland surface. The RMSE results using FY operational algorithm in the grassland and farmland surface are 2.44cm and 6.13cm, respectively.

SeaWinds Scatterometer Wind Vector Retrievals Within Hurricanes Using AMSR and NEXRAD to Perform Corrections for Precipitation Effects: Comparison of AMSR and NEXRAD Retrievals of Rain

NASA Technical Reports Server (NTRS)

Weissman, David E.; Hristova-Veleva, Svetla; Callahan, Philip

2006-01-01

The opportunity provided by satellite scatterometers to measure ocean surface winds in strong storms and hurricanes is diminished by the errors in the received backscatter (SIGMA-0) caused by the attenuation, scattering and surface roughening produced by heavy rain. Providing a good rain correction is a very challenging problem, particularly at Ku band (13.4 GHz) where rain effects are strong. Corrections to the scatterometer measurements of ocean surface winds can be pursued with either of two different methods: empirical or physical modeling. The latter method is employed in this study because of the availability of near simultaneous and collocated measurements provided by the MIDORI-II suite of instruments. The AMSR was designed to measure atmospheric water-related parameters on a spatial scale comparable to the SeaWinds scatterometer. These quantities can be converted into volumetric attenuation and scattering at the Ku-band frequency of SeaWinds. Optimal estimates of the volume backscatter and attenuation require a knowledge of the three dimensional distribution of reflectivity on a scale comparable to that of the precipitation. Studies selected near the US coastline enable the much higher resolution NEXRAD reflectivity measurements evaluate the AMSR estimates. We are also conducting research into the effects of different beam geometries and nonuniform beamfilling of precipitation within the field-of-view of the AMSR and the scatterometer. Furthermore, both AMSR and NEXRAD estimates of atmospheric correction can be used to produce corrected SIGMA-0s, which are then input to the JPL wind retrieval algorithm.

Comparative Perspectives on Recent Trends in Land Surface Dynamics in the Grasslands of North and South America

NASA Astrophysics Data System (ADS)

Henebry, G. M.; Valle De Carvalho E Oliveira, P.; Zheng, B.; de Beurs, K.; Owsley, B.

2015-12-01

In our current era of intensive earth observation the time is ripe to shift away from studies relying on single sensors or single products to the synergistic use of multiple sensors and products at complementary spatial, temporal, and spectral scales. The use of multiple time series can not only reveal hotspots of change in land surface dynamics, but can indicate plausible proximate causes of the changes and suggest their possible consequences. Here we explore recent trends in the land surface dynamics of exemplary semi-arid grasslands in the western hemisphere, including the shortgrass prairie of eastern Colorado and New Mexico, the sandhills prairie of Nebraska, the "savana gramineo-lenhosa" variety of cerrado in central Brazil, and the pampas of Argentina. Observational datasets include (1) NBAR-based vegetation indices, land surface temperature, and evapotranspiration from MODIS, (2) air temperature, water vapor, and vegetation optical depth from AMSR-E and AMSR2, (3) surface air temperature, water vapor, and relative humidity from AIRS, and (4) surface shortwave, longwave, and total net flux from CERES. The spatial resolutions of these nested data include 500 m, 1000 m, 0.05 degree, 25 km, and 1 degree. We apply the nonparametric Seasonal Kendall trend test to each time series independently to identify areas of significant change. We then examine polygons of co-occurrence of significant change in two or more types of products using the surface radiation and energy budgets as guides to interpret the multiple changes. Changes occurring across broad areas are more likely to be of climatic origin; whereas, changes that are abrupt in space and time and of limited area are more likely anthropogenic. Results illustrate the utility of considering multiple remote sensing products as complementary views of land surface dynamics.

Comparison of DMSP SSM/I and Landsat 7 ETM+ Sea Ice Concentrations During Summer Melt

NASA Technical Reports Server (NTRS)

Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro; Koblinsky, Chester J. (Technical Monitor)

2001-01-01

As part of NASA's EOS Aqua sea ice validation program for the Advanced Microwave Scanning Radiometer (AMSR-E), Landsat 7 Enhanced Thematic Mapper (ETM+) images were acquired to develop a sea ice concentration data set with which to validate AMSR-E sea ice concentration retrievals. The standard AMSR-E Arctic sea ice concentration product will be obtained with the enhanced NASA Team (NT2) algorithm. The goal of this study is to assess the accuracy to which the NT2 algorithm, using DMSP Special Sensor Microwave Imager radiances, retrieves sea ice concentrations under summer melt conditions. Melt ponds are currently the largest source of error in the determination of Arctic sea ice concentrations with satellite passive microwave sensors. To accomplish this goal, Landsat 7 ETM+ images of Baffin Bay were acquired under clear sky conditions on the 26th and 27th of June 2000 and used to generate high-resolution sea ice concentration maps with which to compare the NT2 retrievals. Based on a linear regression analysis of 116 25-km samples, we find that overall the NT2 retrievals agree well with the Landsat concentrations. The regression analysis yields a correlation coefficient of 0.98. In areas of high melt ponding, the NT2 retrievals underestimate the sea ice concentrations by about 12% compared to the Landsat values.

Synergistic Utilization of Microwave Satellite Data and GRACE-Total Water Storage Anomaly for Improving Available Water Capacity Prediction in Lower Mekong Basin

NASA Astrophysics Data System (ADS)

Gupta, M.; Bolten, J. D.; Lakshmi, V.

2015-12-01

The Mekong River is the longest river in Southeast Asia and the world's eighth largest in discharge with draining an area of 795,000 km² from the eastern watershed of the Tibetan Plateau to the Mekong Delta including three provinces of China, Myanmar, Lao PDR, Thailand, Cambodia and Viet Nam. This makes the life of people highly vulnerable to availability of the water resources as soil moisture is one of the major fundamental variables in global hydrological cycles. The day-to-day variability in soil moisture on field to global scales is an important quantity for early warning systems for events like flooding and drought. In addition to the extreme situations the accurate soil moisture retrieval are important for agricultural irrigation scheduling and water resource management. The present study proposes a method to determine the effective soil hydraulic parameters directly from information available for the soil moisture state from the recently launched SMAP (L-band) microwave remote sensing observations. Since the optimized parameters are based on the near surface soil moisture information, further constraints are applied during the numerical simulation through the assimilation of GRACE Total Water Storage (TWS) within the physically based land surface model. This work addresses the improvement of available water capacity as the soil hydraulic parameters are optimized through the utilization of satellite-retrieved near surface soil moisture. The initial ranges of soil hydraulic parameters are taken in correspondence with the values available from the literature based on FAO. The optimization process is divided into two steps: the state variable are optimized and the optimal parameter values are then transferred for retrieving soil moisture and streamflow. A homogeneous soil system is considered as the soil moisture from sensors such as AMSR-E/SMAP can only be retrieved for the top few centimeters of soil. To evaluate the performance of the system in helping improve simulation accuracy and whether they can be used to obtain soil moisture profiles at poorly gauged catchments the root mean square error (RMSE) and Mean Bias error (MBE) are used to measure the performance of the simulations.

Multilayered Clouds Identification and Retrieval for CERES Using MODIS

NASA Technical Reports Server (NTRS)

Sun-Mack, Sunny; Minnis, Patrick; Chen, Yan; Yi, Yuhong; Huang, Jainping; Lin, Bin; Fan, Alice; Gibson, Sharon; Chang, Fu-Lung

2006-01-01

Traditionally, analyses of satellite data have been limited to interpreting the radiances in terms of single layer clouds. Generally, this results in significant errors in the retrieved properties for multilayered cloud systems. Two techniques for detecting overlapped clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. The first technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other method uses microwave (MWR) data. The use of BTD, the 11-12 micrometer brightness temperature difference, in conjunction with tau, the retrieved visible optical depth, was suggested by Kawamoto et al. (2001) and used by Pavlonis et al. (2004) as a means to detect multilayered clouds. Combining visible (VIS; 0.65 micrometer) and infrared (IR) retrievals of cloud properties with microwave (MW) retrievals of cloud water temperature Tw and liquid water path LWP retrieved from satellite microwave imagers appears to be a fruitful approach for detecting and retrieving overlapped clouds (Lin et al., 1998, Ho et al., 2003, Huang et al., 2005). The BTD method is limited to optically thin cirrus over low clouds, while the MWR method is limited to ocean areas only. With the availability of VIS and IR data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and MW data from the Advanced Microwave Scanning Radiometer EOS (AMSR-E), both on Aqua, it is now possible to examine both approaches simultaneously. This paper explores the use of the BTD method as applied to MODIS and AMSR-E data taken from the Aqua satellite over non-polar ocean surfaces.

Moisture Fluxes Derived from EOS Aqua Satellite Data for the North Water Polynya Over 2003-2009

NASA Technical Reports Server (NTRS)

Boisvert, Linette N.; Markus, Thorsten; Parkinson, Claire L.; Vihma, Timo

2012-01-01

Satellite data were applied to calculate the moisture flux from the North Water polynya during a series of events spanning 2003-2009. The fluxes were calculated using bulk aerodynamic formulas with the stability effects according to the Monin-Obukhov similarity theory. Input parameters were taken from three sources: air relative humidity, air temperature, and surface temperature from the Atmospheric Infrared Sounder (AIRS) onboard NASA's Earth Observing System (EOS) Aqua satellite, sea ice concentration from the Advanced Microwave Scanning Radiometer (AMSR-E, also onboard Aqua), and wind speed from the ECMWF ERA-Interim reanalysis. Our results show the progression of the moisture fluxes from the polynya during each event, as well as their atmospheric effects after the polynya has closed up. These results were compared to results from studies on other polynyas, and fall within one standard deviation of the moisture flux estimates from these studies. Although the estimated moisture fluxes over the entire study region from AIRS are smaller in magnitude than ERA-Interim, they are more accurate due to improved temperature and relative humidity profiles and ice concentration estimates over the polynya. Error estimates were calculated to be 5.56 x10(exp -3) g/sq. m/ s, only 25% of the total moisture flux, thus suggesting that AIRS and AMSR-E can be used with confidence to study smaller scale features in the Arctic sea ice pack and can capture their atmospheric effects. These findings bode well for larger-scale studies of moisture fluxes over the entire Arctic Ocean and the thinning ice pack.

Soil moisture and temperature algorithms and validation

USDA-ARS?s Scientific Manuscript database

Passive microwave remote sensing of soil moisture has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...

Applying the Karma Provenance tool to NASA's AMSR-E Data Production Stream

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Conover, H.; Regner, K.; Movva, S.; Goodman, H. M.; Pale, B.; Purohit, P.; Sun, Y.

2010-12-01

Current procedures for capturing and disseminating provenance, or data product lineage, are limited in both what is captured and how it is disseminated to the science community. For example, the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) Science Investigator-led Processing System (SIPS) generates Level 2 and Level 3 data products for a variety of geophysical parameters. Data provenance and quality information for these data sets is either very general (e.g., user guides, a list of anomalous data receipt and processing conditions over the life of the missions) or difficult to access or interpret (e.g., quality flags embedded in the data, production history files not easily available to users). Karma is a provenance collection and representation tool designed and developed for data driven workflows such as the productions streams used to produce EOS standard products. Karma records uniform and usable provenance metadata independent of the processing system while minimizing both the modification burden on the processing system and the overall performance overhead. Karma collects both the process and data provenance. The process provenance contains information about the workflow execution and the associated algorithm invocations. The data provenance captures metadata about the derivation history of the data product, including algorithms used and input data sources transformed to generate it. As part of an ongoing NASA funded project, Karma is being integrated into the AMSR-E SIPS data production streams. Metadata gathered by the tool will be presented to the data consumers as provenance graphs, which are useful in validating the workflows and determining the quality of the data product. This presentation will discuss design and implementation issues faced while incorporating a provenance tool into a structured data production flow. Prototype results will also be presented in this talk.

Detection of Rain-on-Snow (ROS) Events Using the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Weather Station Observations

NASA Astrophysics Data System (ADS)

Ryan, E. M.; Brucker, L.; Forman, B. A.

2015-12-01

During the winter months, the occurrence of rain-on-snow (ROS) events can impact snow stratigraphy via generation of large scale ice crusts, e.g., on or within the snowpack. The formation of such layers significantly alters the electromagnetic response of the snowpack, which can be witnessed using space-based microwave radiometers. In addition, ROS layers can hinder the ability of wildlife to burrow in the snow for vegetation, which limits their foraging capability. A prime example occurred on 23 October 2003 in Banks Island, Canada, where an ROS event is believed to have caused the deaths of over 20,000 musk oxen. Through the use of passive microwave remote sensing, ROS events can be detected by utilizing observed brightness temperatures (Tb) from AMSR-E. Tb observed at different microwave frequencies and polarizations depends on snow properties. A wet snowpack formed from an ROS event yields a larger Tb than a typical dry snowpack would. This phenomenon makes observed Tb useful when detecting ROS events. With the use of data retrieved from AMSR-E, in conjunction with observations from ground-based weather station networks, a database of estimated ROS events over the past twelve years was generated. Using this database, changes in measured Tb following the ROS events was also observed. This study adds to the growing knowledge of ROS events and has the potential to help inform passive microwave snow water equivalent (SWE) retrievals or snow cover properties in polar regions.

Evaluation and Application of Gridded Snow Water Equivalent Products for Improving Snowmelt Flood Predictions in the Red River Basin of the North

NASA Astrophysics Data System (ADS)

Schroeder, R.; Jacobs, J. M.; Vuyovich, C.; Cho, E.; Tuttle, S. E.

2017-12-01

Each spring the Red River basin (RRB) of the North, located between the states of Minnesota and North Dakota and southern Manitoba, is vulnerable to dangerous spring snowmelt floods. Flat terrain, low permeability soils and a lack of satisfactory ground observations of snow pack conditions make accurate predictions of the onset and magnitude of major spring flood events in the RRB very challenging. This study investigated the potential benefit of using gridded snow water equivalent (SWE) products from passive microwave satellite missions and model output simulations to improve snowmelt flood predictions in the RRB using NOAA's operational Community Hydrologic Prediction System (CHPS). Level-3 satellite SWE products from AMSR-E, AMSR2 and SSM/I, as well as SWE computed from Level-2 brightness temperatures (Tb) measurements, including model output simulations of SWE from SNODAS and GlobSnow-2 were chosen to support the snowmelt modeling exercises. SWE observations were aggregated spatially (i.e. to the NOAA North Central River Forecast Center forecast basins) and temporally (i.e. by obtaining daily screened and weekly unscreened maximum SWE composites) to assess the value of daily satellite SWE observations relative to weekly maximums. Data screening methods removed the impacts of snow melt and cloud contamination on SWE and consisted of diurnal SWE differences and a temperature-insensitive polarization difference ratio, respectively. We examined the ability of the satellite and model output simulations to capture peak SWE and investigated temporal accuracies of screened and unscreened satellite and model output SWE. The resulting SWE observations were employed to update the SNOW-17 snow accumulation and ablation model of CHPS to assess the benefit of using temporally and spatially consistent SWE observations for snow melt predictions in two test basins in the RRB.

GCOM-W soil moisture and temperature algorithms and validation

USDA-ARS?s Scientific Manuscript database

Passive microwave remote sensing of soil moisture has matured over the past decade as a result of the Advanced Microwave Scanning Radiometer (AMSR) program of JAXA. This program has resulted in improved algorithms that have been supported by rigorous validation. Access to the products and the valida...

Assessing Temporal Stability for Coarse Scale Satellite Moisture Validation in the Maqu Area, Tibet

PubMed Central

Bhatti, Haris Akram; Rientjes, Tom; Verhoef, Wouter; Yaseen, Muhammad

2013-01-01

This study evaluates if the temporal stability concept is applicable to a time series of satellite soil moisture images so to extend the common procedure of satellite image validation. The area of study is the Maqu area, which is located in the northeastern part of the Tibetan plateau. The network serves validation purposes of coarse scale (25–50 km) satellite soil moisture products and comprises 20 stations with probes installed at depths of 5, 10, 20, 40, 80 cm. The study period is 2009. The temporal stability concept is applied to all five depths of the soil moisture measuring network and to a time series of satellite-based moisture products from the Advance Microwave Scanning Radiometer (AMSR-E). The in-situ network is also assessed by Pearsons's correlation analysis. Assessments by the temporal stability concept proved to be useful and results suggest that probe measurements at 10 cm depth best match to the satellite observations. The Mean Relative Difference plot for satellite pixels shows that a RMSM pixel can be identified but in our case this pixel does not overlay any in-situ station. Also, the RMSM pixel does not overlay any of the Representative Mean Soil Moisture (RMSM) stations of the five probe depths. Pearson's correlation analysis on in-situ measurements suggests that moisture patterns over time are more persistent than over space. Since this study presents first results on the application of the temporal stability concept to a series of satellite images, we recommend further tests to become more conclusive on effectiveness to broaden the procedure of satellite validation. PMID:23959237

Preliminary Validation of the AFWA-NASA Blended Snowcover Product Over the Lower Great Lakes region

NASA Technical Reports Server (NTRS)

Hall, D. K.; Montesano, P. M.; Foster, J. L.; Riggs, G. A.; Kelly, R. E. J.; Czajkowski, K.

2007-01-01

A new snow product created using the standard Moderate-Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) snow cover and snow-water equivalent products has been evaluated for the Lower Great Lakes region during the winter of 2002- 03. National Weather Service Co-Operative Observing Network stations and student-acquired snow data were used as ground truth. An interpolation scheme was used to map snow cover on the ground from the station measurements for each day of the study period. It is concluded that this technique does not represent the actual ground conditions adequately to permit evaluation of the new snow product in an absolute sense. However, use of the new product was found to improve the mapping of snow cover as compared to using either the MODIS or AMSR-E product, alone. Plans for further analysis are discussed.

The potential of remotely sensed soil moisture for operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S.; Bierkens, M. F.

2013-12-01

Nowadays, remotely sensed soil moisture is readily available from multiple space born sensors. The high temporal resolution and global coverage make these products very suitable for large-scale land-surface applications. The potential to use these products in operational flood forecasting has thus far not been extensively studied. In this study, we evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the timing and height of the flood peak and low flows. EFAS is used for operational flood forecasting in Europe and uses a distributed hydrological model for flood predictions for lead times up to 10 days. Satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of only discharge observations. Discharge observations are available at the outlet and at six additional locations throughout the catchment. To assimilate soil moisture data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, derived from a detailed model-satellite soil moisture comparison study, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are used in that the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 10-15% on average, compared to assimilation of discharge only. The rank histograms show that the forecast is not biased. The timing errors in the flood predictions are decreased when soil moisture data is used and imminent floods can be forecasted with skill one day earlier. In conclusion, our study shows that assimilation of satellite soil moisture increases the performance of flood forecasting systems for large catchments, like the Upper Danube. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of future soil moisture missions with a higher spatial resolution like SMAP to improve near-real time flood forecasting in large catchments.

The implementation of sea ice model on a regional high-resolution scale

NASA Astrophysics Data System (ADS)

Prasad, Siva; Zakharov, Igor; Bobby, Pradeep; McGuire, Peter

2015-09-01

The availability of high-resolution atmospheric/ocean forecast models, satellite data and access to high-performance computing clusters have provided capability to build high-resolution models for regional ice condition simulation. The paper describes the implementation of the Los Alamos sea ice model (CICE) on a regional scale at high resolution. The advantage of the model is its ability to include oceanographic parameters (e.g., currents) to provide accurate results. The sea ice simulation was performed over Baffin Bay and the Labrador Sea to retrieve important parameters such as ice concentration, thickness, ridging, and drift. Two different forcing models, one with low resolution and another with a high resolution, were used for the estimation of sensitivity of model results. Sea ice behavior over 7 years was simulated to analyze ice formation, melting, and conditions in the region. Validation was based on comparing model results with remote sensing data. The simulated ice concentration correlated well with Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Ocean and Sea Ice Satellite Application Facility (OSI-SAF) data. Visual comparison of ice thickness trends estimated from the Soil Moisture and Ocean Salinity satellite (SMOS) agreed with the simulation for year 2010-2011.

A Wiener-Wavelet-Based filter for de-noising satellite soil moisture retrievals

NASA Astrophysics Data System (ADS)

Massari, Christian; Brocca, Luca; Ciabatta, Luca; Moramarco, Tommaso; Su, Chun-Hsu; Ryu, Dongryeol; Wagner, Wolfgang

2014-05-01

The reduction of noise in microwave satellite soil moisture (SM) retrievals is of paramount importance for practical applications especially for those associated with the study of climate changes, droughts, floods and other related hydrological processes. So far, Fourier based methods have been used for de-noising satellite SM retrievals by filtering either the observed emissivity time series (Du, 2012) or the retrieved SM observations (Su et al. 2013). This contribution introduces an alternative approach based on a Wiener-Wavelet-Based filtering (WWB) technique, which uses the Entropy-Based Wavelet de-noising method developed by Sang et al. (2009) to design both a causal and a non-causal version of the filter. WWB is used as a post-retrieval processing tool to enhance the quality of observations derived from the i) Advanced Microwave Scanning Radiometer for the Earth observing system (AMSR-E), ii) the Advanced SCATterometer (ASCAT), and iii) the Soil Moisture and Ocean Salinity (SMOS) satellite. The method is tested on three pilot sites located in Spain (Remedhus Network), in Greece (Hydrological Observatory of Athens) and in Australia (Oznet network), respectively. Different quantitative criteria are used to judge the goodness of the de-noising technique. Results show that WWB i) is able to improve both the correlation and the root mean squared differences between satellite retrievals and in situ soil moisture observations, and ii) effectively separates random noise from deterministic components of the retrieved signals. Moreover, the use of WWB de-noised data in place of raw observations within a hydrological application confirms the usefulness of the proposed filtering technique. Du, J. (2012), A method to improve satellite soil moisture retrievals based on Fourier analysis, Geophys. Res. Lett., 39, L15404, doi:10.1029/ 2012GL052435 Su,C.-H.,D.Ryu, A. W. Western, and W. Wagner (2013), De-noising of passive and active microwave satellite soil moisture time series, Geophys. Res. Lett., 40,3624-3630, doi:10.1002/grl.50695. Sang Y.-F., D. Wang, J.-C. Wu, Q.-P. Zhu, and L. Wang (2009), Entropy-Based Wavelet De-noising Method for Time Series Analysis, Entropy, 11, pp. 1123-1148, doi:10.3390/e11041123.

Floods, floodplains, delta plains — A satellite imaging approach

NASA Astrophysics Data System (ADS)

Syvitski, James P. M.; Overeem, Irina; Brakenridge, G. Robert; Hannon, Mark

2012-08-01

Thirty-three lowland floodplains and their associated delta plains are characterized with data from three remote sensing systems (AMSR-E, SRTM and MODIS). These data provide new quantitative information to characterize Late Quaternary floodplain landscapes and their penchant for flooding over the last decade. Daily proxy records for discharge since 2002 and for each of the 33 river systems can be derived with novel Advanced Microwave Scanning Radiometer (AMSR-E) methods. A descriptive framework based on analysis of Shuttle Radar Topography Mission (SRTM) data is used to capture the major landscape-scale floodplain elements or zones: 1) container valleys with their long and narrow pathways of largely sediment transit and bypass, 2) floodplain depressions that act as loci for frequent flooding and sediment storage, 3) zones of nodal avulsions common to many continental scale rivers, and often located seaward of container valleys, and 4) coastal floodplains and delta plains that offer both sediment bypass and storage but under the influence of marine processes. The SRTM data allow mapping of smaller-scale architectural elements in unprecedented systematic manner. Floodplain depressions were found to play a major role, which may largely be overlooked in conceptual floodplain models. Lastly, MODIS data (independently and combined with AMSR-E) allows the tracking of flood hydrographs and pathways and sedimentation patterns on a near-daily timescale worldwide. These remote-sensing data show that 85% of the studied major river systems experienced extensive flooding in the last decade. A new quantitative paradigm of floodplain processes, honoring the frequency and extent of floods, can be develop by careful analysis of these new remotely sensed data.

Global Survey and Statistics of Radio-Frequency Interference in AMSR-E Land Observations

NASA Technical Reports Server (NTRS)

Njoku, Eni G.; Ashcroft, Peter; Chan, Tsz K.; Li, Li

2005-01-01

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RFI at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing.

Aerosol optical depth under "clear" sky conditions derived from sea surface reflection of lidar signals.

PubMed

He, Min; Hu, Yongxiang; Huang, Jian Ping; Stamnes, Knut

2016-12-26

There are considerable demands for accurate atmospheric correction of satellite observations of the sea surface or subsurface signal. Surface and sub-surface reflection under "clear" atmospheric conditions can be used to study atmospheric correction for the simplest possible situation. Here "clear" sky means a cloud-free atmosphere with sufficiently small aerosol particles. The "clear" aerosol concept is defined according to the spectral dependence of the scattering cross section on particle size. A 5-year combined CALIPSO and AMSR-E data set was used to derive the aerosol optical depth (AOD) from the lidar signal reflected from the sea surface. Compared with the traditional lidar-retrieved AOD, which relies on lidar backscattering measurements and an assumed lidar ratio, the AOD retrieved through the surface reflectance method depends on both scattering and absorption because it is based on two-way attenuation of the lidar signal transmitted to and then reflected from the surface. The results show that the clear sky AOD derived from the surface signal agrees with the clear sky AOD available in the CALIPSO level 2 database in the westerly wind belt located in the southern hemisphere, but yields significantly higher aerosol loadings in the tropics and in the northern hemisphere.

Error Characterisation and Merging of Active and Passive Microwave Soil Moisture Data Sets

NASA Astrophysics Data System (ADS)

Wagner, Wolfgang; Gruber, Alexander; de Jeu, Richard; Parinussa, Robert; Chung, Daniel; Dorigo, Wouter; Reimer, Christoph; Kidd, Richard

2015-04-01

As part of the Climate Change Initiative (CCI) programme of the European Space Agency (ESA) a data fusion system has been developed which is capable of ingesting surface soil moisture data derived from active and passive microwave sensors (ASCAT, AMSR-E, etc.) flown on different satellite platforms and merging them to create long and consistent time series of soil moisture suitable for use in climate change studies. The so-created soil moisture data records (latest version: ESA CCI SM v02.1 released on 5/12/2014) are freely available and can be obtained from http://www.esa-soilmoisture-cci.org/. As described by Wagner et al. (2012) the principle steps of the data fusion process are: 1) error characterisation, 2) matching to account for data set specific biases, and 3) merging. In this presentation we present the current data fusion process and discuss how new error characterisation methods, such as the increasingly popular triple collocation method as discussed for example by Zwieback et al. (2012) may be used to improve it. The main benefit of an improved error characterisation would be a more reliable identification of the best performing microwave soil moisture retrieval(s) for each grid point and each point in time. In case that two or more satellite data sets provides useful information, the estimated errors can be used to define the weights with which each satellite data set are merged, i.e. the lower its error the higher its weight. This is expected to bring a significant improvement over the current data fusion scheme which is not yet based on quantitative estimates of the retrieval errors but on a proxy measure, namely the vegetation optical depth (Dorigo et al., 2015): over areas with low vegetation passive soil moisture retrievals are used, while over areas with moderate vegetation density active retrievals are used. In transition areas, where both products correlate well, both products are being used in a synergistic way: on time steps where only one of the products is available, the estimate of the respective product is used, while on days where both active and passive sensors provide an estimate, their observations are averaged. REFERENCES Dorigo, W.A., A. Gruber, R. de Jeu, W. Wagner, T. Stacke, A. Löw, C. Albergel, L. Brocca, D. Chung, R. Parinussa, R. Kidd (2015) Evaluation of the ESA CCI soil moisture product using ground-based observations, Remote Sensing of Environment, in press. Wagner, W., W. Dorigo, R. de Jeu, D. Fernandez, J. Benveniste, E. Haas, M. Ertl (2012) Fusion of active and passive microwave observations to create an Essential Climate Variable data record on soil moisture, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals), Volume I-7, XXII ISPRS Congress, Melbourne, Australia, 25 August-1 September 2012, 315-321. Zwieback, S., K. Scipal, W. Dorigo, W. Wagner (2012) Structural and statistical properties of the collocation technique for error characterization, Nonlinear Processes in Geophysics, 19, 69-80.

An empirical understanding of triple collocation evaluation measure

NASA Astrophysics Data System (ADS)

Scipal, Klaus; Doubkova, Marcela; Hegyova, Alena; Dorigo, Wouter; Wagner, Wolfgang

2013-04-01

Triple collocation method is an advanced evaluation method that has been used in the soil moisture field for only about half a decade. The method requires three datasets with an independent error structure that represent an identical phenomenon. The main advantages of the method are that it a) doesn't require a reference dataset that has to be considered to represent the truth, b) limits the effect of random and systematic errors of other two datasets, and c) simultaneously assesses the error of three datasets. The objective of this presentation is to assess the triple collocation error (Tc) of the ASAR Global Mode Surface Soil Moisture (GM SSM 1) km dataset and highlight problems of the method related to its ability to cancel the effect of error of ancillary datasets. In particular, the goal is to a) investigate trends in Tc related to the change in spatial resolution from 5 to 25 km, b) to investigate trends in Tc related to the choice of a hydrological model, and c) to study the relationship between Tc and other absolute evaluation methods (namely RMSE and Error Propagation EP). The triple collocation method is implemented using ASAR GM, AMSR-E, and a model (either AWRA-L, GLDAS-NOAH, or ERA-Interim). First, the significance of the relationship between the three soil moisture datasets was tested that is a prerequisite for the triple collocation method. Second, the trends in Tc related to the choice of the third reference dataset and scale were assessed. For this purpose the triple collocation is repeated replacing AWRA-L with two different globally available model reanalysis dataset operating at different spatial resolution (ERA-Interim and GLDAS-NOAH). Finally, the retrieved results were compared to the results of the RMSE and EP evaluation measures. Our results demonstrate that the Tc method does not eliminate the random and time-variant systematic errors of the second and the third dataset used in the Tc. The possible reasons include the fact a) that the TC method could not fully function with datasets acting at very different spatial resolutions, or b) that the errors were not fully independent as initially assumed.

Quantifying Uncertainties in Land Surface Microwave Emissivity Retrievals

NASA Technical Reports Server (NTRS)

Tian, Yudong; Peters-Lidard, Christa D.; Harrison, Kenneth W.; Prigent, Catherine; Norouzi, Hamidreza; Aires, Filipe; Boukabara, Sid-Ahmed; Furuzawa, Fumie A.; Masunaga, Hirohiko

2012-01-01

Uncertainties in the retrievals of microwave land surface emissivities were quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including SSM/I, TMI and AMSR-E, were studied. Our results show that there are considerable differences between the retrievals from different sensors and from different groups over these two land surface types. In addition, the mean emissivity values show different spectral behavior across the frequencies. With the true emissivity assumed largely constant over both of the two sites throughout the study period, the differences are largely attributed to the systematic and random errors in the retrievals. Generally these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 14% (312 K) over desert and 17% (320 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.52% (26 K). In particular, at 85.0/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are mostly likely caused by rain/cloud contamination, which can lead to random errors up to 1017 K under the most severe conditions.

Finland Validation of the New Blended Snow Product

NASA Technical Reports Server (NTRS)

Kim, E. J.; Casey, K. A.; Hallikainen, M. T.; Foster, J. L.; Hall, D. K.; Riggs, G. A.

2008-01-01

As part of an ongoing effort to validate satellite remote sensing snow products for the recentlydeveloped U.S. Air Force Weather Agency (AFWA) - NASA blended snow product, Satellite and in-situ data for snow extent and snow water equivalent (SWE) are evaluated in Finland for the 2006-2007 snow season Finnish Meteorological Institute (FMI) daily weather station data and Finnish Environment Institute (SYKE) bi-monthly snow course data are used as ground truth. Initial comparison results display positive agreement between the AFWA NASA Snow Algorithm (ANSA) snow extent and SWE maps and in situ data, with discrepancies in accordance with known AMSR-E and MODIS snow mapping limitations. Future ANSA product improvement plans include additional validation and inclusion of fractional snow cover in the ANSA data product. Furthermore, the AMSR-E 19 GHz (horizontal channel) with the difference between ascending and descending satellite passes (Diurnal Amplitude Variations, DAV) will be used to detect the onset of melt, and QuikSCAT scatterometer data (14 GHz) will be used to map areas of actively melting snow.

The efficacy of calibrating hydrologic model using remotely sensed evapotranspiration and soil moisture for streamflow prediction

NASA Astrophysics Data System (ADS)

Kunnath-Poovakka, A.; Ryu, D.; Renzullo, L. J.; George, B.

2016-04-01

Calibration of spatially distributed hydrologic models is frequently limited by the availability of ground observations. Remotely sensed (RS) hydrologic information provides an alternative source of observations to inform models and extend modelling capability beyond the limits of ground observations. This study examines the capability of RS evapotranspiration (ET) and soil moisture (SM) in calibrating a hydrologic model and its efficacy to improve streamflow predictions. SM retrievals from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and daily ET estimates from the CSIRO MODIS ReScaled potential ET (CMRSET) are used to calibrate a simplified Australian Water Resource Assessment - Landscape model (AWRA-L) for a selection of parameters. The Shuffled Complex Evolution Uncertainty Algorithm (SCE-UA) is employed for parameter estimation at eleven catchments in eastern Australia. A subset of parameters for calibration is selected based on the variance-based Sobol' sensitivity analysis. The efficacy of 15 objective functions for calibration is assessed based on streamflow predictions relative to control cases, and relative merits of each are discussed. Synthetic experiments were conducted to examine the effect of bias in RS ET observations on calibration. The objective function containing the root mean square deviation (RMSD) of ET result in best streamflow predictions and the efficacy is superior for catchments with medium to high average runoff. Synthetic experiments revealed that accurate ET product can improve the streamflow predictions in catchments with low average runoff.

An Intercomparison of Vegetation Products from Satellite-based Observations used for Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Vreugdenhil, Mariette; de Jeu, Richard; Wagner, Wolfgang; Dorigo, Wouter; Hahn, Sebastian; Bloeschl, Guenter

2013-04-01

Vegetation and its water content affect active and passive microwave soil moisture retrievals and need to be taken into account in such retrieval methodologies. This study compares the vegetation parameterisation that is used in the TU-Wien soil moisture retrieval algorithm to other vegetation products, such as the Vegetation Optical Depth (VOD), Net Primary Production (NPP) and Leaf Area Index (LAI). When only considering the retrieval algorithm for active microwaves, which was developed by the TU-Wien, the effect of vegetation on the backscattering coefficient is described by the so-called slope [1]. The slope is the first derivative of the backscattering coefficient in relation to the incidence angle. Soil surface backscatter normally decreases quite rapidly with the incidence angle over bare or sparsely vegetated soils, whereas the contribution of dense vegetation is fairly uniform over a large range of incidence angles. Consequently, the slope becomes less steep with increasing vegetation. Because the slope is a derivate of noisy backscatter measurements, it is characterised by an even higher level of noise. Therefore, it is averaged over several years assuming that the state of the vegetation doesn't change inter-annually. The slope is compared to three dynamic vegetation products over Australia, the VOD, NPP and LAI. The VOD was retrieved from AMSR-E passive microwave data using the VUA-NASA retrieval algorithm and provides information on vegetation with a global coverage of approximately every two days [2]. LAI is defined as half the developed area of photosynthetically active elements of the vegetation per unit horizontal ground area. In this study LAI is used from the Geoland2 products derived from SPOT Vegetation*. The NPP is the net rate at which plants build up carbon through photosynthesis and is a model-based estimate from the BiosEquil model [3, 4]. Results show that VOD and slope correspond reasonably well over vegetated areas, whereas in arid areas, where the microwave signals mostly stem from the soil surface and deeper soil layers, they are negatively correlated. A second comparison of monthly values of both vegetation parameters to modelled NPP data shows that particularly over dry areas the VOD corresponds better to the NPP, with r=0.79 for VOD-NPP and r=-0.09 for slope-NPP. 1. Wagner, W., et al., A Study of Vegetation Cover Effects on ERS Scatterometer Data. IEEE Transactions on Geoscience and Remote Sensing, 1999. 37(2): p. 938-948. 2. Owe, M., R. de Jeu, and J. Walker, A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. Geoscience and Remote Sensing, IEEE Transactions on, 2001. 39(8): p. 1643-1654. 3. Raupach, M.R., et al., Balances of Water, Carbon, Nitrogen and Phosphorus in Australian Landscapes: (1) Project Description and Results, 2001, Sustainable Minerals Institute, CSIRO Land and Water. 4. Raupach, M.R., et al., Balances of Water, Carbon, Nitrogen and Phosporus in Australian Landscapes: (2) Model Formulation and Testing, 2001, Sustainable Minerals Institute, CSIRO Land and Water. * These products are the joint property of INRA, CNES and VITO under copyright of Geoland2. They are generated from the SPOT VEGETATION data under copyright CNES and distribution by VITO.

A Marine Boundary Layer Water Vapor Climatology Derived from Microwave and Near-Infrared Imagery

NASA Astrophysics Data System (ADS)

Millan Valle, L. F.; Lebsock, M. D.; Teixeira, J.

2017-12-01

The synergy of the collocated Advanced Microwave Scanning Radiometer (AMSR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global estimates of partial marine planetary boundary layer water vapor. AMSR microwave radiometry provides the total column water vapor, while MODIS near-infrared imagery provides the water vapor above the cloud layers. The difference between the two gives the vapor between the surface and the cloud top, which may be interpreted as the boundary layer water vapor. Comparisons against radiosondes, and GPS-Radio occultation data demonstrate the robustness of these boundary layer water vapor estimates. We exploit the 14 years of AMSR-MODIS synergy to investigate the spatial, seasonal, and inter-annual variations of the boundary layer water vapor. Last, it is shown that the measured AMSR-MODIS partial boundary layer water vapor can be generally prescribed using sea surface temperature, cloud top pressure and the lifting condensation level. The multi-sensor nature of the analysis demonstrates that there exists more information on boundary layer water vapor structure in the satellite observing system than is commonly assumed when considering the capabilities of single instruments. 2017 California Institute of Technology. U.S. Government sponsorship acknowledged.

Interacting Effects of Leaf Water Potential and Biomass on Vegetation Optical Depth

NASA Astrophysics Data System (ADS)

Momen, M.; Wood, J. D.; Novick, K. A.; Pockman, W.; Konings, A. G.

2017-12-01

Remotely-sensed microwave observations of vegetation optical depth (VOD) have been widely used to examine vegetation responses to climate. Such studies have alternately found that VOD is sensitive to both biomass and canopy water content. However, the relative impacts of changes in phenology or water stress on VOD have not been disentangled. In particular, understanding whether leaf water potential (LWP) affects VOD may permit the assimilation of satellite observations into new large-scale plant hydraulic models. Despite extensive validation of the relationship between satellite-derived VOD estimates and vegetation density, relatively few studies have explicitly sought to validate the sensitivity of VOD to canopy water status, and none have studied the effect of variations in LWP on VOD. In this work, we test the sensitivity of VOD to variations in LWP, and present a conceptual framework which relates VOD to a combination of leaf water potential and total biomass including leaves, whose dynamics can be measured through leaf area index, and woody biomass. We used in-situ measurements of LWP data to validate the conceptual model in mixed deciduous forests in Indiana and Missouri, as well as a pinion-juniper woodland in New Mexico. Observed X-band VOD from the AMSR-E and AMSR2 satellites showed dynamics similar to those reconstructed VOD signals based on the new conceptual model which employs in-situ LWP data (R2=0.60-0.80). Because LWP data are not available at global scales, we further estimated ecosystem LWP based on remotely sensed surface soil moisture to better understand the sensitivity of VOD across ecosystems. At the global scale, incorporating a combination of biomass and water potential in the reconstructed VOD signal increased correlations with VOD about 15% compared to biomass alone and about 30% compared to water potential alone. In wetter regions with denser and taller canopy heights, VOD has a higher correlation with leaf area index than with water stress and vice versa in drier regions (see figure 1). Therefore, variations in both phenology and leaf water potential must be accounted for to accurately interpret the dynamics of VOD observations for ecological applications.

The AMSR2 Satellite-based Microwave Snow Algorithm (SMSA) to estimate regional to global snow depth and snow water equivalent

NASA Astrophysics Data System (ADS)

Kelly, R. E. J.; Saberi, N.; Li, Q.

2017-12-01

With moderate to high spatial resolution (<1 km) regional to global snow water equivalent (SWE) observation approaches yet to be fully scoped and developed, the long-term satellite passive microwave record remains an important tool for cryosphere-climate diagnostics. A new satellite microwave remote sensing approach is described for estimating snow depth (SD) and snow water equivalent (SWE). The algorithm, called the Satellite-based Microwave Snow Algorithm (SMSA), uses Advanced Microwave Scanning Radiometer - 2 (AMSR2) observations aboard the Global Change Observation Mission - Water mission launched by the Japan Aerospace Exploration Agency in 2012. The approach is unique since it leverages observed brightness temperatures (Tb) with static ancillary data to parameterize a physically-based retrieval without requiring parameter constraints from in situ snow depth observations or historical snow depth climatology. After screening snow from non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]), moderate and shallow snow depths are estimated by minimizing the difference between Dense Media Radiative Transfer model estimates (Tsang et al., 2000; Picard et al., 2011) and AMSR2 Tb observations to retrieve SWE and SD. Parameterization of the model combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Evaluation of the SMSA performance is achieved using in situ snow depth data from a variety of standard and experiment data sources. Results presented from winter seasons 2012-13 to 2016-17 illustrate the improved performance of the new approach in comparison with the baseline AMSR2 algorithm estimates and approach the performance of the model assimilation-based approach of GlobSnow. Given the variation in estimation power of SWE by different land surface/climate models and selected satellite-derived passive microwave approaches, SMSA provides SWE estimates that are independent of real or near real-time in situ and model data.

Evaluation of Enhanced High Resolution MODIS/AMSR-E SSTs and the Impact on Regional Weather Forecast

NASA Technical Reports Server (NTRS)

Schiferl, Luke D.; Fuell, Kevin K.; Case, Jonathan L.; Jedlovec, Gary J.

2010-01-01

Over the last few years, the NASA Short-term Prediction Research and Transition (SPoRT) Center has been generating a 1-km sea surface temperature (SST) composite derived from retrievals of the Moderate Resolution Imaging Spectroradiometer (MODIS) for use in operational diagnostics and regional model initialization. With the assumption that the day-to-day variation in the SST is nominal, individual MODIS passes aboard the Earth Observing System (EOS) Aqua and Terra satellites are used to create and update four composite SST products each day at 0400, 0700, 1600, and 1900 UTC, valid over the western Atlantic and Caribbean waters. A six month study from February to August 2007 over the marine areas surrounding southern Florida was conducted to compare the use of the MODIS SST composite versus the Real-Time Global SST analysis to initialize the Weather Research and Forecasting (WRF) model. Substantial changes in the forecast heat fluxes were seen at times in the marine boundary layer, but relatively little overall improvement was measured in the sensible weather elements. The limited improvement in the WRF model forecasts could be attributed to the diurnal changes in SST seen in the MODIS SST composites but not accounted for by the model. Furthermore, cloud contamination caused extended periods when individual passes of MODIS were unable to update the SSTs, leading to substantial SST latency and a cool bias during the early summer months. In order to alleviate the latency problems, the SPoRT Center recently enhanced its MODIS SST composite by incorporating information from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) instruments as well as the Operational Sea Surface Temperature and Sea Ice Analysis. These enhancements substantially decreased the latency due to cloud cover and improved the bias and correlation of the composites at available marine point observations. While these enhancements improved upon the modeled cold bias using the original MODIS SSTs, the discernable impacts on the WRF model were still somewhat limited. This paper explores several factors that may have contributed to this result. First, the original methodology to initialize the model used the most recent SST composite available in a hypothetical real ]time configuration, often matching the forecast initial time with an SST field that was 5-8 hours offset. To minimize the differences that result from the diurnal variations in SST, the previous day fs SST composite is incorporated at a time closest to the model initialization hour (e.g. 1600 UTC composite at 1500 UTC model initialization). Second, the diurnal change seen in the MODIS SST composites was not represented by the WRF model in previous simulations, since the SSTs were held constant throughout the model integration. To address this issue, we explore the use of a water skin-temperature diurnal cycle prediction capability within v3.1 of the WRF model to better represent fluctuations in marine surface forcing. Finally, the verification of the WRF model is limited to very few over-water sites, many of which are located near the coastlines. In order to measure the open ocean improvements from the AMSR-E, we could use an independent 2-dimensional, satellite-derived data set to validate the forecast model by applying an object-based verification method. Such a validation technique could aid in better understanding the benefits of the mesoscale SST spatial structure to regional models applications.

Summer Sea Ice Motion from the 18 GHz Channel of AMSR-E and the Exchange of Sea Ice between the Pacific and Atlantic Sectors

NASA Technical Reports Server (NTRS)

Kwok, Ronald

2008-01-01

We demonstrate that sea ice motion in summer can be derived reliably from the 18GHz channel of the AMSR-E instrument on the EOS Aqua platform. The improved spatial resolution of this channel with its lower sensitivity to atmospheric moisture seems to have alleviated various issues that have plagued summer motion retrievals from shorter wavelength observations. Two spatial filters improve retrieval quality: one reduces some of the microwave signatures associated with synoptic-scale weather systems and the other removes outliers. Compared with daily buoy drifts, uncertainties in motion are approx.3-4 km/day. Using the daily motion fields, we examine five years of summer ice area exchange between the Pacific and Atlantic sectors of the Arctic Ocean. With the sea-level pressure patterns during the summer of 2006 and 2007 favoring the export of sea ice into the Atlantic Sector, the regional outflow is approx.21% and approx.15% of the total sea ice retreat in the Pacific sector.

Determination of ice water path in ice-over-water cloud systems using combined MODIS and AMSR-E measurements

NASA Astrophysics Data System (ADS)

Huang, Jianping; Minnis, Patrick; Lin, Bing; Yi, Yuhong; Fan, T.-F.; Sun-Mack, Sunny; Ayers, J. K.

2006-11-01

To provide more accurate ice cloud microphysical properties, the multi-layered cloud retrieval system (MCRS) is used to retrieve ice water path (IWP) in ice-over-water cloud systems globally over oceans using combined instrument data from Aqua. The liquid water path (LWP) of lower-layer water clouds is estimated from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) measurements. The properties of the upper-level ice clouds are then derived from Moderate Resolution Imaging Spectroradiometer (MODIS) measurements by matching simulated radiances from a two-cloud-layer radiative transfer model. The results show that the MCRS can significantly improve the accuracy and reduce the over-estimation of optical depth and IWP retrievals for ice-over-water cloud systems. The mean daytime ice cloud optical depth and IWP for overlapped ice-over-water clouds over oceans from Aqua are 7.6 and 146.4 gm-2, respectively, down from the initial single-layer retrievals of 17.3 and 322.3 gm-2. The mean IWP for actual single-layer clouds is 128.2 gm-2.

Evaluation of Passive Multilayer Cloud Detection Using Preliminary CloudSat and CALIPSO Cloud Profiles

NASA Astrophysics Data System (ADS)

Minnis, P.; Sun-Mack, S.; Chang, F.; Huang, J.; Nguyen, L.; Ayers, J. K.; Spangenberg, D. A.; Yi, Y.; Trepte, C. R.

2006-12-01

During the last few years, several algorithms have been developed to detect and retrieve multilayered clouds using passive satellite data. Assessing these techniques has been difficult due to the need for active sensors such as cloud radars and lidars that can "see" through different layers of clouds. Such sensors have been available only at a few surface sites and on aircraft during field programs. With the launch of the CALIPSO and CloudSat satellites on April 28, 2006, it is now possible to observe multilayered systems all over the globe using collocated cloud radar and lidar data. As part of the A- Train, these new active sensors are also matched in time ad space with passive measurements from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer - EOS (AMSR-E). The Clouds and the Earth's Radiant Energy System (CERES) has been developing and testing algorithms to detect ice-over-water overlapping cloud systems and to retrieve the cloud liquid path (LWP) and ice water path (IWP) for those systems. One technique uses a combination of the CERES cloud retrieval algorithm applied to MODIS data and a microwave retrieval method applied to AMSR-E data. The combination of a CO2-slicing cloud retireval technique with the CERES algorithms applied to MODIS data (Chang et al., 2005) is used to detect and analyze such overlapped systems that contain thin ice clouds. A third technique uses brightness temperature differences and the CERES algorithms to detect similar overlapped methods. This paper uses preliminary CloudSat and CALIPSO data to begin a global scale assessment of these different methods. The long-term goals are to assess and refine the algorithms to aid the development of an optimal combination of the techniques to better monitor ice 9and liquid water clouds in overlapped conditions.

Evaluation of Passive Multilayer Cloud Detection Using Preliminary CloudSat and CALIPSO Cloud Profiles

NASA Astrophysics Data System (ADS)

Minnis, P.; Sun-Mack, S.; Chang, F.; Huang, J.; Nguyen, L.; Ayers, J. K.; Spangenberg, D. A.; Yi, Y.; Trepte, C. R.

2005-05-01

During the last few years, several algorithms have been developed to detect and retrieve multilayered clouds using passive satellite data. Assessing these techniques has been difficult due to the need for active sensors such as cloud radars and lidars that can "see" through different layers of clouds. Such sensors have been available only at a few surface sites and on aircraft during field programs. With the launch of the CALIPSO and CloudSat satellites on April 28, 2006, it is now possible to observe multilayered systems all over the globe using collocated cloud radar and lidar data. As part of the A- Train, these new active sensors are also matched in time ad space with passive measurements from the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer - EOS (AMSR-E). The Clouds and the Earth's Radiant Energy System (CERES) has been developing and testing algorithms to detect ice-over-water overlapping cloud systems and to retrieve the cloud liquid path (LWP) and ice water path (IWP) for those systems. One technique uses a combination of the CERES cloud retrieval algorithm applied to MODIS data and a microwave retrieval method applied to AMSR-E data. The combination of a CO2-slicing cloud retireval technique with the CERES algorithms applied to MODIS data (Chang et al., 2005) is used to detect and analyze such overlapped systems that contain thin ice clouds. A third technique uses brightness temperature differences and the CERES algorithms to detect similar overlapped methods. This paper uses preliminary CloudSat and CALIPSO data to begin a global scale assessment of these different methods. The long-term goals are to assess and refine the algorithms to aid the development of an optimal combination of the techniques to better monitor ice 9and liquid water clouds in overlapped conditions.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2013-11-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model for flood predictions with lead times up to 10 days. For this study, satellite-derived soil moisture from ASCAT, AMSR-E and SMOS is assimilated into the EFAS system for the Upper Danube basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into EFAS, an Ensemble Kalman Filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure optimal performance of the EnKF. For the validation, additional discharge observations not used in the EnKF, are used as an independent validation dataset. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the Mean Absolute Error (MAE) of the ensemble mean is reduced by 65%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of base flows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the Continuous Ranked Probability Score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more data is assimilated into the system and the best performance is achieved with the assimilation of both discharge and satellite observations. The additional gain is highest when discharge observations from both upstream and downstream areas are used in combination with the soil moisture data. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

The suitability of remotely sensed soil moisture for improving operational flood forecasting

NASA Astrophysics Data System (ADS)

Wanders, N.; Karssenberg, D.; de Roo, A.; de Jong, S. M.; Bierkens, M. F. P.

2014-06-01

We evaluate the added value of assimilated remotely sensed soil moisture for the European Flood Awareness System (EFAS) and its potential to improve the prediction of the timing and height of the flood peak and low flows. EFAS is an operational flood forecasting system for Europe and uses a distributed hydrological model (LISFLOOD) for flood predictions with lead times of up to 10 days. For this study, satellite-derived soil moisture from ASCAT (Advanced SCATterometer), AMSR-E (Advanced Microwave Scanning Radiometer - Earth Observing System) and SMOS (Soil Moisture and Ocean Salinity) is assimilated into the LISFLOOD model for the Upper Danube Basin and results are compared to assimilation of discharge observations only. To assimilate soil moisture and discharge data into the hydrological model, an ensemble Kalman filter (EnKF) is used. Information on the spatial (cross-) correlation of the errors in the satellite products, is included to ensure increased performance of the EnKF. For the validation, additional discharge observations not used in the EnKF are used as an independent validation data set. Our results show that the accuracy of flood forecasts is increased when more discharge observations are assimilated; the mean absolute error (MAE) of the ensemble mean is reduced by 35%. The additional inclusion of satellite data results in a further increase of the performance: forecasts of baseflows are better and the uncertainty in the overall discharge is reduced, shown by a 10% reduction in the MAE. In addition, floods are predicted with a higher accuracy and the continuous ranked probability score (CRPS) shows a performance increase of 5-10% on average, compared to assimilation of discharge only. When soil moisture data is used, the timing errors in the flood predictions are decreased especially for shorter lead times and imminent floods can be forecasted with more skill. The number of false flood alerts is reduced when more observational data is assimilated into the system. The added values of the satellite data is largest when these observations are assimilated in combination with distributed discharge observations. These results show the potential of remotely sensed soil moisture observations to improve near-real time flood forecasting in large catchments.

An inversion method for retrieving soil moisture information from satellite altimetry observations

NASA Astrophysics Data System (ADS)

Uebbing, Bernd; Forootan, Ehsan; Kusche, Jürgen; Braakmann-Folgmann, Anne

2016-04-01

Soil moisture represents an important component of the terrestrial water cycle that controls., evapotranspiration and vegetation growth. Consequently, knowledge on soil moisture variability is essential to understand the interactions between land and atmosphere. Yet, terrestrial measurements are sparse and their information content is limited due to the large spatial variability of soil moisture. Therefore, over the last two decades, several active and passive radar and satellite missions such as ERS/SCAT, AMSR, SMOS or SMAP have been providing backscatter information that can be used to estimate surface conditions including soil moisture which is proportional to the dielectric constant of the upper (few cm) soil layers . Another source of soil moisture information are satellite radar altimeters, originally designed to measure sea surface height over the oceans. Measurements of Jason-1/2 (Ku- and C-Band) or Envisat (Ku- and S-Band) nadir radar backscatter provide high-resolution along-track information (~ 300m along-track resolution) on backscatter every ~10 days (Jason-1/2) or ~35 days (Envisat). Recent studies found good correlation between backscatter and soil moisture in upper layers, especially in arid and semi-arid regions, indicating the potential of satellite altimetry both to reconstruct and to monitor soil moisture variability. However, measuring soil moisture using altimetry has some drawbacks that include: (1) the noisy behavior of the altimetry-derived backscatter (due to e.g., existence of surface water in the radar foot-print), (2) the strong assumptions for converting altimetry backscatters to the soil moisture storage changes, and (3) the need for interpolating between the tracks. In this study, we suggest a new inversion framework that allows to retrieve soil moisture information from along-track Jason-2 and Envisat satellite altimetry data, and we test this scheme over the Australian arid and semi-arid regions. Our method consists of: (i) deriving time-invariant spatial patterns (base-functions) by applying principal component analysis (PCA) to simulated soil moisture from a large-scale land surface model. (ii) Estimating time-variable soil moisture evolution by fitting these base functions of (i) to the along-track retracked backscatter coefficients in a least squares sense. (iii) Combining the estimated time-variable amplitudes and the pre-computed base-functions, which results in reconstructed (spatio-temporal) soil moisture information. We will show preliminary results that are compared to available high-resolution soil moisture model data over the region (the Australian Water Resource Assessment, AWRA model). We discuss the possibility of using altimetry-derived soil moisture estimations to improve the simulation skill of soil moisture in the Global Land Data Assimilation System (GLDAS) over Australia.

Application of Artificial Neural Networks to the Development of Improved Multi-Sensor Retrievals of Near-Surface Air Temperature and Humidity Over Ocean

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Clayson, Carol Anne

2012-01-01

Improved estimates of near-surface air temperature and air humidity are critical to the development of more accurate turbulent surface heat fluxes over the ocean. Recent progress in retrieving these parameters has been made through the application of artificial neural networks (ANN) and the use of multi-sensor passive microwave observations. Details are provided on the development of an improved retrieval algorithm that applies the nonlinear statistical ANN methodology to a set of observations from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A) that are currently available from the NASA AQUA satellite platform. Statistical inversion techniques require an adequate training dataset to properly capture embedded physical relationships. The development of multiple training datasets containing only in-situ observations, only synthetic observations produced using the Community Radiative Transfer Model (CRTM), or a mixture of each is discussed. An intercomparison of results using each training dataset is provided to highlight the relative advantages and disadvantages of each methodology. Particular emphasis will be placed on the development of retrievals in cloudy versus clear-sky conditions. Near-surface air temperature and humidity retrievals using the multi-sensor ANN algorithms are compared to previous linear and non-linear retrieval schemes.

A Blended Global Snow Product using Visible, Passive Microwave and Scatterometer Satellite Data

NASA Technical Reports Server (NTRS)

Foster, James L.; Hall, Dorothy K.; Eylander, John B.; Riggs, George A.; Nghiem, Son V.; Tedesco, Marco; Kim, Edward; Montesano, Paul M.; Kelly, Richard E. J.; Casey, Kimberly A.;

Passive Microwave Remote Sensing of Colorado Watersheds Using Calibrated, Enhanced-Resolution Brightness Temperatures (CETB) from AMSR-E and SSM/I for Estimation of Snowmelt Timing

NASA Astrophysics Data System (ADS)

Johnson, M.; Ramage, J. M.; Troy, T. J.; Brodzik, M. J.

2017-12-01

Understanding the timing of snowmelt is critical for water resources management in snow-dominated watersheds. Passive microwave remote sensing has been used to estimate melt-refreeze events through brightness temperature satellite observations taken with sensors like the Special Sensor Microwave Imager (SSM/I) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). Previous studies were limited to lower resolution ( 25 km) datasets, making it difficult to quantify the snowpack in heterogeneous, high-relief areas. This study investigates the use of newly available passive microwave calibrated, enhanced-resolution brightness temperatures (CETB) produced at the National Snow and Ice Data Center to estimate melt timing at much higher spatial resolution ( 3-6 km). CETB datasets generated from SSM/I and AMSR-E records will be used to examine three mountainous basins in Colorado. The CETB datasets retain twice-daily (day/night) observations of brightness temperatures. Therefore, we employ the diurnal amplitude variation (DAV) method to detect melt onset and melt occurrences to determine if algorithms developed for legacy data are valid with the improved CETB dataset. We compare melt variability with nearby stream discharge records to determine an optimum melt onset algorithm using the newly reprocessed data. This study investigates the effectiveness of the CETB product for several locations in Colorado (North Park, Rabbit Ears, Fraser) that were the sites of previous ground/airborne surveys during the NASA Cold Land Processes Field Experiment (CLPX 2002-2003). In summary, this work lays the foundation for the utilization of higher resolution reprocessed CETB data for snow evolution more broadly in a range of environments. Consequently, the new processing methods and improved spatial resolution will enable hydrologists to better analyze trends in snow-dominated mountainous watersheds for more effective water resources management.

Mapping Palm Swamp Wetland Ecosystems in the Peruvian Amazon: a Multi-Sensor Remote Sensing Approach

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schroeder, R.; Pinto, N.; Zimmerman, R.; Horna, V.

2012-12-01

Wetland ecosystems are prevalent in the Amazon basin, especially in northern Peru. Of specific interest are palm swamp wetlands because they are characterized by constant surface inundation and moderate seasonal water level variation. This combination of constantly saturated soils and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, it is critical to develop methods to quantify their spatial extent and inundation state in order to assess their carbon dynamics. Spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We developed a remote sensing methodology using multi-sensor remote sensing data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR), Shuttle Radar Topography Mission (SRTM) DEM, and Landsat to derive maps at 100 meter resolution of palm swamp extent and inundation based on ground data collections; and combined active and passive microwave data from AMSR-E and QuikSCAT to derive inundation extent at 25 kilometer resolution on a weekly basis. We then compared information content and accuracy of the coarse resolution products relative to the high-resolution datasets. The synergistic combination of high and low resolution datasets allowed for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Estimate of the Impact of Absorbing Aerosol Over Cloud on the MODIS Retrievals of Cloud Optical Thickness and Effective Radius Using Two Independent Retrievals of Liquid Water Path

NASA Technical Reports Server (NTRS)

Wilcox, Eric M.; Harshvardhan; Platnick, Steven

2009-01-01

Two independent satellite retrievals of cloud liquid water path (LWP) from the NASA Aqua satellite are used to diagnose the impact of absorbing biomass burning aerosol overlaying boundary-layer marine water clouds on the Moderate Resolution Imaging Spectrometer (MODIS) retrievals of cloud optical thickness (tau) and cloud droplet effective radius (r(sub e)). In the MODIS retrieval over oceans, cloud reflectance in the 0.86-micrometer and 2.13-micrometer bands is used to simultaneously retrieve tau and r(sub e). A low bias in the MODIS tau retrieval may result from reductions in the 0.86-micrometer reflectance, which is only very weakly absorbed by clouds, owing to absorption by aerosols in cases where biomass burning aerosols occur above water clouds. MODIS LWP, derived from the product of the retrieved tau and r(sub e), is compared with LWP ocean retrievals from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E), determined from cloud microwave emission that is transparent to aerosols. For the coastal Atlantic southern African region investigated in this study, a systematic difference between AMSR-E and MODIS LWP retrievals is found for stratocumulus clouds over three biomass burning months in 2005 and 2006 that is consistent with above-cloud absorbing aerosols. Biomass burning aerosol is detected using the ultraviolet aerosol index from the Ozone Monitoring Instrument (OMI) on the Aura satellite. The LWP difference (AMSR-E minus MODIS) increases both with increasing tau and increasing OMI aerosol index. During the biomass burning season the mean LWP difference is 14 g per square meters, which is within the 15-20 g per square meter range of estimated uncertainties in instantaneous LWP retrievals. For samples with only low amounts of overlaying smoke (OMI AI less than or equal to 1) the difference is 9.4, suggesting that the impact of smoke aerosols on the mean MODIS LWP is 5.6 g per square meter. Only for scenes with OMI aerosol index greater than 2 does the average LWP difference and the estimated bias in MODIS cloud optical thickness attributable to the impact of overlaying biomass burning aerosol exceed the instantaneous uncertainty in the retrievals.

Operational Implementation of Sea Ice Concentration Estimates from the AMSR2 Sensor

NASA Technical Reports Server (NTRS)

Meier, Walter N.; Stewart, J. Scott; Liu, Yinghui; Key, Jeffrey; Miller, Jeffrey A.

2017-01-01

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAA's operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum-minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.

Estimating Snow Water Storage in North America Using CLM4, DART, and Snow Radiance Data Assimilation

NASA Technical Reports Server (NTRS)

Kwon, Yonghwan; Yang, Zong-Liang; Zhao, Long; Hoar, Timothy J.; Toure, Ally M.; Rodell, Matthew

2016-01-01

This paper addresses continental-scale snow estimates in North America using a recently developed snow radiance assimilation (RA) system. A series of RA experiments with the ensemble adjustment Kalman filter are conducted by assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature T(sub B) at 18.7- and 36.5-GHz vertical polarization channels. The overall RA performance in estimating snow depth for North America is improved by simultaneously updating the Community Land Model, version 4 (CLM4), snow/soil states and radiative transfer model (RTM) parameters involved in predicting T(sub B) based on their correlations with the prior T(sub B) (i.e., rule-based RA), although degradations are also observed. The RA system exhibits a more mixed performance for snow cover fraction estimates. Compared to the open-loop run (0.171m RMSE), the overall snow depth estimates are improved by 1.6% (0.168m RMSE) in the rule-based RA whereas the default RA (without a rule) results in a degradation of 3.6% (0.177mRMSE). Significant improvement of the snow depth estimates in the rule-based RA as observed for tundra snow class (11.5%, p < 0.05) and bare soil land-cover type (13.5%, p < 0.05). However, the overall improvement is not significant (p = 0.135) because snow estimates are degraded or marginally improved for other snow classes and land covers, especially the taiga snow class and forest land cover (7.1% and 7.3% degradations, respectively). The current RA system needs to be further refined to enhance snow estimates for various snow types and forested regions.

Estimating surface soil moisture from SMAP observations using a Neural Network technique.

PubMed

Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

2018-01-01

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models

NASA Astrophysics Data System (ADS)

Spennemann, P. C.; Salvia, M.; Ruscica, R. C.; Sörensson, A. A.; Grings, F.; Karszenbaum, H.

2018-02-01

In regions of strong Land-Atmosphere (L-A) interaction, soil moisture (SM) conditions can impact the atmosphere through modulating the land surface fluxes. The importance of the identification of L-A interaction regions lies in the potential improvement of the weather/seasonal forecast and the better understanding of the physical mechanisms involved. This study aims to compare the terrestrial segment of the L-A interaction from satellite products and climate models, motivated by previous modeling studies pointing out southeastern South America (SESA) as a L-A hotspot during austral summer. In addition, the L-A interaction under dry or wet anomalous conditions over SESA is analyzed. To identify L-A hotspots the AMSRE-LPRM SM and MODIS land surface temperature products; coupled climate models and uncoupled land surface models were used. SESA highlights as a strong L-A interaction hotspot when employing different metrics, temporal scales and independent datasets, showing consistency between models and satellite estimations. Both AMSRE-LPRM bands (X and C) are consistent showing a strong L-A interaction hotspot over the Pampas ecoregion. Intensification and a larger spatial extent of the L-A interaction for dry summers was observed in both satellite products and models compared to wet summers. These results, which were derived from measured physical variables, are encouraging and promising for future studies analyzing L-A interactions. L-A interaction analysis is proposed here as a meeting point between remote sensing and climate modelling communities of Argentina, within a region with the highest agricultural and livestock production of the continent, but with an important lack of in-situ SM observations.

Current Operational Use of and Future Needs for Microwave Imagery at NOAA

NASA Astrophysics Data System (ADS)

Goldberg, M.; McWilliams, G.; Chang, P.

2017-12-01

There are many applications of microwave imagery served by NOAA's operational products and services. They include the use of microwave imagery and derived products for monitoring precipitation, tropical cyclones, sea surface temperature under all weather conditions, wind speed, snow and ice cover, and even soil moisture. All of NOAA's line offices including the National Weather Service, National Ocean Service, National Marine Fisheries Service, and Office of Oceanic and Atmospheric Research rely on microwave imagery. Currently microwave imagery products used by NOAA come from a constellation of satellites that includes Air Force's Special Sensor Microwave Imager Sounder (SSMIS), the Japanese Advanced Microwave Scanning Radiometer (AMSR), the Navy's WindSat, and NASA's Global Precipitation Monitoring (GPM) Microwave Imager (GMI). Follow-on missions for SSMIS are very uncertain, JAXA approval for a follow-on to AMSR2 is still pending, and GMI is a research satellite (lacking high-latitude coverage) with no commitment for operational continuity. Operational continuity refers to a series of satellites, so when one satellite reaches its design life a new satellite is launched. EUMETSAT has made a commitment to fly a microwave imager in the mid-morning orbit. China and Russia have demonstrated on-orbit microwave imagers. Of utmost importance to NOAA, however, is the quality, access, and latency of the data This presentation will focus on NOAA's current requirements for microwave imagery data which, for the most part, are being fulfilled by AMSR2, SSMIS, and WindSat. It will include examples of products and applications of microwave imagery at NOAA. We will also discuss future needs, especially for improved temporal resolution which hopefully can be met by an international constellation of microwave imagers. Finally, we will discuss what we are doing to address the potential gap in imagery.

Variability and Anomalous Trends in the Global Sea Ice Cover

NASA Technical Reports Server (NTRS)

Comiso, Josefino C.

2012-01-01

The advent of satellite data came fortuitously at a time when the global sea ice cover has been changing rapidly and new techniques are needed to accurately assess the true state and characteristics of the global sea ice cover. The extent of the sea ice in the Northern Hemisphere has been declining by about -4% per decade for the period 1979 to 2011 but for the period from 1996 to 2010, the rate of decline became even more negative at -8% per decade, indicating an acceleration in the decline. More intriguing is the drastically declining perennial sea ice area, which is the ice that survives the summer melt and observed to be retreating at the rate of -14% per decade during the 1979 to 2012 period. Although a slight recovery occurred in the last three years from an abrupt decline in 2007, the perennial ice extent was almost as low as in 2007 in 2011. The multiyear ice, which is the thick component of the perennial ice and regarded as the mainstay of the Arctic sea ice cover is declining at an even higher rate of -19% per decade. The more rapid decline of the extent of this thicker ice type means that the volume of the ice is also declining making the survival of the Arctic ice in summer highly questionable. The slight recovery in 2008, 2009 and 2010 for the perennial ice in summer was likely associated with an apparent cycle in the time series with a period of about 8 years. Results of analysis of concurrent MODIS and AMSR-E data in summer also provide some evidence of more extensive summer melt and meltponding in 2007 and 2011 than in other years. Meanwhile, the Antarctic sea ice cover, as observed by the same set of satellite data, is showing an unexpected and counter intuitive increase of about 1 % per decade over the same period. Although a strong decline in ice extent is apparent in the Bellingshausen/ Amundsen Seas region, such decline is more than compensated by increases in the extent of the sea ice cover in the Ross Sea region. The results of analysis of MODIS, AMSR-E and SSM/I data reveal that the sea ice production rate at the coastal polynyas along the Ross Ice Shelf has been increasing since 1992. This also means that the salinization rate and the formation of bottom water in the region are going up as well. Simulation studies indicate that the stronger production rate is likely associated with the ozone hole that has caused a deepening of the lows in the West Antarctic region and therefore stronger winds off the Ross Ice Shelf. Stronger winds causes larger coastal polynyas near the shelf and hence an enhanced ice production in the region during the autumn and winter period. Results of analysis of temperature data from MODIS and AMSR-E shows that the area and concentration of the sea ice cover are highly correlated with surface temperature for both the Arctic and Antarctic, especially in the seasonal regions where the correlation coefficients are about 0.9. Abnormally high sea surface temperatures (SSTs) and surface ice temperatures (SITs) were also observed in 2007 and 2011when drastic reductions in the summer ice cover occurred, This phenomenon is consistent with the expected warming of the upper layer of the Arctic Ocean on account of ice-albedo feedback. Changes in atmospheric circulation are also expected to have a strong influence on the sea ice cover but the results of direct correlation analyses of the sea ice cover with the Northern and the Southern Annular Mode indices show relatively weak correlations, This might be due in part to the complexity of the dynamics of the system that can be further altered by some phenomena like the Antarctic Circumpolar Wave and extra polar processes like the El Nino Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (POD),

Inter-Sensor Comparison of Microwave Land Surface Emissivity Products to Improve Precipitation Retrievals

NASA Astrophysics Data System (ADS)

Norouzi, H.; Temimi, M.; Turk, J.; Prigent, C.; Furuzawa, F.; Tian, Y.

2013-12-01

Microwave land surface emissivity acts as the background signal to estimate rain rate, cloud liquid water, and total precipitable water. Therefore, its accuracy can directly affect the uncertainty of such measurements. Over land, unlike over oceans, the microwave emissivity is relatively high and and varies significantly as surface conditions and land cover change. Lack of ground truth measurement of microwave emissivity especially on global scale has made the uncertainty analysis of this parameter very challenging. The present study investigates the consistency among the existing global land emissivity estimates from different microwave sensors. The products are determined from various sensors and frequencies ranging from 7 to 90 GHz. The selected emissivity products in this study are from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) by NOAA - Cooperative remote Sensing and Science and Technology Center (CREST), the Special Sensor Microwave Imager (SSM/I) by The Centre National de la Recherche Scientifique (CNRS) in France, TRMM Microwave Imager (TMI) by Nagoya University, Japan, and WindSat by NASA Jet Propulsion Laboratory (JPL). The emissivity estimates are based on different algorithms and ancillary data sets. This work investigates the difference among these emissivity products from 2003 to 2008 dynamically and spectrally. The similarities and discrepancies of the retrievals are studied at different land cover types. The mean relative difference (MRD) and other statistical parameters are calculated temporally for all five years of the study. Some inherent discrepancies between the selected products can be attributed to the difference in geometry in terms of incident angle, spectral response, and the foot print size which can affect the estimations. The results reveal that in lower frequencies (=<19 GHz) ancillary data especially skin temperature data set is the major source of difference in emissivity retrievals, while in higher frequencies (>19 GHz) the residuals of atmospheric effect on the signal cause inconsistency among the products. The time series and correlation between emissivity maps were analyzed over different land classes to assess the consistency of emissivity variations with geophysical variable such as soil moisture, precipitation, and vegetation.

An Uncertainty Data Set for Passive Microwave Satellite Observations of Warm Cloud Liquid Water Path

NASA Astrophysics Data System (ADS)

Greenwald, Thomas J.; Bennartz, Ralf; Lebsock, Matthew; Teixeira, João.

2018-04-01

The first extended comprehensive data set of the retrieval uncertainties in passive microwave observations of cloud liquid water path (CLWP) for warm oceanic clouds has been created for practical use in climate applications. Four major sources of systematic errors were considered over the 9-year record of the Advanced Microwave Scanning Radiometer-EOS (AMSR-E): clear-sky bias, cloud-rain partition (CRP) bias, cloud-fraction-dependent bias, and cloud temperature bias. Errors were estimated using a unique merged AMSR-E/Moderate resolution Imaging Spectroradiometer Level 2 data set as well as observations from the Cloud-Aerosol Lidar with Orthogonal Polarization and the CloudSat Cloud Profiling Radar. To quantify the CRP bias more accurately, a new parameterization was developed to improve the inference of CLWP in warm rain. The cloud-fraction-dependent bias was found to be a combination of the CRP bias, an in-cloud bias, and an adjacent precipitation bias. Globally, the mean net bias was 0.012 kg/m2, dominated by the CRP and in-cloud biases, but with considerable regional and seasonal variation. Good qualitative agreement between a bias-corrected AMSR-E CLWP climatology and ship observations in the Northeast Pacific suggests that the bias estimates are reasonable. However, a possible underestimation of the net bias in certain conditions may be due in part to the crude method used in classifying precipitation, underscoring the need for an independent method of detecting rain in warm clouds. This study demonstrates the importance of combining visible-infrared imager data and passive microwave CLWP observations for estimating uncertainties and improving the accuracy of these observations.

A Comparison of Satellite-Derived Snow Maps with a Focus on Ephemeral Snow in North Carolina

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Fuhrmann, Christopher M.; Perry, L. Baker; Riggs, George A.; Robinson, David A.; Foster, James L.

2010-01-01

In this paper, we focus on the attributes and limitations of four commonly-used daily snowcover products with respect to their ability to map ephemeral snow in central and eastern North Carolina. We show that the Moderate-Resolution Imaging Spectroradiometer (MODIS) fractional snow-cover maps can delineate the snow-covered area very well through the use of a fully-automated algorithm, but suffer from the limitation that cloud cover precludes mapping some ephemeral snow. The semi-automated Interactive Multi-sensor Snow and ice mapping system (IMS) and Rutgers Global Snow Lab (GSL) snow maps are often able to capture ephemeral snow cover because ground-station data are employed to develop the snow maps, The Rutgers GSL maps are based on the IMS maps. Finally, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) provides some good detail of snow-water equivalent especially in deeper snow, but may miss ephemeral snow cover because it is often very thin or wet; the AMSR-E maps also suffer from coarse spatial resolution. We conclude that the southeastern United States represents a good test region for validating the ability of satellite snow-cover maps to capture ephemeral snow cover,

An Axisymmetric View of Concentric Eyewall Evolution in Hurricane Rita (2005)

DTIC Science & Technology

2012-08-01

scientific objectives]. Research and operational aircraft missions were conducted with National Oceanic and Atmospheric Administration WP-3D (NOAA P-3...Advanced Microwave Scanning Radiometer– Earth Observing System (AMSR-E) sat- ellites illustrate the evolution from a single to concentric eyewall. In Fig. 2a...National Center for Atmospheric Research,* Boulder, Colorado MICHAEL T. MONTGOMERY Naval Postgraduate School, Monterey, California, and NOAA/AOML

"Newer, bigger, older" with NASA GIBS

NASA Astrophysics Data System (ADS)

Schmaltz, J. E.; Alarcon, C.; Boller, R. A.; Cechini, M. F.; De Cesare, C.; De Luca, A. P.; Hall, J. R.; Huang, T.; King, J.; Plesea, L.; Pressley, N. N.; Roberts, J. T.; Rodriguez, J. D.; Thompson, C. K.

2015-12-01

The year 2015 witnessed a vast expansion of NASA's Global Imagery Browse Services (GIBS) in a number of dimensions. Near real time imagery was added from a slew of additional sensors including GPM, SMAP, AMSR2, VIIRS, CERES, MOPITT, SSMI, and Aquarius, many of these representing measurements that had not been available in GIBS previously. The SMAP layers are also pioneering a new capability for GIBS to display individual granules. Higher resolution imagery, up to 30m/pixel, is now available in GIBS for some sensors, including ASTER GDEM and L1T and Web-Enabled Landsat Data (WELD). The imagery record is being extended into the past with the entire record of data from MODIS and AMSR-E reprocessing campaigns.

Estimation of global snow cover using passive microwave data

NASA Astrophysics Data System (ADS)

Chang, Alfred T. C.; Kelly, Richard E.; Foster, James L.; Hall, Dorothy K.

2003-04-01

This paper describes an approach to estimate global snow cover using satellite passive microwave data. Snow cover is detected using the high frequency scattering signal from natural microwave radiation, which is observed by passive microwave instruments. Developed for the retrieval of global snow depth and snow water equivalent using Advanced Microwave Scanning Radiometer EOS (AMSR-E), the algorithm uses passive microwave radiation along with a microwave emission model and a snow grain growth model to estimate snow depth. The microwave emission model is based on the Dense Media Radiative Transfer (DMRT) model that uses the quasi-crystalline approach and sticky particle theory to predict the brightness temperature from a single layered snowpack. The grain growth model is a generic single layer model based on an empirical approach to predict snow grain size evolution with time. Gridding to the 25 km EASE-grid projection, a daily record of Special Sensor Microwave Imager (SSM/I) snow depth estimates was generated for December 2000 to March 2001. The estimates are tested using ground measurements from two continental-scale river catchments (Nelson River and the Ob River in Russia). This regional-scale testing of the algorithm shows that for passive microwave estimates, the average daily snow depth retrieval standard error between estimated and measured snow depths ranges from 0 cm to 40 cm of point observations. Bias characteristics are different for each basin. A fraction of the error is related to uncertainties about the grain growth initialization states and uncertainties about grain size changes through the winter season that directly affect the parameterization of the snow depth estimation in the DMRT model. Also, the algorithm does not include a correction for forest cover and this effect is clearly observed in the retrieval. Finally, error is also related to scale differences between in situ ground measurements and area-integrated satellite estimates. With AMSR-E data, improvements to snow depth and water equivalent estimates are expected since AMSR-E will have twice the spatial resolution of the SSM/I and will be able to characterize better the subnivean snow environment from an expanded range of microwave frequencies.

About uncertainties in sea ice thickness retrieval from satellite radar altimetry: results from the ESA-CCI Sea Ice ECV Project Round Robin Exercise

NASA Astrophysics Data System (ADS)

Kern, S.; Khvorostovsky, K.; Skourup, H.; Rinne, E.; Parsakhoo, Z. S.; Djepa, V.; Wadhams, P.; Sandven, S.

2014-03-01

One goal of the European Space Agency Climate Change Initiative sea ice Essential Climate Variable project is to provide a quality controlled 20 year long data set of Arctic Ocean winter-time sea ice thickness distribution. An important step to achieve this goal is to assess the accuracy of sea ice thickness retrieval based on satellite radar altimetry. For this purpose a data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and collocated observations of snow and sea ice freeboard from Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) air-borne campaigns, of sea ice draft from moored and submarine Upward Looking Sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer aboard EOS (AMSR-E) and the Warren Climatology (Warren et al., 1999). An inter-comparison of the snow depth data sets stresses the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. This is confirmed by a comparison of snow freeboard measured during OIB and CryoVEx and snow freeboard computed from radar altimetry. For first-year ice the agreement between OIB and AMSR-E snow depth within 0.02 m suggests AMSR-E snow depth as an appropriate alternative. Different freeboard-to-thickness and freeboard-to-draft conversion approaches are realized. The mean observed ULS sea ice draft agrees with the mean sea ice draft computed from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the realized approaches is able to reproduce the seasonal cycle in sea ice draft observed by moored ULS satisfactorily. A sensitivity analysis of the freeboard-to-thickness conversion suggests: in order to obtain sea ice thickness as accurate as 0.5 m from radar altimetry, besides a freeboard estimate with centimetre accuracy, an ice-type dependent sea ice density is as mandatory as a snow depth with centimetre accuracy.

Recent Improvements in Retrieving Near-Surface Air Temperature and Humidity Using Microwave Remote Sensing

NASA Technical Reports Server (NTRS)

Roberts, J. Brent

2010-01-01

Detailed studies of the energy and water cycles require accurate estimation of the turbulent fluxes of moisture and heat across the atmosphere-ocean interface at regional to basin scale. Providing estimates of these latent and sensible heat fluxes over the global ocean necessitates the use of satellite or reanalysis-based estimates of near surface variables. Recent studies have shown that errors in the surface (10 meter)estimates of humidity and temperature are currently the largest sources of uncertainty in the production of turbulent fluxes from satellite observations. Therefore, emphasis has been placed on reducing the systematic errors in the retrieval of these parameters from microwave radiometers. This study discusses recent improvements in the retrieval of air temperature and humidity through improvements in the choice of algorithms (linear vs. nonlinear) and the choice of microwave sensors. Particular focus is placed on improvements using a neural network approach with a single sensor (Special Sensor Microwave/Imager) and the use of combined sensors from the NASA AQUA satellite platform. The latter algorithm utilizes the unique sampling available on AQUA from the Advanced Microwave Scanning Radiometer (AMSR-E) and the Advanced Microwave Sounding Unit (AMSU-A). Current estimates of uncertainty in the near-surface humidity and temperature from single and multi-sensor approaches are discussed and used to estimate errors in the turbulent fluxes.

Comparing the Accuracy of AMSRE, AMSR2, SSMI and SSMIS Satellite Radiometer Ice Concentration Products with One-Meter Resolution Visible Imagery in the Arctic

NASA Astrophysics Data System (ADS)

Peterson, E. R.; Stanton, T. P.

2016-12-01

Determining ice concentration in the Arctic is necessary to track significant changes in sea ice edge extent. Sea ice concentrations are also needed to interpret data collected by in-situ instruments like buoys, as the amount of ice versus water in a given area determines local solar heating. Ice concentration products are now routinely derived from satellite radiometers including the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Special Sensor Microwave Imager (SSMI), and the Special Sensor Microwave Imager/Sounder (SSMIS). While these radiometers are viewed as reliable to monitor long-term changes in sea ice extent, their accuracy should be analyzed, and compared to determine which radiometer performs best over smaller features such as melt ponds, and how seasonal conditions affect accuracy. Knowledge of the accuracy of radiometers at high resolution can help future researchers determine which radiometer to use, and be aware of radiometer shortcomings in different ice conditions. This will be especially useful when interpreting data from in-situ instruments which deal with small scale measurements. In order to compare these passive microwave radiometers, selected high spatial resolution one-meter resolution Medea images, archived at the Unites States Geological Survey, are used for ground truth comparison. Sea ice concentrations are derived from these images in an interactive process, although estimates are not perfect ground truth due to exposure of images, shadowing and cloud cover. 68 images are retrieved from the USGS website and compared with 9 useable, collocated SSMI, 33 SSMIS, 36 AMSRE, and 14 AMSR2 ice concentrations in the Arctic Ocean. We analyze and compare the accuracy of radiometer instrumentation in differing ice conditions.

The melting sea ice of Arctic polar cap in the summer solstice month and the role of ocean

NASA Astrophysics Data System (ADS)

Lee, S.; Yi, Y.

2014-12-01

The Arctic sea ice is becoming smaller and thinner than climatological standard normal and more fragmented in the early summer. We investigated the widely changing Arctic sea ice using the daily sea ice concentration data. Sea ice data is generated from brightness temperature data derived from the sensors: Defense Meteorological Satellite Program (DMSP)-F13 Special Sensor Microwave/Imagers (SSM/Is), the DMSP-F17 Special Sensor Microwave Imager/Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA Earth Observing System (EOS) Aqua satellite. We tried to figure out appearance of arctic sea ice melting region of polar cap from the data of passive microwave sensors. It is hard to explain polar sea ice melting only by atmosphere effects like surface air temperature or wind. Thus, our hypothesis explaining this phenomenon is that the heat from deep undersea in Arctic Ocean ridges and the hydrothermal vents might be contributing to the melting of Arctic sea ice.

Validating Microwave-Based Satellite Rain Rate Retrievals Over TRMM Ground Validation Sites

NASA Astrophysics Data System (ADS)

Fisher, B. L.; Wolff, D. B.

2008-12-01

Multi-channel, passive microwave instruments are commonly used today to probe the structure of rain systems and to estimate surface rainfall from space. Until the advent of meteorological satellites and the development of remote sensing techniques for measuring precipitation from space, there was no observational system capable of providing accurate estimates of surface precipitation on global scales. Since the early 1970s, microwave measurements from satellites have provided quantitative estimates of surface rainfall by observing the emission and scattering processes due to the existence of clouds and precipitation in the atmosphere. This study assesses the relative performance of microwave precipitation estimates from seven polar-orbiting satellites and the TRMM TMI using four years (2003-2006) of instantaneous radar rain estimates obtained from Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) sites at Kwajalein, Republic of the Marshall Islands (KWAJ) and Melbourne, Florida (MELB). The seven polar orbiters include three different sensor types: SSM/I (F13, F14 and F15), AMSU-B (N15, N16 and N17), and AMSR-E. The TMI aboard the TRMM satellite flies in a sun asynchronous orbit between 35 S and 35 N latitudes. The rain information from these satellites are combined and used to generate several multi-satellite rain products, namely the Goddard TRMM Multi-satellite Precipitation Analysis (TMPA), NOAA's CPC Morphing Technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN). Instantaneous rain rates derived from each sensor were matched to the GV estimates in time and space at a resolution of 0.25 degrees. The study evaluates the measurement and error characteristics of the various satellite estimates through inter-comparisons with GV radar estimates. The GV rain observations provided an empirical ground-based reference for assessing the relative performance of each sensor and sensor class. Because the relative performance of the rain algorithms depends on the underlying surface terrain, the data for MELB was further stratified into ocean, land and coast categories using a 0.25 terrain mask. Relative to GV, AMSR-E and the TMI exhibited the highest correlation and skill over the full dynamic range of observed rain rates at both validation sites. The AMSU sensors, on the other hand, exhibited the lowest correlation and skill, though all sensors performed reasonably well compared to GV. The general tendency was for the microwave sensors to overestimate rain rates below 1 mm/hr where the sampling was highest and to underestimate the high rain rates above 10 mm/hr where the sampling was lowest. Underestimation of the low rain rate regime is attributed to difficulties of detecting and measuring low rain rates, while overestimation over the oceans was attributed largely to saturation of the brightness temperatures at high rain rates. Overall biases depended on the relative differences in the total rainfall at the extremes and the performance of each sensor at the nominal rain rates.

ASCAT soil moisture data assimilation through the Ensemble Kalman Filter for improving streamflow simulation in Mediterranean catchments

NASA Astrophysics Data System (ADS)

Loizu, Javier; Massari, Christian; Álvarez-Mozos, Jesús; Casalí, Javier; Goñi, Mikel

2016-04-01

Assimilation of Surface Soil Moisture (SSM) observations obtained from remote sensing techniques have been shown to improve streamflow prediction at different time scales of hydrological modeling. Different sensors and methods have been tested for their application in SSM estimation, especially in the microwave region of the electromagnetic spectrum. The available observation devices include passive microwave sensors such as the Advanced Microwave Scanning Radiometer - Earth Observation System (AMSR-E) onboard the Aqua satellite and the Soil Moisture and Ocean Salinity (SMOS) mission. On the other hand, active microwave systems include Scatterometers (SCAT) onboard the European Remote Sensing satellites (ERS-1/2) and the Advanced Scatterometer (ASCAT) onboard MetOp-A satellite. Data assimilation (DA) include different techniques that have been applied in hydrology and other fields for decades. These techniques include, among others, Kalman Filtering (KF), Variational Assimilation or Particle Filtering. From the initial KF method, different techniques were developed to suit its application to different systems. The Ensemble Kalman Filter (EnKF), extensively applied in hydrological modeling improvement, shows its capability to deal with nonlinear model dynamics without linearizing model equations, as its main advantage. The objective of this study was to investigate whether data assimilation of SSM ASCAT observations, through the EnKF method, could improve streamflow simulation of mediterranean catchments with TOPLATS hydrological complex model. The DA technique was programmed in FORTRAN, and applied to hourly simulations of TOPLATS catchment model. TOPLATS (TOPMODEL-based Land-Atmosphere Transfer Scheme) was applied on its lumped version for two mediterranean catchments of similar size, located in northern Spain (Arga, 741 km2) and central Italy (Nestore, 720 km2). The model performs a separated computation of energy and water balances. In those balances, the soil is divided into two layers, the upper Surface Zone (SZ), and the deeper Transmission Zone (TZ). In this study, the SZ depth was fixed to 5 cm, for adequate assimilation of observed data. Available data was distributed as follows: first, the model was calibrated for the 2001-2007 period; then the 2007-2010 period was used for satellite data rescaling purposes. Finally, data assimilation was applied during the validation (2010-2013) period. Application of the EnKF required the following steps: 1) rescaling of satellite data, 2) transformation of rescaled data into Soil Water Index (SWI) through a moving average filter, where a T = 9 calibrated value was applied, 3) generation of a 50 member ensemble through perturbation of inputs (rainfall and temperature) and three selected parameters, 4) validation of the ensemble through the compliance of two criteria based on ensemble's spread, mean square error and skill and, 5) Kalman Gain calculation. In this work, comparison of three satellite data rescaling techniques: 1) cumulative distribution Function (CDF) matching, 2) variance matching and 3) linear least square regression was also performed. Results obtained in this study showed slight improvements of hourly Nash-Sutcliffe Efficiency (NSE) in both catchments, with the different rescaling methods evaluated. Larger improvements were found in terms of seasonal simulated volume error reduction.

A Conceptual Approach to Assimilating Remote Sensing Data to Improve Soil Moisture Profile Estimates in a Surface Flux/Hydrology Model. Part 1; Overview

NASA Technical Reports Server (NTRS)

Crosson, William L.; Laymon, Charles A.; Inguva, Ramarao; Schamschula, Marius; Caulfield, John

1998-01-01

Knowledge of the amount of water in the soil is of great importance to many earth science disciplines. Soil moisture is a key variable in controlling the exchange of water and energy between the land surface and the atmosphere. Thus, soil moisture information is valuable in a wide range of applications including weather and climate, runoff potential and flood control, early warning of droughts, irrigation, crop yield forecasting, soil erosion, reservoir management, geotechnical engineering, and water quality. Despite the importance of soil moisture information, widespread and continuous measurements of soil moisture are not possible today. Although many earth surface conditions can be measured from satellites, we still cannot adequately measure soil moisture from space. Research in soil moisture remote sensing began in the mid 1970s shortly after the surge in satellite development. Recent advances in remote sensing have shown that soil moisture can be measured, at least qualitatively, by several methods. Quantitative measurements of moisture in the soil surface layer have been most successful using both passive and active microwave remote sensing, although complications arise from surface roughness and vegetation type and density. Early attempts to measure soil moisture from space-borne microwave instruments were hindered by what is now considered sub-optimal wavelengths (shorter than 5 cm) and the coarse spatial resolution of the measurements. L-band frequencies between 1 and 3 GHz (10-30 cm) have been deemed optimal for detection of soil moisture in the upper few centimeters of soil. The Electronically Steered Thinned Array Radiometer (ESTAR), an aircraft-based instrument operating a 1,4 GHz, has shown great promise for soil moisture determination. Initiatives are underway to develop a similar instrument for space. Existing space-borne synthetic aperture radars (SARS) operating at C- and L-band have also shown some potential to detect surface wetness. The advantage of radar is its much higher resolution than passive microwave systems, but it is currently hampered by surface roughness effects and the lack of a good algorithm based on a single frequency and single polarization. In addition, its repeat frequency is generally low (about 40 days). In the meantime, two new radiometers offer some hope for remote sensing of soil moisture from space. The Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), launched in November 1997, possesses a 10.65 GHz channel and the Advanced Microwave Scanning Radiometer (AMSR) on both the ADEOS-11 and Earth Observing System AM-1 platforms to be launched in 1999 possesses a 6.9 GHz channel. Aside from issues about interference from vegetation, the coarse resolution of these data will provide considerable challenges pertaining to their application. The resolution of TMI is about 45 km and that of AMSR is about 70 km. These resolutions are grossly inconsistent with the scale of soil moisture processes and the spatial variability of factors that control soil moisture. Scale disparities such as these are forcing us to rethink how we assimilate data of various scales in hydrologic models. Of particular interest is how to assimilate soil moisture data by reconciling the scale disparity between what we can expect from present and future remote sensing measurements of soil moisture and modeling soil moisture processes. It is because of this disparity between the resolution of space-based sensors and the scale of data needed for capturing the spatial variability of soil moisture and related properties that remote sensing of soil moisture has not met with more widespread success. Within a single footprint of current sensors at the wavelengths optimal for this application, in most cases there is enormous heterogeneity in soil moisture created by differences in landcover, soils and topography, as well as variability in antecedent precipitation. It is difficult to interpret the meaning of 'mean' soil moisture under such conditions and even more difficult to apply such a value. Because of the non-linear relationships between near-surface soil moisture and other variables of interest, such as surface energy fluxes and runoff, mean soil moisture has little applicability at such large scales. It is for these reasons that the use of remote sensing in conjunction with a hydrologic model appears to be of benefit in capturing the complete spatial and temporal structure of soil moisture. This paper is Part I of a four-part series describing a method for intermittently assimilating remotely-sensed soil moisture information to improve performance of a distributed land surface hydrology model. The method, summarized in section II, involves the following components, each of which is detailed in the indicated section of the paper or subsequent papers in this series: Forward radiative transfer model methods (section II and Part IV); Use of a Kalman filter to assimilate remotely-sensed soil moisture estimates with the model profile (section II and Part IV); Application of a soil hydrology model to capture the continuous evolution of the soil moisture profile within and below the root zone (section III); Statistical aggregation techniques (section IV and Part II); Disaggregation techniques using a neural network approach (section IV and Part III); and Maximum likelihood and Bayesian algorithms for inversely solving for the soil moisture profile in the upper few cm (Part IV).

Towards Understanding the Timing and Frequency of Rain-on-Snow (ROS) Events in Alaska

NASA Astrophysics Data System (ADS)

Pan, C.; Kirchner, P. B.; Kimball, J. S.; Kim, Y.; Kamp, U.

2017-12-01

Rain-on-snow (ROS) events affect ecosystem processes at multiple spatial and temporal scales including hydrology, carbon cycling, wildlife movement and human transportation and result in marked changes to snowpack processes including enhanced snow melt, surface albedo and energy balance. Changes in the surface structure of the snowpack are visible through optical remote sensing and changes in the relative content and distribution of water, air and ice in the snowpack are detectable using passive microwave remote sensing. This project aims to develop ROS products to elucidate changes in frequency and distribution in ROS events using satellite data products derived from both optical and passive microwave satellite records. To detect ROS events, we use downscaled brightness temperature measurements derived from vertical and horizontal polarizations at 19 and 37 GHz from the Advanced Microwave Scanning Radiometer (AMSR-E/2) passive microwave satellites. Preliminary results indicate an overall classification accuracy of 77.6% relative to in situ weather observations including surface air temperature, precipitation, and snow depth. ROS events are spatially distributed largely to elevations below 500 m and occur most frequently on northern to western aspects in addition to southeastern. Regional ROS hot spots occur in southwest Alaska characterized by warmer climates and transient snowcover. The seasonal timing of ROS events indicates increasing frequency during the fall and spring months.

Remote Sensing of Terrestrial Water Storage and Application to Drought Monitoring

NASA Technical Reports Server (NTRS)

Rodell, Matt

2007-01-01

Terrestrial water storage (TWS) consists of groundwater, soil moisture and permafrost, surface water, snow and ice, and wet biomass. TWS variability tends to be dominated by snow and ice in polar and alpine regions, by soil moisture in mid-latitudes, and by surface water in wet, tropical regions such as the Amazon (Rodell and Famiglietti, 2001; Bates et al., 2007). Drought may be defined as a period of abnormally dry weather long enough to cause significant deficits in one or more of the TWS components. Thus, along with observations of the agricultural and socioeconomic impacts, measurements of TWS and its components enable quantification of drought severity. Each of the TWS components exhibits significant spatial variability, while installation and maintenance of sufficiently dense monitoring networks is costly and labor-intensive. Thus satellite remote sensing is an appealing alternative to traditional measurement techniques. Several current remote sensing instruments are able to detect variations in one or more TWS variables, including the Advanced Microwave Scanning Radiometer (AMSR) on NASA's Aqua satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's Terra and Aqua. Future satellite missions have been proposed to improve this capability, including the European Space Agency's Soil Moisture Ocean Salinity mission (SMOS) and the Soil Moisture Active Passive (SMAP), Surface Water Ocean Topography (SWOT), and Snow and Cold Land Processes (SCLP) missions recommended by the US National Academy of Science's Decadal Survey for Earth Science (NRC, 2007). However, only one remote sensing technology is able to monitor changes in TWS from the land surface to the base of the deepest aquifer: satellite gravimetry. This paper focuses on NASA's Gravity Recovery and Climate Experiment mission (GRACE; http://www.csr.utexas.edu/grace/) and its potential as a tool for drought monitoring.

Comparison of global cloud liquid water path derived from microwave measurements with CERES-MODIS

NASA Astrophysics Data System (ADS)

Yi, Y.; Minnis, P.; Huang, J.; Lin, B.; Ayers, K.; Sun-Mack, S.; Fan, A.

Cloud liquid water path LWP is a crucial parameter for climate studies due to the link that it provides between the atmospheric hydrological and radiative budgets Satellite-based visible infrared techniques such as the Visible Infrared Solar Split-Window Technique VISST can retrieve LWP for water clouds assumes single-layer over a variety of surfaces If the water clouds are overlapped by ice clouds the LWP of the underlying clouds can not be retrieved by such techniques However microwave techniques may be used to retrieve the LWP underneath ice clouds due to the microwave s insensitivity to cloud ice particles LWP is typically retrieved from satellite-observed microwave radiances only over ocean due to variations of land surface temperature and emissivity Recently Deeter and Vivekanandan 2006 developed a new technique for retrieving LWP over land In order to overcome the sensitivity to land surface temperature and emissivity their technique is based on a parameterization of microwave polarization-difference signals In this study a similar regression-based technique for retrieving LWP over land and ocean using Advanced Microwave Scanning Radiometer - EOS AMSR-E measurements is developed Furthermore the microwave surface emissivities are also derived using clear-sky fields of view based on the Clouds and Earth s Radiant Energy System Moderate-resolution Imaging Spectroradiometer CERES-MODIS cloud mask These emissivities are used in an alternate form of the technique The results are evaluated using independent measurements such

Palm Swamp Wetland Ecosystems of the Upper Amazon: Characterizing their Distribution and Inundation State Using Multiple Resolution Microwave Remote Sensing

NASA Astrophysics Data System (ADS)

Podest, E.; McDonald, K. C.; Schröder, R.; Pinto, N.; Zimmermann, R.; Horna, V.

2011-12-01

Palm swamp wetlands are prevalent in the Amazon basin, including extensive regions in northern Peru. These ecosystems are characterized by constant surface inundation and moderate seasonal water level variation. The combination of constantly saturated soils, giving rise to low oxygen conditions, and warm temperatures year-round can lead to considerable methane release to the atmosphere. Because of the widespread occurrence and expected sensitivity of these ecosystems to climate change, knowledge of their spatial extent and inundation state is crucial for assessing the associated land-atmosphere carbon exchange. Precise spatio-temporal information on palm swamps is difficult to gather because of their remoteness and difficult accessibility. Spaceborne microwave remote sensing is an effective tool for characterizing these ecosystems since it is sensitive to surface water and vegetation structure and allows monitoring large inaccessible areas on a temporal basis regardless of atmospheric conditions or solar illumination. We are developing a remote sensing methodology using multiple resolution microwave remote sensing data to determine palm swamp distribution and inundation state over focus regions in the Amazon basin in northern Peru. For this purpose, two types of multi-temporal microwave data are used: 1) high-resolution (100 m) data from the Advanced Land Observing Satellite (ALOS) Phased Array L-Band Synthetic Aperture Radar (PALSAR) to derive maps of palm swamp extent and inundation from dual-polarization fine-beam and multi-temporal HH-polarized ScanSAR, and 2) coarse resolution (25 km) combined active and passive microwave data from QuikSCAT and AMSR-E to derive inundated area fraction on a weekly basis. We compare information content and accuracy of the coarse resolution products to the PALSAR-based datasets to ensure information harmonization. The synergistic combination of high and low resolution datasets will allow for characterization of palm swamps and assessment of their flooding status. This work has been undertaken partly within the framework of the JAXA ALOS Kyoto & Carbon Initiative. PALSAR data have been provided by JAXA/EORC. Portions of this work were carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Gravity changes, soil moisture and data assimilation

NASA Astrophysics Data System (ADS)

Walker, J.; Grayson, R.; Rodell, M.; Ellet, K.

2003-04-01

Remote sensing holds promise for near-surface soil moisture and snow mapping, but current techniques do not directly resolve the deeper soil moisture or groundwater. The benefits that would arise from improved monitoring of variations in terrestrial water storage are numerous. The year 2002 saw the launch of NASA's Gravity Recovery And Climate Experiment (GRACE) satellites, which are mapping the Earth's gravity field at such a high level of precision that we expect to be able to infer changes in terrestrial water storage (soil moisture, groundwater, snow, ice, lake, river and vegetation). The project described here has three distinct yet inter-linked components that all leverage off the same ground-based monitoring and land surface modelling framework. These components are: (i) field validation of a relationship between soil moisture and changes in the Earth's gravity field, from ground- and satellite-based measurements of changes in gravity; (ii) development of a modelling framework for the assimilation of gravity data to constrain land surface model predictions of soil moisture content (such a framework enables the downscaling and disaggregation of low spatial (500 km) and temporal (monthly) resolution measurements of gravity change to finer spatial and temporal resolutions); and (iii) further refining the downscaling and disaggregation of space-borne gravity measurements by making use of other remotely sensed information, such as the higher spatial (25 km) and temporal (daily) resolution remotely sensed near-surface soil moisture measurements from the Advanced Microwave Scanning Radiometer (AMSR) instruments on Aqua and ADEOS II. The important field work required by this project will be in the Murrumbidgee Catchment, Australia, where an extensive soil moisture monitoring program by the University of Melbourne is already in place. We will further enhance the current monitoring network by the addition of groundwater wells and additional soil moisture sites. Ground-based gravity measurements will also be made on a monthly basis at each monitoring site. There will be two levels of modelling and monitoring; regional across the entire Murrumbidgee Catchment (100,000 km2), and local across a small sub-catchment (150 km2).

The impact of horizontal heterogeneities, cloud fraction, and cloud dynamics on warm cloud effective radii and liquid water path from CERES-like Aqua MODIS retrievals

NASA Astrophysics Data System (ADS)

Painemal, D.; Minnis, P.; Sun-Mack, S.

2013-05-01

The impact of horizontal heterogeneities, liquid water path (LWP from AMSR-E), and cloud fraction (CF) on MODIS cloud effective radius (re), retrieved from the 2.1 μm (re2.1) and 3.8 μm (re3.8) channels, is investigated for warm clouds over the southeast Pacific. Values of re retrieved using the CERES Edition 4 algorithms are averaged at the CERES footprint resolution (~ 20 km), while heterogeneities (Hσ) are calculated as the ratio between the standard deviation and mean 0.64 μm reflectance. The value of re2.1 strongly depends on CF, with magnitudes up to 5 μm larger than those for overcast scenes, whereas re3.8 remains insensitive to CF. For cloudy scenes, both re2.1 and re3.8 increase with Hσ for any given AMSR-E LWP, but re2.1 changes more than for re3.8. Additionally, re3.8 - re2.1 differences are positive (< 1 μm) for homogeneous scenes (Hσ < 0.2) and LWP > 50 g m-2, and negative (up to -4 μm) for larger Hσ. Thus, re3.8 - re2.1 differences are more likely to reflect biases associated with cloud heterogeneities rather than information about the cloud vertical structure. The consequences for MODIS LWP are also discussed.

Downscaling Satellite Data for Predicting Catchment-scale Root Zone Soil Moisture with Ground-based Sensors and an Ensemble Kalman Filter

NASA Astrophysics Data System (ADS)

Lin, H.; Baldwin, D. C.; Smithwick, E. A. H.

2015-12-01

Predicting root zone (0-100 cm) soil moisture (RZSM) content at a catchment-scale is essential for drought and flood predictions, irrigation planning, weather forecasting, and many other applications. Satellites, such as the NASA Soil Moisture Active Passive (SMAP), can estimate near-surface (0-5 cm) soil moisture content globally at coarse spatial resolutions. We develop a hierarchical Ensemble Kalman Filter (EnKF) data assimilation modeling system to downscale satellite-based near-surface soil moisture and to estimate RZSM content across the Shale Hills Critical Zone Observatory at a 1-m resolution in combination with ground-based soil moisture sensor data. In this example, a simple infiltration model within the EnKF-model has been parameterized for 6 soil-terrain units to forecast daily RZSM content in the catchment from 2009 - 2012 based on AMSRE. LiDAR-derived terrain variables define intra-unit RZSM variability using a novel covariance localization technique. This method also allows the mapping of uncertainty with our RZSM estimates for each time-step. A catchment-wide satellite-to-surface downscaling parameter, which nudges the satellite measurement closer to in situ near-surface data, is also calculated for each time-step. We find significant differences in predicted root zone moisture storage for different terrain units across the experimental time-period. Root mean square error from a cross-validation analysis of RZSM predictions using an independent dataset of catchment-wide in situ Time-Domain Reflectometry (TDR) measurements ranges from 0.060-0.096 cm3 cm-3, and the RZSM predictions are significantly (p < 0.05) correlated with TDR measurements [r = 0.47-0.68]. The predictive skill of this data assimilation system is similar to the Penn State Integrated Hydrologic Modeling (PIHM) system. Uncertainty estimates are significantly (p < 0.05) correlated to cross validation error during wet and dry conditions, but more so in dry summer seasons. Developing an EnKF-model system that downscales satellite data and predicts catchment-scale RZSM content is especially timely, given the anticipated release of SMAP surface moisture data in 2015.

Evaluation of three different data fusion approaches that uses satellite soil moisture from different passive microwave sensors to construct one consistent climate record

NASA Astrophysics Data System (ADS)

van der Schalie, Robin; de Jeu, Richard; Kerr, Yann; Wigneron, Jean-Pierre; Rodríguez-Fernández, Nemesio; Al-Yaari, Amen; Drusch, Matthias; Mecklenburg, Susanne; Dolman, Han

2016-04-01

Datasets that are derived from satellite observations are becoming increasingly important for measuring key parameters of the Earth's climate and are therefore crucial in research on climate change, giving the opportunity to researchers to detect anomalies and long-term trends globally. One of these key parameters is soil moisture (SM), which has a large impact on water, energy and biogeochemical cycles worldwide. A long-term SM data record from active and passive microwave satellite observations was developed as part of ESA's Climate Change Initiative (ESA-CCI-SM, http://www.esa-soilmoisture-cci.org/). Currently the dataset covers a period from 1978 to 2014 and is updated regularly, observations from a several microwave satellites including: ERS-1, ERS-2, METOP-A, Nimbus 7 SMMR, DMSP SSM/I, TRMM TMI, Aqua AMSRE, Coriolis WindSat, and GCOM-W1 AMSR2. In 2009, ESA launched the Soil Moisture and Ocean Salinity (SMOS, Kerr et al., 2010) mission, carrying onboard a unique L-band radiometer, but its SM retrievals are not yet part of this dataset. Due to the different radiometric characteristics of SMOS, integrating SMOS into the ESA-CCI-SM dataset is not straight forward. Therefore several approaches have been tested to fuse soil moisture retrievals from SMOS and AMSRE, which currently forms the basis of the passive microwave part within ESA-CCI-SM project. These approaches are: 1. A Neural Network Fusion approach (Rodríguez-Fernández et al., 2015), 2. A regression approach (Wigneron et al., 2004; Al-Yaari et al., 2015) and 3. A radiative transfer based approach, using the Land Parameter Retrieval Model (Van der Schalie et al., 2016). This study evaluates the three different approaches and tests their skills against multiple datasets, including MERRA-Land, ERA-Interim/Land, the current ESA-CCI-SM v2.2 and in situ measurements from the International Soil Moisture Network and present a recommendation for the potential integration of SMOS soil moisture into the ESA-CCI-SM dataset. This recommendation is based on a series of statistical metrics (i.e. correlation, unbiased root mean square error, bias, spatial correspondence and single to noise ratios (Gruber et al., 2015)) and will provide guidelines for a seamless integration. References Al-Yaari, A., Wigneron, J.P., Kerr, Y., De Jeu, R.A.M., Rodriguez-Fernandez, N., Van der Schalie, R., Al Bitar, A., Mialon, A., Richaume, P., Dolman, A., and Ducharne, A. (2015), "Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations", Remote Sensing of Environment, IN PRESS. Gruber, A., Su, C.-H., Zwieback, S., Crowd, W., Dorigo, W., and Wagner, W. (2015), "Recent advances in (soil moisture) triple collocation analysis", Int. J. Appl. Earth Observ. Geoinf, doi: http://dx.doi.org/10.1016/j.jag.2015.09.002. Kerr, Y.H., Waldteufel, P., Wigneron, J.P., Delwart, S., Cabot, F., Boutin, J., Escorihuela, M.J., Font, J., Reul, N., Gruhier, C., Juglea, S.E., Drinkwater, M.R., Hahne, A., Martin-Neira, M., and Mecklenburg, S. (2010), "The SMOS mission: New tool for monitoring key elements of the global water cycle", Proceedings of the IEEE, vol. 98, no. 5, doi: 10.1109/JPROC.2010.2043043. Rodríguez-Fernández, N.J., Aires, F., Richaume, P., Kerr, Y.H., Prigent, C., Kolassa, J., Cabot, F., Jiménez, C., Mahmoodi, A., and Drusch, M. (2015), "Soil Moisture Retrieval Using Neural Networks: Application to SMOS", IEEE Trans. on Geosc. and Remote Sens., vol. 53, no. 11, doi: 10.1109/TGRS.2015.2430845. Van der Schalie, R., Kerr, Y.H., Wigneron, J.P., Rodriguez-Fernandez, N.J., Al-Yaari, A., and De Jeu, R.A.M. (2015), "Global SMOS Soil Moisture Retrievals from The Land Parameter Retrieval Model", Int. J. Appl. Earth Observ. Geoinf, doi: http://dx.doi.org/10.1016/j.jag.2015.08.005. Wigneron J.-P., Calvet, J.-C., De Rosnay, P., Kerr, Y., Waldteufel, P., Saleh, K., Escorihuela, M.J. and Kruszewski, A. (2004), "Soil Moisture Retrievals from Bi-Angular L-band Passive Microwave Observations", IEEE Trans. Geosc. Remote Sens. Let., vol. 1, no. 4, pp. 277-281.

Solutions Network Formulation Report. Visible/Infrared Imager/Radiometer Suite and Advanced Microwave Scanning Radiometer Data Products for National Drought Monitor Decision Support

NASA Technical Reports Server (NTRS)

Estep, Leland

2007-01-01

Drought effects are either direct or indirect depending on location, population, and regional economic vitality. Common direct effects of drought are reduced crop, rangeland, and forest productivity; increased fire hazard; reduced water levels; increased livestock and wildlife mortality rates; and damage to wildlife and fish habitat. Indirect impacts follow on the heels of direct impacts. For example, a reduction in crop, rangeland, and forest productivity may result in reduced income for farmers and agribusiness, increased prices for food and timber, unemployment, reduced tax revenues, increased crime, foreclosures on bank loans to farmers and businesses, migration, and disaster relief programs. In the United States alone, drought is estimated to result in annual losses of between $6 - 8 billion. Recent sustained drought in the United States has made decision-makers aware of the impacts of climate change on society and environment. The eight major droughts that occurred in the United States between 1980 and 1999 accounted for the largest percentage of weather-related monetary losses. Monitoring drought and its impact that occurs at a variety of scales is an important government activity -- not only nationally but internationally as well. The NDMC (National Drought Mitigation Center) and the USDA (U.S. Department of Agriculture) RMA (Risk Management Agency) have partnered together to develop a DM-DSS (Drought Monitoring Decision Support System). This monitoring system will be an interactive portal that will provide users the ability to visualize and assess drought at all levels. This candidate solution incorporates atmospherically corrected VIIRS data products, such as NDVI (Normalized Difference Vegetation Index) and Ocean SST (sea surface temperature), and AMSR-E soil moisture data products into two NDMC vegetation indices -- VegDRI (Vegetation Drought Response Index) and VegOUT (Vegetation Outlook) -- which are then input into the DM-DSS.

Estimation of wind speeds inside Super Typhoon Nepartak from AMSR2 low-frequency brightness temperatures

NASA Astrophysics Data System (ADS)

Zhang, Lei; Yin, Xiaobin; Shi, Hanqing; Wang, Zhenzhan; Xu, Qing

2018-04-01

Accurate estimations of typhoon-level winds are highly desired over the western Pacific Ocean. A wind speed retrieval algorithm is used to retrieve the wind speeds within Super Typhoon Nepartak (2016) using 6.9- and 10.7-GHz brightness temperatures from the Japanese Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on board the Global Change Observation Mission-Water 1 (GCOM-W1) satellite. The results show that the retrieved wind speeds clearly represent the intensification process of Super Typhoon Nepartak. A good agreement is found between the retrieved wind speeds and the Soil Moisture Active Passive wind speed product. The mean bias is 0.51 m/s, and the root-mean-square difference is 1.93 m/s between them. The retrieved maximum wind speeds are 59.6 m/s at 04:45 UTC on July 6 and 71.3 m/s at 16:58 UTC on July 6. The two results demonstrate good agreement with the results reported by the China Meteorological Administration and the Joint Typhoon Warning Center. In addition, Feng-Yun 2G (FY-2G) satellite infrared images, Feng-Yun 3C (FY-3C) microwave atmospheric sounder data, and AMSR2 brightness temperature images are also used to describe the development and structure of Super Typhoon Nepartak.

Recent Enhancements in NOAA's JPSS Land Product Suite and Key Operational Applications

NASA Astrophysics Data System (ADS)

Csiszar, I. A.; Yu, Y.; Zhan, X.; Vargas, M.; Ek, M. B.; Zheng, W.; Wu, Y.; Smirnova, T. G.; Benjamin, S.; Ahmadov, R.; James, E.; Grell, G. A.

2017-12-01

A suite of operational land products has been produced as part of NOAA's Joint Polar Satellite System (JPSS) program to support a wide range of operational applications in environmental monitoring, prediction, disaster management and mitigation, and decision support. The Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) and the operational JPSS satellite series forms the basis of six fundamental and multiple additional added-value environmental data records (EDRs). A major recent improvement in the land-based VIIRS EDRs has been the development of global gridded products, providing a format and science content suitable for ingest into NOAA's operational land surface and coupled numerical weather prediction models. VIIRS near-real-time Green Vegetation Fraction is now in the process of testing for full operational use, while land surface temperature and albedo are under testing and evaluation. The operational 750m VIIRS active fire product, including fire radiative power, is used to support emission modeling and air quality applications. Testing the evaluation for operational NOAA implementation of the improved 375m VIIRS active fire product is also underway. Added-value and emerging VIIRS land products include vegetation health, phenology, near-real-time surface type and surface condition change, and other biogeophysical variables. As part of the JPSS program, a global soil moisture data product has also been generated from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor on the GCOM-W1 (Global Change Observation Mission - Water 1) satellite since July 2012. This product is included in the blended NESDIS Soil Moisture Operational Products System, providing soil moisture data as a critical input for land surface modeling.

Assessment of NASA GISS CMIP5 and Post-CMIP5 Simulated Clouds and TOA Radiation Budgets Using Satellite Observations. Part I: Cloud Fraction and Properties

NASA Technical Reports Server (NTRS)

Stanfield, Ryan E.; Dong, Xiquan; Xi, Baike; Kennedy, Aaron; Del Genio, Anthony D.; Minnia, Patrick; Jiang, Jonathan H.

2014-01-01

Although many improvements have been made in phase 5 of the Coupled Model Intercomparison Project (CMIP5), clouds remain a significant source of uncertainty in general circulation models (GCMs) because their structural and optical properties are strongly dependent upon interactions between aerosol/cloud microphysics and dynamics that are unresolved in such models. Recent changes to the planetary boundary layer (PBL) turbulence and moist convection parameterizations in the NASA GISS Model E2 atmospheric GCM(post-CMIP5, hereafter P5) have improved cloud simulations significantly compared to its CMIP5 (hereafter C5) predecessor. A study has been performed to evaluate these changes between the P5 and C5 versions of the GCM, both of which used prescribed sea surface temperatures. P5 and C5 simulated cloud fraction (CF), liquid water path (LWP), ice water path (IWP), cloud water path (CWP), precipitable water vapor (PWV), and relative humidity (RH) have been compared to multiple satellite observations including the Clouds and the Earth's Radiant Energy System-Moderate Resolution Imaging Spectroradiometer (CERES-MODIS, hereafter CM), CloudSat- Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; hereafter CC), Atmospheric Infrared Sounder (AIRS), and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Although some improvements are observed in the P5 simulation on a global scale, large improvements have been found over the southern midlatitudes (SMLs), where correlations increased and both bias and root-mean-square error (RMSE) significantly decreased, in relation to the previous C5 simulation, when compared to observations. Changes to the PBL scheme have resulted in improved total column CFs, particularly over the SMLs where marine boundary layer (MBL) CFs have increased by nearly 20% relative to the previous C5 simulation. Globally, the P5 simulated CWPs are 25 gm22 lower than the previous C5 results. The P5 version of the GCM simulates PWV and RH higher than its C5 counterpart and agrees well with the AMSR-E and AIRS observations. The moister atmospheric conditions simulated by P5 are consistent with the CF comparison and provide a strong support for the increase in MBL clouds over the SMLs. Over the tropics, the P5 version of the GCM simulated total column CFs and CWPs are slightly lower than the previous C5 results, primarily as a result of the shallower tropical boundary layer in P5 relative to C5 in regions outside the marine stratocumulus decks.

Antarctic Sea-Ice Freeboard and Estimated Thickness from NASA's ICESat and IceBridge Observations

NASA Technical Reports Server (NTRS)

Yi, Donghui; Kurtz, Nathan; Harbeck, Jeremy; Manizade, Serdar; Hofton, Michelle; Cornejo, Helen G.; Zwally, H. Jay; Robbins, John

2016-01-01

ICESat completed 18 observational campaigns during its lifetime from 2003 to 2009. Data from all of the 18 campaign periods are used in this study. Most of the operational periods were between 34 and 38 days long. Because of laser failure and orbit transition from 8-day to 91-day orbit, there were four periods lasting 57, 16, 23, and 12 days. IceBridge data from 2009, 2010, and 2011 are used in this study. Since 2009, there are 19 Airborne Topographic Mapper (ATM) campaigns, and eight Land, Vegetation, and Ice Sensor (LVIS) campaigns over the Antarctic sea ice. Freeboard heights are derived from ICESat, ATM and LVIS elevation and waveform data. With nominal densities of snow, water, and sea ice, combined with snow depth data from AMSR-E/AMSR2 passive microwave observation over the southern ocean, sea-ice thickness is derived from the freeboard. Combined with AMSR-E/AMSR2 ice concentration, sea-ice area and volume are also calculated. During the 2003-2009 period, sea-ice freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of the growth and decay of the Antarctic pack ice. We found no significant trend of thickness or area for the Antarctic sea ice during the ICESat period. IceBridge sea ice freeboard and thickness data from 2009 to 2011 over the Weddell Sea and Amundsen and Bellingshausen Seas are compared with the ICESat results.

Joint DEnKF-albedo assimilation scheme that considers the common land model subgrid heterogeneity and a snow density-based observation operator for improving snow depth simulations

NASA Astrophysics Data System (ADS)

Xu, Jianhui; Zhang, Feifei; Zhao, Yi; Shu, Hong; Zhong, Kaiwen

2016-07-01

For the large-area snow depth (SD) data sets with high spatial resolution in the Altay region of Northern Xinjiang, China, we present a deterministic ensemble Kalman filter (DEnKF)-albedo assimilation scheme that considers the common land model (CoLM) subgrid heterogeneity. In the albedo assimilation of DEnKF-albedo, the assimilated albedos over each subgrid tile are estimated with the MCD43C1 bidirectional reflectance distribution function (BRDF) parameters product and CoLM calculated solar zenith angle. The BRDF parameters are hypothesized to be consistent over all subgrid tiles within a specified grid. In the SCF assimilation of DEnKF-albedo, a DEnKF combining a snow density-based observation operator considers the effects of the CoLM subgrid heterogeneity and is employed to assimilate MODIS SCF to update SD states over all subgrid tiles. The MODIS SCF over a grid is compared with the area-weighted sum of model predicted SCF over all the subgrid tiles within the grid. The results are validated with in situ SD measurements and AMSR-E product. Compared with the simulations, the DEnKF-albedo scheme can reduce errors of SD simulations and accurately simulate the seasonal variability of SD. Furthermore, it can improve simulations of SD spatiotemporal distribution in the Altay region, which is more accurate and shows more detail than the AMSR-E product.

Assessment of NASA GISS E2 CMIP5 and Post-CMIP5 Simulated Clouds and TOA Radiation Budgets Using Satellite Observations: Cloud fraction and properties

NASA Astrophysics Data System (ADS)

Stanfield, R.; Dong, X.; Xi, B.; Kennedy, A. D.; Del Genio, A. D.; Minnis, P.; Jiang, J. H.

2013-12-01

Recent changes to boundary layer turbulence and convection parameterizations of the NASA GISS E2 GCM have led to drastic improvements in the newest Post-CMIP5 (P5) model simulations. A study has been performed to evaluate these changes. Variables including Cloud Fraction (CF), Liquid Water Path (LWP), Ice Water Path (IWP), Cloud Water Path (LWP+IWP, CWP), Precipitable Water Vapor (PWV), and Relative Humidity (RH), from P5 and its CMIP5 (C5) predecessor have been compared to multiple satellite observations including CERES-MODIS (CM), CloudSat/CALIPSO (CC), AIRS, and AMSR-E. P5 simulations show drastic improvements for regional CFs, resulting in better correlations with observations. The largest improvements were found over the Southern Mid-Latitudes (SMLs), where newly implemented changes to the boundary layer turbulence parameterization increased low-level CF by ~20% while generating less optically thick clouds. The double InterTropical Convergence Zone (ITCZ) issue that plagues many GCMs, including previous GISS C5 simulations, is also removed with the new changes to convection parameterizations when decoupled from the ocean. P5 simulations show a decrease in global CWP, more closely resembling CC and CM observations. Globally, P5 simulated PWV is in better agreement with AMSR-R and AIRS, particularly over the SML oceans. RH comparisons show improvement when compared with AIRS. Spatial and variability analyses using Taylor diagrams indicate overall better correlations and smaller standard deviations in PWV and RH comparisons between P5/C5 simulations and AMSR-R/AIRS observations than CF and CWP/LWP/IWP comparisons.

Enhanced-Resolution Satellite Microwave Brightness Temperature Records for Mapping Boreal-Arctic Landscape Freeze-Thaw Heterogeneity

NASA Astrophysics Data System (ADS)

Kim, Y.; Du, J.; Kimball, J. S.

2017-12-01

The landscape freeze-thaw (FT) status derived from satellite microwave remote sensing is closely linked to vegetation phenology and productivity, surface energy exchange, evapotranspiration, snow/ice melt dynamics, and trace gas fluxes over land areas affected by seasonally frozen temperatures. A long-term global satellite microwave Earth System Data Record of daily landscape freeze-thaw status (FT-ESDR) was developed using similar calibrated 37GHz, vertically-polarized (V-pol) brightness temperatures (Tb) from SMMR, SSM/I, and SSMIS sensors. The FT-ESDR shows mean annual spatial classification accuracies of 90.3 and 84.3 % for PM and AM overpass retrievals relative surface air temperature (SAT) measurement based FT estimates from global weather stations. However, the coarse FT-ESDR gridding (25-km) is insufficient to distinguish finer scale FT heterogeneity. In this study, we tested alternative finer scale FT estimates derived from two enhanced polar-grid (3.125-km and 6-km resolution), 36.5 GHz V-pol Tb records derived from calibrated AMSR-E and AMSR2 sensor observations. The daily FT estimates are derived using a modified seasonal threshold algorithm that classifies daily Tb variations in relation to grid cell-wise FT thresholds calibrated using ERA-Interim reanalysis based SAT, downscaled using a digital terrain map and estimated temperature lapse rates. The resulting polar-grid FT records for a selected study year (2004) show mean annual spatial classification accuracies of 90.1% (84.2%) and 93.1% (85.8%) for respective PM (AM) 3.125km and 6-km Tb retrievals relative to in situ SAT measurement based FT estimates from regional weather stations. Areas with enhanced FT accuracy include water-land boundaries and mountainous terrain. Differences in FT patterns and relative accuracy obtained from the enhanced grid Tb records were attributed to several factors, including different noise contributions from underlying Tb processing and spatial mismatches between Tb retrievals and SAT calibrated FT thresholds.

Constraints on the Hydrologic Settings and Recharge of the Freshwater Lenses in Kuwait

NASA Astrophysics Data System (ADS)

Milewski, A.; Sultan, M.; Al-Dousari, A.

2010-12-01

The majority of the World’s arid and semi-arid countries receive rare, yet extreme, precipitation events. Recharge is minimal due to high evaporation and low infiltration rates. We show that Kuwait experiences geologic and hydrologic settings that are quite different, conditions that promote groundwater recharge. Kuwait is generally flat (slope: 2m/km) and is largely covered (80% of Kuwait’s land) by alluvial deposits with high infiltration capacities; these conditions inhibit runoff and promote infiltration and recharge of aquifers. On the average Kuwait receives 200 mm/yr over a few, but intensive events. Groundwater flows from the SW to the NE and the salinity increases along the flow gradient reaching salinities of 150,000 TDS in the NE. The presence of saline and hypersaline groundwater on local and/or regional scales in arid and hyperarid environments is usually considered as unwelcome news to hydrogeologists. That is not the case everywhere in Kuwait. In the southern regions, infiltrating fresh water mixes with the saline groundwater (TDS: 5,000 to 10,000) in the unconfined aquifers rendering it unsuitable for drinking and irrigation purposes, whereas in the northern regions, infiltrating water form lenses of fresh water on top of the highly saline (TDS >35,000) unconfined aquifers. Using the Raudhatain Watershed (3,696 km^2) in northern Kuwait as our test site, and knowing the locations of fresh water lenses in the watershed, we identified settings which facilitate the formation of these lenses and used these criteria to identify additional potential occurrences. Identified criteria include the presence of gentle slopes, permeable surface material, infrequent yet intensive (>20mm/hr) precipitation events, drainage depressions to collect the limited runoff, and presence of regional unconfined saline aquifers. Approximately 20 locations (size: 3 km2 to 150 km^2) were identified. Over the investigated period (1998- 2006), 25 precipitation events were reported, five of which exceeded 20 mm/hr; no flows were reported at the watershed outlet and no long-term ponding was detected on Landsat TM images acquired shortly after (1 to 14 days) each of the precipitation events suggesting that infiltration is quite high. This suggestion is supported by: (1) examination of NDVI images (from Landsat TM) and soil moisture images (from AMSR-E) which show that the observed increases in soil moisture content and vegetation index following a large precipitation events are not restricted to the valley network, and (2) the highly porous nature of the mapped soils (e.g., gravel, sand) and the high infiltration rates (up to 9m/day) reported for these soils. Using the SWAT continuous rainfall runoff model and taking advantage of global remote sensing datasets and GIS technologies, we estimate: (1) the average annual precipitation, runoff, and recharge at: 837 x 106m3, 6.9 x 10^6m^3, and 636 x 10^6m^3, respectively, and (2) recharge in the identified depressions at 41.6 x 10^6m^3. Results demonstrate the enhanced opportunities for groundwater recharge in the examined watershed and highlight the potential for similar applications in arid areas elsewhere.

The new Cloud Dynamics and Radiation Database algorithms for AMSR2 and GMI: exploitation of the GPM observational database for operational applications

NASA Astrophysics Data System (ADS)

Cinzia Marra, Anna; Casella, Daniele; Martins Costa do Amaral, Lia; Sanò, Paolo; Dietrich, Stefano; Panegrossi, Giulia

2017-04-01

Two new precipitation retrieval algorithms for the Advanced Microwave Scanning Radiometer 2 (AMSR2) and for the GPM Microwave Imager (GMI) are presented. The algorithms are based on the Cloud Dynamics and Radiation Database (CDRD) Bayesian approach and represent an evolution of the previous version applied to Special Sensor Microwave Imager/Sounder (SSMIS) observations, and used operationally within the EUMETSAT Satellite Application Facility on support to Operational Hydrology and Water Management (H-SAF). These new products present as main innovation the use of an extended database entirely empirical, derived from coincident radar and radiometer observations from the NASA/JAXA Global Precipitation Measurement Core Observatory (GPM-CO) (Dual-frequency Precipitation Radar-DPR and GMI). The other new aspects are: 1) a new rain-no-rain screening approach; 2) the use of Empirical Orthogonal Functions (EOF) and Canonical Correlation Analysis (CCA) both in the screening approach, and in the Bayesian algorithm; 2) the use of new meteorological and environmental ancillary variables to categorize the database and mitigate the problem of non-uniqueness of the retrieval solution; 3) the development and implementations of specific modules for computational time minimization. The CDRD algorithms for AMSR2 and GMI are able to handle an extremely large observational database available from GPM-CO and provide the rainfall estimate with minimum latency, making them suitable for near-real time hydrological and operational applications. As far as CDRD for AMSR2, a verification study over Italy using ground-based radar data and over the MSG full disk area using coincident GPM-CO/AMSR2 observations has been carried out. Results show remarkable AMSR2 capabilities for rainfall rate (RR) retrieval over ocean (for RR > 0.25 mm/h), good capabilities over vegetated land (for RR > 1 mm/h), while for coastal areas the results are less certain. Comparisons with NASA GPM products, and with ground-based radar data, show that CDRD for AMSR2 is able to depict very well the areas of high precipitation over all surface types. Similarly, preliminary results of the application of CDRD for GMI are also shown and discussed, highlighting the advantage of the availability of high frequency channels (> 90 GHz) for precipitation retrieval over land and coastal areas.

78 FR 8684 - Twelfth Meeting: RTCA Special Committee 222, Inmarsat AMS(R)S

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-06

... Committee 222, Inmarsat AMS(R)S AGENCY: Federal Aviation Administration (FAA), U.S. Department of Transportation (DOT). ACTION: Meeting Notice of RTCA Special Committee 222, Inmarsat AMS(R)S. SUMMARY: The FAA is..., Inmarsat AMS(R)S. DATES: The meeting will be held February 20, 2013, from 1:00 p.m.--4:00 p.m. ADDRESSES...

78 FR 61446 - Fourteenth Meeting: RTCA Special Committee 222, Inmarsat AMS(R)S

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-03

... Committee 222, Inmarsat AMS(R)S AGENCY: Federal Aviation Administration (FAA), U.S. Department of Transportation (DOT). ACTION: Meeting Notice of RTCA Special Committee 222, Inmarsat AMS(R)S. SUMMARY: The FAA is..., Inmarsat AMS(R)S DATES: The meeting will be held November 19, 2013 from 9:00 a.m.-5:00 p.m. ADDRESSES: The...

The impact of horizontal heterogeneities, cloud fraction, and liquid water path on warm cloud effective radii from CERES-like Aqua MODIS retrievals

NASA Astrophysics Data System (ADS)

Painemal, D.; Minnis, P.; Sun-Mack, S.

2013-10-01

The impact of horizontal heterogeneities, liquid water path (LWP from AMSR-E), and cloud fraction (CF) on MODIS cloud effective radius (re), retrieved from the 2.1 μm (re2.1) and 3.8 μm (re3.8) channels, is investigated for warm clouds over the southeast Pacific. Values of re retrieved using the CERES algorithms are averaged at the CERES footprint resolution (∼20 km), while heterogeneities (Hσ) are calculated as the ratio between the standard deviation and mean 0.64 μm reflectance. The value of re2.1 strongly depends on CF, with magnitudes up to 5 μm larger than those for overcast scenes, whereas re3.8 remains insensitive to CF. For cloudy scenes, both re2.1 and re3.8 increase with Hσ for any given AMSR-E LWP, but re2.1 changes more than for re3.8. Additionally, re3.8-re2.1 differences are positive (<1 μm) for homogeneous scenes (Hσ < 0.2) and LWP > 45 gm-2, and negative (up to -4 μm) for larger Hσ. While re3.8-re2.1 differences in homogeneous scenes are qualitatively consistent with in situ microphysical observations over the region of study, negative differences - particularly evinced in mean regional maps - are more likely to reflect the dominant bias associated with cloud heterogeneities rather than information about the cloud vertical structure. The consequences for MODIS LWP are also discussed.

Improved large-scale hydrological modelling through the assimilation of streamflow and downscaled satellite soil moisture observations.

NASA Astrophysics Data System (ADS)

López López, Patricia; Wanders, Niko; Sutanudjaja, Edwin; Renzullo, Luigi; Sterk, Geert; Schellekens, Jaap; Bierkens, Marc

2015-04-01

The coarse spatial resolution of global hydrological models (typically > 0.25o) often limits their ability to resolve key water balance processes for many river basins and thus compromises their suitability for water resources management, especially when compared to locally-tunes river models. A possible solution to the problem may be to drive the coarse resolution models with high-resolution meteorological data as well as to assimilate ground-based and remotely-sensed observations of key water cycle variables. While this would improve the modelling resolution of the global model, the impact of prediction accuracy remains largely an open question. In this study we investigated the impact that assimilating streamflow and satellite soil moisture observations have on global hydrological model estimation, driven by coarse- and high-resolution meteorological observations, for the Murrumbidgee river basin in Australia. The PCR-GLOBWB global hydrological model is forced with downscaled global climatological data (from 0.5o downscaled to 0.1o resolution) obtained from the WATCH Forcing Data (WFDEI) and local high resolution gauging station based gridded datasets (0.05o), sourced from the Australian Bureau of Meteorology. Downscaled satellite derived soil moisture (from 0.5o downscaled to 0.1o resolution) from AMSR-E and streamflow observations collected from 25 gauging stations are assimilated using an ensemble Kalman filter. Several scenarios are analysed to explore the added value of data assimilation considering both local and global climatological data. Results show that the assimilation of streamflow observations result in the largest improvement of the model estimates. The joint assimilation of both streamflow and downscaled soil moisture observations leads to further improved in streamflow simulations (10% reduction in RMSE), mainly in the headwater catchments (up to 10,000 km2). Results also show that the added contribution of data assimilation, for both soil moisture and streamflow, is more pronounced when the global meteorological data are used to force the models. This is caused by the higher uncertainty and coarser resolution of the global forcing. This study demonstrates that it is possible to improve hydrological simulations forced by coarse resolution meteorological data with downscaled satellite soil moisture and streamflow observations and bring them closer to a hydrological model forced with local climatological data. These findings are important in light of the efforts that are currently done to go to global hyper-resolution modelling and can significantly help to advance this research.

Resolution Enhancement of Spaceborne Radiometer Images

NASA Technical Reports Server (NTRS)

Krim, Hamid

2001-01-01

Our progress over the last year has been along several dimensions: 1. Exploration and understanding of Earth Observatory System (EOS) mission with available data from NASA. 2. Comprehensive review of state of the art techniques and uncovering of limitations to be investigated (e.g. computational, algorithmic ...). and 3. Preliminary development of resolution enhancement algorithms. With the advent of well-collaborated satellite microwave radiometers, it is now possible to obtain long time series of geophysical parameters that are important for studying the global hydrologic cycle and earth radiation budget. Over the world's ocean, these radiometers simultaneously measure profiles of air temperature and the three phases of atmospheric water (vapor, liquid, and ice). In addition, surface parameters such as the near surface wind speed, the sea surface temperature, and the sea ice type and concentration can be retrieved. The special sensor microwaves imager SSM/I has wide application in atmospheric remote sensing over the ocean and provide essential inputs to numerical weather-prediction models. SSM/I data has also been used for land and ice studies, including snow cover classification measurements of soil and plant moisture contents, atmospheric moisture over land, land surface temperature and mapping polar ice. The brightness temperature observed by SSM/I is function of the effective brightness temperature of the earth's surface and the emission scattering and attenuation of the atmosphere. Advanced Microwave Scanning Radiometer (AMSR) is a new instrument that will measure the earth radiation over the spectral range from 7 to 90 GHz. Over the world's ocean, it will be possible to retrieve the four important geographical parameters SST, wind speed, vertically integrated water vapor, vertically integrated cloud liquid water L.

Aqua Satellite Orbiting Earth Artist Concept

NASA Image and Video Library

2002-05-08

NASA Aqua satellite carries six state-of-the-art instruments in a near-polar low-Earth orbit. Aqua is seen in this artist concept orbiting Earth. The six instruments are the Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU-A), the Humidity Sounder for Brazil (HSB), the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), the Moderate Resolution Imaging Spectroradiometer (MODIS), and Clouds and the Earth's Radiant Energy System (CERES). Each has unique characteristics and capabilities, and all six serve together to form a powerful package for Earth observations. http://photojournal.jpl.nasa.gov/catalog/PIA18156

Key Provenance of Earth Science Observational Data Products

NASA Astrophysics Data System (ADS)

Conover, H.; Plale, B.; Aktas, M.; Ramachandran, R.; Purohit, P.; Jensen, S.; Graves, S. J.

2011-12-01

As the sheer volume of data increases, particularly evidenced in the earth and environmental sciences, local arrangements for sharing data need to be replaced with reliable records about the what, who, how, and where of a data set or collection. This is frequently called the provenance of a data set. While observational data processing systems in the earth sciences have a long history of capturing metadata about the processing pipeline, current processes are limited in both what is captured and how it is disseminated to the science community. Provenance capture plays a role in scientific data preservation and stewardship precisely because it can automatically capture and represent a coherent picture of the what, how and who of a particular scientific collection. It reflects the transformations that a data collection underwent prior to its current form and the sequence of tasks that were executed and data products applied to generate a new product. In the NASA-funded Instant Karma project, we examine provenance capture in earth science applications, specifically the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) Science Investigator-led Processing system (SIPS). The project is integrating the Karma provenance collection and representation tool into the AMSR-E SIPS production environment, with an initial focus on Sea Ice. This presentation will describe capture and representation of provenance that is guided by the Open Provenance Model (OPM). Several things have become clear during the course of the project to date. One is that core OPM entities and relationships are not adequate for expressing the kinds of provenance that is of interest in the science domain. OPM supports name-value pair annotations that can be used to augment what is known about the provenance entities and relationships, but in Karma, annotations cannot be added during capture, but only after the fact. This limits the capture system's ability to record something it learned about an entity after the event of its creation in the provenance record. We will discuss extensions to the Open Provenance Model (OPM) and modifications to the Karma tool suite to address this issue, more efficient representations of earth science kinds of provenance, and definition of metadata structures for capturing related knowledge about the data products and science algorithms used to generate them. Use scenarios for provenance information is an active topic of investigation. It has additionally become clear through the project that not all provenance is created equal. In processing pipelines, some provenance is repetitive and uninteresting. Because of the volume of provenance, this obscures what are the interesting pieces of provenance. Methodologies to reveal science-relevant provenance will be presented, along with a preview of the AMSR-E Provenance Browser.

The radiative response of the lower troposphere to moisture intrusions into the Arctic

NASA Astrophysics Data System (ADS)

Johansson, Erik; Devasthale, Abhay; Tjernström, Michael; Ekman, Annica M. L.; L'Ecuyer, Tristan

2016-04-01

Water vapour (WV) transport into the Arctic occurs on daily to seasonal time scales and affects the Arctic atmosphere and surface energy balance in a number of ways. Extreme transport events, hereafter referred to as WV intrusions (WVI), account for a significant fraction of the total transport of water vapour into the Arctic. Considering their overall impact on the total moisture transport, WVIs are expected to strongly influence the radiative properties of the lower troposphere. Being a potent greenhouse gas, WV has a warming effect on the surface via its longwave forcing. As a result, WVIs have potential to warm the sea-ice surface and depending on their strength and degree of persistence, precondition accelerated melting of sea ice in subsequent months following the intrusion WVIs also affect the prevalent thermodynamical characteristics of the lowermost troposphere such as the presence of temperature and humidity inversions. They can further modulate cloud formation processes by changing the local thermodynamics. Characterizing the response of the lower troposphere to WVIs is therefore important, mainly to improve our understanding of the processes, affecting, air-sea-ice interactions. In this context, the aim of the present study is to provide observationally based insights into how the lower troposphere radiatively responds to WVIs, defined as events that exceed 90-percentile value of the poleward meridional moisture flux across 70° N. Using the combined lidar and radar (CloudSat+CALIPSO) data from the A-Train constellation of satellites from 2006 through 2010 together with data from AMSR-E, AIRS and MODIS, we examine the dominant circulation patterns that favour WVI and the surface response to WVI. We further quantify changes in cloudiness and cloud radiative effects during WVI.

Optimality in Data Assimilation

NASA Astrophysics Data System (ADS)

Nearing, Grey; Yatheendradas, Soni

2016-04-01

It costs a lot more to develop and launch an earth-observing satellite than it does to build a data assimilation system. As such, we propose that it is important to understand the efficiency of our assimilation algorithms at extracting information from remote sensing retrievals. To address this, we propose that it is necessary to adopt completely general definition of "optimality" that explicitly acknowledges all differences between the parametric constraints of our assimilation algorithm (e.g., Gaussianity, partial linearity, Markovian updates) and the true nature of the environmetnal system and observing system. In fact, it is not only possible, but incredibly straightforward, to measure the optimality (in this more general sense) of any data assimilation algorithm as applied to any intended model or natural system. We measure the information content of remote sensing data conditional on the fact that we are already running a model and then measure the actual information extracted by data assimilation. The ratio of the two is an efficiency metric, and optimality is defined as occurring when the data assimilation algorithm is perfectly efficient at extracting information from the retrievals. We measure the information content of the remote sensing data in a way that, unlike triple collocation, does not rely on any a priori presumed relationship (e.g., linear) between the retrieval and the ground truth, however, like triple-collocation, is insensitive to the spatial mismatch between point-based measurements and grid-scale retrievals. This theory and method is therefore suitable for use with both dense and sparse validation networks. Additionally, the method we propose is *constructive* in the sense that it provides guidance on how to improve data assimilation systems. All data assimilation strategies can be reduced to approximations of Bayes' law, and we measure the fractions of total information loss that are due to individual assumptions or approximations in the prior (i.e., the model uncertainty distribution), and in the likelihood (i.e., the observation operator and observation uncertainty distribution). In this way, we can directly identify the parts of a data assimilation algorithm that contribute most to assimilation error in a way that (unlike traditional DA performance metrics) considers nonlinearity in the model and observation and non-optimality in the fit between filter assumptions and the real system. To reiterate, the method we propose is theoretically rigorous but also dead-to-rights simple, and can be implemented in no more than a few hours by a competent programmer. We use this to show that careful applications of the Ensemble Kalman Filter use substantially less than half of the information contained in remote sensing soil moisture retrievals (LPRM, AMSR-E, SMOS, and SMOPS). We propose that this finding may explain some of the results from several recent large-scale experiments that show lower-than-expected value to assimilating soil moisture retrievals into land surface models forced by high-quality precipitation data. Our results have important implications for the SMAP mission because over half of the SMAP-affiliated "early adopters" plan to use the EnKF as their primary method for extracting information from SMAP retrievals.

Sea ice motion from low-resolution satellite sensors: An alternative method and its validation in the Arctic

NASA Astrophysics Data System (ADS)

Lavergne, T.; Eastwood, S.; Teffah, Z.; Schyberg, H.; Breivik, L.-A.

2010-10-01

The retrieval of sea ice motion with the Maximum Cross-Correlation (MCC) method from low-resolution (10-15 km) spaceborne imaging sensors is challenged by a dominating quantization noise as the time span of displacement vectors is shortened. To allow investigating shorter displacements from these instruments, we introduce an alternative sea ice motion tracking algorithm that builds on the MCC method but relies on a continuous optimization step for computing the motion vector. The prime effect of this method is to effectively dampen the quantization noise, an artifact of the MCC. It allows for retrieving spatially smooth 48 h sea ice motion vector fields in the Arctic. Strategies to detect and correct erroneous vectors as well as to optimally merge several polarization channels of a given instrument are also described. A test processing chain is implemented and run with several active and passive microwave imagers (Advanced Microwave Scanning Radiometer-EOS (AMSR-E), Special Sensor Microwave Imager, and Advanced Scatterometer) during three Arctic autumn, winter, and spring seasons. Ice motion vectors are collocated to and compared with GPS positions of in situ drifters. Error statistics are shown to be ranging from 2.5 to 4.5 km (standard deviation for components of the vectors) depending on the sensor, without significant bias. We discuss the relative contribution of measurement and representativeness errors by analyzing monthly validation statistics. The 37 GHz channels of the AMSR-E instrument allow for the best validation statistics. The operational low-resolution sea ice drift product of the EUMETSAT OSI SAF (European Organisation for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility) is based on the algorithms presented in this paper.

Testing Snow Melt Algorithms in High Relief Topography Using Calibrated Enhanced-Resolution Brightness Temperatures, Hunza River Basin, Pakistan

NASA Astrophysics Data System (ADS)

Ramage, J. M.; Brodzik, M. J.; Hardman, M.; Troy, T. J.

2017-12-01

Snow is a vital part of the terrestrial hydrological cycle, a crucial resource for people and ecosystems. In mountainous regions snow is extensive, variable, and challenging to document. Snow melt timing and duration are important factors affecting the transfer of snow mass to soil moisture and runoff. Passive microwave brightness temperature (Tb) changes at 36 and 18 GHz are a sensitive way to detect snow melt onset due to their sensitivity to the abrupt change in emissivity. They are widely used on large icefields and high latitude watersheds. The coarse resolution ( 25 km) of historically available data has precluded effective use in high relief, heterogeneous regions, and gaps between swaths also create temporal data gaps at lower latitudes. New enhanced resolution data products generated from a scatterometer image reconstruction for radiometer (rSIR) technique are available at the original frequencies. We use these Calibrated Enhanced-resolution Brightness (CETB) Temperatures Earth System Data Records (ESDR) to evaluate existing snow melt detection algorithms that have been used in other environments, including the cross polarized gradient ratio (XPGR) and the diurnal amplitude variations (DAV) approaches. We use the 36/37 GHz (3.125 km resolution) and 18/19 GHz (6.25 km resolution) vertically and horizontally polarized datasets from the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Radiometer for EOS (AMSR-E) and evaluate them for use in this high relief environment. The new data are used to assess glacier and snow melt records in the Hunza River Basin [area 13,000 sq. km, located at 36N, 74E], a tributary to the Upper Indus Basin, Pakistan. We compare the melt timing results visually and quantitatively to the corresponding EASE-Grid 2.0 25-km dataset, SRTM topography, and surface temperatures from station and reanalysis data. The new dataset is coarser than the topography, but is able to differentiate signals of melt/refreeze timing for different altitudes and land cover in this remote area with significant hazards from snow melt and glacier discharge. The improved spatial resolution, enhanced to 3-6 km, and retaining twice daily observations is a key improvement to fully analyze snowpack melt characteristics in remote mountainous regions.

Towards Year-round Estimation of Terrestrial Water Storage over Snow-Covered Terrain via Multi-sensor Assimilation of GRACE/GRACE-FO and AMSR-E/AMSR-2.

NASA Astrophysics Data System (ADS)

Wang, J.; Xue, Y.; Forman, B. A.; Girotto, M.; Reichle, R. H.

2017-12-01

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth's terrestrial hydrologic cycle and has provided an unprecedented observational constraint for global land surface models. However, the coarse-scale (in space and time), vertically-integrated measure of terrestrial water storage (TWS) limits GRACE's applicability to smaller scale hydrologic applications. In order to enhance model-based estimates of TWS while effectively adding resolution (in space and time) to the coarse-scale TWS retrievals, a multi-variate, multi-sensor data assimilation framework is presented here that simultaneously assimilates gravimetric retrievals of TWS in conjunction with passive microwave (PMW) brightness temperature (Tb) observations over snow-covered terrain. The framework uses the NASA Catchment Land Surface Model (Catchment) and an ensemble Kalman filter (EnKF). A synthetic assimilation experiment is presented for the Volga river basin in Russia. The skill of the output from the assimilation of synthetic observations is compared with that of model estimates generated without the benefit of assimilating the synthetic observations. It is shown that the EnKF framework improves modeled estimates of TWS, snow depth, and snow mass (a.k.a. snow water equivalent). The data assimilation routine produces a conditioned (updated) estimate that is more accurate and contains less uncertainty during both the snow accumulation phase of the snow season as well as during the snow ablation season.

Usability and Interoperability Improvements for an EASE-Grid 2.0 Passive Microwave Data Product Using CF Conventions

NASA Astrophysics Data System (ADS)

Hardman, M.; Brodzik, M. J.; Long, D. G.

2017-12-01

Beginning in 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, near-global spatial coverage for monitoring changes in hydrologic and cryospheric parameters that include precipitation, soil moisture, surface water, vegetation, snow water equivalent, sea ice concentration and sea ice motion. Historical versions of the gridded passive microwave data sets were produced as flat binary files described in human-readable documentation. This format is error-prone and makes it difficult to reliably include all processing and provenance. Funded by NASA MEaSUREs, we have completely reprocessed the gridded data record that includes SMMR, SSM/I-SSMIS and AMSR-E. The new Calibrated Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) files are self-describing. Our approach to the new data set was to create netCDF4 files that use standard metadata conventions and best practices to incorporate file-level, machine- and human-readable contents, geolocation, processing and provenance metadata. We followed the flexible and adaptable Climate and Forecast (CF-1.6) Conventions with respect to their coordinate conventions and map projection parameters. Additionally, we made use of Attribute Conventions for Dataset Discovery (ACDD-1.3) that provided file-level conventions with spatio-temporal bounds that enable indexing software to search for coverage. Our CETB files also include temporal coverage and spatial resolution in the file-level metadata for human-readability. We made use of the JPL CF/ACDD Compliance Checker to guide this work. We tested our file format with real software, for example, netCDF Command-line Operators (NCO) power tools for unlimited control on spatio-temporal subsetting and concatenation of files. The GDAL tools understand the CF metadata and produce fully-compliant geotiff files from our data. ArcMap can then reproject the geotiff files on-the-fly and work with other geolocated data such as coastlines, with no special work required. We expect this combination of standards and well-tested interoperability to significantly improve the usability of this important ESDR for the Earth Science community.

Extremely Low Passive Microwave Brightness Temperatures Due to Thunderstorms

NASA Technical Reports Server (NTRS)

Cecil, Daniel J.

2015-01-01

Extreme events by their nature fall outside the bounds of routine experience. With imperfect or ambiguous measuring systems, it is appropriate to question whether an unusual measurement represents an extreme event or is the result of instrument errors or other sources of noise. About three weeks after the Tropical Rainfall Measuring Mission (TRMM) satellite began collecting data in Dec 1997, a thunderstorm was observed over northern Argentina with 85 GHz brightness temperatures below 50 K and 37 GHz brightness temperatures below 70 K (Zipser et al. 2006). These values are well below what had previously been observed from satellite sensors with lower resolution. The 37 GHz brightness temperatures are also well below those measured by TRMM for any other storm in the subsequent 16 years. Without corroborating evidence, it would be natural to suspect a problem with the instrument, or perhaps an irregularity with the platform during the first weeks of the satellite mission. Automated quality control flags or other procedures in retrieval algorithms could treat these measurements as errors, because they fall outside the expected bounds. But the TRMM satellite also carries a radar and a lightning sensor, both confirming the presence of an intense thunderstorm. The radar recorded 40+ dBZ reflectivity up to about 19 km altitude. More than 200 lightning flashes per minute were recorded. That same storm's 19 GHz brightness temperatures below 150 K would normally be interpreted as the result of a low-emissivity water surface (e.g., a lake, or flood waters) if not for the simultaneous measurements of such intense convection. This paper will examine records from TRMM and related satellite sensors including SSMI, AMSR-E, and the new GMI to find the strongest signatures resulting from thunderstorms, and distinguishing those from sources of noise. The lowest brightness temperatures resulting from thunderstorms as seen by TRMM have been in Argentina in November and December. For SSMI sensors carried on five DMSP satellites examined so far, the lowest thunderstorm-related brightness temperatures have been from Argentina in November - December and from Minnesota in June-July. The Minnesota cases were associated with spotter reports of large hail, significant severe wind, and tornadoes. Those locations have the record-holders for each satellite. The lowest AMSR-E 36.5 GHz brightness temperatures associated with deep convection have been in Argentina; the lowest 89.0 GHz brightness temperatures were from Typhoon Bolaven in the Philippine Sea. This paper will show examples of cases with the lowest brightness temperatures, and map the locations of these and other storms with brightness temperatures nearly as low. The study is largely motivated by the new GMI sensor on the Global Precipitation Mission core satellite, launched in February 2014, with its high resolution expected to reveal unprecedented low brightness temperatures when extreme events are encountered.

Data sets for snow cover monitoring and modelling from the National Snow and Ice Data Center

NASA Astrophysics Data System (ADS)

Holm, M.; Daniels, K.; Scott, D.; McLean, B.; Weaver, R.

2003-04-01

A wide range of snow cover monitoring and modelling data sets are pending or are currently available from the National Snow and Ice Data Center (NSIDC). In-situ observations support validation experiments that enhance the accuracy of remote sensing data. In addition, remote sensing data are available in near-real time, providing coarse-resolution snow monitoring capability. Time series data beginning in 1966 are valuable for modelling efforts. NSIDC holdings include SMMR and SSM/I snow cover data, MODIS snow cover extent products, in-situ and satellite data collected for NASA's recent Cold Land Processes Experiment, and soon-to-be-released ASMR-E passive microwave products. The AMSR-E and MODIS sensors are part of NASA's Earth Observing System flying on the Terra and Aqua satellites Characteristics of these NSIDC-held data sets, appropriateness of products for specific applications, and data set access and availability will be presented.

Effect of Terrestrial and Marine Organic Aerosol on Regional and Global Climate: Model Development, Application, and Verification with Satellite Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meskhidze, Nicholas; Zhang, Yang; Kamykowski, Daniel

2012-03-28

In this DOE project the improvements to parameterization of marine primary organic matter (POM) emissions, hygroscopic properties of marine POM, marine isoprene derived secondary organic aerosol (SOA) emissions, surfactant effects, new cloud droplet activation parameterization have been implemented into Community Atmosphere Model (CAM 5.0), with a seven mode aerosol module from the Pacific Northwest National Laboratory (PNNL)'s Modal Aerosol Model (MAM7). The effects of marine aerosols derived from sea spray and ocean emitted biogenic volatile organic compounds (BVOCs) on microphysical properties of clouds were explored by conducting 10 year CAM5.0-MAM7 model simulations at a grid resolution 1.9° by 2.5° withmore » 30 vertical layers. Model-predicted relationship between ocean physical and biological systems and the abundance of CCN in remote marine atmosphere was compared to data from the A-Train satellites (MODIS, CALIPSO, AMSR-E). Model simulations show that on average, primary and secondary organic aerosol emissions from the ocean can yield up to 20% increase in Cloud Condensation Nuclei (CCN) at 0.2% Supersaturation, and up to 5% increases in droplet number concentration of global maritime shallow clouds. Marine organics were treated as internally or externally mixed with sea salt. Changes associated with cloud properties reduced (absolute value) the model-predicted short wave cloud forcing from -1.35 Wm-2 to -0.25 Wm-2. By using different emission scenarios, and droplet activation parameterizations, this study suggests that addition of marine primary aerosols and biologically generated reactive gases makes an important difference in radiative forcing assessments. All baseline and sensitivity simulations for 2001 and 2050 using global-through-urban WRF/Chem (GU-WRF) were completed. The main objective of these simulations was to evaluate the capability of GU-WRF for an accurate representation of the global atmosphere by exploring the most accurate configuration of physics options in GWRF for global scale modeling in 2001 at a horizontal grid resolution of 1° x 1°. GU-WRF model output was evaluated using observational datasets from a variety of sources including surface based observations (NCDC and BSRN), model reanalysis (NCEP/ NCAR Reanalysis and CMAP), and remotely-sensed data (TRMM) to evaluate the ability of GU-WRF to simulate atmospheric variables at the surface as well as aloft. Explicit treatment of nanoparticles produced from new particle formation in GU-WRF/Chem-MADRID was achieved by expanding particle size sections from 8 to 12 to cover particles with the size range of 1.16 nm to 11.6m. Simulations with two different nucleation parameterizations were conducted for August 2002 over a global domain at a 4º by 5º horizontal resolution. The results are evaluated against field measurement data from the 2002 Aerosol Nucleation and Real Time Characterization Experiment (ANARChE) in Atlanta, Georgia, as well as satellite and reanalysis data. We have also explored the relationship between clean marine aerosol optical properties and ocean surface wind speed using remotely sensed data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the CALIPSO satellite and the Advanced Microwave Scanning Radiometer (AMSR-E) on board the AQUA satellite. Detailed data analyses were carried out over 15 regions selected to be representative of different areas of the global ocean for the time period from June 2006 to April 2011. We show that for very low (less than 4 m s-1) and very high (more than 12 m s-1) wind speed conditions the mean CALIPSO-derived aerosol optical depth (AOD) has little dependency on the surface wind speed. For an intermediate (between 4 and 12 m s-1) marine AOD was linearly correlated with the surface wind speed values, with a slope of 0.0062 s m-1. Results of our study suggest that considerable improvements to both optical properties of marine aerosols and their production mechanisms can be achieved by discriminating clean marine aerosols (or sea salt particles) from all other types of aerosols present over the ocean.« less

Multisensor Observation and Simulation of Snowfall During the 2003 Wakasa Bay Field Experiment

NASA Technical Reports Server (NTRS)

Johnson, Benjamin T.; Petty, Grant W.; Skofronick-Jackson, Gail; Wang, James W.

2005-01-01

This research seeks to assess and improve the accuracy of microphysical assumptions used in satellite passive microwave radiative transfer models and retrieval algorithms by exploiting complementary observations from satellite radiometers, such as TRMM/AMSR-E/GPM, and coincident aircraft instruments, such as the next generation precipitation radar (PR-2). We focus in particular on aircraft data obtained during the Wakasa Bay field experiment, Japan 2003, pertaining to surface snowfall events. The observations of vertical profiles of reflectivity and Doppler-derived fall speeds are used in conjunction with the radiometric measurements to identify 1-D profiles of precipitation particle types, sizes, and concentrations that are consistent with the observations.

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

NASA Technical Reports Server (NTRS)

Miernecki, Maciej; Wigneron, Jean-Pierre; Lopez-Baeza, Ernesto; Kerr, Yann; DeJeu, Richard; DeLannoy, Gabielle J. M.; Jackson, Tom J.; O'Neill, Peggy E.; Shwank, Mike; Moran, Roberto Fernandez;

Investigation of Seasonal Landscape Freeze/Thaw Cycles in Relation to Cloud Structure in the High Northern Latitudes

NASA Technical Reports Server (NTRS)

Smith, Cosmo

2011-01-01

The seasonal freezing and thawing of Earth's cryosphere (the portion of Earth's surface permanently or seasonally frozen) has an immense impact on Earth's climate as well as on its water, carbon and energy cycles. During the spring, snowmelt and the transition between frozen and non-frozen states lowers Earth's surface albedo. This change in albedo causes more solar radiation to be absorbed by the land surface, raising surface soil and air temperatures as much as 5 C within a few days. The transition of ice into liquid water not only raises the surface humidity, but also greatly affects the energy exchange between the land surface and the atmosphere as the phase change creates a latent energy dominated system. There is strong evidence to suggest that the thawing of the cryosphere during spring and refreezing during autumn is correlated to local atmospheric conditions such as cloud structure and frequency. Understanding the influence of land surface freeze/thaw cycles on atmospheric structure can help improve our understanding of links between seasonal land surface state and weather and climate, providing insight into associated changes in Earth's water, carbon, and energy cycles that are driven by climate change.Information on both the freeze/thaw states of Earth's land surface and cloud characteristics is derived from data sets collected by NOAA's Special Sensor Microwave/Imager (SSM/I), the Advanced Microwave Scanning Radiometer on NASA's Earth Observing System(AMSR-E), NASA's CloudSat, and NASA's SeaWinds-on-QuickSCAT Earth remote sensing satellite instruments. These instruments take advantage of the microwave spectrum to collect an ensemble of atmospheric and land surface data. Our analysis uses data from radars (active instruments which transmit a microwave signal toward Earth and measure the resultant backscatter) and radiometers (passive devices which measure Earth's natural microwave emission) to accurately characterize salient details on Earth's surface and atmospheric states. By comparing the cloud measurements and the surface freeze-thaw data sets, a correlation between the two phenomena can be developed.

Detection and Retrieval of Multi-Layered Cloud Properties Using Satellite Data

NASA Technical Reports Server (NTRS)

Minnis, Patrick; Sun-Mack, Sunny; Chen, Yan; Yi, Helen; Huang, Jian-Ping; Nguyen, Louis; Khaiyer, Mandana M.

2005-01-01

Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO2-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the Aqua satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earth's Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes.

Detection and retrieval of multi-layered cloud properties using satellite data

NASA Astrophysics Data System (ADS)

Minnis, Patrick; Sun-Mack, Sunny; Chen, Yan; Yi, Helen; Huang, Jianping; Nguyen, Louis; Khaiyer, Mandana M.

2005-10-01

Four techniques for detecting multilayered clouds and retrieving the cloud properties using satellite data are explored to help address the need for better quantification of cloud vertical structure. A new technique was developed using multispectral imager data with secondary imager products (infrared brightness temperature differences, BTD). The other methods examined here use atmospheric sounding data (CO2-slicing, CO2), BTD, or microwave data. The CO2 and BTD methods are limited to optically thin cirrus over low clouds, while the MWR methods are limited to ocean areas only. This paper explores the use of the BTD and CO2 methods as applied to Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer EOS (AMSR-E) data taken from the Aqua satellite over ocean surfaces. Cloud properties derived from MODIS data for the Clouds and the Earth's Radiant Energy System (CERES) Project are used to classify cloud phase and optical properties. The preliminary results focus on a MODIS image taken off the Uruguayan coast. The combined MW visible infrared (MVI) method is assumed to be the reference for detecting multilayered ice-over-water clouds. The BTD and CO2 techniques accurately match the MVI classifications in only 51 and 41% of the cases, respectively. Much additional study is need to determine the uncertainties in the MVI method and to analyze many more overlapped cloud scenes.

Methane emission from high-latitude (>50N) lakes: Annual cycle of climatological emissions using satellite-derived lake-ice phenology and freeze-thaw dynamics

NASA Astrophysics Data System (ADS)

Matthews, E.; Romanski, J.; Du, J.; Watts, J. D.

2017-12-01

Lakes are increasingly recognized as potentially important contributors to global methane emissions despite occupying only a few percent of Earth's ice-free land surface. More than 40% of the global lake area lies in regions of amplified warming north of 50˚N. As with wetlands, lake emissions are sensitive to interannual fluctuations in, e.g., temperature and duration of thaw season. Several estimates of CH4emission from high-latitude lakes have been published but none relies on geospatial lake distributions and satellite-based duration and timing of thaw seasons. We report on a climatology of weekly, spatially-explicit methane emissions from high-latitude lakes. Lake break-up and freeze-up dates for lakes >50km^2 were determined from a lake-ice phenology data set derived from brightness temperature (Tb) observations of space-borne Advanced Microwave Scanning Radiometer (AMSR-E/2) sensors. The lake-ice conditions for smaller lakes were estimated using an Earth System Data Record for Land Surface Freeze-Thaw State derived from Tb observations of Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and SSM/I Sounder (SSMIS). Climatologies encompass 2002-2015 for lake ice phenology and 1979 to 2010 for the land surface freeze-thaw state. Climatologies encompass 2003-2014 for ice phenology and 1979 to 2010 for freeze-thaw dynamics. Length and timing of typical methane-emission periods, derived from the satellite data, were integrated with daily diffusive and ebulliative methane fluxes for lake types following the work of Wik et al. (Nature, 2016) to estimate a full annual cycle of emissions from lakes >50˚N. We explored several approaches to estimate the large bursts of emissions observed over short periods during lake-ice breakup immediately prior to full lake thaw since several studies suggest that a substantial fraction of total annual emissions may occur at this time. While highly uncertain, we plan to investigate whether the modest, short-lived but annual uptick in atmospheric methane concentrations in late winter/early spring may be associated with these bursts of methane from lakes.

Variations in the Sea Ice Edge and the Marginal Ice Zone on Different Spatial Scales as Observed from Different Satellite Sensor

NASA Technical Reports Server (NTRS)

Markus, Thorsten; Henrichs, John

2006-01-01

The Marginal sea Ice Zone (MIZ) and the sea ice edge are the most dynamic areas of the sea ice cover. Knowledge of the sea ice edge location is vital for routing shipping in the polar regions. The ice edge is the location of recurrent plankton blooms, and is the habitat for a number of animals, including several which are under severe ecological threat. Polar lows are known to preferentially form along the sea ice edge because of induced atmospheric baroclinicity, and the ice edge is also the location of both vertical and horizontal ocean currents driven by thermal and salinity gradients. Finally, sea ice is both a driver and indicator of climate change and monitoring the position of the ice edge accurately over long time periods enables assessment of the impact of global and regional warming near the poles. Several sensors are currently in orbit that can monitor the sea ice edge. These sensors, though, have different spatial resolutions, different limitations, and different repeat frequencies. Satellite passive microwave sensors can monitor the ice edge on a daily or even twice-daily basis, albeit with low spatial resolution - 25 km for the Special Sensor Microwave Imager (SSM/I) or 12.5 km for the Advanced Microwave Scanning Radiometer (AMSR-E). Although special methods exist that allow the detection of the sea ice edge at a quarter of that nominal resolution (PSSM). Visible and infrared data from the Advanced Very High Resolution Radiometer (AVHRR) and from the Moderate Resolution Imaging Spectroradiometer (MODIS) provide daily coverage at 1 km and 250 m, respectively, but the surface observations me limited to cloud-free periods. The Landsat 7 Enhanced Thematic Mapper (ETM+) has a resolution of 15 to 30 m but is limited to cloud-free periods as well, and does not provide daily coverage. Imagery from Synthetic Aperture Radar (SAR) instruments has resolutions of tens of meters to 100 m, and can be used to distinguish open water and sea ice on the basis of surface and volume scattering characteristics. The Canadian RADARSAT C-band SAR provides data that cover the Arctic Ocean and the MIZ every 3 days. A change-point detection approach was utilized to obtain an ice edge estimate from the RADARSAT data The Quickscat scatterometer provides ice edge information with a resolution of a few kilometers on a near-daily basis. During portions of March and April of 2003 a series of aircraft flights were conducted over the ice edge in the Bering Sea carrying the Polarimetric Scanning Radiometer (PSR), which provides spectral coverage identical with the AMSR-E instrument at a resolution of 500 meters. In this study we investigated these different data sets and analyzed differences in their definition of the sea ice edge and the marginal ice zone and how these differences as well as their individual limitations affect the monitoring of the ice edge dynamics. We also examined how the nature of the sea ice edge, including its location, compactness and shape, changes over the seasons. Our approach was based on calculation of distances between ice edges derived from the satellite and aircraft data sets listed above as well as spectral coherence methods and shape parameters such as tortuosity, curvature, and fractional dimension.

Multi-RTM-based Radiance Assimilation to Improve Snow Estimates

NASA Astrophysics Data System (ADS)

Kwon, Y.; Zhao, L.; Hoar, T. J.; Yang, Z. L.; Toure, A. M.

2015-12-01

Data assimilation of microwave brightness temperature (TB) observations (i.e., radiance assimilation (RA)) has been proven to improve snowpack characterization at relatively small scales. However, large-scale applications of RA require a considerable amount of further efforts. Our objective in this study is to explore global-scale snow RA. In a RA scheme, a radiative transfer model (RTM) is an observational operator predicting TB; therefore, the quality of the assimilation results may strongly depend upon the RTM used as well as the land surface model (LSM). Several existing RTMs show different sensitivities to snowpack properties and thus they simulate significantly different TB. At the global scale, snow physical properties vary widely with local climate conditions. No single RTM has been shown to be able to accurately reproduce the observed TB for such a wide range of snow conditions. In this study, therefore, we hypothesize that snow estimates using a microwave RA scheme can be improved through the use of multiple RTMs (i.e., multi-RTM-based approaches). As a first step, here we use two snowpack RTMs, i.e., the Dense Media Radiative Transfer-Multi Layers model (DMRT-ML) and the Microwave Emission Model for Layered Snowpacks (MEMLS). The Community Land Model version 4 (CLM4) is used to simulate snow dynamics. The assimilation process is conducted by the Data Assimilation Research Testbed (DART), which is a community facility developed by the National Center for Atmospheric Research (NCAR) for ensemble-based data assimilation studies. In the RA experiments, the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB at 18.7 and 36.5 GHz vertical polarization channels are assimilated into the RA system using the ensemble adjustment Kalman filter. The results are evaluated using the Canadian Meteorological Centre (CMC) daily snow depth, the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction, and in-situ snowpack and river discharge observations.

Quantifying the clear-sky bias of satellite-derived infrared LST

NASA Astrophysics Data System (ADS)

Ermida, S. L.; Trigo, I. F.; DaCamara, C.

2017-12-01

Land surface temperature (LST) is one of the most relevant parameters when addressing the physical processes that take place at the surface of the Earth. Satellite data are particularly appropriate for measuring LST over the globe with high temporal resolution. Remote-sensed LST estimation from space-borne sensors has been systematically performed over the Globe for nearly 3 decades and geostationary LST climate data records are now available. The retrieval of LST from satellite observations generally relies on measurements in the thermal infrared (IR) window. Although there is a large number of IR sensors on-board geostationary satellites and polar orbiters suitable for LST retrievals with different temporal and spatial resolutions, the use of IR observations limits LST estimates to clear sky conditions. As a consequence, climate studies based on IR LST are likely to be affected by the restriction of LST data to cloudless conditions. However, such "clear sky bias" has never been quantified and, therefore, the actual impact of relying only on clear sky data is still to be determined. On the other hand, an "all-weather" global LST database may be set up based on passive microwave (MW) measurements which are much less affected by clouds. An 8-year record of all-weather MW LST is here used to quantify the clear-sky bias of IR LST at global scale based on MW observations performed by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) onboard NASA's Aqua satellite. Selection of clear-sky and cloudy pixels is based on information derived from measurements performed by the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the same satellite.

The Impact of Assimilating Precipitation-affected Radiance on Cloud and Precipitation in Goddard WRF-EDAS Analyses

NASA Technical Reports Server (NTRS)

Lin, Xin; Zhang, Sara Q.; Zupanski, M.; Hou, Arthur Y.; Zhang, J.

2015-01-01

High-frequency TMI and AMSR-E radiances, which are sensitive to precipitation over land, are assimilated into the Goddard Weather Research and Forecasting Model- Ensemble Data Assimilation System (WRF-EDAS) for a few heavy rain events over the continental US. Independent observations from surface rainfall, satellite IR brightness temperatures, as well as ground-radar reflectivity profiles are used to evaluate the impact of assimilating rain-sensitive radiances on cloud and precipitation within WRF-EDAS. The evaluations go beyond comparisons of forecast skills and domain-mean statistics, and focus on studying the cloud and precipitation features in the jointed rainradiance and rain-cloud space, with particular attentions on vertical distributions of height-dependent cloud types and collective effect of cloud hydrometers. Such a methodology is very helpful to understand limitations and sources of errors in rainaffected radiance assimilations. It is found that the assimilation of rain-sensitive radiances can reduce the mismatch between model analyses and observations by reasonably enhancing/reducing convective intensity over areas where the observation indicates precipitation, and suppressing convection over areas where the model forecast indicates rain but the observation does not. It is also noted that instead of generating sufficient low-level warmrain clouds as in observations, the model analysis tends to produce many spurious upperlevel clouds containing small amount of ice water content. This discrepancy is associated with insufficient information in ice-water-sensitive radiances to address the vertical distribution of clouds with small amount of ice water content. Such a problem will likely be mitigated when multi-channel multi-frequency radiances/reflectivity are assimilated over land along with sufficiently accurate surface emissivity information to better constrain the vertical distribution of cloud hydrometers.

Mesoscale Air-Sea Interactions along the Gulf Stream: An Eddy-Resolving and Convection-Permitting Coupled Regional Climate Model Study

NASA Astrophysics Data System (ADS)

Hsieh, J. S.; Chang, P.; Saravanan, R.

2017-12-01

Frontal and mesoscale air-sea interactions along the Gulf Stream (GS) during boreal winter are investigated using an eddy-resolving and convection-permitting coupled regional climate model with atmospheric grid resolutions varying from meso-β (27-km) to -r (9-km and 3-km nest) scales in WRF and a 9-km ocean model (ROMS) that explicitly resolves the ocean mesoscale eddies across the North Atlantic basin. The mesoscale wavenumber energy spectra for the simulated surface wind stress and SST demonstrate good agreement with the observed spectra calculated from the observational QuikSCAT and AMSR-E datasets, suggesting that the model well captures the energy cascade of the mesoscale eddies in both the atmosphere and the ocean. Intercomparison among different resolution simulations indicates that after three months of integration the simulated GS path tends to overshoot beyond the separation point in the 27-km WRF coupled experiments than the observed climatological path of the GS, whereas the 3-km nested and 9-km WRF coupled simulations realistically simulate GS separation. The GS overshoot in 27-km WRF coupled simulations is accompanied with a significant SST warming bias to the north of the GS extension. Such biases are associated with the deficiency of wind stress-SST coupling strengths simulated by the coupled model with a coarser resolution in WRF. It is found that the model at 27-km grid spacing can approximately simulate 72% (62%) of the observed mean coupling strength between surface wind stress curl (divergence) and crosswind (downwind) SST gradient while by increasing the WRF resolutions to 9 km or 3 km the coupled model can much better capture the observed coupling strengths.

The CREW intercomparison of SEVIRI cloud retrievals

NASA Astrophysics Data System (ADS)

Hamann, U.; Walther, A.; Bennartz, R.; Thoss, A.; Meirink, J. M.; Roebeling, R.

2012-12-01

About 70% of the earth's surface is covered with clouds. They strongly influence the radiation balance and the water cycle of the earth. Hence the detailed monitoring of cloud properties - such as cloud fraction, cloud top temperature, cloud particle size, and cloud water path - is important to understand the role of clouds in the weather and the climate system. The remote sensing with passive sensors is an essential mean for the global observation of the cloud parameters, but is nevertheless challenging. This presentation focuses on the inter-comparison and validation of cloud physical properties retrievals from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) onboard METEOSAT. For this study we use retrievals from 12 state-of-art algorithms (Eumetsat, KNMI, NASA Langley, NASA Goddard, University Madison/Wisconsin, DWD, DLR, Meteo-France, KMI, FU Berlin, UK MetOffice) that are made available through the common database of the CREW (Cloud Retrieval Evaluation Working) group. Cloud detection, cloud top phase, height, and temperature, as well as optical properties and water path are validated with CLOUDSAT, CALIPSO, MISR, and AMSR-E measurements. Special emphasis is given to challenging retrieval conditions. Semi-transparent clouds over the earth's surface or another cloud layer modify the measured brightness temperature and increase the retrieval uncertainty. The consideration of the three-dimensional radiative effects is especially important for large viewing angles and broken cloud fields. Aerosols might be misclassified as cloud and may increase the retrieval uncertainty, too. Due to the availability of the high number of sophisticated retrieval datasets, the advantages of different retrieval approaches can be examined and suggestions for future retrieval developments can be made. We like to thank Eumetsat for sponsoring the CREW project including this work.nstitutes that participate in the CREW project.

Calibrated, Enhanced-Resolution Brightness Temperature Earth System Data Record: A New Era for Gridded Passive Microwave Data

NASA Astrophysics Data System (ADS)

Hardman, M.; Brodzik, M. J.; Long, D. G.

2017-12-01

Since 1978, the satellite passive microwave data record has been a mainstay of remote sensing of the cryosphere, providing twice-daily, near-global spatial coverage for monitoring changes in hydrologic and cryospheric parameters that include precipitation, soil moisture, surface water, vegetation, snow water equivalent, sea ice concentration and sea ice motion. Up until recently, the available global gridded passive microwave data sets have not been produced consistently. Various projections (equal-area, polar stereographic), a number of different gridding techniques were used, along with various temporal sampling as well as a mix of Level 2 source data versions. In addition, not all data from all sensors have been processed completely and they have not been processed in any one consistent way. Furthermore, the original gridding techniques were relatively primitive and were produced on 25 km grids using the original EASE-Grid definition that is not easily accommodated in modern software packages. As part of NASA MEaSUREs, we have re-processed all data from SMMR, all SSM/I-SSMIS and AMSR-E instruments, using the most mature Level 2 data. The Calibrated, Enhanced-Resolution Brightness Temperature (CETB) Earth System Data Record (ESDR) gridded data are now available from the NSIDC DAAC. The data are distributed as netCDF files that comply with CF-1.6 and ACDD-1.3 conventions. The data have been produced on EASE 2.0 projections at smoothed, 25 kilometer resolution and spatially-enhanced resolutions, up to 3.125 km depending on channel frequency, using the radiometer version of the Scatterometer Image Reconstruction (rSIR) method. We expect this newly produced data set to enable scientists to better analyze trends in coastal regions, marginal ice zones and in mountainous terrain that were not possible with the previous gridded passive microwave data. The use of the EASE-Grid 2.0 definition and netCDF-CF formatting allows users to extract compliant geotiff images and provides for easy importing and correct reprojection interoperability in many standard packages. As a consistently-processed, high-quality satellite passive microwave ESDR, we expect this data set to replace earlier gridded passive microwave data sets, and to pave the way for new insights from higher-resolution derived geophysical products.

Cropland land surface phenology and seasonality in East Africa: Ethiopia, Tanzania, and South Sudan

NASA Astrophysics Data System (ADS)

Alemu, W. G.; Henebry, G. M.

2015-12-01

Most people in East Africa depend on rainfed agriculture. Rainfall in the region has been decreasing recently and is highly variable in space and time leading to high food insecurity. A comprehensive understanding of the regional cropland dynamics is therefore needed. Land surface phenology and land surface seasonality have important roles in monitoring cropland dynamics in a region with sparse coverage of in situ climatic and biophysical observations. However, commonly used optical satellite data are often degraded by cloud cover, aerosols, and dust and they are restricted to daytime observations. Here we used near-daily passive microwave (PM) data at 25 km spatial resolution from a series of microwave radiometers—AMSR-E, FengYun3B/MWRI, AMSR2—to study cropland dynamics for 2003-2013 in three important grain production areas of East Africa: Ethiopia, Tanzania, and South Sudan. PM data can be collected through clouds and at night. Based on Google Earth imagery, we identified several cropland areas corresponding to PM grid cells. Rainfall from TRMM and atmospheric water vapor (V) from PM data displayed temporal patterns that were unimodal in Ethiopia and South Sudan, but bimodal in Tanzania. We fitted convex quadratic models to link growing season increments of V and vegetation optical depth (VOD) to accumulated V (AV). The models yielded high coefficients of determination (r2 ≥0.8) and phenometrics calculated from the parameter coefficients. Peak rainfall lagged peak V, but preceded peak VOD. Growing degree-days (GDD), calculated from the PM air temperature data, displayed a weaker bimodal seasonality in which the lowest values occurred during the peak rainy season, due to the cooling effect of latent heat flux and coupled with higher reflection of insolation by the cloud deck. V as a function of GDD displays quasi-periodic behavior. Drier sites in the region displayed larger (smaller) intra-annual dynamic range of V (GDD) compared to the moister sites.

New Approach for Monitoring Seismic and Volcanic Activities Using Microwave Radiometer Data

NASA Astrophysics Data System (ADS)

Maeda, Takashi; Takano, Tadashi

Interferograms formed from the data of satellite-borne synthetic aperture radar (SAR) enable us to detect slight land-surface deformations related to volcanic eruptions and earthquakes. Currently, however, we cannot determine when land-surface deformations occurred with high time resolution since the time lag between two scenes of SAR used to form interferograms is longer than the recurrent period of the satellite carrying it (several tens of days). In order to solve this problem, we are investigating new approach to monitor seismic and vol-canic activities with higher time resolution from satellite-borne sensor data, and now focusing on a satellite-borne microwave radiometer. It is less subject to clouds and rainfalls over the ground than an infrared spectrometer, so more suitable to observe an emission from land sur-faces. With this advantage, we can expect that thermal microwave energy by increasing land surface temperatures is detected before a volcanic eruption. Additionally, laboratory experi-ments recently confirmed that rocks emit microwave energy when fractured. This microwave energy may result from micro discharges in the destruction of materials, or fragment motions with charged surfaces of materials. We first extrapolated the microwave signal power gener-ated by rock failures in an earthquake from the experimental results and concluded that the microwave signals generated by rock failures near the land surface are strong enough to be detected by a satellite-borne radiometer. Accordingly, microwave energy generated by rock failures associated with a seismic activity is likely to be detected as well. However, a satellite-borne microwave radiometer has a serious problem that its spatial res-olution is too coarse compared to SAR or an infrared spectrometer. In order to raise the possibility of detection, a new methodology to compensate the coarse spatial resolution is es-sential. Therefore, we investigated and developed an analysis method to detect local and faint changes from the data of the Advanced Microwave Scanning Radiometer for Earth-Observation System (AMSR-E) aboard the Aqua satellite, and then an algorithm to evaluate microwave energy from land surfaces. Finally, using this algorithm, we have detected characteristic microwave signals emitted from land surfaces in association with some large earthquakes which occurred in Morocco (2004), Sumatra (2007) and Wenchuan (2008) and some large volcanic eruptions which occurred at Reventador in Ecuador (2002) and Chaiten in Chile (2008). In this presentation, the results of these case studies are presented.

Development and validation of PCR-GLOBWB 2.0: a 5 arc min resolution global hydrology and water resources model

NASA Astrophysics Data System (ADS)

Sutanudjaja, Edwin H.; van Beek, Ludovicus P. H.; Wada, Yoshihide; Wisser, Dominik; de Graaf, Inge E. M.; Straatsma, Menno W.; Bierkens, Marc F. P.

2014-05-01

PCR-GLOBWB (PCRaster Global Water Balance) is a grid-based global hydrological model developed at the Department of Physical Geography, Utrecht University. For each grid cell, PCR-GLOBWB simulates moisture storage in vertically stacked soil layers, as well as the water exchange to the atmosphere and underlying groundwater reservoir. Exchange to the atmosphere comprises of precipitation, evaporation and transpiration, as well as snow accumulation and melt. All fluxes are all simulated by considering vegetation phenology and sub-grid variations in elevation, land cover and soil saturation. The model includes physically-based schemes for runoff-infiltration partitioning, interflow, groundwater recharge and baseflow, as well as river routing of discharge. Here we present and summarize the latest developments of PCR-GLOBWB. The new version of the model, PCR-GLOBWB 2.0, now runs at a spatial resolution of 5 arc min (about 10 km at the equator) and supersedes the previous generation of the model (30 arc min PCR-GLOBWB 1.0, van Beek et al., 2011). PCR-GLOBWB 2.0 consolidates all components that have been introduced since PCR-GLOWB 1.0 was first published (2011). Examples of these new components are: A comprehensive water demand and irrigation module (Wada et al., 2012). A dynamic attribution and return flow of water demand to surface water and groundwater resources (de Graaf et al., 2013). An advanced surface water routing scheme with wetland, lakes and floodplains of variable extent, thus simulating flooding and flood wave attenuation (Winsemius et al., 2013). An online scheme for dynamic withdrawal, allocation and consumptive use of groundwater and surface water resources, including a progressive introduction of reservoirs (Wada et al., 2013). Further development will include the inclusion of a dynamic reservoir operation/optimization scheme and a MODFLOW lateral groundwater flow module (Sutanudjaja et al., 2011; Sutanudjaja et al., 2014). Also, scripts used for deriving the parameterization from global data sources for the original model have been coupled with the model, resulting in a near-scalable model that facilitates its application for different domains and at varying resolutions. Results are very promising. When comparing simulated discharges to those observed by GRDC stations, the coefficient-of-determination is high. Human impacts, altering the seasonal and inter-annual variability of terrestrial water storage (TWS) signals, are well simulated by PCR-GLOBWB 2.0 and evident in the validation of simulated TWS with GRACE satellite observation (Tapley et al., 2004). Moreover, the simulation results of PCR-GLOBWB 2.0 compare well to several other remote sensing products, such as the soil moisture datasets of ERS/MetOp (Wagner et al., 1999) and AMSR-E (de Jeu and Owe, 2003), as well as the GLEAM evaporation product (Miralles et al., 2011). References: de Graaf et al., Advances in Water Resources (2013), http://dx.doi.org/10.1016/j.advwatres.2013.12.002 de Jeu and Owe, International Journal of Remote Sensing (2003), http://dx.doi.org/10.1080/0143116031000095934 Miralles et al., Hydrology and Earth System Sciences (2011), http://dx.doi.org/10.5194/hess-15-967-2011 Sutanudjaja et al., Hydrology and Earth System Sciences (2011), http://dx.doi.org/10.5194/hess-15-2913-2011 Sutanudjaja et al., Water Resources Research (2014), http://dx.doi.org/10.1002/2013WR013807 Tapley et al., Science (2004), http://dx.doi.org/10.1126/science.1099192 van Beek et al., Water Resources Research (2011), http://dx.doi.org/10.1029/2010WR009791 Wada et al., Water Resources Research (2012), http://dx.doi.org/10.1029/2011WR010562 Wada et al., Earth System Dynamics Discussion (2013), http://dx.doi.org/10.5194/esdd-4-355-2013 Wagner et al., Remote Sensing of Environment (1999), http://dx.doi.org/10.1016/S0034-4257(99)00036-X Winsemius et al., Hydrology and Earth System Sciences (2013), http://dx.doi.org/10.5194/hess-17-1871-2013

The EUMETSAT Polar System - Second Generation (EPS-SG) micro-wave imaging (MWI) mission

NASA Astrophysics Data System (ADS)

Bojkov, B. R.; Accadia, C.; Klaes, D.; Canestri, A.; Cohen, M.

2017-12-01

The EUMETSAT Polar System (EPS) will be followed by a second generation system called EPS-SG. This new family of missions will contribute to the Joint Polar System being jointly set up with NOAA in the timeframe 2020-2040. These satellites will fly, like Metop (EPS), in a sun synchronous, low earth orbit at 830 km altitude and 09:30 local time descending node, providing observations over the full globe with revisit times of 12 hours. EPS-SG consists of two different satellites configurations, the EPS-SGa series dedicated to IR and MW sounding, and the EPS-SGb series dedicated to microwave imaging and scatterometry. The EPS-SG family will consist of three successive launches of each satellite-type. The Microwave Imager (MWI) will be hosted on Metop-SGb series of satellites, with the primary objective of supporting Numerical Weather Prediction (NWP) at regional and global scales. Other applications will be observation of surface parameters such as sea ice concentration and hydrology applications. The 18 MWI instrument frequencies range from 18.7 GHz to 183 GHz. All MWI channels up to 89 GHz will measure V- and H polarizations. The MWI was also designed to provide continuity of measurements for select heritage microwave imager channels (e.g. SSM/I, AMSR-E). The additional sounding channels such as the 50-55 and 118 GHz bands will provide additional cloud and precipitation information over sea and land. This combination of channels was successfully tested on the NPOESS Aircraft Sounder Testbed - Microwave Sounder (NAST-M) airborne radiometer, and it is the first time that will be implemented in a conical scanning configuration in a single instrument. An overview of the EPS-SG programme and the MWI instrument will be presented.

Linking soil type and rainfall characteristics towards estimation of surface evaporative capacitance

NASA Astrophysics Data System (ADS)

Or, D.; Bickel, S.; Lehmann, P.

2017-12-01

Separation of evapotranspiration (ET) to evaporation (E) and transpiration (T) components for attribution of surface fluxes or for assessment of isotope fractionation in groundwater remains a challenge. Regional estimates of soil evaporation often rely on plant-based (Penman-Monteith) ET estimates where is E is obtained as a residual or a fraction of potential evaporation. We propose a novel method for estimating E from soil-specific properties, regional rainfall characteristics and considering concurrent internal drainage that shelters soil water from evaporation. A soil-dependent evaporative characteristic length defines a depth below which soil water cannot be pulled to the surface by capillarity; this depth determines the maximal soil evaporative capacitance (SEC). The SEC is recharged by rainfall and subsequently emptied by competition between drainage and surface evaporation (considering canopy interception evaporation). We show that E is strongly dependent on rainfall characteristics (mean annual, number of storms) and soil textural type, with up to 50% of rainfall lost to evaporation in loamy soil. The SEC concept applied to different soil types and climatic regions offers direct bounds on regional surface evaporation independent of plant-based parameterization or energy balance calculations.

Numerical simulation study of polar lows in Russian Arctic: dynamical characteristics

NASA Astrophysics Data System (ADS)

Verezemskaya, Polina; Baranyuk, Anastasia; Stepanenko, Victor

2015-04-01

Polar Lows (hereafter PL) are intensive mesoscale cyclones, appearing above the sea surface, usually behind the arctic front and characterized by severe weather conditions [1]. All in consequence of the global warming PLs started to emerge in the arctic water area as well - in summer and autumn. The research goal is to examine PLs by considering multisensory data and the resulting numerical mesoscale model. The main purpose was to realize which conditions induce PL development in such thermodynamically unusual season and region as Kara sea. In order to conduct the analysis we used visible and infrared images from MODIS (Aqua). Atmospheric water vapor V, cloud liquid water Q content and surface wind fields W were resampled by examining AMSR-E microwave radiometer data (Aqua)[2], the last one was additionally extracted from QuickSCAT scatterometer. We have selected some PL cases in Kara sea, appeared in autumn of 2007-2008. Life span of the PL was between 24 to 36 hours. Vortexes' characteristics were: W from 15m/s, Q and V values: 0.08-0.11 kg/m2 and 8-15 kg/m2 relatively. Numerical experiments were carried out with Weather Research and Forecasting model (WRF), which was installed on supercomputer "Lomonosov" of Research Computing Center of Moscow State University [3]. As initial conditions was used reanalysis data ERA-Interim from European Centre for Medium-Range Weather Forecasts. Numerical experiments were made with 5 km spatial resolution, with Goddard center microphysical parameterization and explicit convection simulation. Modeling fields were compared with satellite observations and shown good accordance. Than dynamic characteristics were analyzed: evolution of potential and absolute vorticity [4], surface heat and momentum fluxes, and CAPE and WISHE mechanisms realization. 1. Polar lows, J. Turner, E.A. Rasmussen, 612, Cambridge University press, Cambridge, 2003. 2. Zabolotskikh, E. V., Mitnik, L. M., & Chapron, B. (2013). New approach for severe marine weather study using satellite passive microwave sensing. Geophysical Research Letters, 40(13), 3347-3350. doi:10.1002/grl.50664 3. V. Sadovnichy, A. Tikhonravov, Vl. Voevodin, and V. Opanasenko "Lomonosov": Supercomputing at Moscow State University. In Contemporary High Performance Computing: From Petascale toward Exascale (Chapman & Hall/CRC Computational Science), pp.283-307, Boca Raton, USA, CRC Press, 2013. 4. B. J. Hoskins, M.E. McIntyre, A.W. Robertson, On the use and significance of isentropic potential vorticity maps, Quarterly journal of the Royal Meteorological Society, OCTOBER 1985, № 470, vol. 111(6).

Physical and Radiative Characteristic and Long-term Variability of the Okhotsk Sea Ice Cover

NASA Technical Reports Server (NTRS)

Nishio, Fumihiko; Comiso, Josefino C.; Gersten, Robert; Nakayama, Masashige; Ukita, Jinro; Gasiewski, Al; Stanko, Boba; Naoki, Kazuhiro

2008-01-01

Much of what we know about the large scale characteristics of the Okhotsk Sea ice cover has been provided by ice concentration maps derived from passive microwave data. To understand what satellite data represent in a highly divergent and rapidly changing environment like the Okhotsk Sea, we take advantage of concurrent satellite, aircraft, and ship data acquired on 7 February and characterized the sea ice cover at different scales from meters to hundreds of kilometers. Through comparative analysis of surface features using co-registered data from visible, infrared and microwave channels we evaluated the general radiative and physical characteristics of the ice cover as well as quantify the distribution of different ice types in the region. Ice concentration maps from AMSR-E using the standard sets of channels, and also only the 89 GHz channel for optimal resolution, are compared with aircraft and high resolution visible data and while the standard set provides consistent results, the 89 GHz provides the means to observe mesoscale patterns and some unique features of the ice cover. Analysis of MODIS data reveals that thick ice types represents about 37% of the ice cover indicating that young and new ice types represent a large fraction of the ice cover that averages about 90% ice concentration according to passive microwave data. These results are used to interpret historical data that indicate that the Okhotsk Sea ice extent and area are declining at a rapid rate of about -9% and -12 % per decade, respectively.

Correcting negatively biased refractivity below ducts in GNSS radio occultation: an optimal estimation approach towards improving planetary boundary layer (PBL) characterization

NASA Astrophysics Data System (ADS)

Wang, Kuo-Nung; de la Torre Juárez, Manuel; Ao, Chi O.; Xie, Feiqin

2017-12-01

Global Navigation Satellite System (GNSS) radio occultation (RO) measurements are promising in sensing the vertical structure of the Earth's planetary boundary layer (PBL). However, large refractivity changes near the top of PBL can cause ducting and lead to a negative bias in the retrieved refractivity within the PBL (below ˜ 2 km). To remove the bias, a reconstruction method with assumption of linear structure inside the ducting layer models has been proposed by Xie et al. (2006). While the negative bias can be reduced drastically as demonstrated in the simulation, the lack of high-quality surface refractivity constraint makes its application to real RO data difficult. In this paper, we use the widely available precipitable water (PW) satellite observation as the external constraint for the bias correction. A new framework is proposed to incorporate optimization into the RO reconstruction retrievals in the presence of ducting conditions. The new method uses optimal estimation to select the best refractivity solution whose PW and PBL height best match the externally retrieved PW and the known a priori states, respectively. The near-coincident PW retrievals from AMSR-E microwave radiometer instruments are used as an external observational constraint. This new reconstruction method is tested on both the simulated GNSS-RO profiles and the actual GNSS-RO data. Our results show that the proposed method can greatly reduce the negative refractivity bias when compared to the traditional Abel inversion.

The Influence of Sea Ice on Primary Production in the Southern Ocean: A Satellite Perspective

NASA Technical Reports Server (NTRS)

Smith, Walker O., Jr.; Comiso, Josefino C.

2007-01-01

Sea ice in the Southern Ocean is a major controlling factor on phytoplankton productivity and growth, but the relationship is modified by regional differences in atmospheric and oceanographic conditions. We used the phytoplankton biomass (binned at 7-day intervals), PAR and cloud cover data from SeaWiFS, ice concentrations data from SSM/I and AMSR-E, and sea-surface temperature data from AVHRR, in combination with a vertically integrated model to estimate primary productivity throughout the Southern Ocean (south of 60"s). We also selected six areas within the Southern Ocean and analyzed the variability of the primary productivity and trends through time, as well as the relationship of sea ice to productivity. We found substantial interannual variability in productivity from 1997 - 2005 in all regions of the Southern Ocean, and this variability appeared to be driven in large part by ice dynamics. The most productive regions of Antarctic waters were the continental shelves, which showed the earliest growth, the maximum biomass, and the greatest areal specific productivity. In contrast, no large, sustained blooms occurred in waters of greater depth (> 1,000 m). We suggest that this is due to the slightly greater mixed layer depths found in waters off the continental shelf, and that the interactive effects of iron and irradiance (that is, increased iron requirements in low irradiance environments) result in the limitation of phytoplankton biomass over large regions of the Southern Ocean.

Study of hydrological extremes - floods and droughts in global river basins using satellite data and model output

NASA Astrophysics Data System (ADS)

Lakshmi, V.; Fayne, J.; Bolten, J. D.

2016-12-01

We will use satellite data from TRMM (Tropical Rainfall Measurement Mission), AMSR (Advanced Microwave Scanning Radiometer), GRACE (Gravity Recovery and Climate Experiment) and MODIS (Moderate Resolution Spectroradiometer) and model output from NASA GLDAS (Global Land Data Assimilation System) to understand the linkages between hydrological variables. These hydrological variables include precipitation soil moisture vegetation index surface temperature ET and total water. We will present results for major river basins such as Amazon, Colorado, Mississippi, California, Danube, Nile, Congo, Yangtze Mekong, Murray-Darling and Ganga-Brahmaputra.The major floods and droughts in these watersheds will be mapped in time and space using the satellite data and model outputs mentioned above. We will analyze the various hydrological variables and conduct a synergistic study during times of flood and droughts. In order to compare hydrological variables between river basins with vastly different climate and land use we construct an index that is scaled by the climatology. This allows us to compare across different climate, topography, soils and land use regimes. The analysis shows that the hydrological variables derived from satellite data and NASA models clearly reflect the hydrological extremes. This is especially true when data from different sensors are analyzed together - for example rainfall data from TRMM and total water data from GRACE. Such analyses will help to construct prediction tools for water resources applications.

Ocean-Atmosphere Interaction Over Agulhas Extension Meanders

NASA Technical Reports Server (NTRS)

Liu, W. Timothy; Xie, Xiaosu; Niiler, Pearn P.

2007-01-01

Many years of high-resolution measurements by a number of space-based sensors and from Lagrangian drifters became available recently and are used to examine the persistent atmospheric imprints of the semi-permanent meanders of the Agulhas Extension Current (AEC), where strong surface current and temperature gradients are found. The sea surface temperature (SST) measured by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the chlorophyll concentration measured by the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) support the identification of the meanders and related ocean circulation by the drifters. The collocation of high and low magnitudes of equivalent neutral wind (ENW) measured by Quick Scatterometer (QuikSCAT), which is uniquely related to surface stress by definition, illustrates not only the stability dependence of turbulent mixing but also the unique stress measuring capability of the scatterometer. The observed rotation of ENW in opposition to the rotation of the surface current clearly demonstrates that the scatterometer measures stress rather than winds. The clear differences between the distributions of wind and stress and the possible inadequacy of turbulent parameterization affirm the need of surface stress vector measurements, which were not available before the scatterometers. The opposite sign of the stress vorticity to current vorticity implies that the atmosphere spins down the current rotation through momentum transport. Coincident high SST and ENW over the southern extension of the meander enhance evaporation and latent heat flux, which cools the ocean. The atmosphere is found to provide negative feedback to ocean current and temperature gradients. Distribution of ENW convergence implies ascending motion on the downwind side of local SST maxima and descending air on the upwind side and acceleration of surface wind stress over warm water (deceleration over cool water); the convection may escalate the contrast of ENW over warm and cool water set up by the dependence of turbulent mixing on stability; this relation exerts a positive feedback to the ENW-SST relation. The temperature sounding measured by the Atmospheric Infrared Sounder(AIRS) is consistent with the spatial coherence between the cloud-top temperature provided by the International Satellite Cloud Climatology Project (ISCCP) and SST. Thus ocean mesoscale SST anomalies associated with the persistent meanders may have a long-term effect well above the midlatitude atmospheric boundary layer, an observation not addressed in the past.

A scattering-based over-land rainfall retrieval algorithm for South Korea using GCOM-W1/AMSR-2 data

NASA Astrophysics Data System (ADS)

Kwon, Young-Joo; Shin, Hayan; Ban, Hyunju; Lee, Yang-Won; Park, Kyung-Ae; Cho, Jaeil; Park, No-Wook; Hong, Sungwook

2017-08-01

Heavy summer rainfall is a primary natural disaster affecting lives and properties in the Korean Peninsula. This study presents a satellite-based rainfall rate retrieval algorithm for the South Korea combining polarization-corrected temperature ( PCT) and scattering index ( SI) data from the 36.5 and 89.0 GHz channels of the Advanced microwave Scanning Radiometer 2 (AMSR-2) onboard the Global Change Observation Mission (GCOM)-W1 satellite. The coefficients for the algorithm were obtained from spatial and temporal collocation data from the AMSR-2 and groundbased automatic weather station rain gauges from 1 July - 30 August during the years, 2012-2015. There were time delays of about 25 minutes between the AMSR-2 observations and the ground raingauge measurements. A new linearly-combined rainfall retrieval algorithm focused on heavy rain for the PCT and SI was validated using ground-based rainfall observations for the South Korea from 1 July - 30 August, 2016. The validation presented PCT and SI methods showed slightly improved results for rainfall > 5 mm h-1 compared to the current ASMR-2 level 2 data. The best bias and root mean square error (RMSE) for the PCT method at AMSR-2 36.5 GHz were 2.09 mm h-1 and 7.29 mm h-1, respectively, while the current official AMSR-2 rainfall rates show a larger bias and RMSE (4.80 mm h-1 and 9.35 mm h-1, respectively). This study provides a scatteringbased over-land rainfall retrieval algorithm for South Korea affected by stationary front rain and typhoons with the advantages of the previous PCT and SI methods to be applied to a variety of spaceborne passive microwave radiometers.

Implementation of the Land, Atmosphere Near Real-Time Capability for EOS (LANCE)

NASA Technical Reports Server (NTRS)

Michael, Karen; Murphy, Kevin; Lowe, Dawn; Masuoka, Edward; Vollmer, Bruce; Tilmes, Curt; Teague, Michael; Ye, Gang; Maiden, Martha; Goodman, H. Michael;

Potential Utility of the Real-Time TMPA-RT Precipitation Estimates in Streamflow Prediction

NASA Technical Reports Server (NTRS)

Su, Fengge; Gao, Huilin; Huffman, George J.; Lettenmaier, Dennis P.

2010-01-01

We investigate the potential utility of the real-time Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA-RT) data for streamflow prediction, both through direct comparisons of TMPA-RT estimates with a gridded gauge product, and through evaluation of streamflow simulations over four tributaries of La Plata Basin (LPB) in South America using the two precipitation products. Our assessments indicate that the relative accuracy and the hydrologic performance of TMPA-RT-based streamflow simulations generally improved after February 2005. The improvements in TMPA-RT since 2005 are closely related to upgrades in the TMPA-RT algorithm in early February, 2005 which include use of additional microwave sensors (AMSR-E and AMSU-B) and implementation of different calibration schemes. Our work suggests considerable potential for hydrologic prediction using purely satellite-derived precipitation estimates (no adjustments by in situ gauges) in parts of the globe where in situ observations are sparse.

The NASA Short-term Prediction and Research Transition (SPoRT) Center: A Research to Operations Test Bed

NASA Technical Reports Server (NTRS)

Jedlovec, Gary J.

2005-01-01

Over the last three years, NASA/MSFC scientists have embarked on an effort to transition unique NASA EOS data/products and research technology to selected NWSEOs in the southeast U.S. This activity, called the Short-term Prediction and - Research Transition (SPoRT) program, supports the NASA Science Mission Directorate and its Earth-Sun System Mission to develop a scientific understanding of the Earth System and its response to natural or human-induced changes that will enable improved prediction capability for climate, weather, and natural hazards. The overarching question related to weather prediction is "How well can weather forecasting duration and reliability be improved by new space-based observations, data assimilation, and modeling?" The transition activity has included the real-time delivery of MODIS data and products to several NWS Forecast Offices. Local NWS FOs have used the MODIS data to complement the coarse resolution GOES data for a number of applications. Specialized products have also been developed and made available to local and remote offices for their weather applications. Data from &e Lightning Mapping Array (LMA) network has been used in severe storm forecasts at several offices in the region. At the regional scale and forecast horizons from 0-1 day, the next generation of high-resolution mesoscale forecast and data assimilation models have been used to provide local offices with unique weather forecasts not otherwise available. The continued use of near red-time infusion of NASA science products into high-resolution mesoscale forecast and decision-making models can be expected to improve the model initialization as well as short-term forecasts. A current focus of SPoRT is to expand collaborations to include contributions from the assimilation of AMSR-E data in the ADASIARPS forecast system (OU), inclusion of MODIS SSTs and AIRS thermodynamic profiles in the WRF, and to extend the distribution of real-time MODIS and AMSR-E data and products to the Florida coastal WFOs. A SPoRT Test bed, together with input from other interagency and university partners, will provide a means and a process to effectively transition ESE observations and technology to NWS operations and decision makers at both the globdnational and regional scales. The transition of emerging experimental products into operations through the SPoRT infrastructure will allow NASA to foster and accelerate the progress of this Science Mission Directorate research strategy over the coming years.

Comparison of soil heavy metal pollution caused by e-waste recycling activities and traditional industrial operations.

PubMed

He, Kailing; Sun, Zehang; Hu, Yuanan; Zeng, Xiangying; Yu, Zhiqiang; Cheng, Hefa

2017-04-01

The traditional industrial operations are well recognized as an important source of heavy metal pollution, while that caused by the e-waste recycling activities, which have sprouted in some developing countries, is often overlooked. This study was carried out to compare the status of soil heavy metal pollution caused by the traditional industrial operations and the e-waste recycling activities in the Pearl River Delta, and assess whether greater attention should be paid to control the pollution arising from e-waste recycling activities. Both the total contents and the chemical fractionation of major heavy metals (As, Cr, Cd, Ni, Pb, Cu, and Zn) in 50 surface soil samples collected from the e-waste recycling areas and 20 soil samples from the traditional industrial zones were determined. The results show that the soils in the e-waste recycling areas were mainly polluted by Cu, Zn, As, and Cd, while Cu, Zn, As, Cd, and Pb were the major heavy metals in the soils from the traditional industrial zones. Statistical analyses consistently show that Cu, Cd, Pb, and Zn in the surface soils from both types of sites were contributed mostly by human activities, while As, Cr, and Ni in the soils were dominated by natural background. No clear distinction was found on the pollution characteristic of heavy metals in the surface soils between the e-waste recycling areas and traditional industrial zones. The potential ecological risk posed by heavy metals in the surface soils from both types of sites, which was dominated by that from Cd, ranged from low to moderate. Given the much shorter development history of e-waste recycling and its largely unregulated nature, significant efforts should be made to crack down on illegal e-waste recycling and strengthen pollution control for related activities.

How surface mounds and depressions change during rainfall events

USDA-ARS?s Scientific Manuscript database

The soil roughness, or microrelief, controls processes occurring on the surface. Although there are numerous studies on how soil roughness affects soil erosion processes, little are focused on quantifying different roughness functions on surface hydrology and erosion, i.e., water diverging and soil...

Predicting Near Real-Time Inundation Occurrence from Complimentary Satellite Microwave Brightness Temperature Observations

NASA Astrophysics Data System (ADS)

Fisher, C. K.; Pan, M.; Wood, E. F.

2017-12-01

Throughout the world, there is an increasing need for new methods and data that can aid decision makers, emergency responders and scientists in the monitoring of flood events as they happen. In many regions, it is possible to examine the extent of historical and real-time inundation occurrence from visible and infrared imagery provided by sensors such as MODIS or the Landsat TM; however, this is not possible in regions that are densely vegetated or are under persistent cloud cover. In addition, there is often a temporal mismatch between the sampling of a particular sensor and a given flood event, leading to limited observations in near real-time. As a result, there is a need for alternative methods that take full advantage of complimentary remotely sensed data sources, such as available microwave brightness temperature observations (e.g., SMAP, SMOS, AMSR2, AMSR-E, and GMI), to aid in the estimation of global flooding. The objective of this work was to develop a high-resolution mapping of inundated areas derived from multiple satellite microwave sensor observations with a daily temporal resolution. This system consists of first retrieving water fractions from complimentary microwave sensors (AMSR-2 and SMAP) which may spatially and temporally overlap in the region of interest. Using additional information in a Random Forest classifier, including high resolution topography and multiple datasets of inundated area (both historical and empirical), the resulting retrievals are spatially downscaled to derive estimates of the extent of inundation at a scale relevant to management and flood response activities ( 90m or better) instead of the relatively coarse resolution water fractions, which are limited by the microwave sensor footprints ( 5-50km). Here we present the training and validation of this method for the 2015 floods that occurred in Houston, Texas. Comparing the predicted inundation against historical occurrence maps derived from the Landsat TM record and MODIS imagery, we find good agreement for most areas and are able to provide a daily mapping given the increased temporal coverage. These results illustrate the feasibility of a near real-time inundation prediction system driven by multi-sensor satellite microwave observations, which can be extended to provide a daily estimate of global flooding.

NASA Team 2 Sea Ice Concentration Algorithm Retrieval Uncertainty

NASA Technical Reports Server (NTRS)

Brucker, Ludovic; Cavalieri, Donald J.; Markus, Thorsten; Ivanoff, Alvaro

2014-01-01

Satellite microwave radiometers are widely used to estimate sea ice cover properties (concentration, extent, and area) through the use of sea ice concentration (IC) algorithms. Rare are the algorithms providing associated IC uncertainty estimates. Algorithm uncertainty estimates are needed to assess accurately global and regional trends in IC (and thus extent and area), and to improve sea ice predictions on seasonal to interannual timescales using data assimilation approaches. This paper presents a method to provide relative IC uncertainty estimates using the enhanced NASA Team (NT2) IC algorithm. The proposed approach takes advantage of the NT2 calculations and solely relies on the brightness temperatures (TBs) used as input. NT2 IC and its associated relative uncertainty are obtained for both the Northern and Southern Hemispheres using the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) TB. NT2 IC relative uncertainties estimated on a footprint-by-footprint swath-by-swath basis were averaged daily over each 12.5-km grid cell of the polar stereographic grid. For both hemispheres and throughout the year, the NT2 relative uncertainty is less than 5%. In the Southern Hemisphere, it is low in the interior ice pack, and it increases in the marginal ice zone up to 5%. In the Northern Hemisphere, areas with high uncertainties are also found in the high IC area of the Central Arctic. Retrieval uncertainties are greater in areas corresponding to NT2 ice types associated with deep snow and new ice. Seasonal variations in uncertainty show larger values in summer as a result of melt conditions and greater atmospheric contributions. Our analysis also includes an evaluation of the NT2 algorithm sensitivity to AMSR-E sensor noise. There is a 60% probability that the IC does not change (to within the computed retrieval precision of 1%) due to sensor noise, and the cumulated probability shows that there is a 90% chance that the IC varies by less than +/-3%. We also examined the daily IC variability, which is dominated by sea ice drift and ice formation/melt. Daily IC variability is the highest, year round, in the MIZ (often up to 20%, locally 30%). The temporal and spatial distributions of the retrieval uncertainties and the daily IC variability is expected to be useful for algorithm intercomparisons, climate trend assessments, and possibly IC assimilation in models.

Generating a global soil evaporation dataset using SMAP soil moisture data to estimate components of the surface water balance

NASA Astrophysics Data System (ADS)

Carbone, E.; Small, E. E.; Badger, A.; Livneh, B.

2016-12-01

Evapotranspiration (ET) is fundamental to the water, energy and carbon cycles. However, our ability to measure ET and partition the total flux into transpiration and evaporation from soil is limited. This project aims to generate a global, observationally-based soil evaporation dataset (E-SMAP): using SMAP surface soil moisture data in conjunction with models and auxiliary observations to observe or estimate each component of the surface water balance. E-SMAP will enable a better understanding of water balance processes and contribute to forecasts of water resource availability. Here we focus on the flux between the soil surface and root zone layers (qbot), which dictates the proportion of water that is available for soil evaporation. Any water that moves from the surface layer to the root zone contributes to transpiration or groundwater recharge. The magnitude and direction of qbot are driven by gravity and the gradient in matric potential. We use a highly discretized Richards Equation-type model (e.g. Hydrus 1D software) with meteorological forcing from the North American Land Data Assimilation System (NLDAS) to estimate qbot. We verify the simulations using SMAP L4 surface and root zone soil moisture data. These data are well suited for evaluating qbot because they represent the most advanced estimate of the surface to root zone soil moisture gradient at the global scale. Results are compared with similar calculations using NLDAS and in situ soil moisture data. Preliminary calculations show that the greatest amount of variability between qbot determined from NLDAS, in situ and SMAP occurs directly after precipitation events. At these times, uncertainties in qbot calculations significantly affect E-SMAP estimates.

Evaluation of NASA GEOS-ADAS Modeled Diurnal Warming Through Comparisons to SEVIRI and AMSR2 SST Observations

NASA Astrophysics Data System (ADS)

Gentemann, C. L.; Akella, S.

2018-02-01

An analysis of the ocean skin Sea Surface Temperature (SST) has been included in the Goddard Earth Observing System (GEOS) - Atmospheric Data Assimilation System (ADAS), Version 5 (GEOS-ADAS). This analysis is based on the GEOS atmospheric general circulation model (AGCM) that simulates near-surface diurnal warming and cool skin effects. Analysis for the skin SST is performed along with the atmospheric state, including Advanced Very High Resolution Radiometer (AVHRR) satellite radiance observations as part of the data assimilation system. One month (September, 2015) of GEOS-ADAS SSTs were compared to collocated satellite Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SSTs to examine how the GEOS-ADAS diurnal warming compares to the satellite measured warming. The spatial distribution of warming compares well to the satellite observed distributions. Specific diurnal events are analyzed to examine variability within a single day. The dependence of diurnal warming on wind speed, time of day, and daily average insolation is also examined. Overall the magnitude of GEOS-ADAS warming is similar to the warming inferred from satellite retrievals, but several weaknesses in the GEOS-AGCM simulated diurnal warming are identified and directly related back to specific features in the formulation of the diurnal warming model.

Observational Analysis of Cloud and Precipitation in Midlatitude Cyclones: Northern Versus Southern Hemisphere Warm Fronts

NASA Technical Reports Server (NTRS)

Naud, Catherine M.; Posselt, Derek J.; van den Heever, Susan C.

2012-01-01

Extratropical cyclones are responsible for most of the precipitation and wind damage in the midlatitudes during the cold season, but there are still uncertainties on how they will change in a warming climate. An ubiquitous problem amongst General Circulation Models (GCMs) is a lack of cloudiness over the southern oceans that may be in part caused by a lack of clouds in cyclones. We analyze CloudSat, CALIPSO and AMSR-E observations for 3 austral and boreal cold seasons and composite cloud frequency of occurrence and precipitation at the warm fronts for northern and southern hemisphere oceanic cyclones. We find that cloud frequency of occurrence and precipitation rate are similar in the early stage of the cyclone life cycle in both northern and southern hemispheres. As cyclones evolve and reach their mature stage, cloudiness and precipitation at the warm front increase in the northern hemisphere but decrease in the southern hemisphere. This is partly caused by lower amounts of precipitable water being available to southern hemisphere cyclones, and smaller increases in wind speed as the cyclones evolve. Southern hemisphere cloud occurrence at the warm front is found to be more sensitive to the amount of moisture in the warm sector than to wind speeds. This suggests that cloudiness in southern hemisphere storms may be more susceptible to changes in atmospheric water vapor content, and thus to changes in surface temperature than their northern hemisphere counterparts. These differences between northern and southern hemisphere cyclones are statistically robust, indicating A-Train-based analyses as useful tools for evaluation of GCMs in the next IPCC report.

Physical and Radiative Characteristics and Long Term Variability of the Okhotsk Sea Ice Cover

NASA Technical Reports Server (NTRS)

Nishio, Fumihiko; Comiso, Josefino C.; Gersten, Robert; Nakayama, Masashige; Ukita, Jinro; Gasiewski, Al; Stanko, Boba; Naoki, Kazuhiro

2007-01-01

Much of what we know about the large scale characteristics of the Okhotsk Sea ice cover comes from ice concentration maps derived from passive microwave data. To understand what these satellite data represents in a highly divergent and rapidly changing environment like the Okhotsk Sea, we analyzed concurrent satellite, aircraft, and ship data and characterized the sea ice cover at different scales from meters to tens of kilometers. Through comparative analysis of surface features using co-registered data from visible, infrared and microwave channels we evaluated how the general radiative and physical characteristics of the ice cover changes as well as quantify the distribution of different ice types in the region. Ice concentration maps from AMSR-E using the standard sets of channels, and also only the 89 GHz channel for optimal resolution, are compared with aircraft and high resolution visible data and while the standard set provides consistent results, the 89 GHz provides the means to observe mesoscale patterns and some unique features of the ice cover. Analysis of MODIS data reveals that thick ice types represents about 37% of the ice cover indicating that young and new ice represent a large fraction of the lice cover that averages about 90% ice concentration, according to passive microwave data. A rapid decline of -9% and -12 % per decade is observed suggesting warming signals but further studies are required because of aforementioned characteristics and because the length of the ice season is decreasing by only 2 to 4 days per decade.

Issues and Solutions for Bringing Heterogeneous Water Cycle Data Sets Together

NASA Technical Reports Server (NTRS)

Acker, James; Kempler, Steven; Teng, William; Belvedere, Deborah; Liu, Zhong; Leptoukh, Gregory

2010-01-01

The water cycle research community has generated many regional to global scale products using data from individual NASA missions or sensors (e.g., TRMM, AMSR-E); multiple ground- and space-based data sources (e.g., Global Precipitation Climatology Project [GPCP] products); and sophisticated data assimilation systems (e.g., Land Data Assimilation Systems [LDAS]). However, it is often difficult to access, explore, merge, analyze, and inter-compare these data in a coherent manner due to issues of data resolution, format, and structure. These difficulties were substantiated at the recent Collaborative Energy and Water Cycle Information Services (CEWIS) Workshop, where members of the NASA Energy and Water cycle Study (NEWS) community gave presentations, provided feedback, and developed scenarios which illustrated the difficulties and techniques for bringing together heterogeneous datasets. This presentation reports on the findings of the workshop, thus defining the problems and challenges of multi-dataset research. In addition, the CEWIS prototype shown at the workshop will be presented to illustrate new technologies that can mitigate data access roadblocks encountered in multi-dataset research, including: (1) Quick and easy search and access of selected NEWS data sets. (2) Multi-parameter data subsetting, manipulation, analysis, and display tools. (3) Access to input and derived water cycle data (data lineage). It is hoped that this presentation will encourage community discussion and feedback on heterogeneous data analysis scenarios, issues, and remedies.

The Generation of Near-Real Time Data Products for MODIS

NASA Astrophysics Data System (ADS)

Teague, M.; Schmaltz, J. E.; Ilavajhala, S.; Ye, G.; Masuoka, E.; Murphy, K. J.; Michael, K.

2010-12-01

The GSFC Terrestrial Information Systems Branch (614.5) operate the Land and Atmospheres Near-real-time Capability for EOS (LANCE-MODIS) system. Other LANCE elements include -AIRS, -MLS, -OMI, and -AMSR-E. LANCE-MODIS incorporates the former Rapid Response system and will, in early 2011, include the Fire Information for Resource Management System (FIRMS). The purpose of LANCE is to provide applications users with a variety of products on a near-real time basis. The LANCE-MODIS data products include Level 1 (L1), L2 fire, snow, sea ice, cloud mask/profiles, aerosols, clouds, land surface reflectance, land surface temperature, and L2G and L3 gridded, daily, land surface reflectance products. Data are available either by ftp access (pull) or by subscription (push) and the L1 and L2 data products are available within an average of 2.5 hours of the observation time. The use of ancillary data products input to the standard science algorithms has been modified in order to obtain these latencies. The resulting products have been approved for applications use by the MODIS Science Team. The http://lance.nasa.gov site provides registration information and extensive information concerning the MODIS data products and imagery including a comparison between the LANCE-MODIS and the standard science-quality products generated by the MODAPS system. The LANCE-MODIS system includes a variety of tools that enable users to manipulate the data products including: parameter, band, and geographic subsetting, re-projection, mosaicing, and generation of data in the GeoTIFF format. In most instances the data resulting from use of these tools has a latency of less than 3 hours. Access to these tools is available through a Web Coverage Service. A Google Earth/Web Mapping Service is available to access image products. LANCE-MODIS supports a wide variety of applications users in civilian, military, and foreign agencies as well as universities and the private sector. Examples of applications are: Flood Mapping, Famine relief, Food and Agriculture, Hazards and Disasters, and Weather.

Monitoring the Snowpack in Remote, Ungauged Mountains

NASA Astrophysics Data System (ADS)

Dozier, J.; Davis, R. E.; Bair, N.; Rittger, K. E.

2013-12-01

Our objective is to estimate seasonal snow volumes, relative to historical trends and extremes, in snow-dominated mountains that have austere infrastructure, sparse gauging, challenges of accessibility, and emerging or enduring insecurity related to water resources. The world's mountains accumulate substantial snow and, in some areas, produce the bulk of the runoff. In ranges like Afghanistan's Hindu Kush, availability of water resources affects US policy, military and humanitarian operations, and national security. The rugged terrain makes surface measurements difficult and also affects the analysis of remotely sensed data. To judge feasibility, we consider two regions, a validation case and a case representing inaccessible mountains. For the validation case, we use the Sierra Nevada of California, a mountain range of extensive historical study, emerging scientific innovation, and conflicting priorities in managing water for agriculture, urban areas, hydropower, recreation, habitat, and flood control. For the austere regional focus, we use the Hindu Kush, where some of the most persistent drought in the world causes food insecurity and combines with political instability, and occasional flooding. Our approach uses a mix of satellite data and spare modeling to present information essential for planning and decision making, ranging from optimization of proposed infrastructure projects to assessment of water resources stored as snow for seasonal forecasts. We combine optical imagery (MODIS on Terra/Aqua), passive microwave data (SSM/I and AMSR-E), retrospective reconstruction with energy balance calculations, and a snowmelt model to establish the retrospective context. With the passive microwave data we bracket the historical range in snow cover volume. The rank orders of total retrieved volume correlates with reconstructions. From a library of historical reconstruction, we find similar cases that provide insights about snow cover distribution at a finer scale than the passive retrievals. Specifically, we examine the decade-long record from Terra and Aqua to bracket the historical record. In the California Sierra Nevada, surface measurements have sufficient spatial and temporal resolution for us to validate our approach, whereas in the Hindu Kush surface data are sparse and access presents significant difficulties.

Concentrations, profiles, and estimated human exposures for polychlorinated dibenzo-p-dioxins and dibenzofurans from electronic waste recycling facilities and a chemical industrial complex in Eastern China

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, J.; Kannan, K.; Cheng, J.

2008-11-15

Electronic shredder waste and dust from e-waste facilities, and leaves and surface soil collected in the vicinity of a large scale e-waste recycling facility in Taizhou, Eastern China, were analyzed for total dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) including 2,3,7,8-substituted congeners. We also determined PCDD/Fs in surface agricultural soils from several provinces in China for comparison with soils from e-waste facilities. Concentrations of total PCDD/Fs were high in all of the matrices analyzed and ranged from 30.9 to 11,400 pg/g for shredder waste, 3460 to 9820 pg/g dry weight for leaves, 2560 to 148,000 pg/g dry weight for workshop-floor dust, and 854more » to 10200 pg/g dry weight for soils. We also analyzed surface soils from a chemical industrial complex (a coke-oven plant, a coal-fired power plant, and a chlor-alkali plant) in Shanghai. Concentrations of total PCDD/Fs in surface soil from the chemical industrial complex were lower than the concentrations found in soils from e-waste recycling plants, but higher than the concentrations found in agricultural soils. Agricultural soils from six cities in China contained low levels of total PCDD/Fs. Profiles of dioxin toxic equivalents (TEQs) of 2,3,7,8-PCDD/Fs in soils from e-waste facilities in Taizhou differed from the profiles found in agricultural soils. The estimated daily intakes of TEQs of PCDD/Fs via soil/dust ingestion and dermal exposure were 2 orders of magnitude higher in people at e-waste recycling facilities than in people at the chemical industrial site, implying greater health risk for humans from dioxin exposures at e-waste recycling facilities. The calculated TEQ exposures for e-waste workers from dust and soil ingestion alone were 2-3 orders of magnitude greater than the exposures from soils in reference locations. 37 refs., 1 fig., 2 tabs.« less

Pollution distribution of heavy metals in surface soil at an informal electronic-waste recycling site.

PubMed

Fujimori, Takashi; Takigami, Hidetaka

2014-02-01

We studied distribution of heavy metals [lead (Pb), copper (Cu) and zinc (Zn)] in surface soil at an electronic-waste (e-waste) recycling workshop near Metro Manila in the Philippines to evaluate the pollution size (spot size, small area or the entire workshop), as well as to assess heavy metal transport into the surrounding soil environment. On-site length-of-stride-scale (~70 cm) measurements were performed at each surface soil point using field-portable X-ray fluorescence (FP-XRF). The surface soil at the e-waste recycling workshop was polluted with Cu, Zn and Pb, which were distributed discretely in surface soil. The site was divided into five areas based on the distance from an entrance gate (y-axis) of the e-waste recycling workshop. The three heavy metals showed similar concentration gradients in the y-axis direction. Zn, Pb and Cu concentrations were estimated to decrease to half of their maximum concentrations at ~3, 7 and 7 m from the pollution spot, respectively, inside the informal e-waste recycling workshop. Distance from an entrance may play an important role in heavy metal transport at the soil surface. Using on-site FP-XRF, we evaluated the metal ratio to characterise pollution features of the solid surface. Variability analysis of heavy metals revealed vanishing surficial autocorrelation over metre ranges. Also, the possibility of concentration prediction at unmeasured points using geostatistical kriging was evaluated, and heavy metals had a relative "small" pollution scales and remained inside the original workshop compared with toxic organohalogen compounds. Thus, exposure to heavy metals may directly influence the health of e-waste workers at the original site rather than the surrounding habitat and environmental media.

SMOS data and extreme events

NASA Astrophysics Data System (ADS)

Kerr, Yann; Wigneron, Jean-Pierre; Ferrazzoli, Paolo; Mahmoodi, Ali; Al-Yaari, Amen; Parrens, Marie; Bitar, Ahmad Al; Rodriguez-Fernandez, Nemesio; Bircher, Simone; Molero-rodenas, Beatriz; Drusch, Matthias; Mecklenburg, Susanne

2017-04-01

The SMOS (Soil Moisture and Ocean Salinity) satellite was successfully launched in November 2009. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surface (with an accuracy goal of 0.04 m3/m3), vegetation water content over land, and ocean salinity. These geophysical features are important as they control the energy balance between the surface and the atmosphere. Their knowledge at a global scale is of interest for climatic and weather researches, and in particular in improving model forecasts. The Soil Moisture and Ocean Salinity mission has now been collecting data for over 7 years. The whole data set has been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS) while operational near real time soil moisture data is now available and assimilation of SMOS data in NWP has proved successful. After 7 years it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. We have also produced a 15 year soil moisture data set by merging SMOS and AMSR using a neural network approach. The purpose of this communication is to present the mission results after more than seven years in orbit in a climatic trend perspective, as through such a period anomalies can be detected. Thereby we benefit from consistent datasets provided through the latest reprocessing using most recent algorithm enhancements. Using the above mentioned products it is possible to follow large events such as the evolution of the droughts in North America, or water fraction evolution over the Amazonian basin. In this occasion we will focus on the analysis of SMOS and ancillary products anomalies to reveal two climatic trends, the temporal evolution of water storage over the Indian continent in relation to rainfall anomalies, and the global impact of El Nino types of events on the general water storage distribution. This presentation shows in detail the use of long term data sets of L-band microwave radiometry in two specific cases, namely droughts and water budget over a large basin. Several other analyses are under way currently. Obviously, vegetation water content, but also dielectric constant, are carrying a wealth of information and some interesting perspectives will be presented.

Determination of 3,6-dinitrobenzo[e]pyrene in surface soil and airborne particles by high-performance liquid chromatography with fluorescence detection.

PubMed

Hasei, Tomohiro; Watanabe, Tetsushi; Hirayama, Teruhisa

2006-11-24

We developed a sensitive analytical method and an efficient clean-up method to quantify 3,6-dinitrobenzo[e]pyrene (3,6-DNBeP) in surface soil and airborne particles. After purification using a silica gel column and two reversed-phase columns, 3,6-DNBeP was reduced to 3,6-diaminobenzo[e]pyrene by a catalyst column and analyzed by high-performance liquid chromatography (HPLC) with a fluorescence detector. 3,6-DNBeP was detected in all of the soil samples and airborne particles examined. The concentration of 3,6-DNBeP in surface soil and airborne particles was determined in the ranges of 347-5007 pg/g of soil and 137-1238 fg/m3, respectively.

Investigation of polar mesocyclones in Arctic Ocean using COSMO-CLM and WRF numerical models and remote sensing data

NASA Astrophysics Data System (ADS)

Varentsov, Mikhail; Verezemskaya, Polina; Baranyuk, Anastasia; Zabolotskikh, Elizaveta; Repina, Irina

2015-04-01

Polar lows (PL), high latitude marine mesoscale cyclones, are an enigmatic atmospheric phenomenon, which could result in windstorm damage of shipping and infrastructure in high latitudes. Because of their small spatial scales, short life times and their tendency to develop in remote data sparse regions (Zahn, Strorch, 2008), our knowledge of their behavior and climatology lags behind that of synoptic-scale cyclones. In case of continuing global warming (IPCC, 2013) and prospects of the intensification of economic activity and marine traffic in Arctic region, the problem of relevant simulation of this phenomenon by numerical models of the atmosphere, which could be used for weather and climate prediction, is especially important. The focus of this paper is researching the ability to simulate polar lows by two modern nonhydrostatic mesoscale numerical models, driven by realistic lateral boundary conditions from ERA-Interim reanalysis: regional climate model COSMO-CLM (Böhm et. al., 2009) and weather prediction and research model (WRF). Fields of wind, pressure and cloudiness, simulated by models, were compared with remote sensing data and ground meteorological observations for several cases, when polar lows were observed, in Norwegian, Kara and Laptev seas. Several types of satellite data were used: atmospheric water vapor, cloud liquid water content and surface wind fields were resampled by examining AMSR-E and AMSR-2 microwave radiometer data (MODIS Aqua, GCOM-W1), and wind fields were additionally extracted from QuickSCAT scatterometer. Infrared and visible pictures of cloud cover were obtained from MODIS (Aqua). Completed comparison shown that COSMO-CLM and WRF models could successfully reproduce evolution of polar lows and their most important characteristics such as size and wind speed in short experiments with WRF model and longer (up to half-year) experiments with COSMO-CLM model. Improvement of the quality of polar lows reproduction by these models in relation to source reanalysis fields were investigated. References: 1. Böhm U. et al. CLM - the climate version of LM: Brief description and long-term applications [Journal] // COSMO Newsletter. - 2006. - Vol. 6. 2. IPCC Fifth Assessment Report: Climate Change 2013 (AR5) Rep.,Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 3. Zahn, M., and H. von Storch (2008), A long-term climatology of North Atlantic polar lows, Geophys. Res. Lett., 35, L22702

40 CFR 279.54 - Used oil management.

Code of Federal Regulations, 2014 CFR

2014-07-01

... the soil, groundwater, or surface water. (d) Secondary containment for existing aboveground tanks... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... out of the system to the soil, groundwater, or surface water. (f) Labels. (1) Containers and...

40 CFR 279.54 - Used oil management.

Code of Federal Regulations, 2011 CFR

2011-07-01

... the soil, groundwater, or surface water. (d) Secondary containment for existing aboveground tanks... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... out of the system to the soil, groundwater, or surface water. (f) Labels. (1) Containers and...

40 CFR 279.54 - Used oil management.

Code of Federal Regulations, 2012 CFR

2012-07-01

... the soil, groundwater, or surface water. (d) Secondary containment for existing aboveground tanks... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... out of the system to the soil, groundwater, or surface water. (f) Labels. (1) Containers and...

40 CFR 279.54 - Used oil management.

Code of Federal Regulations, 2013 CFR

2013-07-01

... the soil, groundwater, or surface water. (d) Secondary containment for existing aboveground tanks... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... out of the system to the soil, groundwater, or surface water. (f) Labels. (1) Containers and...

40 CFR 279.64 - Used oil storage.

Code of Federal Regulations, 2010 CFR

2010-07-01

... soil, groundwater, or surface water. (d) Secondary containment for existing aboveground tanks. Existing... system to the soil, groundwater, or surface water. (e) Secondary containment for new aboveground tanks... containment system from migrating out of the system to the soil, groundwater, or surface water. (f) Labels. (1...

40 CFR 279.64 - Used oil storage.

Code of Federal Regulations, 2011 CFR

2011-07-01

... soil, groundwater, or surface water. (d) Secondary containment for existing aboveground tanks. Existing... system to the soil, groundwater, or surface water. (e) Secondary containment for new aboveground tanks... containment system from migrating out of the system to the soil, groundwater, or surface water. (f) Labels. (1...

Baffin Bay Ice Drift and Export: 2002-2007

NASA Technical Reports Server (NTRS)

Kwok, Ron

2007-01-01

Multiyear estimates of sea ice drift in Baffin Bay and Davis Strait are derived for the first time from the 89 GHz channel of the AMSR-E instrument. Uncertainties in the drift estimates, assessed with Envisat ice motion, are approximately 2-3 km/day. A persistent atmospheric trough, between the coast of Greenland and Baffin Island, drives the prevailing southward drift pattern with average daily displacements in excess of 18-20 km during winter. Over the 5-year record, the ice export ranges between 360 and 675 x 10(exp 3) km(exp 2), with an average of 530 x 10(exp 3) km(exp 2). Sea ice area inflow from the Nares Strait, Lancaster Sound and Jones Sound potentially contribute up to a third of the net area outflow while ice production at the North Water Polynya contributes the balance. Rough estimates of annual volume export give approximately 500-800 km(exp 3). Comparatively, these are approximately 70% and approximately 30% of the annual area and Strait.

Estimating Precipitation Susceptibility in Warm Marine Clouds Using Multi-sensor Aerosol and Cloud Products from A-Train Satellites

NASA Astrophysics Data System (ADS)

Bai, H.; Gong, C.; Wang, M.; Zhang, Z.

2017-12-01

Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol-cloud interactions and constraining aerosol indirect effects. However, large discrepancies exist in the previous satellite estimates of precipitation susceptibility. In this paper, multi-sensor aerosol and cloud products, including those from CALIPSO, CloudSat, MODIS, and AMSR-E from June 2006 to April 2011 are analyzed to estimate precipitation susceptibility (including precipitation frequency susceptibility SPOP, precipitation intensity susceptibility SI, and precipitation rate susceptibility SR) in warm marine clouds. Our results show that SPOP demonstrates relatively robust features throughout independent LWP products and diverse rain products. In contrast, the behaviors of SI are more subject to LWP or rain products. Our results further show that SPOP strongly depends on atmospherics stability, with larger value under more stable environment. Precipitation susceptibility calculated with respect to cloud droplet number concentration (CDNC) is generally much larger than that estimated with respect to aerosol index (AI), which results from the weak dependency of CDNC on AI.

Arctic sea ice concentration observed with SMOS during summer

NASA Astrophysics Data System (ADS)

Gabarro, Carolina; Martinez, Justino; Turiel, Antonio

2017-04-01

The Arctic Ocean is under profound transformation. Observations and model predictions show dramatic decline in sea ice extent and volume [1]. A retreating Arctic ice cover has a marked impact on regional and global climate, and vice versa, through a large number of feedback mechanisms and interactions with the climate system [2]. The launch of the Soil Moisture and Ocean Salinity (SMOS) mission, in 2009, marked the dawn of a new type of space-based microwave observations. Although the mission was originally conceived for hydrological and oceanographic studies [3,4], SMOS is also making inroads in the cryospheric sciences by measuring the thin ice thickness [5,6]. SMOS carries an L-band (1.4 GHz), passive interferometric radiometer (the so-called MIRAS) that measures the electromagnetic radiation emitted by the Earth's surface, at about 50 km spatial resolution, continuous multi-angle viewing, large wide swath (1200-km), and with a 3-day revisit time at the equator, but more frequently at the poles. A novel radiometric method to determine sea ice concentration (SIC) from SMOS is presented. The method uses the Bayesian-based Maximum Likelihood Estimation (MLE) approach to retrieve SIC. The advantage of this approach with respect to the classical linear inversion is that the former takes into account the uncertainty of the tie-point measured data in addition to the mean value, while the latter only uses a mean value of the tie-point data. When thin ice is present, the SMOS algorithm underestimates the SIC due to the low opacity of the ice at this frequency. However, using a synergistic approach with data from other satellite sensors, it is possible to obtain accurate thin ice thickness estimations with the Bayesian-based method. Despite its lower spatial resolution relative to SSMI or AMSR-E, SMOS-derived SIC products are little affected by the atmosphere and the snow (almost transparent at L-band). Moreover L-band measurements are more robust in front of the accelerated metamorphosis and melt processes during summer affecting the ice surface fraction measurements. Therefore, the SMOS SIC dataset has great potential during summer periods in which higher frequency radiometers present high uncertainties determining the SIC. This new dataset can contribute to complement ongoing monitoring efforts in the Arctic Cryosphere. [1] Comiso, J. C.: Large Decadal Decline of the Arctic Multiyear Ice Cover, Journal of Climate, 25, 1176-1193, 2012. [2] Holland, M. M. and Bitz, C. M.: Polar amplification of climate change in coupled models, Climate Dynamics, 21, 221-232, 2003. [3] Font, J, et al.: SMOS: The Challenging Sea Surface Salinity Measurement from Space'. Proc. IGARSS, no. 5, 649 -665, 2010. [4] Kerr, Y., et al.: The SMOS mission: New tool for monitoring key elements of the global water cycle Proc. IGARSS no. 5, 666-687, 2010. [5] Kaleschke, L., et al.: Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period, Geophys. Res. Lett., doi:10.1029/ 2012GL050916, 2012. [6] Huntemann, et al.: Empirical sea ice thickness retrieval during the freeze up period from SMOS high incident angle observations, The Cryosphere Discuss., 7, 4379-4405, 2013.

Vertical distribution of the soil microbiota along a successional gradient in a glacier forefield.

PubMed

Rime, Thomas; Hartmann, Martin; Brunner, Ivano; Widmer, Franco; Zeyer, Josef; Frey, Beat

2015-03-01

Spatial patterns of microbial communities have been extensively surveyed in well-developed soils, but few studies investigated the vertical distribution of micro-organisms in newly developed soils after glacier retreat. We used 454-pyrosequencing to assess whether bacterial and fungal community structures differed between stages of soil development (SSD) characterized by an increasing vegetation cover from barren (vegetation cover: 0%/age: 10 years), sparsely vegetated (13%/60 years), transient (60%/80 years) to vegetated (95%/110 years) and depths (surface, 5 and 20 cm) along the Damma glacier forefield (Switzerland). The SSD significantly influenced the bacterial and fungal communities. Based on indicator species analyses, metabolically versatile bacteria (e.g. Geobacter) and psychrophilic yeasts (e.g. Mrakia) characterized the barren soils. Vegetated soils with higher C, N and root biomass consisted of bacteria able to degrade complex organic compounds (e.g. Candidatus Solibacter), lignocellulolytic Ascomycota (e.g. Geoglossum) and ectomycorrhizal Basidiomycota (e.g. Laccaria). Soil depth only influenced bacterial and fungal communities in barren and sparsely vegetated soils. These changes were partly due to more silt and higher soil moisture in the surface. In both soil ages, the surface was characterized by OTUs affiliated to Phormidium and Sphingobacteriales. In lower depths, however, bacterial and fungal communities differed between SSD. Lower depths of sparsely vegetated soils consisted of OTUs affiliated to Acidobacteria and Geoglossum, whereas depths of barren soils were characterized by OTUs related to Gemmatimonadetes. Overall, plant establishment drives the soil microbiota along the successional gradient but does not influence the vertical distribution of microbiota in recently deglaciated soils. © 2014 John Wiley & Sons Ltd.

Improvements to the swath-level near-surface atmospheric state parameter retrievals within the NRL Ocean Surface Flux System (NFLUX)

NASA Astrophysics Data System (ADS)

May, J. C.; Rowley, C. D.; Meyer, H.

2017-12-01

The Naval Research Laboratory (NRL) Ocean Surface Flux System (NFLUX) is an end-to-end data processing and assimilation system used to provide near-real-time satellite-based surface heat flux fields over the global ocean. The first component of NFLUX produces near-real-time swath-level estimates of surface state parameters and downwelling radiative fluxes. The focus here will be on the satellite swath-level state parameter retrievals, namely surface air temperature, surface specific humidity, and surface scalar wind speed over the ocean. Swath-level state parameter retrievals are produced from satellite sensor data records (SDRs) from four passive microwave sensors onboard 10 platforms: the Special Sensor Microwave Imager/Sounder (SSMIS) sensor onboard the DMSP F16, F17, and F18 platforms; the Advanced Microwave Sounding Unit-A (AMSU-A) sensor onboard the NOAA-15, NOAA-18, NOAA-19, Metop-A, and Metop-B platforms; the Advanced Technology Microwave Sounder (ATMS) sensor onboard the S-NPP platform; and the Advanced Microwave Scannin Radiometer 2 (AMSR2) sensor onboard the GCOM-W1 platform. The satellite SDRs are translated into state parameter estimates using multiple polynomial regression algorithms. The coefficients to the algorithms are obtained using a bootstrapping technique with all available brightness temperature channels for a given sensor, in addition to a SST field. For each retrieved parameter for each sensor-platform combination, unique algorithms are developed for ascending and descending orbits, as well as clear vs cloudy conditions. Each of the sensors produces surface air temperature and surface specific humidity retrievals. The SSMIS and AMSR2 sensors also produce surface scalar wind speed retrievals. Improvement is seen in the SSMIS retrievals when separate algorithms are used for the even and odd scans, with the odd scans performing better than the even scans. Currently, NFLUX treats all SSMIS scans as even scans. Additional improvement in all of the surface retrievals comes from using a 3-hourly SST field, as opposed to a daily SST field.

Enhanced hemispheric-scale snow mapping through the blending of optical and microwave satellite data

NASA Astrophysics Data System (ADS)

Armstrong, R. L.; Brodzik, M. J.; Savoie, M.; Knowles, K.

2003-04-01

Snow cover is an important variable for climate and hydrologic models due to its effects on energy and moisture budgets. Seasonal snow can cover more than 50% of the Northern Hemisphere land surface during the winter resulting in snow cover being the land surface characteristic responsible for the largest annual and interannual differences in albedo. Passive microwave satellite remote sensing can augment measurements based on visible satellite data alone because of the ability to acquire data through most clouds or during darkness as well as to provide a measure of snow depth or water equivalent. Global snow cover fluctuation can now be monitored over a 24 year period using passive microwave data (Scanning Multichannel Microwave Radiometer (SMMR) 1978-1987 and Special Sensor Microwave/Imager (SSM/I), 1987-present). Evaluation of snow extent derived from passive microwave algorithms is presented through comparison with the NOAA Northern Hemisphere weekly snow extent data. For the period 1978 to 2002, both passive microwave and visible data sets show a similar pattern of inter-annual variability, although the maximum snow extents derived from the microwave data are consistently less than those provided by the visible satellite data and the visible data typically show higher monthly variability. Decadal trends and their significance are compared for the two data types. During shallow snow conditions of the early winter season microwave data consistently indicate less snow-covered area than the visible data. This underestimate of snow extent results from the fact that shallow snow cover (less than about 5.0 cm) does not provide a scattering signal of sufficient strength to be detected by the algorithms. As the snow cover continues to build during the months of January through March, as well as throughout the melt season, agreement between the two data types continually improves. This occurs because as the snow becomes deeper and the layered structure more complex, the negative spectral gradient driving the passive microwave algorithm is enhanced. Because the current generation of microwave snow algorithms is unable to consistently detect shallow and intermittent snow, we combine visible satellite data with the microwave data in a single blended product to overcome this problem. For the period 1978 to 2002 we combine data from the NOAA weekly snow charts with passive microwave data from the SMMR and SSM/I brightness temperature record. For the current and future time period we blend MODIS and AMSR-E data sets, both of which have greatly enhanced spatial resolution compared to the earlier data sources. Because it is not possible to determine snow depth or snow water equivalent from visible data, the regions where only the NOAA or MODIS data indicate snow are defined as "shallow snow". However, because our current blended product is being developed in the 25 km EASE-Grid and the MODIS data being used are in the Climate Modelers Grid (CMG) at approximately 5 km (0.05 deg.) the blended product also includes percent snow cover over the larger grid cell. A prototype version of the blended MODIS/AMSR-E product will be available in near real-time from NSIDC during the 2002-2003 winter season.

Sensitivity of Support Vector Machine Predictions of Passive Microwave Brightness Temperature Over Snow-covered Terrain in High Mountain Asia

NASA Astrophysics Data System (ADS)

Ahmad, J. A.; Forman, B. A.

2017-12-01

High Mountain Asia (HMA) serves as a water supply source for over 1.3 billion people, primarily in south-east Asia. Most of this water originates as snow (or ice) that melts during the summer months and contributes to the run-off downstream. In spite of its critical role, there is still considerable uncertainty regarding the total amount of snow in HMA and its spatial and temporal variation. In this study, the NASA Land Information Systems (LIS) is used to model the hydrologic cycle over the Indus basin. In addition, the ability of support vector machines (SVM), a machine learning technique, to predict passive microwave brightness temperatures at a specific frequency and polarization as a function of LIS-derived land surface model output is explored in a sensitivity analysis. Multi-frequency, multi-polarization passive microwave brightness temperatures as measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) over the Indus basin are used as training targets during the SVM training process. Normalized sensitivity coefficients (NSC) are then computed to assess the sensitivity of a well-trained SVM to each LIS-derived state variable. Preliminary results conform with the known first-order physics. For example, input states directly linked to physical temperature like snow temperature, air temperature, and vegetation temperature have positive NSC's whereas input states that increase volume scattering such as snow water equivalent or snow density yield negative NSC's. Air temperature exhibits the largest sensitivity coefficients due to its inherent, high-frequency variability. Adherence of this machine learning algorithm to the first-order physics bodes well for its potential use in LIS as the observation operator within a radiance data assimilation system aimed at improving regional- and continental-scale snow estimates.

Diagnosing Warm Frontal Cloud Formation in a GCM: A Novel Approach Using Conditional Subsetting

NASA Technical Reports Server (NTRS)

Booth, James F.; Naud, Catherine M.; DelGenio, Anthony D.

2013-01-01

This study analyzes characteristics of clouds and vertical motion across extratropical cyclone warm fronts in the NASA Goddard Institute for Space Studies general circulation model. The validity of the modeled clouds is assessed using a combination of satellite observations from CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis. The analysis focuses on developing cyclones, to test the model's ability to generate their initial structure. To begin, the extratropical cyclones and their warm fronts are objectively identified and cyclone-local fields are mapped into a vertical transect centered on the surface warm front. To further isolate specific physics, the cyclones are separated using conditional subsetting based on additional cyclone-local variables, and the differences between the subset means are analyzed. Conditional subsets are created based on 1) the transect clouds and 2) vertical motion; 3) the strength of the temperature gradient along the warm front, as well as the storm-local 4) wind speed and 5) precipitable water (PW). The analysis shows that the model does not generate enough frontal cloud, especially at low altitude. The subsetting results reveal that, compared to the observations, the model exhibits a decoupling between cloud formation at high and low altitudes across warm fronts and a weak sensitivity to moisture. These issues are caused in part by the parameterized convection and assumptions in the stratiform cloud scheme that are valid in the subtropics. On the other hand, the model generates proper covariability of low-altitude vertical motion and cloud at the warm front and a joint dependence of cloudiness on wind and PW.

Rain-on-snow and ice layer formation detection using passive microwave radiometry: An arctic perspective

NASA Astrophysics Data System (ADS)

Langlois, A.; Royer, A.; Montpetit, B.; Johnson, C. A.; Brucker, L.; Dolant, C.; Richards, A.; Roy, A.

2015-12-01

With the current changes observed in the Arctic, an increase in occurrence of rain-on-snow (ROS) events has been reported in the Arctic (land) over the past few decades. Several studies have established that strong linkages between surface temperatures and passive microwaves do exist, but the contribution of snow properties under winter extreme events such as rain-on-snow events (ROS) and associated ice layer formation need to be better understood that both have a significant impact on ecosystem processes. In particular, ice layer formation is known to affect the survival of ungulates by blocking their access to food. Given the current pronounced warming in northern regions, more frequent ROS can be expected. However, one of the main challenges in the study of ROS in northern regions is the lack of meteorological information and in-situ measurements. The retrieval of ROS occurrence in the Arctic using satellite remote sensing tools thus represents the most viable approach. Here, we present here results from 1) ROS occurrence formation in the Peary caribou habitat using an empirically developed ROS algorithm by our group based on the gradient ratio, 2) ice layer formation across the same area using a semi-empirical detection approach based on the polarization ratio spanning between 1978 and 2013. A detection threshold was adjusted given the platform used (SMMR, SSM/I and AMSR-E), and initial results suggest high-occurrence years as: 1981-1982, 1992-1993; 1994-1995; 1999-2000; 2001-2002; 2002-2003; 2003-2004; 2006-2007; 2007-2008. A trend in occurrence for Banks Island and NW Victoria Island and linkages to caribou population is presented.

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI AMSR-E, SSM/I, AMSU-B and the TRMM PR

NASA Technical Reports Server (NTRS)

Fisher, Brad; Wolff, David B.

2010-01-01

Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25 resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.

40 CFR 279.45 - Used oil storage at transfer facilities.

Code of Federal Regulations, 2010 CFR

2010-07-01

... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... into the containment system from migrating out of the system to the soil, groundwater, or surface water... system to the soil, groundwater, or surface water. (g) Labels. (1) Containers and aboveground tanks used...

40 CFR 279.45 - Used oil storage at transfer facilities.

Code of Federal Regulations, 2012 CFR

2012-07-01

... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... into the containment system from migrating out of the system to the soil, groundwater, or surface water... system to the soil, groundwater, or surface water. (g) Labels. (1) Containers and aboveground tanks used...

40 CFR 279.45 - Used oil storage at transfer facilities.

Code of Federal Regulations, 2014 CFR

2014-07-01

... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... into the containment system from migrating out of the system to the soil, groundwater, or surface water... system to the soil, groundwater, or surface water. (g) Labels. (1) Containers and aboveground tanks used...

40 CFR 279.45 - Used oil storage at transfer facilities.

Code of Federal Regulations, 2011 CFR

2011-07-01

... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... into the containment system from migrating out of the system to the soil, groundwater, or surface water... system to the soil, groundwater, or surface water. (g) Labels. (1) Containers and aboveground tanks used...

40 CFR 279.45 - Used oil storage at transfer facilities.

Code of Federal Regulations, 2013 CFR

2013-07-01

... containment system from migrating out of the system to the soil, groundwater, or surface water. (e) Secondary... into the containment system from migrating out of the system to the soil, groundwater, or surface water... system to the soil, groundwater, or surface water. (g) Labels. (1) Containers and aboveground tanks used...

SPCZ variability in 30 years of high temporal and spatial resolution satellite data

NASA Astrophysics Data System (ADS)

Haffke, C. M.; Magnusdottir, G.

2010-12-01

A recently developed spatial-temporal statistical model for objectively identifying the ITCZ is modified and adapted for the South Pacific Convergence Zone (SPCZ). Using a Bayesian statistical framework along with a Markov random field algorithm, the model determines the presence or absence of the SPCZ in instantaneous satellite observations. From this, SPCZ location, area, and intensity of convection are determined. The model incorporates satellite observations, prior knowledge of typical SPCZ location, and information from neighboring pixels in space and time to determine whether a given pixel is labeled as SPCZ or non-SPCZ. The model ‘learns’ to identify the SPCZ through manual labeling and is ultimately designed to emulate the way a human observer would identify the SPCZ given a sequence of satellite images of different fields. The statistical model has several advantages over previous labeling methods, such as threshholding an observed field (such as IR). Threshholding may identify isolated areas of convection as SPCZ that a human observer would not include. Similarly it may omit areas within a human identified SPCZ region that are not active during the current time frame. The three types of satellite observations that were used as input to the original ITCZ detection model were infrared and visible GOES data as well as total precipitable water. GOES infrared is available every three hours from 1980-present, GOES visible has usable observations every 24 hours from 1995-present, and total precipitable water, a composite of all available microwave data (e.g. SSM/I, TMI, AMSR-E), is available every six hours from 1995-present. We shall describe the model development efforts that are mainly focused on including additional sources of high temporal and spatial resolution satellite observations (such as quikSCAT surface winds and TRMM precipitation). Other products such as sea surface temperature and mixed-layer ocean heat content are used for understanding coupling with the ocean and for model validation. Model output, or identified SPCZ regions, provide the means to track the SPCZ ‘envelope’ and determine variability in location, area, and intensity of convection on diurnal to decadal time scales. The length of the recently archived satellite data set (1980-2009) allows for a thorough examination of seasonal and interannual variability, which will be presented. We also aim to quantify SPCZ interaction with ENSO and the MJO.

The surface-pore integrated effect of soil organic matter on retention and transport of pharmaceuticals and personal care products in soils.

PubMed

Qin, Qin; Chen, Xijuan; Zhuang, Jie

2017-12-01

This study examines a surface-pore integrated mechanism that allows soil organic matter (SOM) to influence the retention and transport of three representative pharmaceuticals and personal care products (PPCPs)-ibuprofen, carbamazepine, and bisphenol A-in agricultural soil. A series of sorption-desorption batch tests and breakthrough column experiments were conducted using manured and non-manured soils. Results show that SOM could substantially influence the environmental behaviors of PPCPs via two mechanisms: surface-coating and pore-filling. Surface-coating with molecular SOM decreases the sorption of dissociated PPCPs (e.g., ibuprofen) but increases the sorption of non-dissociated PPCPs (e.g., carbamazepine and bisphenol A), while pore-filling with colloidal SOM enhances the retention of all the PPCPs by providing nano-/micro-pores that limit diffusion. The higher retention and lower mobility of PPCPs in soil microaggregates than in bulk soils suggest that SOM content and SOM-altered soil pore structure could exert a coupled effect on PPCP retention. Differences in the elution of PPCPs with low surface tension solution (i.e., 20% ethanol) in the presence and absence of SOM indicate that PPCPs prefer to remain in SOM-filled pores. Overall, ibuprofen has a high environmental risk, whereas carbamazepine and bisphenol A could be readily retarded in agricultural soils (with a loamy clay texture). This study implies that SOM accrual (particularly pore-filling SOM) has a high potential for reducing the off-site risks of PPCPs by increasing soil nano-/micro-porosity. Copyright © 2017 Elsevier B.V. All rights reserved.

Concentrations, profiles, and estimated human exposures for polychlorinated dibenzo-p-dioxins and dibenzofurans from electronic waste recycling facilities and a chemical industrial complex in Eastern China.

PubMed

Ma, Jing; Kannan, Kurunthachalam; Cheng, Jinping; Horii, Yuichi; Wu, Qian; Wang, Wenhua

2008-11-15

Environmental pollution arising from electronic waste (e-waste) disposal and recycling has received considerable attention in recent years. Treatment, at low temperatures, of e-wastes that contain polyvinylchloride and related polymers can release polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). Although several studies have reported trace metals and polybrominated diphenyl ethers (PBDEs) released from e-waste recycling operations, environmental contamination and human exposure to PCDD/Fs from e-waste recycling operations are less well understood. In this study, electronic shredder waste and dust from e-waste facilities, and leaves and surface soil collected in the vicinity of a large scale e-waste recycling facility in Taizhou, Eastern China, were analyzed for total PCDD/ Fs including 2,3,7,8-substituted congeners. We also determined PCDD/Fs in surface agricultural soils from several provinces in China for comparison with soils from e-waste facilities. Concentrations of total PCDD/Fs were high in all of the matrices analyzed and ranged from 30.9 to 11400 pg/g for shredder waste, 3460 to 9820 pg/g dry weight for leaves, 2560 to 148000 pg/g dry weight for workshop-floor dust, and 854 to 10200 pg/g dry weight for soils. We also analyzed surface soils from a chemical industrial complex (a coke-oven plant, a coal-fired power plant, and a chlor-alkali plant) in Shanghai. Concentrations of total PCDD/Fs in surface soil (44.5-531 pg/g dry wt) from the chemical industrial complex were lower than the concentrations found in soils from e-waste recycling plants, but higher than the concentrations found in agricultural soils. Agricultural soils from six cities in China contained low levels (3.44-33.8 pg/g dry wt) of total PCDD/Fs. Profiles of dioxin toxic equivalents (TEQs) of 2,3,7,8-PCDD/Fs in soils from e-waste facilities in Taizhou differed from the profiles found in agricultural soils. The estimated daily intakes of TEQs of PCDD/ Fs via soil/dust ingestion and dermal exposure (2.3 and 0.363 pg TEQ/kg bw/day for children and adults, respectively) were 2 orders of magnitude higher in people at e-waste recycling facilities than in people at the chemical industrial site (0.021 and 0.0053 pg TEQ/kg bw/day for children and adults, respectively), implying greater health risk for humans from dioxin exposures at e-waste recycling facilities. The calculated TEQ exposures for e-waste workers from dust and soil ingestion alone were 2-3 orders of magnitude greater than the exposures from soils in reference locations.

Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

NASA Technical Reports Server (NTRS)

Girotto, Manuela

2018-01-01

Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

Concepts on tracking the impact of tropical cyclones through the coastal zone

NASA Astrophysics Data System (ADS)

Syvitski, J. P.; Hannon, M. T.; Kettner, A. J.; Bachman, S.

2009-12-01

WAVEWATCH III™ (Tolman, 2009) models the evolution of wind wave spectra under influence of wind, breaking, nonlinear interactions, bottom interaction (including shoaling and refraction), currents, water level changes and ice concentrations. The NOAA/NCEP data system offers global estimates every 3 hr at 1° x 1.25° for wind speed and direction at 10m asl, wave direction, height, and period. These and other derived parameters are useful in characterizing wave conditions as tropical cyclones approach landfall. The Tropical Rainfall Measuring Mission or TRMM based precipitation estimates a global 0.25° x 0.25° grid between 50° N-S produced within ≈7 hours of observation time. Estimates are derived from the Passive Microwave Radiometer, Precipitation Radar, and Visible-Infrared Scanner), plus data from: i) SSM/I ii) low-orbit GOES IR and TIROS Operational Vertical Sounder, iii) AMSR-E, iv) AMSU-B, and v) rain gauge data run through algorithm 3B-43. Data are served by the Goddard Distributed Active Archive Center. Evapotranspiration estimates are from the MODIS ET (MOD16) algorithm developed by Mu et al. (2007), based on the Penman-Monteith equation, modified with satellite information that uses: (1) vapor pressure deficit and minimum air temperature constraints on stomatal conductance; (2) leaf area index as a scalar for estimating canopy conductance; (3) the Enhanced Vegetation Index; and (4) a calculation of soil evaporation. TopoFlow is a spatially distributed hydrologic model able to ingest the TRMM and EV data through a suite of hydrologic processes (e.g. snowmelt, precipitation, evapotranspiration, infiltration, channel and overland flow, shallow subsurface flow, and flow diversions) to evolve in time in response to climatic forcings. Modeled or gauged discharge can then be coupled to sediment flux models to provide factor of 2 estimates of sediment flux (Syvitski et al. 2007, Kettner et al. 2008, Syvitski and Milliman 2007). The MODIS satellite constellation can track storm fronts and tropical cyclones and sense sediment discharged, resuspension of shoreline sediment, and be used to observe the dimensions and dynamics of delta flooding and delta-plain aggradation (Syvitski et al. 2009). An integrated workflow involving these models and data system will be presented outlining their use in characterizing sediment flux within the coastal zone.

Chemical resistance and cleanability of glazed surfaces

NASA Astrophysics Data System (ADS)

Hupa, Leena; Bergman, Roger; Fröberg, Linda; Vane-Tempest, Stina; Hupa, Mikko; Kronberg, Thomas; Pesonen-Leinonen, Eija; Sjöberg, Anna-Maija

2005-06-01

Adhesion of soil on glazed surfaces and their cleanability depends on chemical composition, phase composition, and roughness of the surface. The surface can be glossy consisting mainly of a smooth glassy phase. A matt and rough surface consists of a glassy phase and one or more crystalline phases. The origin and composition of the crystalline phases affect the chemical resistance and the cleanability of the surface. Fifteen experimental glossy and matt glazes were soaked in a slightly alkaline cleaning agent solution. The surfaces were spin-coated with sebum, i.e. a soil component typical for sanitary facilities. After wiping out the soil film in a controlled manner, the surface conditions and the soil left were evaluated with colour measurements, SEM/EDXA and COM. The results show that wollastonite-type crystals in the glaze surfaces were attacked in aqueous solutions containing typical cleaning agents. This corrosion led to significant decrease in the cleanability of the surface. The other crystal types observed, i.e. diopside and quartz crystals were not corroded, and the cleanability of glazes containing only these crystals was not changed in the cleaning agent exposures. Also the glassy phase was found to be attacked in some formulations leading to a somewhat decreased cleanability. The repeated soiling and cleaning procedures indicated that soil is accumulated on rough surfaces and surfaces which were clearly corroded by the cleaning agent.

Combined radar-radiometer surface soil moisture and roughness estimation

USDA-ARS?s Scientific Manuscript database

A robust physics-based combined radar-radiometer, or Active-Passive, surface soil moisture and roughness estimation methodology is presented. Soil moisture and roughness retrieval is performed via optimization, i.e., minimization, of a joint objective function which constrains similar resolution rad...

Snow cover data records from satellite and conventional measurements

NASA Astrophysics Data System (ADS)

Derksen, C.; Brown, R.; Wang, L.

2008-12-01

A major goal of snow-related research in the Climate Research Division of Environment Canada is the development of consistent snow cover information from satellite and in situ data sources for climate monitoring and model evaluation. This work involves new satellite algorithm development for reliable mapping of snow water equivalent (SWE), snow cover extent (SCE) and snow cover onset and melt dates, evaluation of existing snow cover products such as the NOAA weekly data set with in situ and satellite data, and the reconstruction and reanalysis of snow cover information from the application of physical snow models, geostatistics and data assimilation methods. In the context of the International Polar Year, a major effort is being made to develop and evaluate snow cover information over the Arctic region with a particular focus on the dynamic spring melt period where positive feedbacks to the climate system are more pronounced. Assessment of the NOAA daily and weekly SCE products with MODIS and QuikSCAT derived datasets identified a systematic late bias of 2-3 weeks in snow-off dates over northern Canada. This bias was not observed over northern Eurasia which suggests that regional differences in variables such as lake fraction and cloud cover are systematically influencing the accuracy of the NOAA product over northern Canada. Considerable progress has been made in deriving passive microwave derived SWE information over sub- Arctic regions of North America where pre-existing algorithms were unable to account for the influence of forest cover and lake ice. Previous uncertainties in retrieving SWE across the boreal forest have been resolved with the combination of 18.7 and 10.7 GHz measurements from the Advanced Microwave Scanning Radiometer (AMSR-E; 2002-present). Full time series development (1978-onwards) remains problematic, however, because 10.7 GHz measurements are not available from the Special Sensor Microwave/Imager (1987-present). Satellite measurements coupled with lake ice model simulations have illustrated frequency dependent, seasonally evolving relationships between brightness temperature and lake fraction across tundra regions. A potential solution based on the temporal evolution of 37 GHz AMSR-E measurements shows some promise as this was found to be significantly correlated with field measurements of tundra SWE, and to be relatively insensitive to lake fraction. New pan-Arctic (N 60°N) snowmelt onset and end date records (2000-2006) were produced from enhanced resolution (4.45 km) QuikSCAT (QSCAT) Ku-band backscatter measurements. The goal is to merge this with melt onset information from other components of the cryosphere (e.g. glaciers, ice caps, ice sheets, lake ice, sea ice) to provide an integrated circumpolar melt onset and duration dataset for climate monitoring and research on cryosphere-climate links and feedbacks. A major challenge is expanding the relatively short time period of Ku-band satellite measurements with historical C-band data (i.e. from ERS-1). Geostatistical methods and snow cover modeling were used to develop a 10-km gridded SWE dataset over Quebec from 1970-2005 for climate studies and evaluation of the performance of the Canadian Regional Climate Model.

Multi-Timescale Analysis of the Spatial Representativeness of In Situ Soil Moisture Data within Satellite Footprints

NASA Astrophysics Data System (ADS)

Molero, B.; Leroux, D. J.; Richaume, P.; Kerr, Y. H.; Merlin, O.; Cosh, M. H.; Bindlish, R.

2018-01-01

We conduct a novel comprehensive investigation that seeks to prove the connection between spatial scales and timescales in surface soil moisture (SM) within the satellite footprint ( 50 km). Modeled and measured point series at Yanco and Little Washita in situ networks are first decomposed into anomalies at timescales ranging from 0.5 to 128 days, using wavelet transforms. Then, their degree of spatial representativeness is evaluated on a per-timescale basis by comparison to large spatial scale data sets (the in situ spatial average, SMOS, AMSR2, and ECMWF). Four methods are used for this: temporal stability analysis (TStab), triple collocation (TC), percentage of correlated areas (CArea), and a new proposed approach that uses wavelet-based correlations (WCor). We found that the mean of the spatial representativeness values tends to increase with the timescale but so does their dispersion. Locations exhibit poor spatial representativeness at scales below 4 days, while either very good or poor representativeness at seasonal scales. Regarding the methods, TStab cannot be applied to the anomaly series due to their multiple zero-crossings, and TC is suitable for week and month scales but not for other scales where data set cross-correlations are found low. In contrast, WCor and CArea give consistent results at all timescales. WCor is less sensitive to the spatial sampling density, so it is a robust method that can be applied to sparse networks (one station per footprint). These results are promising to improve the validation and downscaling of satellite SM series and the optimization of SM networks.

Index for characterizing post-fire soil environments in temperate coniferous forests

USGS Publications Warehouse

Jain, Theresa B.; Pilliod, David S.; Graham, Russell T.; Lentile, Leigh B.; Sandquist, Jonathan E.

2012-01-01

Many scientists and managers have an interest in describing the environment following a fire to understand the effects on soil productivity, vegetation growth, and wildlife habitat, but little research has focused on the scientific rationale for classifying the post-fire environment. We developed an empirically-grounded soil post-fire index (PFI) based on available science and ecological thresholds. Using over 50 literature sources, we identified a minimum of five broad categories of post-fire outcomes: (a) unburned, (b) abundant surface organic matter ( > 85% surface organic matter), (c) moderate amount of surface organic matter ( ≥ 40 through 85%), (d) small amounts of surface organic matter ( < 40%), and (e) absence of surface organic matter (no organic matter left). We then subdivided each broad category on the basis of post-fire mineral soil colors providing a more fine-tuned post-fire soil index. We related each PFI category to characteristics such as soil temperature and duration of heating during fire, and physical, chemical, and biological responses. Classifying or describing post-fire soil conditions consistently will improve interpretations of fire effects research and facilitate communication of potential responses or outcomes (e.g., erosion potential) from fires of varying severities.

Re-evaluating the Cloud Lifetime Effect: Does Precipitation Suppression Always Lead to an Increased Cloud Extent in Warm Clouds?

NASA Astrophysics Data System (ADS)

Douglas, A.; L'Ecuyer, T.

2017-12-01

Aerosol influences on cloud lifetime remain a poorly understood pathway of aerosol-cloud-radiation interaction with large margins of error according to the fifth IPCC report. Increases in cloud lifetime are attributed to changes in cloud extent due to the suppression of precipitation by increased aerosol concentrations. The dependence of changes in cloud fraction and probability of precipitation on aerosol perturbations for controlled cloud regimes will be investigated using A-Train measurements. CloudSat, MODIS, and AMSR-E measurements from 2006 to 2010 are sorted into regimes established using stability to describe local meteorology, and relative humidity and liquid water path to describe cloud morphology. Holding the thermodynamic and meteorological environments constant allows variations in precipitation and cloud extent owing to regime-specific cloud lifetime effects to be attributed to aerosol perturbations. The relationship between precipitation suppression, cloud extent, and liquid water path will be analyzed. The cloud lifetime effect will be constrained using regimes in the hopes of improving our understanding of precipitation-aerosol interactions.

Radiometric correction of scatterometric wind measurements

NASA Technical Reports Server (NTRS)

1995-01-01

Use of a spaceborne scatterometer to determine the ocean-surface wind vector requires accurate measurement of radar backscatter from ocean. Such measurements are hindered by the effect of attenuation in the precipitating regions over sea. The attenuation can be estimated reasonably well with the knowledge of brightness temperatures observed by a microwave radiometer. The NASA SeaWinds scatterometer is to be flown on the Japanese ADEOS2. The AMSR multi-frequency radiometer on ADEOS2 will be used to correct errors due to attenuation in the SeaWinds scatterometer measurements. Here we investigate the errors in the attenuation corrections. Errors would be quite small if the radiometer and scatterometer footprints were identical and filled with uniform rain. However, the footprints are not identical, and because of their size one cannot expect uniform rain across each cell. Simulations were performed with the SeaWinds scatterometer (13.4 GHz) and AMSR (18.7 GHz) footprints with gradients of attenuation. The study shows that the resulting wind speed errors after correction (using the radiometer) are small for most cases. However, variations in the degree of overlap between the radiometer and scatterometer footprints affect the accuracy of the wind speed measurements.

Mapping soil features from multispectral scanner data

NASA Technical Reports Server (NTRS)

Kristof, S. J.; Zachary, A. L.

1974-01-01

In being able to identify quickly gross variations in soil features, the computer-aided classification of multispectral scanner data can be an effective aid to soil surveying. Variations in soil tone are easily seen as well as variations in features related to soil tone, e.g., drainage patterns and organic matter content. Changes in surface texture also affect the reflectance properties of soils. Inasmuch as conventional soil classes are based on both surface and subsurface soil characteristics, the technique described here can be expected only to augment and not replace traditional soil mapping.

Sea Ice Concentration Estimation Using Active and Passive Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Zhang, Y.; Li, F.; Zhang, S.; Zhu, T.

2017-12-01

In this abstract, a decision-level fusion method by utilizing SAR and passive microwave remote sensing data for sea ice concentration estimation is investigated. Sea ice concentration product from passive microwave concentration retrieval methods has large uncertainty within thin ice zone. Passive microwave data including SSM/I, AMSR-E, and AMSR-2 provide daily and long time series observations covering whole polar sea ice scene, and SAR images provide rich sea ice details with high spatial resolution including deformation and polarimetric features. In the proposed method, the merits from passive microwave data and SAR data are considered. Sea ice concentration products from ASI and sea ice category label derived from CRF framework in SAR imagery are calibrated under least distance protocol. For SAR imagery, incident angle and azimuth angle were used to correct backscattering values from slant range to ground range in order to improve geocoding accuracy. The posterior probability distribution between category label from SAR imagery and passive microwave sea ice concentration product is modeled and integrated under Bayesian network, where Gaussian statistical distribution from ASI sea ice concentration products serves as the prior term, which represented as an uncertainty of sea ice concentration. Empirical model based likelihood term is constructed under Bernoulli theory, which meets the non-negative and monotonically increasing conditions. In the posterior probability estimation procedure, final sea ice concentration is obtained using MAP criterion, which equals to minimize the cost function and it can be calculated with nonlinear iteration method. The proposed algorithm is tested on multiple satellite SAR data sets including GF-3, Sentinel-1A, RADARSAT-2 and Envisat ASAR. Results show that the proposed algorithm can improve the accuracy of ASI sea ice concentration products and reduce the uncertainty along the ice edge.

Application of the two-source energy balance model to partition evapotranspiration in an arid wine vineyard

NASA Astrophysics Data System (ADS)

Kool, Dilia; Kustas, William P.; Agam, Nurit

2016-04-01

The partitioning of evapotranspiration (ET) into transpiration (T), a productive water use, and soil water evaporation (E), which is generally considered a water loss, is highly relevant to agriculture in the light of increasing desertification and water scarcity. This task is challenged by the complexity of soil and plant interactions, coupled with changes in atmospheric and soil water content conditions. Many of the processes controlling water/energy exchange are not adequately modeled. The two-source energy balance model (TSEB) was evaluated and adapted for independent E and T estimations in an isolated drip-irrigated wine vineyard in the arid Negev desert. The TSEB model estimates ET by computing vegetation and soil energy fluxes using remotely sensed composite surface temperature, local weather data (solar radiation, air temperature and humidity, and wind speed), and vegetation metrics (row spacing, canopy height and width, and leaf area). The soil and vegetation energy fluxes are computed numerically using a system of temperature gradient and resistance equations; where soil and canopy temperatures are derived from the composite surface temperature. For estimation of ET, the TSEB model has been shown to perform well for various agricultural crops under a wide range of environmental conditions, but validation of T and E fluxes is limited to one study in a well-watered cotton crop. Extending the TSEB approach to water-limited vineyards demands careful consideration regarding how the complex canopy structure of vineyards will influence the accuracy of the partitioning between E and T. Data for evaluation of the TSEB model were collected over a season (bud break till harvest). Composite, canopy, and soil surface temperatures were measured using infrared thermometers. The composite vegetation and soil surface energy fluxes were assessed using independent measurements of net radiation, and soil, sensible and latent heat flux. The below canopy energy balance was assessed at the dry midrow position as well as the wet irrigated position directly underneath the vine row, where net radiation and soil heat flux were measured, sensible heat flux was computed indirectly, and E was calculated as the residual. While the below canopy energy balance approach used in this study allowed continuous assessment of E at daily intervals, instantaneous E fluxes could not be assessed due to vertical variability in shading below the canopy. Seasonal partitioning indicated that total E amounted to 9-11% of ET. Initial evaluation of the TSEB model indicated that discrepancies between modeled and measured fluxes can largely be attributed to net radiation partitioning. In addition, large diurnal variation at the soil surface requires adaptation of the soil heat flux formulations. Improved estimation of energy fluxes by accounting for the relatively complex canopy structure of vineyards will be highlighted.

Relation between L-band soil emittance and soil water content

NASA Technical Reports Server (NTRS)

Stroosnijder, L.; Lascano, R. J.; Van Bavel, C. H. M.; Newton, R. W.

1986-01-01

An experimental relation between soil emittance (E) at L-band and soil surface moisture content (M) is compared with a theoretical one. The latter depends on the soil dielectric constant, which is a function of both soil moisture content and of soil texture. It appears that a difference of 10 percent in the surface clay content causes a change in the estimate of M on the order of 0.02 cu m/cu m. This is based on calculations with a model that simulates the flow of water and energy, in combination with a radiative transfer model. It is concluded that an experimental determination of the E-M relation for each soil type is not required, and that a rough estimate of the soil texture will lead to a sufficiently accurate estimate of soil moisture from a general, theoretical relationship obtained by numerical simulation.

An Inter-calibrated Passive Microwave Brightness Temperature Data Record and Ocean Products

NASA Astrophysics Data System (ADS)

Hilburn, K. A.; Wentz, F. J.

2014-12-01

Inter-calibration of passive microwave sensors has been the subject of on-going activity at Remote Sensing Systems (RSS) since 1974. RSS has produced a brightness temperature TB data record that spans the last 28 years (1987-2014) from inter-calibrated passive microwave sensors on 14 satellites: AMSR-E, AMSR2, GMI, SSMI F08-F15, SSMIS F16-F18, TMI, WindSat. Accompanying the TB record are a suite of ocean products derived from the TBs that provide a 28-year record of wind speed, water vapor, cloud liquid, and rain rate; and 18 years (1997-2014) of sea surface temperatures, corresponding to the period for which 6 and/or 10 GHz measurements are available. Crucial to the inter-calibration and ocean product retrieval are a highly accurate radiative transfer model RTM. The RSS RTM has been continually refined for over 30 years and is arguably the most accurate model in the 1-100 GHz spectrum. The current generation of TB and ocean products, produced using the latest version of the RTM, is called Version-7. The accuracy of the Version-7 inter-calibration is estimated to be 0.1 K, based on inter-satellite comparisons and validation of the ocean products against in situ measurements. The data record produced by RSS has had a significant scientific impact. Over just the last 14 years (2000-2013) RSS data have been used in 743 peer-reviewed journal articles. This is an average of 4.5 peer-reviewed papers published every month made possible with RSS data. Some of the most important scientific contributions made by RSS data have been to the study of the climate. The AR5 Report "Climate Change 2013: The Physical Science Basis" by the Intergovernmental Panel on Climate Change (IPCC), the internationally accepted authority on climate change, references 20 peer-reviewed journal papers from RSS scientists. The report makes direct use of RSS water vapor data, RSS atmospheric temperatures from MSU/AMSU, and 9 other datasets that are derived from RSS data. The RSS TB data record is used to produce the NSIDC Sea Ice, NASA Sea Ice, and NASA GPCP Rain Rate datasets. The RSS ocean products are used to produce the NCDC ERSST, CCMP Winds, NOAA Sea Winds, WHOI OA Flux, OSU Wind Stress, and UWISC Cloud Water datasets. The large number of scientific articles and significant impact on the IPCC report demonstrate the success of RSS inter-calibration methodology.

Assessing the relationship between microwave vegetation optical depth and gross primary production

NASA Astrophysics Data System (ADS)

Teubner, Irene E.; Forkel, Matthias; Jung, Martin; Liu, Yi Y.; Miralles, Diego G.; Parinussa, Robert; van der Schalie, Robin; Vreugdenhil, Mariette; Schwalm, Christopher R.; Tramontana, Gianluca; Camps-Valls, Gustau; Dorigo, Wouter A.

2018-03-01

At the global scale, the uptake of atmospheric carbon dioxide by terrestrial ecosystems through photosynthesis is commonly estimated through vegetation indices or biophysical properties derived from optical remote sensing data. Microwave observations of vegetated areas are sensitive to different components of the vegetation layer than observations in the optical domain and may therefore provide complementary information on the vegetation state, which may be used in the estimation of Gross Primary Production (GPP). However, the relation between GPP and Vegetation Optical Depth (VOD), a biophysical quantity derived from microwave observations, is not yet known. This study aims to explore the relationship between VOD and GPP. VOD data were taken from different frequencies (L-, C-, and X-band) and from both active and passive microwave sensors, including the Advanced Scatterometer (ASCAT), the Soil Moisture Ocean Salinity (SMOS) mission, the Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E) and a merged VOD data set from various passive microwave sensors. VOD data were compared against FLUXCOM GPP and Solar-Induced chlorophyll Fluorescence (SIF) from the Global Ozone Monitoring Experiment-2 (GOME-2). FLUXCOM GPP estimates are based on the upscaling of flux tower GPP observations using optical satellite data, while SIF observations present a measure of photosynthetic activity and are often used as a proxy for GPP. For relating VOD to GPP, three variables were analyzed: original VOD time series, temporal changes in VOD (ΔVOD), and positive changes in VOD (ΔVOD≥0). Results show widespread positive correlations between VOD and GPP with some negative correlations mainly occurring in dry and wet regions for active and passive VOD, respectively. Correlations between VOD and GPP were similar or higher than between VOD and SIF. When comparing the three variables for relating VOD to GPP, correlations with GPP were higher for the original VOD time series than for ΔVOD or ΔVOD≥0 in case of sparsely to moderately vegetated areas and evergreen forests, while the opposite was true for deciduous forests. Results suggest that original VOD time series should be used jointly with changes in VOD for the estimation of GPP across biomes, which may further benefit from combining active and passive VOD data.

[Distribution of soil organic carbon in surface soil along a precipitation gradient in loess hilly area].

PubMed

Sun, Long; Zhang, Guang-hui; Luan, Li-li; Li, Zhen-wei; Geng, Ren

2016-02-01

Along the 368-591 mm precipitation gradient, 7 survey sites, i.e. a total 63 investigated plots were selected. At each sites, woodland, grassland, and cropland with similar restoration age were selected to investigate soil organic carbon distribution in surface soil (0-30 cm), and the influence of factors, e.g. climate, soil depth, and land uses, on soil organic carbon distribution were analyzed. The result showed that, along the precipitation gradient, the grassland (8.70 g . kg-1) > woodland (7.88 g . kg-1) > farmland (7.73 g . kg-1) in concentration and the grassland (20.28 kg . m-2) > farmland (19.34 kg . m-2) > woodland (17.14 kg . m-2) in density. The differences of soil organic carbon concentration of three land uses were not significant. Further analysis of pooled data of three land uses showed that the surface soil organic carbon concentration differed significantly at different precipitation levels (P<0.00 1). Significant positive relationship was detected between mean annual precipitation and soil organic carbon concentration (r=0.838, P<0.001) in the of pooled data. From south to north (start from northernmost Ordos), i.e. along the 368-591 mm precipitation gradient, the soil organic carbon increased with annual precipitation 0. 04 g . kg-1 . mm-1, density 0.08 kg . m-2 . mm-1. The soil organic carbon distribution was predicted with mean annual precipitation, soil clay content, plant litter in woodland, and root density in farmland.

Short-term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

NASA Astrophysics Data System (ADS)

Hebert, David A.; Allard, Richard A.; Metzger, E. Joseph; Posey, Pamela G.; Preller, Ruth H.; Wallcraft, Alan J.; Phelps, Michael W.; Smedstad, Ole Martin

2015-12-01

In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1-7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short-term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

Correction of WindScat Scatterometric Measurements by Combining with AMSR Radiometric Data

NASA Technical Reports Server (NTRS)

Song, S.; Moore, R. K.

1996-01-01

The Seawinds scatterometer on the advanced Earth observing satellite-2 (ADEOS-2) will determine surface wind vectors by measuring the radar cross section. Multiple measurements will be made at different points in a wind-vector cell. When dense clouds and rain are present, the signal will be attenuated, thereby giving erroneous results for the wind. This report describes algorithms to use with the advanced mechanically scanned radiometer (AMSR) scanning radiometer on ADEOS-2 to correct for the attenuation. One can determine attenuation from a radiometer measurement based on the excess brightness temperature measured. This is the difference between the total measured brightness temperature and the contribution from surface emission. A major problem that the algorithm must address is determining the surface contribution. Two basic approaches were developed for this, one using the scattering coefficient measured along with the brightness temperature, and the other using the brightness temperature alone. For both methods, best results will occur if the wind from the preceding wind-vector cell can be used as an input to the algorithm. In the method based on the scattering coefficient, we need the wind direction from the preceding cell. In the method using brightness temperature alone, we need the wind speed from the preceding cell. If neither is available, the algorithm can work, but the corrections will be less accurate. Both correction methods require iterative solutions. Simulations show that the algorithms make significant improvements in the measured scattering coefficient and thus is the retrieved wind vector. For stratiform rains, the errors without correction can be quite large, so the correction makes a major improvement. For systems of separated convective cells, the initial error is smaller and the correction, although about the same percentage, has a smaller effect.

VARIABLE CHARGE SOILS: MINERALOGY AND CHEMISTRY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Ranst, Eric; Qafoku, Nikolla; Noble, Andrew

2016-09-19

Soils rich in particles with amphoteric surface properties in the Oxisols, Ultisols, Alfisols, Spodosols and Andisols orders (1) are considered to be variable charge soils (2) (Table 1). The term “variable charge” is used to describe organic and inorganic soil constituents with reactive surface groups whose charge varies with pH and ionic concentration and composition of the soil solution. Such groups are the surface carboxyl, phenolic and amino functional groups of organic materials in soils, and surface hydroxyl groups of Fe and Al oxides, allophane and imogolite. The hydroxyl surface groups are also present on edges of some phyllosilicate mineralsmore » such as kaolinite, mica, and hydroxyl-interlayered vermiculite. The variable charge is developed on the surface groups as a result of adsorption or desorption of ions that are constituents of the solid phase, i.e., H+, and the adsorption or desorption of solid-unlike ions that are not constituents of the solid phase. Highly weathered soils and subsoils (e.g., Oxisols and some Ultisols, Alfisols and Andisols) may undergo isoelectric weathering and reach a “zero net charge” stage during their development. They usually have a slightly acidic to acidic soil solution pH, which is close to either the point of zero net charge (PZNC) (3) or the point of zero salt effect (PZSE) (3). They are characterized by high abundances of minerals with a point of zero net proton charge (PZNPC) (3) at neutral and slightly basic pHs; the most important being Fe and Al oxides and allophane. Under acidic conditions, the surfaces of these minerals are net positively charged. In contrast, the surfaces of permanent charge phyllosilicates are negatively charged regardless of ambient conditions. Variable charge soils therefore, are heterogeneous charge systems.« less

Soil-landscape development and late Quaternary environmental change in coastal Estremadura, Portugal

NASA Astrophysics Data System (ADS)

Daniels, Michael; Haws, Jonathan; Benedetti, Michael; Bicho, Nuno

2015-04-01

This poster integrates soil-landscape analysis with archaeological survey and paleoenvironmental reconstruction. Soils in surface and buried contexts in Estremadura, Portugal, provide evidence of landscape stability and instability, relative age relationships between landforms, and general paleoenvironmental conditions during the late Quaternary. These factors provide insight into the distribution and condition of Paleolithic archaeological sites and help understand the record of human settlement in the region. Late Pleistocene and Holocene dunes extend inland approximately 10 km from coastal source regions. Surface soils in Holocene dunes under maritime pine (Pinus pinaster) forest exhibit A, E, C/Bh and A, C horizon sequences and classify as Quartzipsamments. Surface soils in late Pleistocene dunes exhibit A, E, Bh, Bhs, Bs horizon sequences and classify as Haplorthods. Both Pleistocene and Holocene dunes commonly bury a heavily weathered soil formed in calcareous sandstone. The boundary between underlying buried soils and overlying surface soils is characterized by a lag deposit of medium to coarse, moderately-rounded gravels, underlain immediately by subsurface Bt and Bss horizons. The lag deposit and absence of buried A horizons both indicate intense and/or prolonged surface erosion prior to burial by late Quaternary dunes. Soil-geomorphic relationships therefore suggest at least two distinct episodes of dune emplacement and subsequent landscape stability following an extensive episode late Pleistocene landscape instability and soil erosion. A conceptual model of soil-landscape evolution through the late Quaternary and Holocene results from the integration of soil profile data, proxy paleoenvironmental data, and the partial record of human settled as revealed in the archaeological record.

Porous media augmented with biochar for the retention of E. coli

NASA Astrophysics Data System (ADS)

Kolotouros, Christos A.; Manariotis, Ioannis D.; Karapanagioti, Hrissi K.

2016-04-01

A significant number of epidemic outbreaks has been attributed to waterborne fecal-borne pathogenic microorganisms from contaminated ground water. The transport of pathogenic microorganisms in groundwater is controlled by physical and chemical soil properties like soil structure, texture, percent water saturation, soil ionic strength, pore-size distribution, soil and solution pH, soil surface charge, and concentration of organic carbon in solution. Biochar can increase soil productivity by improving both chemical and physical soil properties. The mixing of biochar into soils may stimulate microbial population and activate dormant soil microorganisms. Furthermore, the application of biochar into soil affects the mobility of microorganisms by altering the physical and chemical properties of the soil, and by retaining the microorganisms on the biochar surface. The aim of this study was to investigate the effect of biochar mixing into soil on the transport of Escherichia coli in saturated porous media. Initially, batch experiments were conducted at two different ionic strengths (1 and 150 mM KCl) and at varying E. coli concentrations in order to evaluate the retention of E. coli on biochar in aqueous solutions. Kinetic analysis was conducted, and three isotherm models were employed to analyze the experimental data. Column experiments were also conducted in saturated sand columns augmented with different biochar contents, in order to examine the effect of biochar on the retention of E. coli. The Langmuir model fitted better the retention experimental data, compared to Freundlich and Tempkin models. The retention of E. coli was enhanced at lower ionic strength. Finally, biochar-augmented sand columns were more capable in retaining E. coli than pure sand columns.

Impacts of Soil-aquifer Heat and Water Fluxes on Simulated Global Climate

NASA Technical Reports Server (NTRS)

Krakauer, N.Y.; Puma, Michael J.; Cook, B. I.

2013-01-01

Climate models have traditionally only represented heat and water fluxes within relatively shallow soil layers, but there is increasing interest in the possible role of heat and water exchanges with the deeper subsurface. Here, we integrate an idealized 50m deep aquifer into the land surface module of the GISS ModelE general circulation model to test the influence of aquifer-soil moisture and heat exchanges on climate variables. We evaluate the impact on the modeled climate of aquifer-soil heat and water fluxes separately, as well as in combination. The addition of the aquifer to ModelE has limited impact on annual-mean climate, with little change in global mean land temperature, precipitation, or evaporation. The seasonal amplitude of deep soil temperature is strongly damped by the soil-aquifer heat flux. This not only improves the model representation of permafrost area but propagates to the surface, resulting in an increase in the seasonal amplitude of surface air temperature of >1K in the Arctic. The soil-aquifer water and heat fluxes both slightly decrease interannual variability in soil moisture and in landsurface temperature, and decrease the soil moisture memory of the land surface on seasonal to annual timescales. The results of this experiment suggest that deepening the modeled land surface, compared to modeling only a shallower soil column with a no-flux bottom boundary condition, has limited impact on mean climate but does affect seasonality and interannual persistence.

Interaction of Escherichia coli with growing salad spinach plants.

PubMed

Warriner, Keith; Ibrahim, Faozia; Dickinson, Matthew; Wright, Charles; Waites, William M

2003-10-01

In this study, the interaction of a bioluminescence-labeled Escherichia coli strain with growing spinach plants was assessed. Through bioluminescence profiles, the direct visualization of E. coli growing around the roots of developing seedlings was accomplished. Subsequent in situ glucuronidase (GUS) staining of seedlings confirmed that E. coli had become internalized within root tissue and, to a limited extent, within hypocotyls. When inoculated seeds were sown in soil microcosms and cultivated for 42 days, E. coli was recovered from the external surfaces of spinach roots and leaves as well as from surface-sterilized roots. When 20-day-old spinach seedlings (from uninoculated seeds) were transferred to soil inoculated with E. coli, the bacterium became established on the plant surface, but internalization into the inner root tissue was restricted. However, for seedlings transferred to a hydroponic system containing 10(2) or 10(3) CFU of E. coli per ml of the circulating nutrient solution, the bacterium was recovered from surface-sterilized roots, indicating that it had been internalized. Differences between E. coli interactions in the soil and those in the hydroponic system may be attributed to greater accessibility of the roots in the latter model. Alternatively, the presence of a competitive microflora in soil may have restricted root colonization by E. coli. The implications of this study's findings with regard to the microbiological safety of minimally processed vegetables are discussed.

On the relationship between land surface infrared emissivity and soil moisture

NASA Astrophysics Data System (ADS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu

2018-01-01

The relationship between surface infrared (IR) emissivity and soil moisture content has been investigated based on satellite measurements. Surface soil moisture content can be estimated by IR remote sensing, namely using the surface parameters of IR emissivity, temperature, vegetation coverage, and soil texture. It is possible to separate IR emissivity from other parameters affecting surface soil moisture estimation. The main objective of this paper is to examine the correlation between land surface IR emissivity and soil moisture. To this end, we have developed a simple yet effective scheme to estimate volumetric soil moisture (VSM) using IR land surface emissivity retrieved from satellite IR spectral radiance measurements, assuming those other parameters impacting the radiative transfer (e.g., temperature, vegetation coverage, and surface roughness) are known for an acceptable time and space reference location. This scheme is applied to a decade of global IR emissivity data retrieved from MetOp-A infrared atmospheric sounding interferometer measurements. The VSM estimated from these IR emissivity data (denoted as IR-VSM) is used to demonstrate its measurement-to-measurement variations. Representative 0.25-deg spatially-gridded monthly-mean IR-VSM global datasets are then assembled to compare with those routinely provided from satellite microwave (MW) multisensor measurements (denoted as MW-VSM), demonstrating VSM spatial variations as well as seasonal-cycles and interannual variability. Initial positive agreement is shown to exist between IR- and MW-VSM (i.e., R2 = 0.85). IR land surface emissivity contains surface water content information. So, when IR measurements are used to estimate soil moisture, this correlation produces results that correspond with those customarily achievable from MW measurements. A decade-long monthly-gridded emissivity atlas is used to estimate IR-VSM, to demonstrate its seasonal-cycle and interannual variation, which is spatially coherent and consistent with that from MW measurements, and, moreover, to achieve our objective of investigating the relationship between land surface IR emissivity and soil moisture.

Impact of metals in surface matrices from formal and informal electronic-waste recycling around Metro Manila, the Philippines, and intra-Asian comparison.

PubMed

Fujimori, Takashi; Takigami, Hidetaka; Agusa, Tetsuro; Eguchi, Akifumi; Bekki, Kanae; Yoshida, Aya; Terazono, Atsushi; Ballesteros, Florencio C

2012-06-30

We report concentrations, enrichment factors, and hazard indicators of 11 metals (Ag, As, Cd, Co, Cu, Fe, In, Mn, Ni, Pb, and Zn) in soil and dust surface matrices from formal and informal electronic waste (e-waste) recycling sites around Metro Manila, the Philippines, referring to soil guidelines and previous data from various e-waste recycling sites in Asia. Surface dust from e-waste recycling sites had higher levels of metal contamination than surface soil. Comparison of formal and informal e-waste recycling sites (hereafter, "formal" and "informal") revealed differences in specific contaminants. Formal dust contained a mixture of serious pollutant metals (Ni, Cu, Pb, and Zn) and Cd (polluted modestly), quite high enrichment metals (Ag and In), and crust-derived metals (As, Co, Fe, and Mn). For informal soil, concentration levels of specific metals (Cd, Co, Cu, Mn, Ni, Pb, and Zn) were similar among Asian recycling sites. Formal dust had significantly higher hazardous risk than the other matrices (p<0.005), excluding informal dust (p=0.059, almost significant difference). Thus, workers exposed to formal dust should protect themselves from hazardous toxic metals (Pb and Cu). There is also a high health risk for children ingesting surface matrices from informal e-waste recycling sites. Copyright © 2012 Elsevier B.V. All rights reserved.

Urbanization Effects on the Vertical Distribution of Soil Microbial Communities and Soil C Storage across Edge-to-Interior Urban Forest Gradients

NASA Astrophysics Data System (ADS)

Rosier, C. L.; Van Stan, J. T., II; Trammell, T. L.

2017-12-01

Urbanization alters environmental conditions such as temperature, moisture, carbon (C) and nitrogen (N) deposition affecting critical soil processes (e.g., C storage). Urban soils experience elevated N deposition (e.g., transportation, industry) and decreased soil moisture via urban heat island that can subsequently alter soil microbial community structure and activity. However, there is a critical gap in understanding how increased temperatures and pollutant deposition influences soil microbial community structure and soil C/N cycling in urban forests. Furthermore, canopy structural differences between individual tree species is a potentially important mechanism facilitating the deposition of pollutants to the soil. The overarching goal of this study is to investigate the influence of urbanization and tree species structural differences on the bacterial and fungal community and C and N content of soils experiencing a gradient of urbanization pressures (i.e., forest edge to interior; 150-m). Soil cores (1-m depth) were collected near the stem (< 0.5 meter) of two tree species with contrasting canopy and bark structure (Fagus grandifolia, vs. Liriodendron tulipifera), and evaluated for soil microbial structure via metagenomic analysis and soil C/N content. We hypothesize that soil moisture constraints coupled with increases in recalcitrant C will decrease gram negative bacteria (i.e., dependent on labile C) while increasing saprophytic fungal community abundance (i.e., specialist consuming recalcitrant C) within both surface and subsurface soils experiencing the greatest urban pressure (i.e., forest edge). We further expect trees located on the edge of forest fragments will maintain greater surface soil (< 20 cm) C concentrations due to decreased soil moisture constraining microbial activity (e.g., slower decay), and increased capture of recalcitrant C stocks from industrial/vehicle emission sources (e.g., black C). Our initial results support our hypotheses that urbanization alters soil microbial community composition via reduced soil moisture and carbon storage potential via deposition gradients. Further analyses will answer important questions regarding how individual tree species alters urban soil C storage, N retention, and microbial dynamics.

SMOS and SMAP: from Lessons Learned to Future Mission Requirements

NASA Astrophysics Data System (ADS)

Kerr, Y. H.; Wigneron, J. P.; Cabot, F.; Escorihuela, M. J.; Anterrieu, E.; Rouge, B.; Rodriguez Fernandez, N.; Bindlish, R.; Khazaal, A.; Al-Bitar, A.; Mialon, A.; Lesthievent, G.

2017-12-01

The SMOS (Soil Moisture and Ocean Salinity) satellite was successfully launched in November 2009. This ESA led mission for Earth Observation is dedicated to provide soil moisture over continental surface, vegetation water content over land, and ocean salinity. The Soil Moisture and Ocean Salinity mission has now been collecting data for over 7 years. TheSoil Moisture Active and Passive for over 2 years.The two data set have been reprocessed (Version 620 for levels 1 and 2 and version 3 for level 3 CATDS) to be merged into one product, while operational near real time soil moisture data is now available and assimilation of SMOS data in NWP has proved successful. After 7 years of L-Band data acquisition, it seems important to start using data for having a look at anomalies and see how they can relate to large scale events. We have also produced a 15 year soil moisture data set by merging SMOS and AMSR using a neural network approach. The purpose of this communication is to present the two mission results after more than seven years in orbit in a climatic trend perspective, as through such a period anomalies can be detected. Thereby we benefit from consistent datasets provided through the latest reprocessing using most recent algorithm enhancements. Using the above mentioned products it is possible to follow large events such as the evolution of the droughts in North America, or water fraction evolution over the Amazonian basin. In this occasion we will focus on the analysis of SMOS and ancillary products anomalies to reveal two climatic trends, the temporal evolution of water storage over the Indian continent in relation to rainfall anomalies, and the global impact of El Nino types of events on the general water storage distribution. This presentation shows in detail the use of long term data sets of L-band microwave radiometry in two specific cases, namely droughts and water budget over a large basin. Several other analyses are under way currently. Obviously, vegetation water content, but also dielectric constant, are carrying a wealth of information and some interesting perspectives will be presented. More important it is now possible to draw conclusions from the lessons learnt and, with the help of the user's community, define the requirements for future missions. And, finally, from these requirement to propose mission scenarii.

Heavy metal contamination of surface soil in electronic waste dismantling area: site investigation and source-apportionment analysis.

PubMed

Jinhui Li; Huabo Duan; Pixing Shi

2011-07-01

The dismantling and disposal of electronic waste (e-waste) in developing countries is causing increasing concern because of its impacts on the environment and risks to human health. Heavy-metal concentrations in the surface soils of Guiyu (Guangdong Province, China) were monitored to determine the status of heavy-metal contamination on e-waste dismantling area with a more than 20 years history. Two metalloids and nine metals were selected for investigation. This paper also attempts to compare the data among a variety of e-waste dismantling areas, after reviewing a number of heavy-metal contamination-related studies in such areas in China over the past decade. In addition, source apportionment of heavy metal in the surface soil of these areas has been analysed. Both the MSW open-burning sites probably contained invaluable e-waste and abandoned sites formerly involved in informal recycling activities are the new sources of soil-based environmental pollution in Guiyu. Although printed circuit board waste is thought to be the main source of heavy-metal emissions during e-waste processing, requirement is necessary to soundly manage the plastic separated from e-waste, which mostly contains heavy metals and other toxic substances.

A laboratory study of colloid and solute transport in surface runoff on saturated soil

NASA Astrophysics Data System (ADS)

Yu, Congrong; Gao, Bin; Muñoz-Carpena, Rafael; Tian, Yuan; Wu, Lei; Perez-Ovilla, Oscar

2011-05-01

SummaryColloids in surface runoff may pose risks to the ecosystems not only because some of them (e.g., pathogens) are toxic, but also because they may facilitate the transport of other contaminants. Although many studies have been conducted to explore colloid fate and transport in the environment, current understanding of colloids in surface runoff is still limited. In this study, we conducted a range of laboratory experiments to examine the transport behavior of colloids in a surface runoff system, made of a soil box packed with quartz sand with four soil drainage outlets and one surface flow outlet. A natural clay colloid (kaolinite) and a conservative chemical tracer (bromide) were applied to the system under a simulated rainfall event (64 mm/h). Effluent soil drainage and surface flow samples were collected to determine the breakthrough concentrations of bromide and kaolinite. Under the experimental conditions tested, our results showed that surface runoff dominated the transport processes. As a result, kaolinite and bromide were found more in surface flow than in soil drainage. Comparisons between the breakthrough concentrations of bromide and kaolinite showed that kaolinite had lower mobility than bromide in the subsurface flow (i.e., soil drainage), but behaved almost identical to bromide in the surface runoff. Student's t-test confirmed the difference between kaolinite and bromide in subsurface flow ( p = 0.02). Spearman's test and linear regression analysis, however, showed a strong 1:1 correlation between kaolinite and bromide in surface runoff ( p < 0.0001). Our result indicate that colloids and chemical solutes may behave similarly in overland flow on bare soils with limited drainage when surface runoff dominates the transport processes.

Using Remote Sensing Platforms to Estimate Near-Surface Soil Properties

NASA Technical Reports Server (NTRS)

Sullivan, D. G.; Shaw, J. N.; Rickman, D.; Mask, P. L.; Wersinger, J. M.; Luvall, J.

2003-01-01

Evaluation of near-surface soil properties via remote sensing (RS) could facilitate soil survey mapping, erosion prediction, fertilization regimes, and allocation of agrochemicals. The objective of this study was to evaluate the relationship between soil spectral signature and near surface soil properties in conventionally managed row crop systems. High resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor (ATLAS) multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil water content, soil organic carbon (SOC), particle size distribution (PSD), and citrate dithionite extractable iron (Fed) content. Surface roughness, soil water content, and crusting were also measured at sampling. Results showed RS data acquired from lands with less than 4 % surface soil water content best approximated near-surface soil properties at the Coastal Plain site where loamy sand textured surfaces were predominant. Utilizing a combination of band ratios in stepwise regression, Fed (r2 = 0.61), SOC (r2 = 0.36), sand (r2 = 0.52), and clay (r2 = 0.76) were related to RS data at the Coastal Plain site. In contrast, the more clayey Ridge and Valley soils had r-squares of 0.50, 0.36, 0.17, and 0.57. for Fed, SOC, sand and clay, respectively. Use of estimated eEmissivity did not generally improve estimates of near-surface soil attributes.

Recent Improvements in AMSR2 Ground-Based RFI Filtering

NASA Astrophysics Data System (ADS)

Scott, J. P.; Gentemann, C. L.; Wentz, F. J.

2015-12-01

Passive satellite radiometer measurements in the microwave frequencies (6-89 GHz) are useful in providing geophysical retrievals of sea surface temperature (SST), atmospheric water vapor, wind speed, rain rate, and more. However, radio frequency interference (RFI) is one of the fastest growing sources of error in these retrievals. RFI can originate from broadcasting satellites, as well as from ground-based instrumentation that makes use of the microwave range. The microwave channel bandwidths used by passive satellite radiometers are often wider than the protected bands allocated for this type of remote sensing, a common practice in microwave radiometer design used to reduce the effect of instrument noise in the observed signal. However, broad channel bandwidths allow greater opportunity for RFI to affect these observations and retrievals. For ground-based RFI, a signal is broadcast directly into the atmosphere which may interfere with the radiometer - its antenna, cold mirror, hot load or the internal workings of the radiometer itself. It is relatively easy to identify and flag RFI from large sources, but more difficult to do so from small, sporadic sources. Ground-based RFI has high spatial and temporal variability, requiring constant, automated detection and removal to avoid spurious trends leaching into the geophysical retrievals. Ascension Island in the South Atlantic Ocean has been one of these notorious ground-based RFI sources, affecting many microwave radiometers, including the AMSR2 radiometer onboard JAXA's GCOM-W1 satellite. Ascension Island RFI mainly affects AMSR2's lower frequency channels (6.9, 7.3, and 10.65 GHz) over a broad spatial region in the South Atlantic Ocean, which makes it challenging to detect and flag this RFI using conventional channel and geophysical retrieval differencing techniques. The authors have developed a new method of using the radiometer's earth counts and hot counts, for the affected channels, to detect an Ascension Island RFI event and flag the data efficiently and accurately, thereby reducing false detections and optimizing retrieval quality and data preservation.

Extreme Thunderstorms as Seen by Satellite

NASA Technical Reports Server (NTRS)

Cecil, Daniel J.

2014-01-01

Extreme events by their nature fall outside the bounds of routine experience. With imperfect or ambiguous measuring systems, it is appropriate to question whether an unusual measurement represents an extreme event or is the result of instrument errors or other sources of noise. About three weeks after the Tropical Rainfall Measuring Mission (TRMM) satellite began collecting data in Dec 1997, a thunderstorm was observed over northern Argentina with 85 GHz brightness temperatures below 50 K and 37 GHz brightness temperatures below 70 K (Zipser et al. 2006). These values are well below what had previously been observed from satellite sensors with lower resolution. The 37 GHz brightness temperatures are also well below those measured by TRMM for any other storm in the subsequent 16 years. Without corroborating evidence, it would be natural to suspect a problem with the instrument, or perhaps an irregularity with the platform during the first weeks of the satellite mission. But the TRMM satellite also carries a radar and a lightning sensor, both confirming the presence of an intense thunderstorm. The radar recorded 40+ dBZ (decibels relative to Z) reflectivity up to about 19 km altitude. More than 200 lightning flashes per minute were recorded. That same storm's 19 GHz brightness temperatures below 150 K would normally be interpreted as the result of a low-emissivity water surface (e.g., a lake, or flood waters) if not for the simultaneous measurements of such intense convection. This paper will examine records from TRMM and related satellite sensors including SSMI and AMSR-E to find the strongest signatures resulting from thunderstorms, and distinguishing those from sources of noise. The lowest brightness temperatures resulting from thunderstorms as seen by TRMM have been in Argentina in November and December. For SSMI sensors carried on five DMSP satellites examined so far, the lowest thunderstorm-related brightness temperatures have been from Argentina in November - December and from Minnesota in June-July. The Minnesota cases were associated with spotter reports of large hail, significant severe wind, and tornadoes. Those locations have the record holders for each satellite. This paper will show examples of cases with the lowest brightness temperatures, and map the locations of these and other storms with brightness temperatures nearly as low. Higher resolution data from the field program MC3E and possibly from IPHEX will be considered for context.

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

NASA Astrophysics Data System (ADS)

Zhao, Changyu; Chen, Haishan; Sun, Shanlei

2018-04-01

Soil enthalpy ( H) contains the combined effects of both soil moisture ( w) and soil temperature ( T) in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different (positive or negative) impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer (i.e., the fifth layer), H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation ( P) prediction, the Huanghe-Huaihe Basin (HHB) and Southeast China (SEC), with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H-P and T-P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer (June) P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors (e.g., T and w).

A field guide to pedoderm and pattern classes

USDA-ARS?s Scientific Manuscript database

Pedoderm and Pattern Classes (PPCs) describe the soil pedoderm (i.e., the air-soil interface), the spatial arrangement (pattern) of plants potentially influencing the soil pedoderm, and evidence of soil redistribution. PPCs provide a record of soil surface features and plant patterns that influence ...

Continuous measurement of soil evaporation in a drip-irrigated wine vineyard in a desert area

USDA-ARS?s Scientific Manuscript database

Evaporation from the soil surface (E) can be a significant source of water loss in arid areas. In sparsely vegetated systems, E is expected to be a function of soil, climate, irrigation regime, precipitation patterns, and plant canopy development, and will therefore change dynamically at both daily ...

Event-based estimation of water budget components using the network of multi-sensor capacitance probes

USDA-ARS?s Scientific Manuscript database

A time-scale-free approach was developed for estimation of water fluxes at boundaries of monitoring soil profile using water content time series. The approach uses the soil water budget to compute soil water budget components, i.e. surface-water excess (Sw), infiltration less evapotranspiration (I-E...

Persistence of Escherichia coli and Salmonella in surface soil following application of liquid hog manure for production of pickling cucumbers.

PubMed

Côté, Caroline; Quessy, Sylvain

2005-05-01

Liquid hog manure is routinely applied to farm land as a crop fertilizer. However, this practice raises food safety concerns, especially when manure is used on fruit and vegetable crops. The objectives of this project were to evaluate the persistence of Escherichia coli and Salmonella in surface soil after application of liquid hog manure to fields where pickling cucumbers were grown and to verify the microbiological quality of harvested cucumbers. Mineral fertilizers were replaced by liquid hog manure at various ratios in the production of pickling cucumbers in a 3-year field study. The experimental design was a randomized complete block comprising four replicates in sandy loam (years 1, 2, and 3) and loamy sand (year 3). Soil samples were taken at a depth of 20 cm every 2 weeks after June application of organic and inorganic fertilizers. Vegetable samples were also taken at harvest time. Liquid hog manure, soil, and vegetable (washed and unwashed) samples were analyzed for the presence of Salmonella and E. coli. An exponential decrease of E. coli populations was observed in surface soil after the application of manure. The estimated average time required to reach undetectable concentrations of E. coli in sandy loam varied from 56 to 70 days, whereas the absence of E. coli was estimated at 77 days in loamy sand. The maximal Salmonella persistence in soil was 54 days. E. coli and Salmonella were not detected in any vegetable samples.

Spatial variability of specific surface area of arable soils in Poland

NASA Astrophysics Data System (ADS)

Sokolowski, S.; Sokolowska, Z.; Usowicz, B.

2012-04-01

Evaluation of soil spatial variability is an important issue in agrophysics and in environmental research. Knowledge of spatial variability of physico-chemical properties enables a better understanding of several processes that take place in soils. In particular, it is well known that mineralogical, organic, as well as particle-size compositions of soils vary in a wide range. Specific surface area of soils is one of the most significant characteristics of soils. It can be not only related to the type of soil, mainly to the content of clay, but also largely determines several physical and chemical properties of soils and is often used as a controlling factor in numerous biological processes. Knowledge of the specific surface area is necessary in calculating certain basic soil characteristics, such as the dielectric permeability of soil, water retention curve, water transport in the soil, cation exchange capacity and pesticide adsorption. The aim of the present study is two-fold. First, we carry out recognition of soil total specific surface area patterns in the territory of Poland and perform the investigation of features of its spatial variability. Next, semivariograms and fractal analysis are used to characterize and compare the spatial variability of soil specific surface area in two soil horizons (A and B). Specific surface area of about 1000 samples was determined by analyzing water vapor adsorption isotherms via the BET method. The collected data of the values of specific surface area of mineral soil representatives for the territory of Poland were then used to describe its spatial variability by employing geostatistical techniques and fractal theory. Using the data calculated for some selected points within the entire territory and along selected directions, the values of semivariance were determined. The slope of the regression line of the log-log plot of semi-variance versus the distance was used to estimate the fractal dimension, D. Specific surface area in A and B horizons was space-dependent, with the range of spatial dependence of about 2.5°. Variogram surfaces showed anisotropy of the specific surface area in both horizons with a trend toward the W to E directions. The smallest fractal dimensions were obtained for W to E directions and the highest values - for S to N directions. * The work was financially supported in part by the ESA Programme for European Cooperating States (PECS), No.98084 "SWEX-R, Soil Water and Energy Exchange/Research", AO3275.

Operationally Merged Satellite Visible/IR and Passive Microwave Sea Ice Information for Improved Sea Ice Forecasts and Ship Routing

DTIC Science & Technology

2015-09-30

microwave sea ice information for improved sea ice forecasts and ship routing W. Meier NASA Goddard Space Flight Center, Cryospheric Sciences Laboratory...updating the initial ice concentration analysis fields along the ice edge. In the past year, NASA Goddard and NRL have generated a merged 4 km AMSR-E...collaborations of three groups: NASA Goddard Space Flight Center ( NASA /GSFC) in Greenbelt, MD, NRL/Oceanography Division located at Stennis Space Center (SSC

Precipitation from the GPM Microwave Imager and Constellation Radiometers

NASA Astrophysics Data System (ADS)

Kummerow, Christian; Randel, David; Kirstetter, Pierre-Emmanuel; Kulie, Mark; Wang, Nai-Yu

2014-05-01

Satellite precipitation retrievals from microwave sensors are fundamentally underconstrained requiring either implicit or explicit a-priori information to constrain solutions. The radiometer algorithm designed for the GPM core and constellation satellites makes this a-priori information explicit in the form of a database of possible rain structures from the GPM core satellite and a Bayesian retrieval scheme. The a-priori database will eventually come from the GPM core satellite's combined radar/radiometer retrieval algorithm. That product is physically constrained to ensure radiometric consistency between the radars and radiometers and is thus ideally suited to create the a-priori databases for all radiometers in the GPM constellation. Until a robust product exists, however, the a-priori databases are being generated from the combination of existing sources over land and oceans. Over oceans, the Day-1 GPM radiometer algorithm uses the TRMM PR/TMI physically derived hydrometer profiles that are available from the tropics through sea surface temperatures of approximately 285K. For colder sea surface temperatures, the existing profiles are used with lower hydrometeor layers removed to correspond to colder conditions. While not ideal, the results appear to be reasonable placeholders until the full GPM database can be constructed. It is more difficult to construct physically consistent profiles over land due to ambiguities in surface emissivities as well as details of the ice scattering that dominates brightness temperature signatures over land. Over land, the a-priori databases have therefore been constructed by matching satellite overpasses to surface radar data derived from the WSR-88 network over the continental United States through the National Mosaic and Multi-Sensor QPE (NMQ) initiative. Databases are generated as a function of land type (4 categories of increasing vegetation cover as well as 4 categories of increasing snow depth), land surface temperature and total precipitable water. One year of coincident observations, generating 20 and 80 million database entries, depending upon the sensor, are used in the retrieval algorithm. The remaining areas such as sea ice and high latitude coastal zones are filled with a combination of CloudSat and AMSR-E plus MHS observations together with a model to create the equivalent databases for other radiometers in the constellation. The most noteworthy result from the Day-1 algorithm is the quality of the land products when compared to existing products. Unlike previous versions of land algorithms that depended upon complex screening routines to decide if pixels were precipitating or not, the current scheme is free of conditional rain statements and appears to produce rain rate with much greater fidelity than previous schemes. There results will be shown.

Assessment of Global Forecast Ocean Assimilation Model (FOAM) using new satellite SST data

NASA Astrophysics Data System (ADS)

Ascione Kenov, Isabella; Sykes, Peter; Fiedler, Emma; McConnell, Niall; Ryan, Andrew; Maksymczuk, Jan

2016-04-01

There is an increased demand for accurate ocean weather information for applications in the field of marine safety and navigation, water quality, offshore commercial operations, monitoring of oil spills and pollutants, among others. The Met Office, UK, provides ocean forecasts to customers from governmental, commercial and ecological sectors using the Global Forecast Ocean Assimilation Model (FOAM), an operational modelling system which covers the global ocean and runs daily, using the NEMO (Nucleus for European Modelling of the Ocean) ocean model with horizontal resolution of 1/4° and 75 vertical levels. The system assimilates salinity and temperature profiles, sea surface temperature (SST), sea surface height (SSH), and sea ice concentration observations on a daily basis. In this study, the FOAM system is updated to assimilate Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Spinning Enhanced Visible and Infrared Imager (SEVIRI) SST data. Model results from one month trials are assessed against observations using verification tools which provide a quantitative description of model performance and error, based on statistical metrics, including mean error, root mean square error (RMSE), correlation coefficient, and Taylor diagrams. A series of hindcast experiments is used to run the FOAM system with AMSR2 and SEVIRI SST data, using a control run for comparison. Results show that all trials perform well on the global ocean and that largest SST mean errors were found in the Southern hemisphere. The geographic distribution of the model error for SST and temperature profiles are discussed using statistical metrics evaluated over sub-regions of the global ocean.

Development and Implementation of Flood Risk Mapping, Water Bodies Monitoring and Climate Information for Human Health

NASA Astrophysics Data System (ADS)

Ceccato, P.; McDonald, K. C.; Jensen, K.; Podest, E.; De La Torre Juarez, M.

2013-12-01

Public health professionals are increasingly concerned about the potential impact that climate variability and change can have on infectious disease. The International Research Institute for Climate and Society (IRI), City College of New York (CCNY) and NASA Jet Propulsion Laboratory (JPL) are developing new products to increase the public health community's capacity to understand, use, and demand the appropriate climate data and climate information to mitigate the public health impacts of climate on vector-borne diseases such as malaria, leishmaniasis, rift valley fever. In this poster we present the new and improved products that have been developed for monitoring water bodies for monitoring and forecasting risks of vector-borne disease epidemics. The products include seasonal inundation patterns in the East African region based on the global mappings of inundated water fraction derived at the 25-km scale from both active and passive microwave instruments QuikSCAT, AMSR-E, SSM/I, ERS, ASCAT, and MODIS and LANDSAT data. We also present how the products are integrated into a knowledge system (IRI Data Library Map room, SERVIR) to support the use of climate and environmental information in climate-sensitive health decision-making.

Exploring scaling issues by using NASA Cold Land Processes Experiment(CLPX-1, IOP3) radiometric data

NASA Technical Reports Server (NTRS)

Tedesco, Marco; Kim, Edward J.; Cline, Don; Graf, Tobias; Koike, Toshio; Armstrong, Richard; Brodzik, Mary; Stankov, Boba; Gasiewski, Al; Klein, Marian

2004-01-01

The NASA Cold-land Processes Field Experiment-1 (CLPX-1) involved several instruments in order to acquire data at different spatial resolutions. Indeed, one of the main tasks of CLPX-1 was to explore scaling issues associated with microwave remote sensing of snowpacks. To achieve this task, microwave brightness temperatures collected at 18.7, 36.5, and 89 GHz at LSOS test site by means of the University of Tokyo s Ground Based Microwave Radiometer-7 (GBMR-7) were compared with brightness temperatures recorded by the NOAA Polarimetric Scanning Radiometer (PSR/A) and by SSM/I and AMSR-E radiometers. Differences between different scales observations were observed and they may be due to the topography of the terrain and to observed footprints. In the case of satellite and airborne data, indeed, it is necessary to consider the heterogeneity of the terrain and the presence of trees inside the observed scene becomes a very important factor. Also when comparing data acquired only by the two satellites, differences were found. Different acquisition times and footprint positions, together with different calibration and validation procedures, can be responsible for the observed differences.

Indonesian drought monitoring from space. A report of SAFE activity: Assessment of drought impact on rice production in Indonesia by satellite remote sensing and dissemination with web-GIS

NASA Astrophysics Data System (ADS)

Shofiyati, Rizatus; Takeuchi, Wataru; Sofan, Parwati; Darmawan, Soni; Awaluddin; Supriatna, Wahyu

2014-06-01

Long droughts experienced in Indonesia in the past are identified as one of the main factors in the failure of rice production. In this regard, special attention to monitor the condition is encouraged to reduce the damage. Currently, various satellite data and approaches can withdraw valuable information for monitoring and anticipating drought hazards. Two types of drought, Meteorology and Agriculture, have been assessed. During the last 10 years, daily and monthly rainfall data derived from TRMM and GSMaP. MTSAT and AMSR-E data have been analyzed to identify meteorological drought. Agricultural drought has been studied by observing the character of some indices (EVI, VCI, VHI, LST, and NDVI) of sixteen-day and monthly MODIS data at a period of 5 years (2009 - 2013). Network for data transfer has been built between LAPAN (data provider), ICALRD (implementer), IAARD Cloud Computing, and University of Tokyo (technical supporter). A Web-GIS based Drought Monitoring Information System has been developed to disseminate the information to end users. This paper describes the implementation of remote sensing drought monitoring model and development of Web-GIS and satellite based information system.

PM Science Working Group Meeting on Spacecraft Maneuvers

NASA Technical Reports Server (NTRS)

Parkinson, Claire L.

1997-01-01

The EOS PM Science Working Group met on May 6, 1997, to examine the issue of spacecraft maneuvers. The meeting was held at NASA Goddard Space Flight Center and was attended by the Team Leaders of all four instrument science teams with instruments on the PM-1 spacecraft, additional representatives from each of the four teams, the PM Project management, and random others. The meeting was chaired by the PM Project Scientist and open to all. The meeting was called in order to untangle some of the concerns raised over the past several months regarding whether or not the PM-1 spacecraft should undergo spacecraft maneuvers to allow the instruments to obtain deep-space views. Two of the Science Teams, those for the Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Clouds and the Earth's Radiant Energy System (CERES), had strongly expressed the need for deep-space views in order to calibrate their instruments properly and conveniently. The other two teams, those for the Advanced Microwave Scanning Radiometer (AMSR-E) and the Atmospheric Infrared Sounder (AIRS), the Advanced Microwave Sounding Unit (AMSU), and the Humidity Sounder for Brazil (HSB), had expressed concerns that the maneuvers involve risks to the instruments and undesired gaps in the data sets.

Hyperparameter Classification of Arctic Sea Ice and Snow Based on Aerial Laser Data, Passive Microwave Data and Field Data

NASA Astrophysics Data System (ADS)

Herzfeld, U. C.; Maslanik, J.; Williams, S.; Sturm, M.; Cavalieri, D.

2006-12-01

In the past year, the Arctic sea-ice cover has been shrinking at an alarming rate. Remote-sensing technologies provide opportunities for observations of the sea ice at unprecedented repetition rates and spatial resolutions. The advance of new observational technologies is not only fascinating, it also brings with it the challenge and necessity to derive adequate new geoinformatical and geomathematical methods as a basis for analysis and geophysical interpretation of new data types. Our research includes validation and analysis of NASA EOS data, development of observational instrumentation and advanced geoinformatics. In this talk we emphasize the close linkage between technological development and geoinformatics along case studies of sea-ice near Point Barrow, Alaska, based on the following data types: AMSR-E and PSR passive microwave data, RADARSAT and ERS SAR data, manually-collected snow-depth data and laser-elevation data from unmanned aerial vehicles. The hyperparameter concept is introduced to facilitate characterization and classification of the same sea-ice properties and spatial structures from these data sets, which differ with respect to spatial resolution, measured parameters and observed geophysical variables. Mathematically, this requires parameter identification in undersampled, oversampled or overprinted situations.

A New 1DVAR Retrieval for AMSR2 and GMI: Validation and Sensitivites

NASA Astrophysics Data System (ADS)

Duncan, D.; Kummerow, C. D.

2015-12-01

A new non-raining retrieval has been developed for microwave imagers and applied to the GMI and AMSR2 sensors. With the Community Radiative Transfer Model (CRTM) as the forward model for the physical retrieval, a 1-dimensional variational method finds the atmospheric state which minimizes the difference between observed and simulated brightness temperatures. A key innovation of the algorithm development is a method to calculate the sensor error covariance matrix that is specific to the forward model employed and includes off-diagonal elements, allowing the algorithm to handle various forward models and sensors with little cross-talk. The water vapor profile is resolved by way of empirical orthogonal functions (EOFs) and then summed to get total precipitable water (TPW). Validation of retrieved 10m wind speed, TPW, and sea surface temperature (SST) is performed via comparison with buoys and radiosondes as well as global models and other remotely sensed products. In addition to the validation, sensitivity experiments investigate the impact of ancillary data on the under-constrained retrieval, a concern for climate data records that strive to be independent of model biases. The introduction of model analysis data is found to aid the algorithm most at high frequency channels and affect TPW retrievals, whereas wind and cloud water retrievals show little effect from ingesting further ancillary data.

Exploiting Soil Moisture, Precipitation, and Streamflow Observations to Evaluate Soil Moisture/Runoff Coupling in Land Surface Models

NASA Astrophysics Data System (ADS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Xia, Y.; Liu, Q.

2018-05-01

Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective of land surface models tasked with characterizing the surface water and energy balance. Temporal variability in this partitioning is due, in part, to changes in prestorm soil moisture, which determine soil infiltration capacity and unsaturated storage. Utilizing the National Aeronautics and Space Administration Soil Moisture Active Passive Level-4 soil moisture product in combination with streamflow and precipitation observations, we demonstrate that land surface models (LSMs) generally underestimate the strength of the positive rank correlation between prestorm soil moisture and event runoff coefficients (i.e., the fraction of rainfall accumulation volume converted into stormflow runoff during a storm event). Underestimation is largest for LSMs employing an infiltration-excess approach for stormflow runoff generation. More accurate coupling strength is found in LSMs that explicitly represent subsurface stormflow or saturation-excess runoff generation processes.

Effects of manure-application practices on curli production by Escherichia coli transported through soil

NASA Astrophysics Data System (ADS)

Truhlar, A. M.; Salvucci, A. E.; Siler, J. D.; Richards, B. K.; Geohring, L.; Walter, M. T.; Hay, A. G.

2014-12-01

The release of Escherichia coli into the environment from untreated manure can pose a threat to human health. Environmental survival of E. coli has been linked to extracellular fibers called curli. We investigated the effect of manure management (surface application followed by incorporation versus immediate incorporation) on the relative abundance of curli-producing E. coli in subsurface drainage effluent. Samples were collected from three dairy farms. The proportion of curli-producing E. coli in the manure storage facilities was uniform across the farms. However, the abundance of curli-producing E. coli was much greater (P < 0.05) in the tile drains of farms performing surface application of manure than in the tile drain of the farm that incorporated manure. This field result was corroborated by controlled soil column experiments; the abundance of curli-producing E. coli in soil column effluents was greater (P < 0.05) when manure was surface-applied than when it was incorporated. Our findings suggest selection pressures resulting from the different manure application methods affected curli production by E. coli isolates transported through soil. Given the importance of curli production in pathogenesis, this work highlights the effect that manure management strategies may have on pathogenesis-associated phenotypes of bacteria in agricultural subsurface runoff.

Relationship between passive microwave-derived snowmelt and surface-measured discharge, Wheaton River, Yukon Territory, Canada

NASA Astrophysics Data System (ADS)

Ramage, J. M.; McKenney, R. A.; Thorson, B.; Maltais, P.; Kopczynski, S. E.

2006-03-01

Snow volume and melt timing are major factors influencing the water cycle at northern high altitudes and latitudes, yet both are hard to quantify or monitor in remote mountainous regions. Twice-daily special sensor microwave imager (SSM/I) passive microwave observations of seasonal snow melt onset in the Wheaton River basin, Yukon Territory, Canada (60 ° 0805N, 134 ° 5345W), are used to test the idea that melt onset date and duration of snowpack melt-refreeze fluctuations control the timing of the early hydrograph peaks with predictable lags. This work uses the SSM/I satellite data from 1988 to 2002 to evaluate the chronology of melt and runoff patterns in the upper Yukon River basin. The Wheaton River is a small (875 km2) tributary to the Yukon, and is a subarctic, partly glacierized heterogeneous basin with near-continuous hydrographic records dating back to 1966. SSM/I pixels are sensitive to melt onset due to the strong increase in snow emissivity, and have a robust signal, in spite of coarse (>25 × 25 km2) pixel resolution and varied terrain. Results show that Wheaton River peak flows closely follow the end of large daily variations in brightness temperature of pixels covering the Wheaton River, but the magnitude of flow is highly variable, as might be expected from interannual snow mass variability. Spring rise in the hydrograph follows the end of high diurnal brightness temperature (Tb) amplitude variations (DAV) by 0 to 5 days approximately 90% of the time for this basin. Subsequent work will compare these findings for a larger (7250 km2), unglacierized tributary, the Ross River, which is farther northeast (61 ° 5940N, 132 ° 2240W) in the Yukon Territory. These techniques will also be used to try to determine the improvement in melt detection and runoff prediction from the higher resolution (15 × 15 km2) advanced microwave scanning radiometer for EOS (AMSR-E) sensor.

Glacial Boundary Features Delineated Using Enhanced-resolution Passive-microwave Data to Determine Melt Season Variation of the Vatnajokull Ice Cap, Iceland

NASA Astrophysics Data System (ADS)

Marzillier, D. M.; Ramage, J. M.

2017-12-01

Temperate glaciers such as those seen in Iceland experience high annual mass flux, thereby responding to small scale changes in Earth's climate. Decadal changes in the glacial margins of Iceland's ice caps are observable in the Landsat record, however twice daily AMSR-E Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) allow for observation on a daily temporal scale and a 3.125 km spatial scale, which can in turn be connected to patterns seen over longer periods of time. Passive microwave data allow for careful observation of melt onset and duration in Iceland's glacial regions by recording changes in emissivity of the ice surface, known as brightness temperature (TB), which is sensitive to fluctuations in the liquid water content of snow and ice seen during melting in glaciated regions. Enhanced resolution of this data set allows for a determination of a threshold that defines the melting season. The XPGR snowmelt algorithm originally presented by Abdalati and Steffen (1995) is used as a comparison with the diurnal amplitude variation (DAV) values on Iceland's Vatnajokull ice cap located at 64.4N, -16.8W. Ground-based air temperature data in this region, digital elevation models (DEMs), and river discharge dominated by glacial runoff are used to confirm the glacial response to changes in global climate. Results show that Iceland glaciers have a bimodal distribution of brightness temperature delineating when the snow/ice is melting and refreezing. Ground based temperatures have increased on a decadal trend. Clear glacial boundaries are visible on the passive microwave delineating strong features, and we are working to understand their variability and contribution to glacier evolution. The passive microwave data set allows connections to be made between observations seen on a daily scale and the long term glacier changes observed by the Landsat satellite record that integrates the overall glacier changes.

Tools for proximal soil sensing

USDA-ARS?s Scientific Manuscript database

Proximal soil sensing (i.e. near-surface geophysical methods) are used to study soil phenomena across spatial scales. Geophysical methods exploit contrasts in physical properties (dielectric permittivity, apparent electrical conductivity or resistivity, magnetic susceptibility) to indirectly measur...

Soil property effects on wind erosion of organic soils

NASA Astrophysics Data System (ADS)

Zobeck, Ted M.; Baddock, Matthew; Scott Van Pelt, R.; Tatarko, John; Acosta-Martinez, Veronica

2013-09-01

Histosols (also known as organic soils, mucks, or peats) are soils that are dominated by organic matter (OM > 20%) in half or more of the upper 80 cm. Forty two states have a total of 21 million ha of Histosols in the United States. These soils, when intensively cropped, are subject to wind erosion resulting in loss of crop productivity and degradation of soil, air, and water quality. Estimating wind erosion on Histosols has been determined by USDA-Natural Resources Conservation Service (NRCS) as a critical need for the Wind Erosion Prediction System (WEPS) model. WEPS has been developed to simulate wind erosion on agricultural land in the US, including soils with organic soil material surfaces. However, additional field measurements are needed to understand how soil properties vary among organic soils and to calibrate and validate estimates of wind erosion of organic soils using WEPS. Soil properties and sediment flux were measured in six soils with high organic contents located in Michigan and Florida, USA. Soil properties observed included organic matter content, particle density, dry mechanical stability, dry clod stability, wind erodible material, and geometric mean diameter of the surface aggregate distribution. A field portable wind tunnel was used to generate suspended sediment and dust from agricultural surfaces for soils ranging from 17% to 67% organic matter. The soils were tilled and rolled to provide a consolidated, friable surface. Dust emissions and saltation were measured using an isokinetic vertical slot sampler aspirated by a regulated suction source. Suspended dust was sampled using a Grimm optical particle size analyzer. Particle density of the saltation-sized material (>106 μm) was inversely related to OM content and varied from 2.41 g cm-3 for the soil with the lowest OM content to 1.61 g cm-3 for the soil with highest OM content. Wind erodible material and the geometric mean diameter of the surface soil were inversely related to dry clod stability. The effect of soil properties on sediment flux varied among flux types. Saltation flux was adequately predicted with simple linear regression models. Dry mechanical stability was the best single soil property linearly related to saltation flux. Simple linear models with soil properties as independent variables were not well correlated with PM10E values (mass flux). A second order polynomial equation with OM as the independent variable was found to be most highly correlated with PM10E values. These results demonstrate that variations in sediment and dust emissions can be linked to soil properties using simple models based on one or more soil properties to estimate saltation mass flux and PM10E values from organic and organic-rich soils.

An Integrated Approach for Accessing Multiple Datasets through LANCE

NASA Astrophysics Data System (ADS)

Murphy, K. J.; Teague, M.; Conover, H.; Regner, K.; Beaumont, B.; Masuoka, E.; Vollmer, B.; Theobald, M.; Durbin, P.; Michael, K.; Boller, R. A.; Schmaltz, J. E.; Davies, D.; Horricks, K.; Ilavajhala, S.; Thompson, C. K.; Bingham, A.

2011-12-01

The NASA/GSFC Land Atmospheres Near-real time Capability for EOS (LANCE) provides imagery for approximately 40 data products from MODIS, AIRS, AMSR-E and OMI to support the applications community in the study of a variety of phenomena. Thirty-six of these products are available within 2.5 hours of observation at the spacecraft. The data set includes the population density data provided by the EOSDIS Socio-Economic Data and Applications Center (SEDAC). The purpose of this paper is to describe the variety of tools that have been developed by LANCE to support user access to the imagery. The long-standing Rapid Response system has been integrated into LANCE and is a major vehicle for the distribution of the imagery to end users. There are presently approximately 10,000 anonymous users per month accessing these imagery. The products are grouped into 14 applications categories such as Smoke Plumes, Pollution, Fires, Agriculture and the selection of any category will make relevant subsets of the 40 products available as possible overlays in an interactive Web Client utilizing Web Mapping Service (WMS) to support user investigations (http://lance2.modaps.eosdis.nasa.gov/wms/). For example, selecting Severe Storms will include 6 products for MODIS, OMI, AIRS, and AMSR-E plus the SEDAC population density data. The client and WMS were developed using open-source technologies such as OpenLayers and MapServer and provides a uniform, browser-based access to data products. All overlays are downloadable in PNG, JPEG, or GeoTiff form up to 200MB per request. The WMS was beta-tested with the user community and substantial performance improvements were made through the use of such techniques as tile-caching. LANCE established a partnership with Physical Oceanography Distributed Active Archive Center (PO DAAC) to develop an alternative presentation for the 40 data products known as the State of the Earth (SOTE). This provides a Google Earth-based interface to the products grouped in the same fashion as the WMS. The SOTE servers stream imagery and data in the OGC KML format and these feeds can be visualized through the Google Earth browser plug-in. SOTE provides visualization through a virtual globe environment by allowing users to interact with the globe via zooming, rotating, and tilting. In addition, SOTE also allows adding custom KML feeds. LANCE also provides datacasting feeds to facilitate user access to imagery for the 40 products and the related HDF-EOS products (available in a variety of formats). These XML-based data feeds contain data attribute and geolocation information, and metadata including an identification of the related application category. Users can subscribe to any feeds through the LANCE web site and use the PO DAAC Feed Reader to filter and view the content. The WMS, SOTE, and datacasting tools can be accessed through http://lance.nasa.gov.

Soil Minerals Affect Extracellular Enzyme Activities in Cold and Warm Environments

NASA Astrophysics Data System (ADS)

Yang, Z.; Morin, M. M.; Graham, D. E.; Wullschleger, S. D.; Gu, B.

2017-12-01

Extracellular enzymes are mainly responsible for degrading and cycling soil organic matter (SOM) in both cold and warm terrestrial ecosystems. Minerals can play important roles in affecting soil enzyme activities, however, the interactions between enzyme and soil minerals remain poorly understood. In this study, we developed a model soil-enzyme system to examine the mineral effects on a hydrolytic enzyme (i.e., β-glucosidase) under both cold (4°C) and relatively warm (20 and 30°C) conditions. Minerals including iron oxides and clays (e.g., kaolinite and montmorillonite) were used to mimic different types of soils, and enzyme adsorption experiments were conducted to determine the enzyme interactions with different mineral surfaces. Time-series experiments were also carried out to measure enzymatic degradation of the organic substrates, such as cellobiose and indican. We observed that fractions of adsorbed enzyme and the hydrolytic activity were higher on iron oxides (e.g., hematite) compared to kaolinite and montmorillonite at given experimental conditions. The degradation of cellobiose was significantly faster than that of indican in the presence of minerals. We also found that the adsorption of enzyme was not dependent on the mineral surface areas, but was controlled by the mineral surface charge. In addition, temperature increase from 4 to 30°C enhanced mineral-assisted glucosidase hydrolysis by 2 to 4 fold, suggesting greater degradation under warmer environments. The present work demonstrates that the enzyme activity is influenced not only by the soil temperature but also by the surface chemistry of soil minerals. Our results highlight the need to consider the physical and chemical properties of minerals in biogeochemical models, which could provide a better prediction for enzyme-facilitated SOM transformations in terrestrial ecosystems.

Influence of crop residues on trifluralin mineralization in a silty clay loam soil.

PubMed

Farenhorst, Annemieke

2007-01-01

Trifluralin is typically applied onto crop residues (trash, stubble) at the soil surface, or onto the bare soil surface after the incorporation of crop residues into the soil. The objective of this study was to quantify the effect of the type and amount of crop residues in soil on trifluralin mineralization in a Wellwood silty clay loam soil. Leaves and stubble of Potato (Solanum tuberosum) (P); Canola (Brassica napus) (C), Wheat (Triticum aestivum) (W), Oats (Avena sativa), (O), and Alfalfa (Medicago sativa) (A) were added to soil microcosms at rates of 2%, 4%, 8% and 16% of the total soil weight (25 g). The type and amount of crop residues in soil had little influence on the trifluralin first-order mineralization rate constant, which ranged from 3.57E-03 day(-1) in soil with 16% A to 2.89E-02 day(-1) in soil with 8% W. The cumulative trifluralin mineralization at 113 days ranged from 1.15% in soil with 16% P to 3.21% in soil with 4% C, again demonstrating that the observed differences across the treatments are not of agronomic or environmental importance.

Thermal Properties of Soils

DTIC Science & Technology

1981-12-01

plagio - clase feldspar and pyroxene. The tine fraction may Surface area and its effects contain the clay "sheet" minerals (i.e. kaolinite. illite...Pyroxene, Kaoliniwe Unified By By Ortho. Plagio . amphibole, Basic clay min. Hematite Soil Soil soil petrogr. X.ray clase clase and Igneous and clay and no

Escherichia coli Transport from Surface-Applied Manure to Subsurface Drains through Artificial Biopores

USDA-ARS?s Scientific Manuscript database

Transport of pathogenic bacteria in soils primarily occurs through soil mesopores and macropores (e.g., biopores and cracks). Field research has demonstrated that biopores and subsurface drains can be hydraulically connected. This research was conducted to investigate the importance of surface conne...

The EUMETSAT Polar System-Second Generation (EPS-SG) micro-wave and sub-millimetre wave imaging missions

NASA Astrophysics Data System (ADS)

Accadia, Christophe; Schlüssel, Peter; Phillips, Pepe L.; Wilson, J. Julian W.

2013-10-01

The EUMETSAT Polar System (EPS) will be followed by a second generation system, EPS-SG, in the 2020-2040 timeframe and contribute to the Joint Polar System being jointly set up with NOAA. Among the various missions which are part of EPS-SG, there are the Microwave Imager (MWI) and the Ice Cloud Imager (ICI). The MWI frequencies are from 18 GHz up to 183 GHz. All MWI channels up to 89 GHz measure both V and H polarisations. The primary objective of the MWI mission is to support Numerical Weather Prediction at regional and global scales. The MWI will not only provide continuity of measurements for some heritage microwave imager channels (e.g. SSM/I, AMSR-E) but will also include additional channels such as the 50-55 / 118 GHz bands. The combined use of these channels will provide more information on cloud and precipitation over sea and land. The ICI will provide measurements over the sub-millimetre spectral range contributing to an innovative characterisation of clouds over the whole globe. The ICI has channels at 183 GHz, 325 GHz and 448 GHz with single V polarisation and two channels at 243 GHz and 664 GHz with both V and H polarisation. The ICI's primary objectives are to support climate monitoring and validation of ice cloud models and the parameterisation of ice clouds in weather and climate models through the provision of ice cloud products.

Heavy metal contamination of soil and water in the vicinity of an abandoned e-waste recycling site: implications for dissemination of heavy metals.

PubMed

Wu, Qihang; Leung, Jonathan Y S; Geng, Xinhua; Chen, Shejun; Huang, Xuexia; Li, Haiyan; Huang, Zhuying; Zhu, Libin; Chen, Jiahao; Lu, Yayin

2015-02-15

Illegal e-waste recycling activity has caused heavy metal pollution in many developing countries, including China. In recent years, the Chinese government has strengthened enforcement to impede such activity; however, the heavy metals remaining in the abandoned e-waste recycling site can still pose ecological risk. The present study aimed to investigate the concentrations of heavy metals in soil and water in the vicinity of an abandoned e-waste recycling site in Longtang, South China. Results showed that the surface soil of the former burning and acid-leaching sites was still heavily contaminated with Cd (>0.39 mg kg(-1)) and Cu (>1981 mg kg(-1)), which exceeded their respective guideline levels. The concentration of heavy metals generally decreased with depth in both burning site and paddy field, which is related to the elevated pH and reduced TOM along the depth gradient. The pond water was seriously acidified and contaminated with heavy metals, while the well water was slightly contaminated since heavy metals were mostly retained in the surface soil. The use of pond water for irrigation resulted in considerable heavy metal contamination in the paddy soil. Compared with previous studies, the reduced heavy metal concentrations in the surface soil imply that heavy metals were transported to the other areas, such as pond. Therefore, immediate remediation of the contaminated soil and water is necessary to prevent dissemination of heavy metals and potential ecological disaster. Copyright © 2014 Elsevier B.V. All rights reserved.

Corporate corruption of science-Another asbestos example.

PubMed

Egilman, David; Monárrez, Rubén

2017-02-01

Kelsh et al. [2007]: Occup Med (Lond) 57:581-589 published a paper reanalyzing one of the few data sources publicly available on mesothelioma amongst brake workers, the Australian Mesothelioma Surveillance Registry (AMSR). This reanalysis was commissioned by lawyers representing the automobile manufacturing companies and did not align with an independent analysis published by Leigh and Driscoll [2003]: Occup Environ Health 9:206-217. We sought to reevaluate the AMSR data ourselves to understand how the company-sponsored research categorized the data. In our re-analysis of the 78 brake-related folios in the AMSR, we determined that 57 were employed brake mechanics, 35 were employed brake mechanics with no other asbestos exposure besides brake work or repair, and 41 of these cases had no other asbestos exposure besides brake work or repair. Our classifications differed significantly from Kelsh et al. We discuss how Kelsh et al. methodically reduced the relevant cases by following overly stringent criteria for inclusion. Am. J. Ind. Med. 60:152-162, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Lunar soil and surface processes studies

NASA Technical Reports Server (NTRS)

Glass, B. P.

1975-01-01

Glass particles in lunar soil were characterized and compared to terrestrial analogues. In addition, useful information was obtained concerning the nature of lunar surface processes (e.g. volcanism and impact), maturity of soils and chemistry and heterogeneity of lunar surface material. It is felt, however, that the most important result of the study was that it demonstrated that the investigation of glass particles from the regolith of planetary bodies with little or no atmospheres can be a powerful method for learning about the surface processes and chemistry of planetary surfaces. Thus, the return of samples from other planetary bodies (especially the terrestrial planets and asteroids) using unmanned spacecraft is urged.

Variable Charge Soils: Mineralogy and Chemistry

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qafoku, Nik; Van Ranst, Eric; Noble, Andrew

2003-11-01

Soils rich in particles with amphoteric surface properties in the Oxisols, Ultisols, Alfisols, Spodosols and Andisols orders (1) are considered variable charge soils (2). The term “variable charge” is used to describe organic and inorganic soil constituents with reactive surface groups whose charge varies with pH, ionic concentration and composition of the soil solution. Such groups are the surface carboxyl, phenolic and amino functional groups of organic materials in soils, and surface hydroxyl groups of Fe and Al oxides, allophane and imogolite. The hydroxyl surface groups are also present on edges of some phyllosilicate minerals such as kaolinite, mica, andmore » hydroxyl-interlayered vermiculite. The variable charge is developed on the surface groups as a result of adsorption or desorption of ions that are constituents of the solid phase, i.e., H+, and the adsorption or desorption of solid-unlike ions that are not constituents of the solid. Highly weathered soils usually undergo isoeletric weathering and reach a “zero net charge” stage during their development. They have a slightly acidic to acidic soil solution pH, which is close to either point of zero net charge (PZNC) (3) or point of zero salt effect (PZSE) (3). They are characterized by high abundances of minerals with a point of zero net proton charge (PZNPC) (3) at neutral and slightly basic pHs; the most important being Fe and Al oxides and allophane. Under acidic conditions, the surfaces of these minerals are net positively charged. In contrast, the surfaces of permanent charge phyllosilicates are negatively charged regardless of ambient conditions. Variable charge soils therefore, are heterogeneous charge systems. The coexistence and interactions of oppositely charged surfaces or particles confers a different pattern of physical and chemical behavior on the soil, relatively to a homogeneously charged system of temperate regions. In some variable charge soils (Oxisols and some Ultisols developed on ferromagnesian-rich parent materials) the surfaces of phyllosilicates are coated to a lesser or greater extent by amorphous or crystalline, oppositely charged nanoparticles of Fe and Al oxides. These coatings exhibit a high reactive surface area and help cementing larger particles with one another. As a result of these electrostatic interactions, stable microaggregates that are difficult to disperse are formed in variable charge soils. Most of highly weathered soils have reached the “advanced stage” of Jackson-Sherman weathering sequence that is characterized by the removal of Na, K, Ca, Mg, and Fe(II), the presence of Fe and Al polymers, and very dilute soil solutions with an ionic strength (IS) of less than 1 mmol L-1. The inter-penetration or overlapping of the diffuse double layers on oppositely charged surfaces may occur in these dilute systems. These diffuse layer interactions may affect the magnitude of the effective charge, i.e., the counter-ion charge (4). In addition, salt adsorption, which is defined as the simultaneous adsorption in equivalent amounts of the cation and anion of an electrolyte with no net release of other ions into the soil solution, appears to be a common phenomenon in these soils. They act as cation- and anion-exchangers and as salt-sorbers. The magnitude of salt adsorption depends strongly on initial IS in the soil solution and the presence in appreciable amounts of oppositely charged surfaces. Among the authors that have made illustrious contributions towards a better understanding of these fascinating soil systems are S. Matson, R.K. Schofield, van Olphen, M.E. Sumner, G.W. Thomas, G.P. Gillman, G. Uehara, B.K.G. Theng, K. Wada, N.J. Barrow, J.W. Bowden, R.J. Hunter and G. Sposito. This entry is mainly based on publications by these authors.« less

Three Principles of Water Flow in Soils

NASA Astrophysics Data System (ADS)

Guo, L.; Lin, H.

2016-12-01

Knowledge of water flow in soils is crucial to understanding terrestrial hydrological cycle, surface energy balance, biogeochemical dynamics, ecosystem services, contaminant transport, and many other Critical Zone processes. However, due to the complex and dynamic nature of non-uniform flow, reconstruction and prediction of water flow in natural soils remain challenging. This study synthesizes three principles of water flow in soils that can improve modeling water flow in soils of various complexity. The first principle, known as the Darcy's law, came to light in the 19th century and suggested a linear relationship between water flux density and hydraulic gradient, which was modified by Buckingham for unsaturated soils. Combining mass balance and the Buckingham-Darcy's law, L.A. Richards quantitatively described soil water change with space and time, i.e., Richards equation. The second principle was proposed by L.A. Richards in the 20th century, which described the minimum pressure potential needed to overcome surface tension of fluid and initiate water flow through soil-air interface. This study extends this principle to encompass soil hydrologic phenomena related to varied interfaces and microscopic features and provides a more cohesive explanation of hysteresis, hydrophobicity, and threshold behavior when water moves through layered soils. The third principle is emerging in the 21st century, which highlights the complex and evolving flow networks embedded in heterogeneous soils. This principle is summarized as: Water moves non-uniformly in natural soils with a dual-flow regime, i.e., it follows the least-resistant or preferred paths when "pushed" (e.g., by storms) or "attracted" (e.g., by plants) or "restricted" (e.g., by bedrock), but moves diffusively into the matrix when "relaxed" (e.g., at rest) or "touched" (e.g., adsorption). The first principle is a macroscopic view of steady-state water flow, the second principle is a microscopic view of interface-based dynamics of water flow, and the third principle combines macroscopic and microscopic consideration to explain a mosaic-like flow regime in soils. Integration of above principles can advance flow theory, measurement, and modeling and can improve management of soil and water resources.

Some Results on Sea Ice Rheology for the Seasonal Ice Zone, Obtained from the Deformation Field of Sea Ice Drift Pattern

NASA Astrophysics Data System (ADS)

Toyota, T.; Kimura, N.

2017-12-01

Sea ice rheology which relates sea ice stress to the large-scale deformation of the ice cover has been a big issue to numerical sea ice modelling. At present the treatment of internal stress within sea ice area is based mostly on the rheology formulated by Hibler (1979), where the whole sea ice area behaves like an isotropic and plastic matter under the ordinary stress with the yield curve given by an ellipse with an aspect ratio (e) of 2, irrespective of sea ice area and horizontal resolution of the model. However, this formulation was initially developed to reproduce the seasonal variation of the perennial ice in the Arctic Ocean. As for its applicability to the seasonal ice zones (SIZ), where various types of sea ice are present, it still needs validation from observational data. In this study, the validity of this rheology was examined for the Sea of Okhotsk ice, typical of the SIZ, based on the AMSR-derived ice drift pattern in comparison with the result obtained for the Beaufort Sea. To examine the dependence on a horizontal scale, the coastal radar data operated near the Hokkaido coast, Japan, were also used. Ice drift pattern was obtained by a maximum cross-correlation method with grid spacings of 37.5 km from the 89 GHz brightness temperature of AMSR-E for the entire Sea of Okhotsk and the Beaufort Sea and 1.3 km from the coastal radar for the near-shore Sea of Okhotsk. The validity of this rheology was investigated from a standpoint of work rate done by deformation field, following the theory of Rothrock (1975). In analysis, the relative rates of convergence were compared between theory and observation to check the shape of yield curve, and the strain ellipse at each grid cell was estimated to see the horizontal variation of deformation field. The result shows that the ellipse of e=1.7-2.0 as the yield curve represents the observed relative conversion rates well for all the ice areas. Since this result corresponds with the yield criterion by Tresca and Von Mises for a 2D plastic matter, it suggests the validity and applicability of this rheology to the SIZ to some extent. However, it was also noted that the variation of the deformation field in the Sea of Okhotsk is much larger than in the Beaufort Sea, which indicates the need for the careful treatment of grid size in the model.

Surface Instabilities From Buried Explosives

DTIC Science & Technology

2009-07-21

interface between soil and air during buried explosions. The purpose of understanding this instability is to determine its effect on local vehicle loading...Except when the target is on the surface, e.g., a tank track, the most important loading mechanism from a buried charge is the impact of soil propelled...rising soil and the air. This unstable 15. SUBJECT TERMS Buried Explosions, Richtmyer-Meshkov Instability, Target Loading, Jetting, 16 SECURITY

The importance of nano-porosity in the stalk-derived biochar to the sorption of 17β-estradiol and retention of it in the greenhouse soil.

PubMed

Zhang, Fengsong; Li, Yanxia; Zhang, Guixiang; Li, Wei; Yang, Lingsheng

2017-04-01

Natural estrogens in greenhouse soils with long-term manure application are becoming a potential threat to adjacent aquatic environment. Porous stalk biochar as a cost-effective adsorbent of estrogen has a strong potential to reduce their transportation from soil to waters. But the dominant adsorption mechanism of estrogen to stalk biochars and retention of estrogen by greenhouse soils amended with biochar are less well known. Element, function groups, total surface area (SA total ), nano-pores of stalk biochars, and chemical structure of 17β-estradiol (E2, length 1.20 nm, width 0.56 nm, thickness 0.48 nm) are integrated in research on E2 sorption behavior in three stalk-derived biochars produced from wheat straw (WS), rice straw (RS), and corn straw (CS), and greenhouse soils amended with optimal biochar. The three biochars had comparable H/C and (O + N)/C, while their aromatic carbon contents and total surface areas (SA total ) both varied as CS > WS > RS. However, WS had the highest sorption capacity (logK oc ), sorption affinity (K f ), and strongest nonlinearity (n). Additionally, the variation of Langmuir maximum adsorption capacity (Q 0 ) was consistent with the trend for SA 1.2-20 (WS > RS > CS) but contrary to the trend for SA total and SA <1.2 (CS > WS > RS). These results indicate that pore-filling dominates the sorption of E2 by biochars and exhibits "sieving effect" and length-directionality-specific via H-bonding between -OH groups on the both ends of E2 in the length direction and polar groups on the inner surface of pores. After the addition of wheat straw biochar, the extent of increase in the sorption affinity for E2 in the soil with low OC content was higher than those in the soil with high OC content. Therefore, the effectiveness for the wheat straw biochar mitigating the risk of E2 in greenhouse soil depended on the compositions of soil, especially organic matter.

3D Visualization of near real-time remote-sensing observation for hurricanes field campaign using Google Earth API

NASA Astrophysics Data System (ADS)

Li, P.; Turk, J.; Vu, Q.; Knosp, B.; Hristova-Veleva, S. M.; Lambrigtsen, B.; Poulsen, W. L.; Licata, S.

2009-12-01

NASA is planning a new field experiment, the Genesis and Rapid Intensification Processes (GRIP), in the summer of 2010 to better understand how tropical storms form and develop into major hurricanes. The DC-8 aircraft and the Global Hawk Unmanned Airborne System (UAS) will be deployed loaded with instruments for measurements including lightning, temperature, 3D wind, precipitation, liquid and ice water contents, aerosol and cloud profiles. During the field campaign, both the spaceborne and the airborne observations will be collected in real-time and integrated with the hurricane forecast models. This observation-model integration will help the campaign achieve its science goals by allowing team members to effectively plan the mission with current forecasts. To support the GRIP experiment, JPL developed a website for interactive visualization of all related remote-sensing observations in the GRIP’s geographical domain using the new Google Earth API. All the observations are collected in near real-time (NRT) with 2 to 5 hour latency. The observations include a 1KM blended Sea Surface Temperature (SST) map from GHRSST L2P products; 6-hour composite images of GOES IR; stability indices, temperature and vapor profiles from AIRS and AMSU-B; microwave brightness temperature and rain index maps from AMSR-E, SSMI and TRMM-TMI; ocean surface wind vectors, vorticity and divergence of the wind from QuikSCAT; the 3D precipitation structure from TRMM-PR and vertical profiles of cloud and precipitation from CloudSAT. All the NRT observations are collected from the data centers and science facilities at NASA and NOAA, subsetted, re-projected, and composited into hourly or daily data products depending on the frequency of the observation. The data products are then displayed on the 3D Google Earth plug-in at the JPL Tropical Cyclone Information System (TCIS) website. The data products offered by the TCIS in the Google Earth display include image overlays, wind vectors, clickable placemarks with vertical profiles for temperature and water vapors and curtain plots along the satellite tracks. Multiple products can be overlaid with individual adjustable opacity control. The time sequence visualization is supported by calendar and Google Earth time animation. The work described here was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Storm-centric view of Tropical Cyclone oceanic wakes

NASA Astrophysics Data System (ADS)

Gentemann, C. L.; Scott, J. P.; Smith, D.

2012-12-01

Tropical cyclones (TCs) have a dramatic impact on the upper ocean. Storm-generated oceanic mixing, high amplitude near-inertial currents, upwelling, and heat fluxes often warm or cool the surface ocean temperatures over large regions near tropical cyclones. These SST anomalies occur to the right (Northern Hemisphere) or left (Southern Hemisphere) of the storm track, varying along and across the storm track. These wide swaths of temperature change have been previously documented by in situ field programs as well as IR and visible satellite data. The amplitude, temporal and spatial variability of these surface temperature anomalies depend primarily upon the storm size, storm intensity, translational velocity, and the underlying ocean conditions. Tropical cyclone 'cold wakes' are usually 2 - 5 °C cooler than pre-storm SSTs, and persist for days to weeks. Since storms that occur in rapid succession typically follow similar paths, the cold wake from one storm can affect development of subsequent storms. Recent studies, on both warm and cold wakes, have mostly focused on small subsets of global storms because of the amount of work it takes to co-locate different data sources to a storm's location. While a number of hurricane/typhoon websites exist that co-locate various datasets to TC locations, none provide 3-dimensional temporal and spatial structure of the ocean-atmosphere necessary to study cold/warm wake development and impact. We are developing a global 3-dimensional storm centric database for TC research. The database we propose will include in situ data, satellite data, and model analyses. Remote Sensing Systems (RSS) has a widely-used storm watch archive which provides the user an interface for visually analyzing collocated NASA Quick Scatterometer (QuikSCAT) winds with GHRSST microwave SSTs and SSM/I, TMI or AMSR-E rain rates for all global tropical cyclones 1999-2009. We will build on this concept of bringing together different data near storm locations when developing the storm-centric database. This database will be made available to researchers via the web display tools previously developed for RSS web pages. The database will provide scientists with a single data format collection of various atmospheric and oceanographic data, and will include all tropical storms since 1998, when the passive MW SSTs from the TMI instrument first became available. Initial results showing an analysis of Typhoon Man-Yi will be presented.

Hydrologic data assimilation with a hillslope-scale-resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Flores, Alejandro N.; Bras, Rafael L.; Entekhabi, Dara

2012-08-01

Soil moisture information is critical for applications like landslide susceptibility analysis and military trafficability assessment. Existing technologies cannot observe soil moisture at spatial scales of hillslopes (e.g., 100 to 102 m) and over large areas (e.g., 102 to 105 km2) with sufficiently high temporal coverage (e.g., days). Physics-based hydrologic models can simulate soil moisture at the necessary spatial and temporal scales, albeit with error. We develop and test a data assimilation framework based on the ensemble Kalman filter for constraining uncertain simulated high-resolution soil moisture fields to anticipated remote sensing products, specifically NASA's Soil Moisture Active-Passive (SMAP) mission, which will provide global L band microwave observation approximately every 2-3 days. The framework directly assimilates SMAP synthetic 3 km radar backscatter observations to update hillslope-scale bare soil moisture estimates from a physics-based model. Downscaling from 3 km observations to hillslope scales is achieved through the data assimilation algorithm. Assimilation reduces bias in near-surface soil moisture (e.g., top 10 cm) by approximately 0.05 m3/m3and expected root-mean-square errors by at least 60% in much of the watershed, relative to an open loop simulation. However, near-surface moisture estimates in channel and valley bottoms do not improve, and estimates of profile-integrated moisture throughout the watershed do not substantially improve. We discuss the implications of this work, focusing on ongoing efforts to improve soil moisture estimation in the entire soil profile through joint assimilation of other satellite (e.g., vegetation) and in situ soil moisture measurements.

The MJO Transition from Shallow to Deep Convection in CloudSat/CALIPSO Data and GISS GCM Simulations

NASA Technical Reports Server (NTRS)

DelGenio, Anthony G.; Chen, Yonghua; Kim, Daehyun; Yao, Mao-Sung

2013-01-01

The relationship between convective penetration depth and tropospheric humidity is central to recent theories of the Madden-Julian oscillation (MJO). It has been suggested that general circulation models (GCMs) poorly simulate the MJO because they fail to gradually moisten the troposphere by shallow convection and simulate a slow transition to deep convection. CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data are analyzed to document the variability of convection depth and its relation to water vapor during the MJO transition from shallow to deep convection and to constrain GCM cumulus parameterizations. Composites of cloud occurrence for 10MJO events show the following anticipatedMJO cloud structure: shallow and congestus clouds in advance of the peak, deep clouds near the peak, and upper-level anvils after the peak. Cirrus clouds are also frequent in advance of the peak. The Advanced Microwave Scanning Radiometer for EarthObserving System (EOS) (AMSR-E) columnwater vapor (CWV) increases by;5 mmduring the shallow- deep transition phase, consistent with the idea of moisture preconditioning. Echo-top height of clouds rooted in the boundary layer increases sharply with CWV, with large variability in depth when CWV is between;46 and 68 mm. International Satellite Cloud Climatology Project cloud classifications reproduce these climatological relationships but correctly identify congestus-dominated scenes only about half the time. A version of the Goddard Institute for Space Studies Model E2 (GISS-E2) GCM with strengthened entrainment and rain evaporation that produces MJO-like variability also reproduces the shallow-deep convection transition, including the large variability of cloud-top height at intermediate CWV values. The variability is due to small grid-scale relative humidity and lapse rate anomalies for similar values of CWV. 1.

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Fate of Escherichia coli O157:H7 and Salmonella in soil and lettuce roots as affected by potential home gardening practices.

PubMed

Erickson, Marilyn C; Liao, Jean; Payton, Alison S; Webb, Cathy C; Ma, Li; Zhang, Guodong; Flitcroft, Ian; Doyle, Michael P; Beuchat, Larry R

2013-12-01

The survival and distribution of enteric pathogens in soil and lettuce systems were investigated in response to several practices (soil amendment supplementation and reduced watering) that could be applied by home gardeners. Leaf lettuce was grown in manure compost/top soil (0:5, 1:5 or 2:5 w/w) mixtures. Escherichia coli O157:H7 or Salmonella was applied at a low or high dose (10(3) or 10(6) colony-forming units (CFU) mL(-1) ) to the soil of seedlings and mid-age plants. Supplementation of top soil with compost did not affect pathogen survival in the soil or on root surfaces, suggesting that nutrients were not a limiting factor. Salmonella populations on root surfaces were 0.7-0.8 log CFU g(-1) lower for mid-age plants compared with seedlings. E. coli O157:H7 populations on root surfaces were 0.8 log CFU g(-1) lower for mid-age plants receiving 40 mL of water compared with plants receiving 75 mL of water on alternate days. Preharvest internalization of E. coli O157:H7 and Salmonella into lettuce roots was not observed at any time. Based on the environmental conditions and high pathogen populations in soil used in this study, internalization of Salmonella or E. coli O157:H7 into lettuce roots did not occur under practices that could be encountered by inexperienced home gardeners. © 2013 Society of Chemical Industry.

Soil-soil solution distribution coefficient of soil organic matter is a key factor for that of radioiodide in surface and subsurface soils.

PubMed

Unno, Yusuke; Tsukada, Hirofumi; Takeda, Akira; Takaku, Yuichi; Hisamatsu, Shun'ichi

2017-04-01

We investigated the vertical distribution of the soil-soil-solution distribution coefficients (K d ) of 125 I, 137 Cs, and 85 Sr in organic-rich surface soil and organic-poor subsurface soil of a pasture and an urban forest near a spent-nuclear-fuel reprocessing plant in Rokkasho, Japan. K d of 137 Cs was highly correlated with water-extractable K + . K d of 85 Sr was highly correlated with water-extractable Ca 2+ and SOC. K d of 125 I - was low in organic-rich surface soil, high slightly below the surface, and lowest in the deepest soil. This kinked distribution pattern differed from the gradual decrease of the other radionuclides. The thickness of the high- 125 I - K d middle layer (i.e., with high radioiodide retention ability) differed between sites. K d of 125 I - was significantly correlated with K d of soil organic carbon. Our results also showed that the layer thickness is controlled by the ratio of K d -OC between surface and subsurface soils. This finding suggests that the addition of SOC might prevent further radioiodide migration down the soil profile. As far as we know, this is the first report to show a strong correlation of a soil characteristic with K d of 125 I - . Further study is needed to clarify how radioiodide is retained and migrates in soil. Copyright © 2017 Elsevier Ltd. All rights reserved.

On the role of "internal variability" on soil erosion assessment

NASA Astrophysics Data System (ADS)

Kim, Jongho; Ivanov, Valeriy; Fatichi, Simone

2017-04-01

Empirical data demonstrate that soil loss is highly non-unique with respect to meteorological or even runoff forcing and its frequency distributions exhibit heavy tails. However, all current erosion assessments do not describe the large associated uncertainties of temporal erosion variability and make unjustified assumptions by relying on central tendencies. Thus, the predictive skill of prognostic models and reliability of national-scale assessments have been repeatedly questioned. In this study, we attempt to reveal that the high variability in soil losses can be attributed to two sources: (1) 'external variability' referring to the uncertainties originating at macro-scale, such as climate, topography, and land use, which has been extensively studied; (2) 'geomorphic internal variability' referring to the micro-scale variations of pedologic properties (e.g., surface erodibility in soils with multi-sized particles), hydrologic properties (e.g., soil structure and degree of saturation), and hydraulic properties (e.g., surface roughness and surface topography). Using data and a physical hydraulic, hydrologic, and erosion and sediment transport model, we show that the geomorphic internal variability summarized by spatio-temporal variability in surface erodibility properties is a considerable source of uncertainty in erosion estimates and represents an overlooked but vital element of geomorphic response. The conclusion is that predictive frameworks of soil erosion should embed stochastic components together with deterministic assessments, if they do not want to largely underestimate uncertainty. Acknowledgement: This study was supported by the Basic Science Research Program of the National Research Foundation of Korea funded by the Ministry of Education (2016R1D1A1B03931886).

The Role of Vegetation and Mulch in Mitigating the Impact of Raindrops on Soils in Urban Vegetated Green Infrastructure Systems

NASA Astrophysics Data System (ADS)

Alizadehtazi, B.; Montalto, F. A.; Sjoblom, K.

2014-12-01

Raindrop impulses applied to soils can break up larger soil aggregates into smaller particles, dispersing them from their original position. The displaced particles can self-stratify, with finer particles at the top forming a crust. Occurrence of this phenomenon reduces the infiltration rate and increases runoff, contributing to downstream flooding, soil erosion, and non point source pollutant loads. Unprotected soil surfaces (e.g. without vegetation canopies, mulch, or other materials), are more susceptible to crust formation due to the higher kinetic energy associated with raindrop impact. By contrast, soil that is protected by vegetation canopies and mulch layers is less susceptible to crust formation, since these surfaces intercept raindrops, dissipating some of their kinetic energy prior to their impact with the soil. Within this context, this presentation presents preliminary laboratory work conducted using a rainfall simulator to determine the ability of new urban vegetation and mulch to minimize soil crust formation. Three different scenarios are compared: a) bare soil, b) soil with mulch cover, and c) soil protected by vegetation canopies. Soil moisture, surface penetration resistance, and physical measurements of the volume of infiltrate and runoff are made on all three surface treatments after simulated rainfall events. The results are used to develop recommendations regarding surface treatment in green infrastructure (GI) system designs, namely whether heavily vegetated GI facilities require mulching to maintain infiltration capacity.

Biochar-attenuated desorption of heavy metals in small arms range soils

USDA-ARS?s Scientific Manuscript database

Stabilization (capping/solidification) and dilution (e.g., washing, chelate-assisted phytoremediation) represent non-removal and removal remediation technologies for heavy metal contaminated soils. Biochar is stable in soil, and contains carboxyl and other surface ligands; these properties are usef...

Preliminary Evaluation of the AFWA-NASA (ANSA) Blended Snow-Cover Product over the Lower Great Lakes Region

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Foster, James L.; Riggs, George A.; Kelly, Richard E. J.; Chien, Janet Y. L.; Montesano, Paul M.

2009-01-01

The Air Force Weather Agency (AFWA) - NASA (ANSA) blended-snow product utilizes EOS standard snow products from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) to map daily snow cover and snow-water equivalent (SWE) globally. We have compared ANSA-derived SWE. with SWE values calculated from snow depths reported at approx.1500 National Climatic Data Center (NCDC) coop stations in the Lower Great Lakes basin. Our preliminary results show that conversion of snow depth to SWE is very sensitive to the choice of snow density (we used either 0.2 or 03 as conversion factors). We found overall better agreement between the ANSA-derived SWE and the co-op station data when we use a snow density of 0.3 to convert the snow depths to SWE. In addition, we show that the ANSA underestimates SWE in densely-forested areas, using January and February 2008 ANSA and co-op data. Furthermore, apparent large SWE changes from one day to the next may be caused by thaw-re-freeze events, and do not always represent a real change in SWE. In the near future we will continue the analysis in the 2006-07 and 2007-08 snow seasons.

Multi-scale assimilation of remotely sensed snow observations for hydrologic estimation

NASA Astrophysics Data System (ADS)

Andreadis, K.; Lettenmaier, D.

2008-12-01

Data assimilation provides a framework for optimally merging model predictions and remote sensing observations of snow properties (snow cover extent, water equivalent, grain size, melt state), ideally overcoming limitations of both. A synthetic twin experiment is used to evaluate a data assimilation system that would ingest remotely sensed observations from passive microwave and visible wavelength sensors (brightness temperature and snow cover extent derived products, respectively) with the objective of estimating snow water equivalent. Two data assimilation techniques are used, the Ensemble Kalman filter and the Ensemble Multiscale Kalman filter (EnMKF). One of the challenges inherent in such a data assimilation system is the discrepancy in spatial scales between the different types of snow-related observations. The EnMKF represents the sample model error covariance with a tree that relates the system state variables at different locations and scales through a set of parent-child relationships. This provides an attractive framework to efficiently assimilate observations at different spatial scales. This study provides a first assessment of the feasibility of a system that would assimilate observations from multiple sensors (MODIS snow cover and AMSR-E brightness temperatures) and at different spatial scales for snow water equivalent estimation. The relative value of the different types of observations is examined. Additionally, the error characteristics of both model and observations are discussed.

Development and evaluation of ice phenology algorithm from space-borne active and passive microwave measurements

NASA Astrophysics Data System (ADS)

Kang, K.; Duguay, C. R.

2013-12-01

The presence (or absence) of ice cover plays an important role in lake-atmosphere interactions at high latitudes during the winter months. Knowledge of ice phenology (i.e. freeze-onset/melt-onset, ice-on/ice-off dates, and ice cover duration) is crucial for understanding both the role of lake ice cover in and its response to regional weather and climate. Shortening of the ice cover season in many regions of the Northern Hemisphere over recent decades has been shown to significantly influence the thermal regime as well as the water quality and quantity of lakes. In this respect, satellite remote sensing instruments are providing invaluable measurements for monitoring changes in timing of ice phenological events and the length of the ice cover (or open water) season on large northern lakes, and also for providing more spatially representative limnological information than available from in situ measurements. In this study, we present a new ice phenology retrieval algorithm developed from the synergistic use of Quick Scatterometer (QuikSCAT), Oceansat-2 Scatterometer (OSCAT) and the Advanced Microwave Scanning Radiometer (AMSR-E). Retrieved ice dates are then evaluated against those derived from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS) 4 km resolution product (2004-2011) during the freeze-up and break-up periods (2002-2012) for 11 lakes (Amadjuak, Nettilling, Great Bear, Great Slave, Manitoba, and Winnipeg in North America as well as Inarijrvi, Ladoga, Onega, Qinghai (Koko Nor), and Baikal in Eurasia). In addition, daily wind speed derived from QuikSCAT/OSCAT is analyzed along with WindSAT surface wind vector products (2002-2012) during the open water season for the large lakes. A detailed evaluation of the new algorithm conducted over Great Slave Lake (GSL) and Great Bear Lake (GBL) reveals that estimated ice-on/ice-off dates are within 4-7 days of those derived from the IMS product. Preliminary analysis of ice dates show that ice-on occurs three to five months earlier and ice-off two months later on GSL and GBL (Canada) compared to Lake Ladoga and Lake Onega (Russia) mostly due to regional climate differences. Overall, the synergistic use of microwave satellite data from various sensors provides an invaluable opportunity for operational monitoring of ice cover on large northern lakes.

Partitioning Evapotranspiration in Semiarid Grassland and Shrubland Ecosystems Using Diurnal Surface Temperature Variation

NASA Technical Reports Server (NTRS)

Moran, M. Susan; Scott, Russell L.; Keefer, Timothy O.; Paige, Ginger B.; Emmerich, William E.; Cosh, Michael H.; O'Neill, Peggy E.

2007-01-01

The encroachment of woody plants in grasslands across the Western U.S. will affect soil water availability by altering the contributions of evaporation (E) and transpiration (T) to total evapotranspiration (ET). To study this phenomenon, a network of flux stations is in place to measure ET in grass- and shrub-dominated ecosystems throughout the Western U.S. A method is described and tested here to partition the daily measurements of ET into E and T based on diurnal surface temperature variations of the soil and standard energy balance theory. The difference between the mid-afternoon and pre-dawn soil surface temperature, termed Apparent Thermal Inertia (I(sub A)), was used to identify days when E was negligible, and thus, ET=T. For other days, a three-step procedure based on energy balance equations was used to estimate Qe contributions of daily E and T to total daily ET. The method was tested at Walnut Gulch Experimental Watershed in southeast Arizona based on Bowen ratio estimates of ET and continuous measurements of surface temperature with an infrared thermometer (IRT) from 2004- 2005, and a second dataset of Bowen ratio, IRT and stem-flow gage measurements in 2003. Results showed that reasonable estimates of daily T were obtained for a multi-year period with ease of operation and minimal cost. With known season-long daily T, E and ET, it is possible to determine the soil water availability associated with grass- and shrub-dominated sites and better understand the hydrologic impact of regional woody plant encroachment.

T Tank Farm Interim Surface Barrier Demonstration - Vadose Zone Monitoring FY09 Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Z. F.; Strickland, Christopher E.; Field, Jim G.

2010-01-01

DOE’s Office of River Protection constructed a temporary surface barrier over a portion of the T Tank Farm as part of the T Farm Interim Surface Barrier Demonstration Project. As part of the demonstration effort, vadose zone moisture is being monitored to assess the effectiveness of the barrier at reducing soil moisture. A solar-powered system was installed to continuously monitor soil water conditions at four locations (i.e., instrument Nests A, B, C, and D) beneath the barrier and outside the barrier footprint as well as site meteorological conditions. Nest A is placed in the area outside the barrier footprint andmore » serves as a control, providing subsurface conditions outside the influence of the surface barrier. Nest B provides subsurface measurements to assess surface-barrier edge effects. Nests C and D are used to assess changes in soil-moisture conditions beneath the interim surface barrier. Each instrument nest is composed of a capacitance probe (CP) with multiple sensors, multiple heat-dissipation units (HDUs), and a neutron probe (NP) access tube. The monitoring results in FY09 are summarized below. The solar panels functioned normally and could provide sufficient power to the instruments. The CP in Nest C after September 20, 2009, was not functional. The CP sensors in Nest B after July 13 and the 0.9-m CP sensor in Nest D before June 10 gave noisy data. Other CPs were functional normally. All the HDUs were functional normally but some pressure-head values measured by HDUs were greater than the upper measurement-limit. The higher-than-upper-limit values might be due to the very wet soil condition and/or measurement error but do not imply the malfunction of the sensors. Similar to FY07 and FY08, in FY09, the soil under natural conditions (Nest A) was generally recharged during the winter period (October-March) and discharged during the summer period (April-September). Soil water conditions above about 1.5-m to 2-m depth from all three types of measurements (i.e., CP, NP and HDU) showed relatively large variation during the seasonal wetting-drying cycle. For the soil below 2-m depth, the seasonal variation of soil water content was relatively small. The construction of the surface barrier was completed in April 2008. In the soil below the surface barrier (Nests C and D), the CP measurements showed that water content at the soil between 0.6-m and 2.3-m depths was very stable, indicating no climatic impacts on soil water condition beneath the barrier. The NP-measured water content showed that soil water drainage seemed occurring in the soil between about 3.4 m (11 ft) and 9.1 m (30 ft) in FY09. The HDU-measured water pressure decreased consistently in the soil above 5-m depth, indicating soil water drainage at these depths of the soil. In the soil below the edge of the surface barrier (Nest B), the CP-measured water content was relatively stable through the year except at the 0.9-m depth; the NP-measured water content showed that soil water drainage was occurring in the soil between about 3.4 m (11 ft) and 9.1 m (30 ft) but at a slightly smaller magnitude than those in Nests C and D; the HDU-measurements show that the pressure head changes in FY09 in Nest B were less than those for C and D but more than those for A. The soil-water-pressure head was more sensitive to soil water regime changes under dry conditions. In the soil beneath the barrier, the theoretical steady-state values of pressure head is equal to the negative of the distance to groundwater table. Hence, it is expected that, in the future, while the water content become stable, the pressure head will keep decreasing for a long time (e.g., many years). These results indicate that the T Tank Farm surface barrier was performing as expected by intercepting the meteoric water from infiltrating into the soil and the soil was becoming drier gradually. The barrier also has some effects on the soil below the barrier edge but at a reduced magnitude.« less

Effective soil hydraulic properties in space and time: some field data analysis and modeling concepts

USDA-ARS?s Scientific Manuscript database

Soil hydraulic properties, which control surface fluxes and storage of water and chemicals in the soil profile, vary in space and time. Spatial variability above the measurement scale (e.g., soil area of 0.07 m2 or support volume of 14 L) must be upscaled appropriately to determine “effective” hydr...

Evaluating Remotely-Sensed Soil Moisture Retrievals Using Triple Collocation Techniques

USDA-ARS?s Scientific Manuscript database

The validation is footprint-scale (~40 km) surface soil moisture retrievals from space is complicated by a lack of ground-based soil moisture instrumentation and challenges associated with up-scaling point-scale measurements from such instrumentation. Recent work has demonstrated the potential of e...

Spatial and diurnal below canopy evaporation in a desert vineyard: measurements and modeling

USDA-ARS?s Scientific Manuscript database

Evaporation from the soil surface (E) can be a significant source of water loss in arid areas. In sparsely vegetated systems, E is expected to be a function of soil, climate, irrigation regime, precipitation patterns, and plant canopy development, and will therefore change dynamically at both daily ...

Depth matters: Soil pH and dilution effects in the northern Great Plains

USDA-ARS?s Scientific Manuscript database

In the northern Great Plans (NGP), surface sampling depths of 0-15.2 cm or 0-20.3 cm are suggested for testing soil characteristics such as pH. However, acidification is often most pronounced near-surface (e.g., <10 cm). Thus, sampling deeper can potentially dilute (increase) pH measurements and the...

Widespread arsenic contamination of soils in residential areas and public spaces: an emerging regulatory or medical crisis?

PubMed

Belluck, D A; Benjamin, S L; Baveye, P; Sampson, J; Johnson, B

2003-01-01

A critical review finds government agencies allow, permit, license, or ignore arsenic releases to surface soils. Release rates are controlled or evaluated using risk-based soil contaminant numerical limits employing standardized risk algorithms, chemical-specific and default input values. United States arsenic residential soil limits, approximately 0.4- approximately 40 ppm, generally correspond to a one-in-one-million to a one-in-ten-thousand incremental cancer risk range via ingestion of or direct contact with contaminated residential soils. Background arsenic surface soil levels often exceed applicable limits. Arsenic releases to surface soils (via, e.g., air emissions, waste recycling, soil amendments, direct pesticide application, and chromated copper arsenic (CCA)-treated wood) can result in greatly elevated arsenic levels, sometimes one to two orders of magnitude greater than applicable numerical limits. CCA-treated wood, a heavily used infrastructure material at residences and public spaces, can release sufficient arsenic to result in surface soil concentrations that exceed numerical limits by one or two orders of magnitude. Although significant exceedence of arsenic surface soil numerical limits would normally result in regulatory actions at industrial or hazardous waste sites, no such pattern is seen at residential and public spaces. Given the current risk assessment paradigm, measured or expected elevated surface soil arsenic levels at residential and public spaces suggest that a regulatory health crisis of sizeable magnitude is imminent. In contrast, available literature and a survey of government agencies conducted for this paper finds no verified cases of human morbidity or mortality resulting from exposure to elevated levels of arsenic in surface soils. This concomitance of an emerging regulatory health crisis in the absence of a medical crisis is arguably partly attributable to inadequate government and private party attention to the issue.

Surface runoff and soil erosion by difference of surface cover characteristics using by an oscillating rainfall simulator

NASA Astrophysics Data System (ADS)

Kim, J. K.; Kim, M. S.; Yang, D. Y.

2017-12-01

Sediment transfer within hill slope can be changed by the hydrologic characteristics of surface material on hill slope. To better understand sediment transfer of the past and future related to climate changes, studies for the changes of soil erosion due to hydrological characteristics changes by surface materials on hill slope are needed. To do so, on-situ rainfall simulating test was conducted on three different surface conditions, i.e. well covered with litter layer condition (a), undisturbed bare condition (b), and disturbed bare condition (c) and these results from rainfall simulating test were compared with that estimated using the Limburg Soil Erosion Model (LISEM). The result from the rainfall simulating tests showed differences in the infiltration rate (a > b > c) and the highest soil erosion rate was occurred on c condition. The result from model also was similar to those from rainfall simulating tests, however, the difference from the value of soil erosion rate between two results was quite large on b and c conditions. These results implied that the difference of surface conditions could change the surface runoff and soil erosion and the result from the erosion model might significantly underestimate on bare surface conditions rather than that from rainfall simulating test.

NCA-LDAS: An Integrated Terrestrial Water Analysis System for Development, Evaluation, and Dissemination of Climate Indicators

NASA Astrophysics Data System (ADS)

Jasinski, M. F.; Arsenault, K. R.; Beaudoing, H. K.; Bolten, J. D.; Borak, J.; Kumar, S.; Peters-Lidard, C. D.; Li, B.; Liu, Y.; Mocko, D. M.; Rodell, M.

2014-12-01

An Integrated Terrestrial Water Analysis System, or NCA-LDAS, has been created to enable development, evaluation, and dissemination of terrestrial hydrologic climate indicators focusing on the continental U.S. The purpose is to provide quantifiable indicators of states and estimated trends in our nation's water stores and fluxes over a wide range of scales and locations, to support improved understanding and management of water resources and numerous related sectors such as agriculture and energy. NCA-LDAS relies on improved modeling of terrestrial hydrology through assimilation of satellite imagery, building upon the legacy of the Land Information System modeling framework (Kumar et al, 2006; Peters-Lidard et al, 2007). It currently employs the Noah or Catchment Land Surface Model, run with a number of satellite data assimilation scenarios. The domain for NCA-LDAS is the continental U.S. at 1/8 degree grid for the period 1979 to present. Satellite-based variables that are assimilated are soil moisture and snow water equivalent from principally microwave sensors such as SMMR, SSM/I and AMSR, snow covered area from multispectral sensors such as AVHRR, and MODIS, and terrestrial water storage from GRACE. Once simulated, output are evaluated in comparison to independent datasets using a variety of metrics using the Land Surface Verification Toolkit (LVT). LVT schemes within NCA-LDAS also include routines for computing standard statistics of time series such means, max, and linear trends, at various scales. The dissemination of the NCA-LDAS, including model descriptions, forcings, parameters, daily output, indicator results and LVT tools, have been made available to the public through dissemination on NASA GES-DISC.

Forms of phosphorus transfer in runoff under no-tillage in a soil treated with successive swine effluents applications.

PubMed

Lourenzi, Cledimar Rogério; Ceretta, Carlos Alberto; Tiecher, Tadeu Luis; Lorensini, Felipe; Cancian, Adriana; Stefanello, Lincon; Girotto, Eduardo; Vieira, Renan Costa Beber; Ferreira, Paulo Ademar Avelar; Brunetto, Gustavo

2015-04-01

Successive swine effluent applications can substantially increase the transfer of phosphorus (P) forms in runoff. The aim of this study was to evaluate P accumulation in the soil and transfer of P forms in surface runoff from a Hapludalf soil under no-tillage subjected to successive swine effluent applications. This research was carried out in the Agricultural Engineering Department of the Federal University of Santa Maria, Brazil, from 2004 to 2007, on a Typic Hapludalf soil. Swine effluent rates of 0, 20, 40, and 80 m3 ha(-1) were broadcast over the soil surface prior to sowing of different species in a crop rotation. Soil samples were collected in stratified layers, and the levels of available P were determined. Samples of water runoff from the soil surface were collected throughout the period, and the available, soluble, particulate, and total P were measured. Successive swine effluent applications led to increases in P availability, especially in the soil surface, and P migration through the soil profile. Transfer of P forms was closely associated with runoff, which is directly related to rainfall volume. Swine effluent applications also reduced surface runoff. These results show that in areas with successive swine effluent applications, practices that promote higher water infiltration into the soil are required, e.g., crop rotation and no-tillage system.

Towards Improved High-Resolution Land Surface Hydrologic Reanalysis Using a Physically-Based Hydrologic Model and Data Assimilation

NASA Astrophysics Data System (ADS)

Shi, Y.; Davis, K. J.; Zhang, F.; Duffy, C.; Yu, X.

2014-12-01

A coupled physically based land surface hydrologic model, Flux-PIHM, has been developed by incorporating a land surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Flux-PIHM has been implemented and manually calibrated at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Model predictions of discharge, point soil moisture, point water table depth, sensible and latent heat fluxes, and soil temperature show good agreement with observations. When calibrated only using discharge, and soil moisture and water table depth at one point, Flux-PIHM is able to resolve the observed 101 m scale soil moisture pattern at the Shale Hills watershed when an appropriate map of soil hydraulic properties is provided. A Flux-PIHM data assimilation system has been developed by incorporating EnKF for model parameter and state estimation. Both synthetic and real data assimilation experiments have been performed at the Shale Hills watershed. Synthetic experiment results show that the data assimilation system is able to simultaneously provide accurate estimates of multiple parameters. In the real data experiment, the EnKF estimated parameters and manually calibrated parameters yield similar model performances, but the EnKF method significantly decreases the time and labor required for calibration. The data requirements for accurate Flux-PIHM parameter estimation via data assimilation using synthetic observations have been tested. Results show that by assimilating only in situ outlet discharge, soil water content at one point, and the land surface temperature averaged over the whole watershed, the data assimilation system can provide an accurate representation of watershed hydrology. Observations of these key variables are available with national and even global spatial coverage (e.g., MODIS surface temperature, SMAP soil moisture, and the USGS gauging stations). National atmospheric reanalysis products, soil databases and land cover databases (e.g., NLDAS-2, SSURGO, NLCD) can provide high resolution forcing and input data. Therefore the Flux-PIHM data assimilation system could be readily expanded to other watersheds to provide regional scale land surface and hydrologic reanalysis with high spatial temporal resolution.

Use of shrub willows (Salix spp.) to develop soil communities during coal mine restoration

USDA-ARS?s Scientific Manuscript database

Afforestation or reforestation in highly degraded environments (e.g. surface mines) is often complicated by the total removal of vegetation and severe soil degradation that occurs during mining operations, necessitating revegetation to be undertaken in tandem with the re-establishment of soil develo...

Measuring surface fluxes in CAPE

NASA Technical Reports Server (NTRS)

Kanemasu, E. T.; D-Shah, T.; Nie, Dalin

1992-01-01

Two stations (site 1612 and site 2008) were operated by the University of Georgia group from 6 July 1991 to 18 August 1991. The following data were collected continuously: surface energy fluxes (i.e., net radiation, soil heat fluxes, sensible heat flux and latent heat flux), air temperature, vapor pressure, soil temperature (at 1 cm depth), and precipitation. Canopy reflectance and light interception data were taken three times at each site between 6 July and 18 August. Soil moisture content was measured twice at each site.

Remedial Investigation Report, Presidio Main Installation, Presidio of San Francisco. Volume 5: Figures 7-15

DTIC Science & Technology

1997-01-01

YFO U A .. SOIL BORING1 3 . 4 SURFACE SOIL SAM SSURFACES COVERE S::::::::::::::::i-•. PAVEMENT OR BUll ...EXPLANW SOIL BORING .A SURFACE SOIL SAIN LII. PVMNOR BUll .5____iSTAIN ED AREAS LITHOOGY E/DUNOTES: 1. ALL...WCALXI ~q~qJO~i II~ %~1, z U 0 a LL 0 L c 0S-0 F- tr C14 Uj- Ui -Z w- z zow w0 m Z z z 0 0on coLi/ in z On.. 0 LL -J Ua. z C 0 w D TIPo 44f -lot a26

Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model

USDA-ARS?s Scientific Manuscript database

Studies of global hydrologic cycles, carbon cycles and climate change are greatly facilitated when global estimates of evapotranspiration (E) are available. We have developed an air-relative-humidity-based two-source (ARTS) E model that simulates the surface energy balance, soil water balance, and e...

Streamflow estimation in ungauged basins using remote sensed hydrological data

NASA Astrophysics Data System (ADS)

Vasquez, Nicolas; Vargas, Ximena

2017-04-01

In several parts of the world the scarcity of streamflow gauging stations produces an important deficit of information, and calibrating these basins remains a challenge for hydrologists. Improvements in remote sensing have provided significant information about hydrological cycle, which can be used to calibrate a hydrological model when streamflow information is not available. Several satellite products related to snow, evapotranspiration, soil moisture, among other variables provide essential information about hydrological processes, and can be used to calibrate physically based hydrological models. Despite this useful information, other aspects are unknown like aquifers dimensions or precipitation heterogeneity. We calibrated three snow driven basins in the Coquimbo Region in Northern Chile, using fSCA from MODIS (MOD10 and MYD10) and NDSI from Landsat. We also considered the MOD16 product to estimate evapotranspiration. Soil Moisture from AMSR-E was considered but it was not useful due to the spatial resolution of the product and the high heterogeneity of the terrain. The Cold Regional Hydrological Modal (CHRM) was selected to represent the hydrological processes due to the importance of snow processes which are, by far, the most important in this area, where precipitation falls as snow principally in winter (June to August) and the melting period begins in spring (September) and ends in the beginning of summer (December and January). The inputs used in the model are precipitation, temperature, short wave radiation, wind speed and relative humidity. The meteorological information was obtained from stations available in the area, and distributed spatially using orographic gradients for wind and precipitation and lapse rates for air temperature and dew point temperature. Short wave radiation was computed and corrected by cloud cover data from MODIS. Streamflow data was available but it was not used in the calibration process. The three basins are Cochiguaz river at Peñón (676 km2), Derecho river at Alcohuaz (338 km2) and Toro river in confluence with La Laguna river (468 km2). These sub-basins are part of the Elqui river basins and are located in the Andes Cordillera, Chile. The mean altitude are 3508 (m.a.s.l), 3543 (m.a.s.l) and 3625 (m.a.s.l) respectively. For the calibration period (2002 to 2014), the NSE of the fSCA are 0.85 and 0.87 for Cochiguaz and Derecho rivers. The Toro river was separated in two rivers: Vacas Heladas and Malo. NSE for these two last basins are 0.77 and 0.78. For ET, the analysis relies on the number of pixels inside each basin, but annually, the R2 are 0.62, 0.43, 0.46 and 0.58 for the four sub-basins. Some biases are noticed when ET is analyzed. For streamflow, the NSE were 0.64, 0.34 and 0.08 for Cochiguaz, Derecho and Toro river in the calibration period. Additionally, due to the uncertainty about the aquifers dimensions, a sensitivity analysis was performed.

Rapid soil development after windthrow disturbance in pristine forests.

Treesearch

B.T. Bormann; H. Spaltenstein; M.H. McClellan; F.C. Ugolini; K. Cromack; S.M. Nay

1995-01-01

1. We examined how rapidly soils can change during secondary succession by observing soil development on 350-year chronosequences in three pristine forest ecosystems in south-east Alaska. 2. Soil surfaces, created by different windthrow events of known or estimated age, were examined within each of three forest stands (0.5-2.0 ha plots; i.e. a within-stand...

Evaluating RGB photogrammetry and multi-temporal digital surface models for detecting soil erosion

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Seeger, Manuel

2013-04-01

Photogrammetry is a widely used tool for generating high-resolution digital surface models. Unmanned Aerial Vehicles (UAVs), equipped with a Red Green Blue (RGB) camera, have great potential in quickly acquiring multi-temporal high-resolution orthophotos and surface models. Such datasets would ease the monitoring of geomorphological processes, such as local soil erosion and rill formation after heavy rainfall events. In this study we test a photogrammetric setup to determine data requirements for soil erosion studies with UAVs. We used a rainfall simulator (5 m2) and above a rig with attached a Panasonic GX1 16 megapixel digital camera and 20mm lens. The soil material in the simulator consisted of loamy sand at an angle of 5 degrees. Stereo pair images were taken before and after rainfall simulation with 75-85% overlap. Acquired images were automatically mosaicked to create high-resolution orthorectified images and digital surface models (DSM). We resampled the DSM to different spatial resolutions to analyze the effect of cell size to the accuracy of measured rill depth and soil loss estimations, and determined an optimal cell size (thus flight altitude). Furthermore, the high spatial accuracy of the acquired surface models allows further analysis of rill formation and channel initiation related to e.g. surface roughness. We suggest implementing near-infrared and temperature sensors to combine soil moisture and soil physical properties with surface morphology for future investigations.

How far can we prevent further physical soil degradation in the future?

NASA Astrophysics Data System (ADS)

Horn, Rainer

2017-04-01

Arable as well as forest soils are exposed to increasing external stresses, which coincide with a further and deeper reaching soil degradation, which may result in an aggravation of hydraulic, gaseous, thermal but also physicochemical and chemical soil functions. The decline coincides with a simultaneous reduction in useable land areas and worsens food production amongst others. Therefore, it is mandatory, that stable soil structure from the surface down to depth prevents soil compaction, sustains water infiltration, reduces rates of soil erosion by water and wind in each case to the minimum possible under the soil, terrain, land use, and climatic conditions in which the soils occur. It improves organic carbon storage in soils and optimizes microbial activity and functions. These benefits coincide with sustainable soil properties and soil management systems, which prevent - deep mechanical stress propagation which can cause irreversible soil deformation, - loss of surface soil layers with coinciding organic and mineral nutrient pool available for microbial processing and plant uptake, - Truncation of soil horizons, or damage on private and public infrastructures (roads, houses) and downstream fields. In order to prevent negative impacts on soils, it is recommended, that A) concerning prevention of soil compaction - stresses applied to soils shall not exceed the mechanical soil stability to maintain the actual functioning of chemical, physical and biological processes and to utilize their resilience (i.e. the elasticity), - land use management strategies have to be related to the actual soil properties in order to optimize plant growth, yield, filtering and buffering of infiltrating water, and carbon sequestration. B) soil erosion by - water, wind, and tillage is counteracted by an adequate surface soil stability including a site specific residue management (e.g. conservation tillage), controlled traffic and harvesting, ecological grassland use strategies (e.g. fodder production and harvesting, adequate animal grazing), - wind is furthermore minimized by adequate hedgerow plantations, continuous cover crop growth, optimized particle bindings by water, infiltrating organic acids, appropriate grazing intensity. Agroforestry can be considered as an additional positive measure to reduce soil erosion risks generally and to ameliorate degraded sites. C) -plant cover on slopes remains untouched, overgrazing and consecutive soil homogenization especially under moist climatic conditions must be prevented but adjusted to the actual structure stability of the hillsides. The communication of these findings followed by application of such measures can help farmers and foresters as well as landowners to prevent (further) physical soil degradation in the future.

Sensitivity of Land Surface Parameters on Thunderstorm Simulation through HRLDAS-WRF Coupling Mode

NASA Astrophysics Data System (ADS)

Kumar, Dinesh; Kumar, Krishan; Mohanty, U. C.; Kisore Osuri, Krishna

2016-07-01

Land surface characteristics play an important role in large scale, regional and mesoscale atmospheric process. Representation of land surface characteristics can be improved through coupling of mesoscale atmospheric models with land surface models. Mesoscale atmospheric models depend on Land Surface Models (LSM) to provide land surface variables such as fluxes of heat, moisture, and momentum for lower boundary layer evolution. Studies have shown that land surface properties such as soil moisture, soil temperature, soil roughness, vegetation cover, have considerable effect on lower boundary layer. Although, the necessity to initialize soil moisture accurately in NWP models is widely acknowledged, monitoring soil moisture at regional and global scale is a very tough task due to high spatial and temporal variability. As a result, the available observation network is unable to provide the required spatial and temporal data for the most part of the globe. Therefore, model for land surface initializations rely on updated land surface properties from LSM. The solution for NWP land-state initialization can be found by combining data assimilation techniques, satellite-derived soil data, and land surface models. Further, it requires an intermediate step to use observed rainfall, satellite derived surface insolation, and meteorological analyses to run an uncoupled (offline) integration of LSM, so that the evolution of modeled soil moisture can be forced by observed forcing conditions. Therefore, for accurate land-state initialization, high resolution land data assimilation system (HRLDAS) is used to provide the essential land surface parameters. Offline-coupling of HRLDAS-WRF has shown much improved results over Delhi, India for four thunder storm events. The evolution of land surface variables particularly soil moisture, soil temperature and surface fluxes have provided more realistic condition. Results have shown that most of domain part became wetter and warmer after assimilation of soil moisture and soil temperature at the initial condition which helped to improve the exchange fluxes at lower atmospheric level. Mixing ratio were increased along with elevated theta-e at lower level giving a signature of improvement in LDAS experiment leading to a suitable condition for convection. In the analysis, moisture convergence, mixing ratio and vertical velocities have improved significantly in terms of intensity and time lag. Surface variables like soil moisture, soil temperature, sensible heat flux and latent heat flux have progressed in a possible realistic pattern. Above discussion suggests that assimilation of soil moisture and soil temperature improves the overall simulations significantly.

Release of chlorinated, brominated and mixed halogenated dioxin-related compounds to soils from open burning of e-waste in Agbogbloshie (Accra, Ghana).

PubMed

Tue, Nguyen Minh; Goto, Akitoshi; Takahashi, Shin; Itai, Takaaki; Asante, Kwadwo Ansong; Kunisue, Tatsuya; Tanabe, Shinsuke

2016-01-25

Although complex mixtures of dioxin-related compounds (DRCs) can be released from informal e-waste recycling, DRC contamination in African e-waste recycling sites has not been investigated. This study examined the concentrations of DRCs including chlorinated, brominated, mixed halogenated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs, PBDD/Fs, PXDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) in surface soil samples from the Agbogbloshie e-waste recycling site in Ghana. PCDD/F and PBDD/F concentrations in open burning areas (18-520 and 83-3800 ng/g dry, respectively) were among the highest reported in soils from informal e-waste sites. The concentrations of PCDFs and PBDFs were higher than those of the respective dibenzo-p-dioxins, suggesting combustion and PBDE-containing plastics as principal sources. PXDFs were found as more abundant than PCDFs, and higher brominated analogues occurred at higher concentrations. The median total WHO toxic equivalent (TEQ) concentration in open burning soils was 7 times higher than the U.S. action level (1000 pg/g), with TEQ contributors in the order of PBDFs>PCDD/Fs>PXDFs. DRC emission to soils over the e-waste site as of 2010 was estimated, from surface soil lightness based on the correlations between concentrations and lightness, at 200mg (95% confidence interval 93-540 mg) WHO-TEQ over three years. People living in Agbogbloshie are potentially exposed to high levels of not only chlorinated but also brominated DRCs, and human health implications need to be assessed in future studies. Copyright © 2015 Elsevier B.V. All rights reserved.

An initial assessment of a SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States

NASA Astrophysics Data System (ADS)

Mishra, Vikalp; Ellenburg, W. Lee; Griffin, Robert E.; Mecikalski, John R.; Cruise, James F.; Hain, Christopher R.; Anderson, Martha C.

2018-06-01

The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation over Continental U.S. (CONUS). The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of -0.022 and -0.019 and unbiased RMSD of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal correlation of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (>55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly, however, gain statistics show that TIR-Downscaled SSM slightly outperformed SMAP-E.

Transport and modeling of estrogenic hormones in a dairy farm effluent through undisturbed soil lysimeters.

PubMed

Steiner, Laure D; Bidwell, Vincent J; Di, Hong J; Cameron, Keith C; Northcott, Grant L

2010-04-01

The presence of endocrine-disrupting chemicals, including estrone (E1) and 17beta-estradiol (E2), in surface waters has been associated with physiological dysfunction in a number of aquatic organisms. One source of surface and groundwater contamination with E1 and E2 is the land application of animal wastes. The processes involved in the transport of these hormones in the soil, when applied with animal wastes, are still unclear. Therefore, a field-transport experiment was carried out, where a dairy farm effluent spiked with E1 and E2 was applied on large (50 cm diameter and 70 cm depth) undisturbed soil lysimeters. The concentrations of E1 and E2 in the leachate were monitored over a 3-month period, during which irrigation was applied. The experimental data suggest that E1 and E2 were transported through preferential/macropore flow pathways. The data from the experiment also show that E1 and E2 are leached earlier than the inert tracer (bromide). This observation can be explained either by the presence of antecedent concentrations in the soil or by an enhanced transport of E1 and E2 through the soil. A state-space mixing-cell model was further developed in order to describe the transport of E1 and E2 by three transport processes in parallel. The inverse modeling of the leaching data did not support the hypothesis that antecedent concentrations of estrogens could be responsible for the observed breakthrough curves but confirmed that estrogens were transported mainly via preferential/macropore flow and also via an enhanced transport. The parameter values that characterized this enhanced transport strongly suggest that this enhanced transport is mediated by colloids. For the first time, the simultaneous transport of E1 and E2 was modeled under transient conditions, taking into account the advection-dispersion, preferential/macropore flow, and colloidal-enhanced transport processes as well as E1 and E2 dissipation in the soil. These findings have major implications in terms of management practices to decrease E1 and E2 transport and water contamination.

Toward Linking Aboveground Vegetation Properties and Soil Microbial Communities Using Remote Sensing

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Yuki; Gilbert, Jack A.; Larsen, Peter E.

2014-04-01

Despite their vital role in terrestrial ecosystem function, the distributions and dynamics of soil microbial communities (SMCs) are poorly understood. Vegetation and soil properties are the primary factors that influence SMCs. This paper discusses the potential effectiveness of remote sensing science and technologies for mapping SMC biogeography by characterizing surface biophysical properties (e.g., plant traits and community composition) strongly correlated with SMCs. Using remotely sensed biophysical properties to predict SMC distributions is extremely challenging because of the intricate interactions between biotic and abiotic factors and between above- and belowground ecosystems. However, the integration of biophysical and soil remote sensing withmore » geospatial information about the e nvironment holds great promise for mapping SMC biogeography. Additional research needs invol ve microbial taxonomic definition, soil environmental complexity, and scaling strategies. The collaborative effort of experts from diverse disciplines is essential to linking terrestrial surface biosphere observations with subsurface microbial community distributions using remote sensing.« less

Soil Organic Matter Stabilization via Mineral Interactions in Forest Soils with Varying Saturation Frequency

NASA Astrophysics Data System (ADS)

Possinger, A. R.; Inagaki, T.; Bailey, S. W.; Kogel-Knabner, I.; Lehmann, J.

2017-12-01

Soil carbon (C) interaction with minerals and metals through surface adsorption and co-precipitation processes is important for soil organic C (SOC) stabilization. Co-precipitation (i.e., the incorporation of C as an "impurity" in metal precipitates as they form) may increase the potential quantity of mineral-associated C per unit mineral surface compared to surface adsorption: a potentially important and as yet unaccounted for mechanism of C stabilization in soil. However, chemical, physical, and biological characterization of co-precipitated SOM as such in natural soils is limited, and the relative persistence of co-precipitated C is unknown, particularly under dynamic environmental conditions. To better understand the relationships between SOM stabilization via organometallic co-precipitation and environmental variables, this study compares mineral-SOM characteristics across a forest soil (Spodosol) hydrological gradient with expected differences in co-precipitation of SOM with iron (Fe) and aluminum (Al) due to variable saturation frequency. Soils were collected from a steep, well-drained forest soil transect with low, medium, and high frequency of water table intrusion into surface soils (Hubbard Brook Experimental Forest, Woodstock, NH). Lower saturation frequency soils generally had higher C content, C/Fe, C/Al, and other indicators of co-precipitation interactions resulting from SOM complexation, transport, and precipitation, an important process of Spodosol formation. Preliminary Fe X-ray Absorption Spectroscopic (XAS) characterization of SOM and metal chemistry in low frequency profiles suggest co-precipitation of SOM in the fine fraction (<20 µm). Short-term (10d) aerobic incubation of high and low saturation frequency soils showed greater SOC mineralization per unit soil C for low saturation frequency (i.e., higher co-precipitation) soils; however, increased mineralization may be attributed to non-mineral associated fractions of SOM. Further work to identify the component of SOM contributing to rapid mineralization using 13C-labeled substrates will link the observed chemical characteristics (13C-NMR, C K-edge XANES, and Fe XAS) of mineral-organic associations resulting from varying saturation frequency with mechanisms driving mineralization processes.

Soil moisture remote sensing: State of the science

USDA-ARS?s Scientific Manuscript database

Satellites (e.g., SMAP, SMOS) using passive microwave techniques, in particular at L band frequency, have shown good promise for global mapping of near-surface (0-5 cm) soil moisture at a spatial resolution of 25-40 km and temporal resolution of 2-3 days. C- and X-band soil moisture records date bac...

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

NASA Technical Reports Server (NTRS)

Eagleson, Peter S.; Jasinski, Michael F.

1988-01-01

The estimation of the spatially variable surface moisture and heat fluxes of natural, semivegetated landscapes is difficult due to the highly random nature of the vegetation (e.g., plant species, density, and stress) and the soil (e.g., moisture content, and soil hydraulic conductivity). The solution to that problem lies, in part, in the use of satellite remotely sensed data, and in the preparation of those data in terms of the physical properties of the plant and soil. The work was focused on the development and testing of a stochastic geometric canopy-soil reflectance model, which can be applied to the physically-based interpretation of LANDSAT images. The model conceptualizes the landscape as a stochastic surface with bulk plant and soil reflective properties. The model is particularly suited for regional scale investigations where the quantification of the bulk landscape properties, such as fractional vegetation cover, is important on a pixel by pixel basis. A summary of the theoretical analysis and the preliminary testing of the model with actual aerial radiometric data is provided.

Climate Prediction Center - United States Drought Information

Science.gov Websites

• Crop Moisture Indices • Soil Moisture Percentiles (based on NLDAS) • Standardized Runoff Index (based /Minimum • Mean Surface Hydrology (based on NLDAS) • Total Soil Moisture • Total SM Change • MOSAIC Soil Moisture Profile • NOAH Soil Moisture Profile • NOAH Soil T Profile • Evaporation • E-P Â

Global Precipitation Measurement, Validation, and Applications Integrated Hydrologic Validation to Improve Physical Precipitation Retrievals for GPM

NASA Technical Reports Server (NTRS)

Peters-Lidar, Christa D.; Tian, Yudong; Kenneth, Tian; Harrison, Kenneth; Kumar, Sujay

2011-01-01

Land surface modeling and data assimilation can provide dynamic land surface state variables necessary to support physical precipitation retrieval algorithms over land. It is well-known that surface emission, particularly over the range of frequencies to be included in the Global Precipitation Measurement Mission (GPM), is sensitive to land surface states, including soil properties, vegetation type and greenness, soil moisture, surface temperature, and snow cover, density, and grain size. In order to investigate the robustness of both the land surface model states and the microwave emissivity and forward radiative transfer models, we have undertaken a multi-site investigation as part of the NASA Precipitation Measurement Missions (PMM) Land Surface Characterization Working Group. Specifically, we will demonstrate the performance of the Land Information System (LIS; http://lis.gsfc.nasa.gov; Peters-Lidard et aI., 2007; Kumar et al., 2006) coupled to the Joint Center for Satellite Data Assimilation (JCSDA's) Community Radiative Transfer Model (CRTM; Weng, 2007; van Deist, 2009). The land surface is characterized by complex physical/chemical constituents and creates temporally and spatially heterogeneous surface properties in response to microwave radiation scattering. The uncertainties in surface microwave emission (both surface radiative temperature and emissivity) and very low polarization ratio are linked to difficulties in rainfall detection using low-frequency passive microwave sensors (e.g.,Kummerow et al. 2001). Therefore, addressing these issues is of utmost importance for the GPM mission. There are many approaches to parameterizing land surface emission and radiative transfer, some of which have been customized for snow (e.g., the Helsinki University of Technology or HUT radiative transfer model;) and soil moisture (e.g., the Land Surface Microwave Emission Model or LSMEM).

Role of Vegetation and Mulch in Mitigating the Effects of Raindrop Impact on Runoff and Infiltration from Urban Vegetated Green Infrastructure

NASA Astrophysics Data System (ADS)

Alizadehtazi, B.; Montalto, F. A.

2013-12-01

Rain drop impact causes soil crust formation which, in turn, reduces infiltration rates and increases runoff, contributing to soil erosion, downstream flooding and non point source pollutant loads. Unprotected soil surfaces (e.g. without vegetation canopies, mulch, or other materials), are more susceptible to crust formation due to the higher kinetic energy associated with raindrop impact. This impulse breaks larger soil aggregates into smaller particles and disperses soil from its original position. The displaced soil particles self-stratify, with finer particles at the top forming the crust. By contrast, soil that is protected by vegetation canopies and mulch layers is less susceptible to crust formation, since these surfaces intercept raindrops, dissipating some of their kinetic energy prior to their impact with the soil. Very little research has sought to quantify the effect that canopies and mulch can have on this phenomenon. This presentation presents preliminary findings from ongoing study conducted using rainfall simulator to determine the ability of new urban vegetation and mulch to minimize soil crust formation. Three different scenarios are compared: a) bare soil, b) soil with mulch cover, and c) soil protected by vegetation canopies. Soil moisture, surface penetration resistance, and physical measurements of the volume of infiltrate and runoff are made on all three surface treatments after simulated rainfall events. The results are used to discuss green infrastructure facility maintenance and design strategies, namely whether heavily vegetated GI facilities require mulching to maintain infiltration capacity.

Applications of NASA Earth Observation Imagery in Google Earth Engine to Estimate Glacier Trends and Water Availability in Chile's Aconcagua Watershed

NASA Astrophysics Data System (ADS)

Webb, M. J.; Babis, B.; Deland, S.; McGurk, G.

2017-12-01

The Aconcagua basin of Central Chile, just north of the capital city of Santiago, is characterized by the glaciated Andes to the east, which supply meltwater runoff to the lower fertile river valleys. Known for the production of fruit and vegetable crops, the region is experiencing stressed hydrologic resources as a result of anomalous climate conditions and anthropogenic water consumption. Traditionally, the wet and cool winter months account for 80 percent of Aconcagua's total annual precipitation, while dry and warm conditions prevail during the summer months. Consequently, the basin depends on seasonal glacial accumulation to provide water storage for the dry season when up to 67 percent of water is derived from glacial runoff. Overall, 70 percent of regional water is consumed by agricultural practices, specifically the fruit and vegetable farming that thrives in Aconcagua's Mediterranean-type climate. Globally, weather intensification and the rising zero-degree isotherm are poised to threaten the stability and longevity of glacial water resources. In recent years, Chile has experienced periods of prolonged drought as well as glacier shrinkage. The Aconcagua basin is especially vulnerable to these changes as a consequence of its agricultural economies and reliance on sub-tropical glaciers for water resources. Aconcagua is among the top three regions contributing to Chile's gross domestic product (GDP). Furthermore, in 2011 the Chilean government announced plans to increase the national land under irrigation by 57 percent by 2022. In partnership with the Chilean Ministry of Agriculture, the objective of this research was to integrate NASA Earth observations in conjunction with in situ river discharge measurements into Google Earth Engine to enhance regional understanding of current and future climate conditions in Chile. The remotely-sensed datasets included Landsat TM/OLI derived glacial extent, Terra MODIS snow cover and surface temperature, and Aqua AMSR-E/GCOM-W1 AMSR2 snow water equivalent, and TRMM precipitation datasets. Pearson's Product Moment Correlational Coefficient statistical test was applied to the remotely-sensed datasets and discharge measurements to study climatic correlations to water resource availability on a temporal and spatial scale.

C-band Joint Active/Passive Dual Polarization Sea Ice Detection

NASA Astrophysics Data System (ADS)

Keller, M. R.; Gifford, C. M.; Winstead, N. S.; Walton, W. C.; Dietz, J. E.

2017-12-01

A technique for synergistically-combining high-resolution SAR returns with like-frequency passive microwave emissions to detect thin (<30 cm) ice under the difficult conditions of late melt and freeze-up is presented. As the Arctic sea ice cover thins and shrinks, the algorithm offers an approach to adapting existing sensors monitoring thicker ice to provide continuing coverage. Lower resolution (10-26 km) ice detections with spaceborne radiometers and scatterometers are challenged by rapidly changing thin ice. Synthetic Aperture Radar (SAR) is high resolution (5-100m) but because of cross section ambiguities automated algorithms have had difficulty separating thin ice types from water. The radiometric emissivity of thin ice versus water at microwave frequencies is generally unambiguous in the early stages of ice growth. The method, developed using RADARSAT-2 and AMSR-E data, uses higher-ordered statistics. For the SAR, the COV (coefficient of variation, ratio of standard deviation to mean) has fewer ambiguities between ice and water than cross sections, but breaking waves still produce ice-like signatures for both polarizations. For the radiometer, the PRIC (polarization ratio ice concentration) identifies areas that are unambiguously water. Applying cumulative statistics to co-located COV levels adaptively determines an ice/water threshold. Outcomes from extensive testing with Sentinel and AMSR-2 data are shown in the results. The detection algorithm was applied to the freeze-up in the Beaufort, Chukchi, Barents, and East Siberian Seas in 2015 and 2016, spanning mid-September to early November of both years. At the end of the melt, 6 GHz PRIC values are 5-10% greater than those reported by radiometric algorithms at 19 and 37 GHz. During freeze-up, COV separates grease ice (<5 cm thick) from water. As the ice thickens, the COV is less reliable, but adding a mask based on either the PRIC or the cross-pol/co-pol SAR ratio corrects for COV deficiencies. In general, the dual-sensor detection algorithm reports 10-15% higher total ice concentrations than operational scatterometer or radiometer algorithms, mostly from ice edge and coastal areas. In conclusion, the algorithm presented combines high-resolution SAR returns with passive microwave emissions for automated ice detection at SAR resolutions.

Evaluating the influence of antecedent soil moisture on variability of the North American Monsoon precipitation in the coupled MM5/VIC modeling system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhu, Chunmei; Leung, Lai R.; Gochis, David

2009-11-29

The influence of antecedent soil moisture on North American monsoon system (NAMS) precipitation variability was explored using the MM5 mesoscale model coupled with the Variable Infiltration Capacity (VIC) land surface model. Sensitivity experiments were performed with extreme wet and dry initial soil moisture conditions for both the 1984 wet monsoon year and the 1989 dry year. The MM5-VIC model reproduced the key features of NAMS in 1984 and 1989 especially over northwestern Mexico. Our modeling results indicate that the land surface has memory of the initial soil wetness prescribed at the onset of the monsoon that persists over most ofmore » the region well into the monsoon season (e.g. until August). However, in contrast to the classical thermal contrast concept, where wetter soils lead to cooler surface temperatures, less land-sea thermal contrast, weaker monsoon circulations and less precipitation, the coupled model consistently demonstrated a positive soil moisture – precipitation feedback. Specifically, anomalously wet premonsoon soil moisture always lead to enhanced monsoon precipitation, and the reverse was also true. The surface temperature changes induced by differences in surface energy flux partitioning associated with pre-monsoon soil moisture anomalies changed the surface pressure and consequently the flow field in the coupled model, which in turn changed moisture convergence and, accordingly, precipitation patterns. Both the largescale circulation change and local land-atmospheric interactions in response to premonsoon soil moisture anomalies play important roles in the coupled model’s positive soil moisture monsoon precipitation feedback. However, the former may be sensitive to the strength and location of the thermal anomalies, thus leaving open the possibility of both positive and negative soil moisture precipitation feedbacks.« less

Elevated [CO2] mitigates the effect of surface drought by stimulating root growth to access sub-soil water.

PubMed

Uddin, Shihab; Löw, Markus; Parvin, Shahnaj; Fitzgerald, Glenn J; Tausz-Posch, Sabine; Armstrong, Roger; O'Leary, Garry; Tausz, Michael

2018-01-01

Through stimulation of root growth, increasing atmospheric CO2 concentration ([CO2]) may facilitate access of crops to sub-soil water, which could potentially prolong physiological activity in dryland environments, particularly because crops are more water use efficient under elevated [CO2] (e[CO2]). This study investigated the effect of drought in shallow soil versus sub-soil on agronomic and physiological responses of wheat to e[CO2] in a glasshouse experiment. Wheat (Triticum aestivum L. cv. Yitpi) was grown in split-columns with the top (0-30 cm) and bottom (31-60 cm; 'sub-soil') soil layer hydraulically separated by a wax-coated, root-penetrable layer under ambient [CO2] (a[CO2], ∼400 μmol mol-1) or e[CO2] (∼700 μmol mol-1) [CO2]. Drought was imposed from stem-elongation in either the top or bottom soil layer or both by withholding 33% of the irrigation, resulting in four water treatments (WW, WD, DW, DD; D = drought, W = well-watered, letters denote water treatment in top and bottom soil layer, respectively). Leaf gas exchange was measured weekly from stem-elongation until anthesis. Above-and belowground biomass, grain yield and yield components were evaluated at three developmental stages (stem-elongation, anthesis and maturity). Compared with a[CO2], net assimilation rate was higher and stomatal conductance was lower under e[CO2], resulting in greater intrinsic water use efficiency. Elevated [CO2] stimulated both above- and belowground biomass as well as grain yield, however, this stimulation was greater under well-watered (WW) than drought (DD) throughout the whole soil profile. Imposition of drought in either or both soil layers decreased aboveground biomass and grain yield under both [CO2] compared to the well-watered treatment. However, the greatest 'CO2 fertilisation effect' was observed when drought was imposed in the top soil layer only (DW), and this was associated with e[CO2]-stimulation of root growth especially in the well-watered bottom layer. We suggest that stimulation of belowground biomass under e[CO2] will allow better access to sub-soil water during grain filling period, when additional water is converted into additional yield with high efficiency in Mediterranean-type dryland agro-ecosystems. If sufficient water is available in the sub-soil, e[CO2] may help mitigating the effect of drying surface soil.

Effect of Space Radiation Processing on Lunar Soil Surface Chemistry: X-Ray Photoelectron Spectroscopy Studies

NASA Technical Reports Server (NTRS)

Dukes, C.; Loeffler, M.J.; Baragiola, R.; Christoffersen, R.; Keller, J.

2009-01-01

Current understanding of the chemistry and microstructure of the surfaces of lunar soil grains is dominated by a reference frame derived mainly from electron microscopy observations [e.g. 1,2]. These studies have shown that the outermost 10-100 nm of grain surfaces in mature lunar soil finest fractions have been modified by the combined effects of solar wind exposure, surface deposition of vapors and accretion of impact melt products [1,2]. These processes produce surface-correlated nanophase Feo, host grain amorphization, formation of surface patinas and other complex changes [1,2]. What is less well understood is how these changes are reflected directly at the surface, defined as the outermost 1-5 atomic monolayers, a region not easily chemically characterized by TEM. We are currently employing X-ray Photoelectron Spectroscopy (XPS) to study the surface chemistry of lunar soil samples that have been previously studied by TEM. This work includes modification of the grain surfaces by in situ irradiation with ions at solar wind energies to better understand how irradiated surfaces in lunar grains change their chemistry once exposed to ambient conditions on earth.

Predicting root zone soil moisture with soil properties and satellite near-surface moisture data across the conterminous United States

NASA Astrophysics Data System (ADS)

Baldwin, D.; Manfreda, S.; Keller, K.; Smithwick, E. A. H.

2017-03-01

Satellite-based near-surface (0-2 cm) soil moisture estimates have global coverage, but do not capture variations of soil moisture in the root zone (up to 100 cm depth) and may be biased with respect to ground-based soil moisture measurements. Here, we present an ensemble Kalman filter (EnKF) hydrologic data assimilation system that predicts bias in satellite soil moisture data to support the physically based Soil Moisture Analytical Relationship (SMAR) infiltration model, which estimates root zone soil moisture with satellite soil moisture data. The SMAR-EnKF model estimates a regional-scale bias parameter using available in situ data. The regional bias parameter is added to satellite soil moisture retrievals before their use in the SMAR model, and the bias parameter is updated continuously over time with the EnKF algorithm. In this study, the SMAR-EnKF assimilates in situ soil moisture at 43 Soil Climate Analysis Network (SCAN) monitoring locations across the conterminous U.S. Multivariate regression models are developed to estimate SMAR parameters using soil physical properties and the moderate resolution imaging spectroradiometer (MODIS) evapotranspiration data product as covariates. SMAR-EnKF root zone soil moisture predictions are in relatively close agreement with in situ observations when using optimal model parameters, with root mean square errors averaging 0.051 [cm3 cm-3] (standard error, s.e. = 0.005). The average root mean square error associated with a 20-fold cross-validation analysis with permuted SMAR parameter regression models increases moderately (0.082 [cm3 cm-3], s.e. = 0.004). The expected regional-scale satellite correction bias is negative in four out of six ecoregions studied (mean = -0.12 [-], s.e. = 0.002), excluding the Great Plains and Eastern Temperate Forests (0.053 [-], s.e. = 0.001). With its capability of estimating regional-scale satellite bias, the SMAR-EnKF system can predict root zone soil moisture over broad extents and has applications in drought predictions and other operational hydrologic modeling purposes.

Fate of Escherichia coli O157: H7 in agricultural soils amended with different organic fertilizers.

PubMed

Yao, Zhiyuan; Yang, Li; Wang, Haizhen; Wu, Jianjun; Xu, Jianming

2015-10-15

Five organic fertilizers (vermicompost, pig manure, chicken manure, peat and oil residue) were applied to agricultural soils to study their effects on the survival of Escherichia coli O157:H7 (E. coli O157:H7). Results showed that E. coli O157:H7 survival changed greatly after organic fertilizers application, with shorter td values (survival time needed to reach the detection limit of 100 CFU g(-1)) (12.57±6.57 days) in soils amended with chicken manure and the longest (25.65±7.12 days) in soils amended with pig manure. Soil pH, EC and free Fe/Al (hydro) oxides were significant explanatory factors for E. coli O157:H7 survival in the original soils. Soil constituents (minerals and organic matter) and changes in their surface charges with pH increased the effect of soil pH on E. coli O157:H7 survival. However, electrical conductivity played a more important role in regulating E. coli O157:H7 survival in fertilizer-amended soils. This study highlighted the importance of choosing appropriate organic fertilizers in the preharvest environment to reduce food-borne bacterial contamination. Copyright © 2015 Elsevier B.V. All rights reserved.

Generation of Level 3 SMMR and SSM/I Brightness Temperatures for the Period 1978-1999

NASA Technical Reports Server (NTRS)

Partington, Kim

1999-01-01

The NOAA/NASA Pathfinder Program was initially designed to assure that certain key remote sensing data sets of particular significance to global change research were scientifically validated, consistently processed and made readily available to the research community at minimal cost. Through this Program the National Snow and Ice Data Center (NSIDC), University of Colorado has successfully processed, archived and distributed the Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I) Level 3 (EASE-Grid format) Pathfinder data sets for the period 1978 to 1999. These data are routinely distributed to approximately 150 researchers through various media including CD-ROM, 8 mm tape, ftp and the EOS Information Management System (IMS). At NSIDC these data are currently being applied in the development and validation of algorithms to derive snow water equivalent (NASA NAG5-6636), the mapping of frozen ground and the detection of the onset of melt over ice sheets, sea ice and snow cover. The EASE-Grid format, developed at NSIDC in conjunction with the SMMR-SSM/I Pathfinder project has also been applied to Advanced Very High Resolution Radiometer (AVHRR) and TOVS Pathfinder data, as well as ancillary data such as digital elevation, land cover classification and several in situ data sets. EASE-Grid will also be used for all land products derived from the NASA EOS AMSR-E instrument.

Using machine learning for real-time estimates of snow water equivalent in the watersheds of Afghanistan

NASA Astrophysics Data System (ADS)

Bair, Edward H.; Abreu Calfa, Andre; Rittger, Karl; Dozier, Jeff

2018-05-01

In the mountains, snowmelt often provides most of the runoff. Operational estimates use imagery from optical and passive microwave sensors, but each has its limitations. An accurate approach, which we validate in Afghanistan and the Sierra Nevada USA, reconstructs spatially distributed snow water equivalent (SWE) by calculating snowmelt backward from a remotely sensed date of disappearance. However, reconstructed SWE estimates are available only retrospectively; they do not provide a forecast. To estimate SWE throughout the snowmelt season, we consider physiographic and remotely sensed information as predictors and reconstructed SWE as the target. The period of analysis matches the AMSR-E radiometer's lifetime from 2003 to 2011, for the months of April through June. The spatial resolution of the predictions is 3.125 km, to match the resolution of a microwave brightness temperature product. Two machine learning techniques - bagged regression trees and feed-forward neural networks - produced similar mean results, with 0-14 % bias and 46-48 mm RMSE on average. Nash-Sutcliffe efficiencies averaged 0.68 for all years. Daily SWE climatology and fractional snow-covered area are the most important predictors. We conclude that these methods can accurately estimate SWE during the snow season in remote mountains, and thereby provide an independent estimate to forecast runoff and validate other methods to assess the snow resource.

ICESat Observations of Seasonal and Interannual Variations of Sea-Ice Freeboard and Estimated Thickness in the Weddell Sea, Antarctica (2003-2009)

NASA Technical Reports Server (NTRS)

Yi, Donghui; Robbins, John W.

2010-01-01

Sea-ice freeboard heights for 17 ICESat campaign periods from 2003 to 2009 are derived from ICESat data. Freeboard is combined with snow depth from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data and nominal densities of snow, water and sea ice, to estimate sea-ice thickness. Sea-ice freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of growth and decay of the Weddell Sea (Antarctica) pack ice. During October-November, sea ice grows to its seasonal maximum both in area and thickness; the mean freeboards are 0.33-0.41 m and the mean thicknesses are 2.10-2.59 m. During February-March, thinner sea ice melts away and the sea-ice pack is mainly distributed in the west Weddell Sea; the mean freeboards are 0.35-0.46 m and the mean thicknesses are 1.48-1.94 m. During May-June, the mean freeboards and thicknesses are 0.26-0.29 m and 1.32-1.37 m, respectively. The 6 year trends in sea-ice extent and volume are (0.023+/-0.051) x 10(exp 6)sq km/a (0.45%/a) and (0.007+/-1.0.092) x 10(exp 3)cu km/a (0.08%/a); however, the large standard deviations indicate that these positive trends are not statistically significant.

Physicochemical properties and stability of thousand-year-old soil organic matter in boreal paleopodzols

NASA Astrophysics Data System (ADS)

Feng, Wen-Ting; Klaminder, Jonatan; Boily, Jean-Francois

2013-04-01

Soil organic matter (SOM) stabilization mechanisms are key to predict carbon (C) cycle responses to climate change, especially in critically sensitive ecosystems, such as the arctic and boreal ecosystems of Scandinavia (IPCC 2007). Interactions between organic matter and soil mineral components can be of particular importance. Their impacts on SOM stability are however not fully resolved. In this study, we present an exhaustive physicochemical characterization of SOM and soil mineral components of boreal paleopodzols formed over several thousands of years in northern Sweden. We also test the hypothesis that old SOM in these environments is strongly associated to mineral surfaces. This work was specifically focused on two relict podzolic profiles capped by more recently developed podzolic profile. Each of the three profiles consisted of a well developed E-horizon and of an underlying B-horizon enriched in secondary weathering products. Soil C age was greater with increasing depth, with the deepest horizon dating from the mid-Holocene. Organic C loadings, expressed in terms of C mass per mineral surface area, decreased from 0.52 to 0.31 mg C m-2 from deep to the deepest B horizons. A monolayer coating model could thus be used to suggest that C was mainly bonded to unsaturated mineral surfaces. Scanning electron microscopy and energy dispersive X-ray spectroscopy showed that, unlike in younger B-horizon, the oldest C of the deepest B-horizon did not accumulate in clusters. It was instead distributed more homogenously at the micrometer scale with soil mineral particles. X-ray photoelectron spectroscopy moreover showed that the top 1-10 nm of the mineral surfaces contained proportions of aliphatic-C, ether/alcohol-C, and amide-C that varied greatly amongst the three B horizons but not among the three E horizons. Different composition of SOM remained in deep E and B horizons, thereby suggesting a selective SOM preservation process that is controlled by the properties of the mineral matrix. Our findings therefore support the concept that soil mineral surfaces impact SOM stability. The importance of SOM-mineral surfaces complexation was demonstrated further through combined temperature-programmed desorption mass spectrometric Fourier transform infrared (TPD-MS-FTIR) experiments pointing to highly resilient forms of SOM associated to mineral particle surfaces. In summary, our study suggests organic matter sorption on mineral surfaces is important for SOM preservation at the millennial scale. Predicting the long-term fate of C in boreal regions should consequently account for such types of organo-mineral associations.

Pleistocene permafrost features in soils in the South-western Italian Alps

NASA Astrophysics Data System (ADS)

D'Amico, Michele; Catoni, Marcella; Bonifacio, Eleonora; Zanini, Ermanno

2015-04-01

Because of extensive Pleistocenic glaciations which erased most of the previously existing soils, slope steepness and climatic conditions favoring soil erosion, most soils observed on the Alps (and in other mid-latitude mountain ranges) developed only during the Holocene. However, in few sites, particularly in the outermost sections of the Alpine range, Pleistocene glaciers covered only small and scattered surfaces because of the low altitude reached in the basins, and ancient soils could be preserved for long periods of time on particularly stable surfaces. In some cases, these soils retain good memories of past periglacial activity. We described and sampled soils on stable surfaces in the Upper Tanaro valley, Ligurian Alps (Southwestern Piemonte, Italy). The sampling sites were between 600 to 1600 m of altitude, under present day lower montane Castanea sativa/Ostrya carpinifolia forests, montane Fagus sylvatica and Pinus uncinata forests or montane heath/grazed grassland, on different quartzitic substrata. The surface morphology often showed strongly developed, fossil periglacial patterned ground forms, such as coarse stone circles on flat surfaces, or stone stripes on steeper slopes. The stone circles could be up to 5 m wide, while the sorted stripes could be as wide as 12-15 m. A strong lateral cryogenic textural sorting characterized the fine fraction too, with sand dominating close to the stone rims of the patterned ground features and silt and clay the central parts. The surface 60-120 cm of the soils were podzolized during the Holocene; as a result of the textural lateral sorting, the thickness of the podzolic E and Bs horizons varied widely across the patterns. The lower boundary of the Holocene Podzols was abrupt, and corresponded with dense layers with thick coarse laminar structure and illuvial silt accumulation (Cjj horizons). Dense Cjj diapiric inclusions were sometimes preserved in the central parts of the patterns. Where cover beds were developed, more superimposed podzol cycles were observed: the deeper podzols, included in the dense layer, were strongly cryoturbated and showed convoluted horizons and buried organic horizons. The presence of the dense Cjj horizons also influenced surface soil hydrology, which in turn influenced the expression of E and Bs horizons, in addition to textural lateral variability. In conclusion, surface morphology and soil properties evidence the presence of permafrost during cold Pleistocene phases, with an active layer 60-120 cm thick, associated with a particularly strong cryoturbation. However, all the permafrost features were not necessarily formed during the same periods, and dating of different materials would be necessary in order to obtain precise paleoenvironmental reconstructions of cold Quaternary phases in the Alps.

Culturable fungi in potting soils and compost.

PubMed

Haas, Doris; Lesch, Susanne; Buzina, Walter; Galler, Herbert; Gutschi, Anna Maria; Habib, Juliana; Pfeifer, Bettina; Luxner, Josefa; Reinthaler, Franz F

2016-11-01

In the present study the spectrum and the incidence of fungi in potting soils and compost was investigated. Since soil is one of the most important biotopes for fungi, relatively high concentrations of fungal propagules are to be expected. For detection of fungi, samples of commercial soils, compost and soils from potted plants (both surface and sub-surface) were suspended and plated onto several mycological media. The resulting colonies were evaluated qualitatively and quantitatively. The results from the different sampling series vary, but concentrations on the surface of potted plants and in commercial soils are increased tenfold compared to compost and sub-surface soils. Median values range from 9.5 × 10(4) colony forming units (CFU)/g to 5.5 × 10(5) CFU/g. The spectrum of fungi also varies in the soils. However, all sampling series show high proportion of Aspergillus and Penicillium species, including potentially pathogenic species such as Aspergillus fumigatus. Cladosporium, a genus dominant in the ambient air, was found preferably in samples which were in contact with the air. The results show that potentially pathogenic fungi are present in soils. Immunocompromised individuals should avoid handling soils or potted plants in their immediate vicinity. © The Author 2016. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

[Effects of the grain size and thickness of dust deposits on soil water and salt movement in the hinterland of the Taklimakan Desert].

PubMed

Sun, Yan-Wei; Li, Sheng-Yu; Xu, Xin-Wen; Zhang, Jian-Guo; Li, Ying

2009-08-01

By using mcirolysimeter, a laboratory simulation experiment was conducted to study the effects of the grain size and thickness of dust deposits on the soil water evaporation and salt movement in the hinterland of the Taklimakan Desert. Under the same initial soil water content and deposition thickness condition, finer-textured (<0.063 mm) deposits promoted soil water evaporation, deeper soil desiccation, and surface soil salt accumulation, while coarse-textured (0.063-2 mm) deposits inhibited soil water evaporation and decreased deeper soil water loss and surface soil salt accumulation. The inhibition effect of the grain size of dust deposits on soil water evaporation had an inflection point at the grain size 0.20 mm, i. e., increased with increasing grain size when the grain size was 0.063-0.20 mm but decreased with increasing grain size when the grain size was > 0.20 mm. With the increasing thickness of dust deposits, its inhibition effect on soil water evaporation increased, and there existed a logarithmic relationship between the dust deposits thickness and water evaporation. Surface soil salt accumulation had a negative correlation with dust deposits thickness. In sum, the dust deposits in study area could affect the stability of arid desert ecosystem.

Comparing Noah-MP simulations of energy and water fluxes in the soil-vegetation-atmosphere continuum with plot scale measurements

NASA Astrophysics Data System (ADS)

Gayler, Sebastian; Wöhling, Thomas; Högy, Petra; Ingwersen, Joachim; Wizemann, Hans-Dieter; Wulfmeyer, Volker; Streck, Thilo

2013-04-01

During the last years, land-surface models have proven to perform well in several studies that compared simulated fluxes of water and energy from the land surface to the atmosphere against measured fluxes at the plot-scale. In contrast, considerable deficits of land-surface models have been identified to simulate soil water fluxes and vertical soil moisture distribution. For example, Gayler et al. (2013) showed that simplifications in the representation of root water uptake can result in insufficient simulations of the vertical distribution of soil moisture and its dynamics. However, in coupled simulations of the terrestrial water cycle, both sub-systems, the atmosphere and the subsurface hydrogeo-system, must fit together and models are needed, which are able to adequately simulate soil moisture, latent heat flux, and their interrelationship. Consequently, land-surface models must be further improved, e.g. by incorporation of advanced biogeophysics models. To improve the conceptual realism in biophysical and hydrological processes in the community land surface model Noah, this model was recently enhanced to Noah-MP by a multi-options framework to parameterize individual processes (Niu et al., 2011). Thus, in Noah-MP the user can choose from several alternative models for vegetation and hydrology processes that can be applied in different combinations. In this study, we evaluate the performance of different Noah-MP model settings to simulate water and energy fluxes across the land surface at two contrasting field sites in South-West Germany. The evaluation is done in 1D offline-mode, i.e. without coupling to an atmospheric model. The atmospheric forcing is provided by measured time series of the relevant variables. Simulation results are compared with eddy covariance measurements of turbulent fluxes and measured time series of soil moisture at different depths. The aims of the study are i) to carve out the most appropriate combination of process parameterizations in Noah-MP to simultaneously match the different components of the water and energy cycle at the field sites under consideration, and ii) to estimate the uncertainty in model structure. We further investigate the potential to improve simulation results by incorporating concepts of more advanced root water uptake models from agricultural field scale models into the land-surface-scheme. Gayler S, Ingwersen J, Priesack E, Wöhling T, Wulfmeyer V, Streck T (2013): Assessing the relevance of sub surface processes for the simulation of evapotranspiration and soil moisture dynamics with CLM3.5: Comparison with field data and crop model simulations. Environ. Earth Sci., 69(2), under revision. Niu G-Y, Yang Z-L, Mitchell KE, Chen F, Ek MB, Barlage M, Kumar A, Manning K, Niyogi D, Rosero E, Tewari M and Xia Y (2011): The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements. Journal of Geophysical Research 116(D12109).

Monitoring the Vadose Zone Moisture Regime Below a Surface Barrier

NASA Astrophysics Data System (ADS)

Zhang, Z. F.; Strickland, C. E.; Field, J. G.

2009-12-01

A 6000 m2 interim surface barrier has been constructed over a portion of the T Tank Farm in the Depart of Energy’s Hanford site. The purpose of using a surface barrier was to reduce or eliminate the infiltration of meteoric precipitation into the contaminated soil zone due to past leaks from Tank T-106 and hence to reduce the rate of movement of the plume. As part of the demonstration effort, vadose zone moisture is being monitored to assess the effectiveness of the barrier on the reduction of soil moisture flow. A vadose zone monitoring system was installed to measure soil water conditions at four horizontal locations (i.e., instrument Nests A, B, C, and D) outside, near the edge of, and beneath the barrier. Each instrument nest consists of a capacitance probe with multiple sensors, multiple heat-dissipation units, and a neutron probe access tube used to measure soil-water content and soil-water pressure. Nest A serves as a control by providing subsurface conditions outside the influence of the surface barrier. Nest B provides subsurface measurements to assess barrier edge effects. Nests C and D are used to assess the impact of the surface barrier on soil-moisture conditions beneath it. Monitoring began in September 2006 and continues to the present. To date, the monitoring system has provided high-quality data. Results show that the soil beneath the barrier has been draining from the shallower depth. The lack of climate-caused seasonal variation of soil water condition beneath the barrier indicates that the surface barrier has minimized water exchange between the soil and the atmosphere.

Using SMAP to identify structural errors in hydrologic models

NASA Astrophysics Data System (ADS)

Crow, W. T.; Reichle, R. H.; Chen, F.; Xia, Y.; Liu, Q.

2017-12-01

Despite decades of effort, and the development of progressively more complex models, there continues to be underlying uncertainty regarding the representation of basic water and energy balance processes in land surface models. Soil moisture occupies a central conceptual position between atmosphere forcing of the land surface and resulting surface water fluxes. As such, direct observations of soil moisture are potentially of great value for identifying and correcting fundamental structural problems affecting these models. However, to date, this potential has not yet been realized using satellite-based retrieval products. Using soil moisture data sets produced by the NASA Soil Moisture Active/Passive mission, this presentation will explore the use of the remotely-sensed soil moisture data products as a constraint to reject certain types of surface runoff parameterizations within a land surface model. Results will demonstrate that the precision of the SMAP Level 4 Surface and Root-Zone soil moisture product allows for the robust sampling of correlation statistics describing the true strength of the relationship between pre-storm soil moisture and subsequent storm-scale runoff efficiency (i.e., total storm flow divided by total rainfall both in units of depth). For a set of 16 basins located in the South-Central United States, we will use these sampled correlations to demonstrate that so-called "infiltration-excess" runoff parameterizations under predict the importance of pre-storm soil moisture for determining storm-scale runoff efficiency. To conclude, we will discuss prospects for leveraging this insight to improve short-term hydrologic forecasting and additional avenues for SMAP soil moisture products to provide process-level insight for hydrologic modelers.

Multiple soil nutrient competition between plants, microbes, and mineral surfaces: model development, parameterization, and example applications in several tropical forests

NASA Astrophysics Data System (ADS)

Zhu, Q.; Riley, W. J.; Tang, J.; Koven, C. D.

2015-03-01

Soil is a complex system where biotic (e.g., plant roots, micro-organisms) and abiotic (e.g., mineral surfaces) consumers compete for resources necessary for life (e.g., nitrogen, phosphorus). This competition is ecologically significant, since it regulates the dynamics of soil nutrients and controls aboveground plant productivity. Here we develop, calibrate, and test a nutrient competition model that accounts for multiple soil nutrients interacting with multiple biotic and abiotic consumers. As applied here for tropical forests, the Nutrient COMpetition model (N-COM) includes three primary soil nutrients (NH4+, NO3-, and POx (representing the sum of PO43-, HPO42-, and H2PO4-)) and five potential competitors (plant roots, decomposing microbes, nitrifiers, denitrifiers, and mineral surfaces). The competition is formulated with a quasi-steady-state chemical equilibrium approximation to account for substrate (multiple substrates share one consumer) and consumer (multiple consumers compete for one substrate) effects. N-COM successfully reproduced observed soil heterotrophic respiration, N2O emissions, free phosphorus, sorbed phosphorus, and free NH4+ at a tropical forest site (Tapajos). The overall model posterior uncertainty was moderately well constrained. Our sensitivity analysis revealed that soil nutrient competition was primarily regulated by consumer-substrate affinity rather than environmental factors such as soil temperature or soil moisture. Our results imply that the competitiveness (from most to least competitive) followed this order: (1) for NH4+, nitrifiers ~ decomposing microbes > plant roots, (2) for NO3-, denitrifiers ~ decomposing microbes > plant roots, (3) for POx, mineral surfaces > decomposing microbes ~ plant roots. Although smaller, plant relative competitiveness is of the same order of magnitude as microbes. We then applied the N-COM model to analyze field nitrogen and phosphorus perturbation experiments in two tropical forest sites (in Hawaii and Puerto Rico) not used in model development or calibration. Under soil inorganic nitrogen and phosphorus elevated conditions, the model accurately replicated the experimentally observed competition among different nutrient consumers. Although we used as many observations as we could obtain, more nutrient addition experiments in tropical systems would greatly benefit model testing and calibration. In summary, the N-COM model provides an ecologically consistent representation of nutrient competition appropriate for land BGC models integrated in Earth System Models.

Interactive Effects of Climate Change and Decomposer Communities on the Stabilization of Wood-Derived Carbon Pools: Catalyst for a New Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Resh, Sigrid C.

Globally, forest soils store ~two-thirds as much carbon (C) as the atmosphere. Although wood makes up the majority of forest biomass, the importance of wood contributions to soil C pools is unknown. Even with recent advances in the mechanistic understanding of soil processes, integrative studies tracing C input pathways and biological fluxes within and from soils are lacking. Therefore, our research objectives were to assess the impact of different fungal decay pathways (i.e., white-rot versus brown-rot)—in interaction with wood quality, soil temperature, wood location (i.e., soil surface and buried in mineral soil), and soil texture—on the transformation of woody materialmore » into soil CO 2 efflux, dissolved organic carbon (DOC), and soil C pools. The use of 13C-depleted woody biomass harvested from the Rhinelander, WI free-air carbon dioxide enrichment (Aspen-FACE) experiment affords the unique opportunity to distinguish the wood-derived C from other soil C fluxes and pools. We established 168 treatment plots across six field sites (three sand and three loam textured soil). Treatment plots consisted of full-factorial design with the following treatments: 1. Wood chips from elevated CO 2, elevated CO 2 + O 3, or ambient atmosphere AspenFACE treatments; 2. Inoculated with white rot (Bjerkandera adusta) or brown rot (Gloeophyllum sepiarium) pure fungal cultures, or the original suite of endemic microbial community on the logs; and 3. Buried (15cm in soil as a proxy for coarse roots) or surface applied wood chips. We also created a warming treatment using open-topped, passive warming chambers on a subset of the above treatments. Control plots with no added wood (“no chip control”) were incorporated into the research design. Soils were sampled for initial δ 13C values, CN concentrations, and bulk density. A subset of plots were instrumented with lysimeters for sampling soil water and temperature data loggers for measuring soil temperatures. To determine the early pathways of decomposition, we measured soil surface CO 2 efflux, dissolved organic C (DOC), and DO 13C approximately monthly over two growing seasons from a subsample of the research plots. To determine the portion of soil surface CO 2 efflux attributable to wood-derived C, we used Keeling plot techniques to estimate the associated δ 13C values of the soil CO 2 efflux. We measured the δ 13CO 2 once during the peak of each growing season. Initial values for soil δ 13C values and CN concentrations averaged across the six sites were -26.8‰ (standard error = 0.04), 2.46% (se = 0.11), and 0.15% (se = 0.01), respectively. The labeled wood chips from the Aspen FACE treatments had an average δ13C value of -39.5‰ (se 0.10). The >12 ‰ isotopic difference between the soil and wood chip δ 13C values provides the basis for tracking the wood-derived C through the early stages of decomposition and subsequent storage in the soil. Across our six research sites, average soil surface CO 2 efflux ranged from 1.04 to 2.00 g CO 2 m -2 h -1 for the first two growing seasons. No wood chip controls had an average soil surface CO 2 efflux of 0.67 g CO 2 m -2 h -1 or about half of that of the wood chip treatment plots. Wood-derived CO 2 efflux was higher for loam textured soils relative to sands (0.70 and 0.54 g CO 2 m -2 h -1, respectively; p = 0.045)), for surface relative to buried wood chip treatments (0.92 and 0.39 g CO 2 m -2 h -1, respectively; p < 0.001), for warmed relative to ambient temperature treatments (0.99 and 0.78 g CO 2 m -2 h -1, respectively; 0.004), and for natural rot relative to brown and white rots (0.93, 0.82, and 0.78 g CO 2 m -2 h -1, respectively; p = 0.068). Our first two growing seasons of soil surface CO 2 efflux data show that wood chip location (i.e., surface vs. buried chip application) is very important, with surface chips loosing twice the wood-derived CO 2. The DOC data support this trend for greater loss of ecosystem C from surface chips. This has strong implications for the importance of root and buried wood for ecosystem C retention. This strong chip location effect on wood-derived C loss was significantly modified by soil texture, soil temperature, decomposer communities, and wood quality as effected by potential future CO 2 and O 3 levels.« less

Modelling and interpreting biologically crusted dryland soil sub-surface structure using automated micropenetrometry

NASA Astrophysics Data System (ADS)

Hoon, Stephen R.; Felde, Vincent J. M. N. L.; Drahorad, Sylvie L.; Felix-Henningsen, Peter

2015-04-01

Soil penetrometers are used routinely to determine the shear strength of soils and deformable sediments both at the surface and throughout a depth profile in disciplines as diverse as soil science, agriculture, geoengineering and alpine avalanche-safety (e.g. Grunwald et al. 2001, Van Herwijnen et al. 2009). Generically, penetrometers comprise two principal components: An advancing probe, and a transducer; the latter to measure the pressure or force required to cause the probe to penetrate or advance through the soil or sediment. The force transducer employed to determine the pressure can range, for example, from a simple mechanical spring gauge to an automatically data-logged electronic transducer. Automated computer control of the penetrometer step size and probe advance rate enables precise measurements to be made down to a resolution of 10's of microns, (e.g. the automated electronic micropenetrometer (EMP) described by Drahorad 2012). Here we discuss the determination, modelling and interpretation of biologically crusted dryland soil sub-surface structures using automated micropenetrometry. We outline a model enabling the interpretation of depth dependent penetration resistance (PR) profiles and their spatial differentials using the model equations, σ {}(z) ={}σ c0{}+Σ 1n[σ n{}(z){}+anz + bnz2] and dσ /dz = Σ 1n[dσ n(z) /dz{} {}+{}Frn(z)] where σ c0 and σ n are the plastic deformation stresses for the surface and nth soil structure (e.g. soil crust, layer, horizon or void) respectively, and Frn(z)dz is the frictional work done per unit volume by sliding the penetrometer rod an incremental distance, dz, through the nth layer. Both σ n(z) and Frn(z) are related to soil structure. They determine the form of σ {}(z){} measured by the EMP transducer. The model enables pores (regions of zero deformation stress) to be distinguished from changes in layer structure or probe friction. We have applied this method to both artificial calibration soils in the laboratory, and in-situ field studies. In particular, we discuss the nature and detection of surface and buried (fossil) subsurface Biological Soil Crusts (BSCs), voids, macroscopic particles and compositional layers. The strength of surface BSCs and the occurrence of buried BSCs and layers has been detected at sub millimetre scales to depths of 40mm. Our measurements and field observations of PR show the importance of morphological layering to overall BSC functions (Felde et al. 2015). We also discuss the effect of penetrometer shaft and probe-tip profiles upon the theoretical and experimental curves, EMP resolution and reproducibility, demonstrating how the model enables voids, buried biological soil crusts, exotic particles, soil horizons and layers to be distinguished one from another. This represents a potentially important contribution to advancing understanding of the relationship between BSCs and dryland soil structure. References: Drahorad SL, Felix-Henningsen P. (2012) An electronic micropenetrometer (EMP) for field measurements of biological soil crust stability, J. Plant Nutr. Soil Sci., 175, 519-520 Felde V.J.M.N.L., Drahorad S.L., Felix-Henningsen P., Hoon S.R. (2015) Ongoing oversanding induces biological soil crust layering - a new approach for BSC structure elucidation determined from high resolution penetration resistance data (submitted) Grunwald, S., Rooney D.J., McSweeney K., Lowery B. (2001) Development of pedotransfer functions for a profile cone penetrometer, Geoderma, 100, 25-47 Van Herwijnen A., Bellaire S., Schweizer J. (2009) Comparison of micro-structural snowpack parameters derived from penetration resistance measurements with fracture character observations from compression tests, Cold Regions Sci. {& Technol.}, 59, 193-201

Community composition of ammonia-oxidizing bacteria and archaea in rice field soil as affected by nitrogen fertilization.

PubMed

Wang, Yanan; Ke, Xiubin; Wu, Liqin; Lu, Yahai

2009-02-01

Little information is available on the ecology of ammonia-oxidizing bacteria (AOB) and archaea (AOA) in flooded rice soils. Consequently, a microcosm experiment was conducted to determine the effect of nitrogen fertilizer on the composition of AOB and AOA communities in rice soil by using molecular analyses of ammonia monooxygenase gene (amoA) fragments. Experimental treatments included three levels of N (urea) fertilizer, i.e. 50, 100 and 150 mgNkg(-1) soil. Soil samples were operationally divided into four fractions: surface soil, bulk soil deep layer, rhizosphere and washed root material. NH(4)(+)-N was the dominant form of N in soil porewater and increased with N fertilization. Cloning and sequencing of amoA gene fragments showed that the AOB community in the rice soil consisted of three major groups, i.e. Nitrosomonas communis cluster, Nitrosospira cluster 3a and cluster 3b. The sequences related to Nitrosomonas were predominant. There was a clear effect of N fertilizer and soil depth on AOB community composition based on terminal restriction fragment length polymorphism fingerprinting. Nitrosomonas appeared to be more abundant in the potentially oxic or micro-oxic fractions, including surface soil, rhizosphere and washed root material, than the deep layer of anoxic bulk soil. Furthermore, Nitrosomonas increased relatively in the partially oxic fractions and that of Nitrosospira decreased with the increasing application of N fertilizer. However, AOA community composition remained unchanged according to the denaturing gradient gel electrophoresis analyses.

Assembling Large, Multi-Sensor Climate Datasets Using the SciFlo Grid Workflow System

NASA Astrophysics Data System (ADS)

Wilson, B. D.; Manipon, G.; Xing, Z.; Fetzer, E.

2008-12-01

NASA's Earth Observing System (EOS) is the world's most ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the A-Train platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the cloud scenes from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time matchups between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, and assemble merged datasets for further scientific and statistical analysis. To meet these large-scale challenges, we are utilizing a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data query, access, subsetting, co-registration, mining, fusion, and advanced statistical analysis. SciFlo is a semantically-enabled ("smart") Grid Workflow system that ties together a peer-to-peer network of computers into an efficient engine for distributed computation. The SciFlo workflow engine enables scientists to do multi-instrument Earth Science by assembling remotely-invokable Web Services (SOAP or http GET URLs), native executables, command-line scripts, and Python codes into a distributed computing flow. A scientist visually authors the graph of operation in the VizFlow GUI, or uses a text editor to modify the simple XML workflow documents. The SciFlo client & server engines optimize the execution of such distributed workflows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The engine transparently moves data to the operators, and moves operators to the data (on the dozen trusted SciFlo nodes). SciFlo also deploys a variety of Data Grid services to: query datasets in space and time, locate & retrieve on-line data granules, provide on-the-fly variable and spatial subsetting, and perform pairwise instrument matchups for A-Train datasets. These services are combined into efficient workflows to assemble the desired large-scale, merged climate datasets. SciFlo is currently being applied in several large climate studies: comparisons of aerosol optical depth between MODIS, MISR, AERONET ground network, and U. Michigan's IMPACT aerosol transport model; characterization of long-term biases in microwave and infrared instruments (AIRS, MLS) by comparisons to GPS temperature retrievals accurate to 0.1 degrees Kelvin; and construction of a decade-long, multi-sensor water vapor climatology stratified by classified cloud scene by bringing together datasets from AIRS/AMSU, AMSR-E, MLS, MODIS, and CloudSat (NASA MEASUREs grant, Fetzer PI). The presentation will discuss the SciFlo technologies, their application in these distributed workflows, and the many challenges encountered in assembling and analyzing these massive datasets.

Assembling Large, Multi-Sensor Climate Datasets Using the SciFlo Grid Workflow System

NASA Astrophysics Data System (ADS)

Wilson, B.; Manipon, G.; Xing, Z.; Fetzer, E.

2009-04-01

NASA's Earth Observing System (EOS) is an ambitious facility for studying global climate change. The mandate now is to combine measurements from the instruments on the "A-Train" platforms (AIRS, AMSR-E, MODIS, MISR, MLS, and CloudSat) and other Earth probes to enable large-scale studies of climate change over periods of years to decades. However, moving from predominantly single-instrument studies to a multi-sensor, measurement-based model for long-duration analysis of important climate variables presents serious challenges for large-scale data mining and data fusion. For example, one might want to compare temperature and water vapor retrievals from one instrument (AIRS) to another instrument (MODIS), and to a model (ECMWF), stratify the comparisons using a classification of the "cloud scenes" from CloudSat, and repeat the entire analysis over years of AIRS data. To perform such an analysis, one must discover & access multiple datasets from remote sites, find the space/time "matchups" between instruments swaths and model grids, understand the quality flags and uncertainties for retrieved physical variables, assemble merged datasets, and compute fused products for further scientific and statistical analysis. To meet these large-scale challenges, we are utilizing a Grid computing and dataflow framework, named SciFlo, in which we are deploying a set of versatile and reusable operators for data query, access, subsetting, co-registration, mining, fusion, and advanced statistical analysis. SciFlo is a semantically-enabled ("smart") Grid Workflow system that ties together a peer-to-peer network of computers into an efficient engine for distributed computation. The SciFlo workflow engine enables scientists to do multi-instrument Earth Science by assembling remotely-invokable Web Services (SOAP or http GET URLs), native executables, command-line scripts, and Python codes into a distributed computing flow. A scientist visually authors the graph of operation in the VizFlow GUI, or uses a text editor to modify the simple XML workflow documents. The SciFlo client & server engines optimize the execution of such distributed workflows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The engine transparently moves data to the operators, and moves operators to the data (on the dozen trusted SciFlo nodes). SciFlo also deploys a variety of Data Grid services to: query datasets in space and time, locate & retrieve on-line data granules, provide on-the-fly variable and spatial subsetting, perform pairwise instrument matchups for A-Train datasets, and compute fused products. These services are combined into efficient workflows to assemble the desired large-scale, merged climate datasets. SciFlo is currently being applied in several large climate studies: comparisons of aerosol optical depth between MODIS, MISR, AERONET ground network, and U. Michigan's IMPACT aerosol transport model; characterization of long-term biases in microwave and infrared instruments (AIRS, MLS) by comparisons to GPS temperature retrievals accurate to 0.1 degrees Kelvin; and construction of a decade-long, multi-sensor water vapor climatology stratified by classified cloud scene by bringing together datasets from AIRS/AMSU, AMSR-E, MLS, MODIS, and CloudSat (NASA MEASUREs grant, Fetzer PI). The presentation will discuss the SciFlo technologies, their application in these distributed workflows, and the many challenges encountered in assembling and analyzing these massive datasets.

The fate and transport of reproductive hormones and their conjugates in the environment (Invited)

NASA Astrophysics Data System (ADS)

Casey, F. X.; Shrestha, S. L.; Hakk, H.; Smith, D. J.; Larsen, G. L.; Padmanabhan, G.

2009-12-01

Reproductive steroid hormones can disrupt the endocrine system of some species at ng/L concentrations. Sources of steroid hormones to the environment include human waste water effluents or manure produced at animal feeding operations (AFOs). Steroid hormones, such as 17β-estradiol (E2) and estrone (E1), undergo various fate and transport processes, and laboratory studies have shown that they do not persist long (hours to few days), and have very little if any mobility in soil. Nonetheless, steroid hormones are detected at frequencies and concentrations of concern in the natural environment that would suggest their moderate persistence and mobility. One theory that may partially explain the disparity between field and laboratory studies is that conjugated forms of hormones are more mobile than their deconjugated counterparts. Glucuronide and sulfate conjugates are found in abundance in animal waste and are more soluble than their deconjugated forms. Laboratory studies were conducted to study the fate of a major urinary E2 conjugate, 17β-estradiol glucuronide (E2G), in a Hamar soil (Sandy, mixed, frigid typic Endoaquolls) from the surface and subsurface horizons. Speciation studies using batch sorption indicated that E2G degraded to E2 and E1 within 24 hours in the upper horizon soil with organic carbon content (OC) of 1.35%; whereas it persisted more in the lower horizon soil containing 0.32% OC. For initial concentrations of 2.8-28 mg/L, more than 15% of the applied dose concentration was still intact in the conjugate form in the aqueous phase for 3 - 14 days, in the lower horizon soil. The decline of E2G in the aqueous phase in the upper horizon soil was approximated with a first-order rate constant (k), which ranged from -0.208 to -0.279/h. The k values ranged from -0.006 to -0.016/h for the lower soil horizon. The differences in k values between the two horizons could be attributed to differences in bacterial activity and/or differences in sorption capacities. The upper horizon would generally have more biological activity and perhaps more E2G bio-degradation. Also, the higher OC of the upper horizon would result in greater sorption of the hydrophobic hormones. Our results may have important implications for on-farm manure management. A prevailing practice is to inject manure slurry below the soil surface to reduce ammonia volatilization and odor. If slurries are injected too deep, then conjugated hormones in the manure could potentially be placed at soil depths that have less capacity to degrade and a greater potential to be transported. Time windows of 24 hours for surface manure application and 3 - 14 days for subsurface manure application may be decisive in determining whether E2G will be transported during runoff and leaching events.

Impervious Surfaces Alter Soil Bacterial Communities in Urban Areas: A Case Study in Beijing, China

PubMed Central

Hu, Yinhong; Dou, Xiaolin; Li, Juanyong; Li, Feng

2018-01-01

The rapid expansion of urbanization has caused land cover change, especially the increasing area of impervious surfaces. Such alterations have significant effects on the soil ecosystem by impeding the exchange of gasses, water, and materials between soil and the atmosphere. It is unclear whether impervious surfaces have any effects on soil bacterial diversity and community composition. In the present study, we conducted an investigation of bacterial communities across five typical land cover types, including impervious surfaces (concrete), permeable pavement (bricks with round holes), shrub coverage (Buxus megistophylla Levl.), lawns (Festuca elata Keng ex E. Alexeev), and roadside trees (Sophora japonica Linn.) in Beijing, to explore the response of bacteria to impervious surfaces. The soil bacterial communities were addressed by high-throughput sequencing of the bacterial 16S rRNA gene. We found that Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, Chloroflexi, and Firmicutes were the predominant phyla in urban soils. Soil from impervious surfaces presented a lower bacterial diversity, and differed greatly from other types of land cover. Soil bacterial diversity was predominantly affected by Zn, dissolved organic carbon (DOC), and soil moisture content (SMC). The composition of the bacterial community was similar under shrub coverage, roadside trees, and lawns, but different from beneath impervious surfaces and permeable pavement. Variance partitioning analysis showed that edaphic properties contributed to 12% of the bacterial community variation, heavy metal pollution explained 3.6% of the variation, and interaction between the two explained 33% of the variance. Together, our data indicate that impervious surfaces induced changes in bacterial community composition and decrease of bacterial diversity. Interactions between edaphic properties and heavy metals were here found to change the composition of the bacterial community and diversity across areas with different types of land cover, and soil properties play a more important role than heavy metals. PMID:29545776

Polynya dynamics and associated atmospheric forcing at the Ronne Ice Shelf

NASA Astrophysics Data System (ADS)

Ebner, Lars; Heinemann, Günther

2014-05-01

The Ronne Ice Shelf is known as one of the most active regions of polynya developments around the Antarctic continent. Low temperatures are prevailing throughout the whole year, particularly in winter. It is generally recognized that polynya formations are primarily forced by offshore winds and secondarily by ocean currents. Many authors have addressed this issue previously at the Ross Ice Shelf and Adélie Coast and connected polynya dynamics to strong katabatic surge events. Such investigations of atmospheric dynamics and simultaneous polynya occurrence are still severely underrepresented for the southwestern part of the Weddell Sea and especially for the Ronne Ice Shelf. Due to the very flat terrain gradients of the ice shelf katabatic winds are of minor importance in that area. Other atmospheric processes must therefore play a crucial role for polynya developments at the Ronne Ice Shelf. High-resolution simulations have been carried out for the Weddell Sea region using the non-hydrostatic NWP model COSMO from the German Meteorological Service (DWD). For the austral autumn and winter (March to August) 2008 daily forecast simulations were conducted with the consideration of daily sea-ice coverage deduced from the passive microwave system AMSR-E. These simulations are used to analyze the synoptic and mesoscale atmospheric dynamics of the Weddell Sea region and find linkages to polynya occurrence at the Ronne Ice Shelf. For that reason, the relation between the surface wind speed, the synoptic pressure gradient in the free atmosphere and polynya area is investigated. Seven significant polynya events are identified for the simulation period, three in the autumn and four in the winter season. It can be shown that in almost all cases synoptic cyclones are the primary polynya forcing systems. In most cases the timely interaction of several passing cyclones in the northern and central Weddell Sea leads to maintenance of a strong synoptic pressure gradient above the Ronne Ice Shelf. This strong synoptic forcing results in a moderate to strong offshore surface wind. It turned out that these synoptic depressions lead to strong barrier winds above the northwestern Ronne Ice Shelf and along the eastern flank of the Antarctic Peninsula. The fact, that these barrier winds often appear prior or during the initial break up of sea ice at the shelf ice edge, suggest that this mesoscale wind phenomenon plays a crucial role for polynya development. Furthermore, even mesoscale cyclogenesis above the Ronne Ice Shelf and the following northeastward passage of such a system can break up sea-ice cover under large-scale stationary weather conditions.

Simulation of Runoff Hydrograph on Soil Surfaces with Different Microtopography Using a Travel Time Method at the Plot Scale

PubMed Central

Zhao, Longshan; Wu, Faqi

2015-01-01

In this study, a simple travel time-based runoff model was proposed to simulate a runoff hydrograph on soil surfaces with different microtopographies. Three main parameters, i.e., rainfall intensity (I), mean flow velocity (v m) and ponding time of depression (t p), were inputted into this model. The soil surface was divided into numerous grid cells, and the flow length of each grid cell (l i) was then calculated from a digital elevation model (DEM). The flow velocity in each grid cell (v i) was derived from the upstream flow accumulation area using v m. The total flow travel time through each grid cell to the surface outlet was the sum of the sum of flow travel times along the flow path (i.e., the sum of l i/v i) and t p. The runoff rate at the slope outlet for each respective travel time was estimated by finding the sum of the rain rate from all contributing cells for all time intervals. The results show positive agreement between the measured and predicted runoff hydrographs. PMID:26103635

Simulation of Runoff Hydrograph on Soil Surfaces with Different Microtopography Using a Travel Time Method at the Plot Scale.

PubMed

Zhao, Longshan; Wu, Faqi

2015-01-01

In this study, a simple travel time-based runoff model was proposed to simulate a runoff hydrograph on soil surfaces with different microtopographies. Three main parameters, i.e., rainfall intensity (I), mean flow velocity (vm) and ponding time of depression (tp), were inputted into this model. The soil surface was divided into numerous grid cells, and the flow length of each grid cell (li) was then calculated from a digital elevation model (DEM). The flow velocity in each grid cell (vi) was derived from the upstream flow accumulation area using vm. The total flow travel time through each grid cell to the surface outlet was the sum of the sum of flow travel times along the flow path (i.e., the sum of li/vi) and tp. The runoff rate at the slope outlet for each respective travel time was estimated by finding the sum of the rain rate from all contributing cells for all time intervals. The results show positive agreement between the measured and predicted runoff hydrographs.