Use of reflected GNSS SNR data to retrieve either soil moisture or vegetation height from a wheat crop

NASA Astrophysics Data System (ADS)

Zhang, Sibo; Roussel, Nicolas; Boniface, Karen; Ha, Minh Cuong; Frappart, Frédéric; Darrozes, José; Baup, Frédéric; Calvet, Jean-Christophe

2017-09-01

This work aims to estimate soil moisture and vegetation height from Global Navigation Satellite System (GNSS) Signal to Noise Ratio (SNR) data using direct and reflected signals by the land surface surrounding a ground-based antenna. Observations are collected from a rainfed wheat field in southwestern France. Surface soil moisture is retrieved based on SNR phases estimated by the Least Square Estimation method, assuming the relative antenna height is constant. It is found that vegetation growth breaks up the constant relative antenna height assumption. A vegetation-height retrieval algorithm is proposed using the SNR-dominant period (the peak period in the average power spectrum derived from a wavelet analysis of SNR). Soil moisture and vegetation height are retrieved at different time periods (before and after vegetation's significant growth in March). The retrievals are compared with two independent reference data sets: in situ observations of soil moisture and vegetation height, and numerical simulations of soil moisture, vegetation height and above-ground dry biomass from the ISBA (interactions between soil, biosphere and atmosphere) land surface model. Results show that changes in soil moisture mainly affect the multipath phase of the SNR data (assuming the relative antenna height is constant) with little change in the dominant period of the SNR data, whereas changes in vegetation height are more likely to modulate the SNR-dominant period. Surface volumetric soil moisture can be estimated (R2 = 0.74, RMSE = 0.009 m3 m-3) when the wheat is smaller than one wavelength (˜ 19 cm). The quality of the estimates markedly decreases when the vegetation height increases. This is because the reflected GNSS signal is less affected by the soil. When vegetation replaces soil as the dominant reflecting surface, a wavelet analysis provides an accurate estimation of the wheat crop height (R2 = 0.98, RMSE = 6.2 cm). The latter correlates with modeled above-ground dry biomass of the wheat from stem elongation to ripening. It is found that the vegetation height retrievals are sensitive to changes in plant height of at least one wavelength. A simple smoothing of the retrieved plant height allows an excellent matching to in situ observations, and to modeled above-ground dry biomass.

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Case, Jonathan; Zavodsky, Bradley; Jedlovec, Gary

2014-01-01

Soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) instrument are assimilated into the Noah land surface model (LSM) within the NASA Land Information System (LIS). Before assimilation, SMOS retrievals are bias-corrected to match the model climatological distribution using a Cumulative Distribution Function (CDF) matching approach. Data assimilation is done via the Ensemble Kalman Filter. The goal is to improve the representation of soil moisture within the LSM, and ultimately to improve numerical weather forecasts through better land surface initialization. We present a case study showing a large area of irrigation in the lower Mississippi River Valley, in an area with extensive rice agriculture. High soil moisture value in this region are observed by SMOS, but not captured in the forcing data. After assimilation, the model fields reflect the observed geographic patterns of soil moisture. Plans for a modeling experiment and operational use of the data are given. This work helps prepare for the assimilation of Soil Moisture Active/Passive (SMAP) retrievals in the near future.

Resolving inter-annual terrestrial water storage variations using microwave-based surface soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

Due to their shallow vertical support, remotely-sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (S). However, advances in our ability to esti...

Land Surface Modeling and Data Assimilation to Support Physical Precipitation Retrievals for GPM

NASA Technical Reports Server (NTRS)

Peters-Lidard, Christa D.; Tian. Yudong; Kumar, Sujay; Geiger, James; Choudhury, Bhaskar

2010-01-01

Objective: The objective of this proposal is to provide a routine land surface modeling and data assimilation capability for GPM in order to provide global land surface states that are 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 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. Therefore, providing a robust capability to routinely provide these critical land states is essential to support GPM-era physical retrieval algorithms over land.

Surface soil moisture retrieval over a Mediterranean semi-arid region using X-band TerraSAR-X SAR data

NASA Astrophysics Data System (ADS)

Azza, Gorrab; Zribi, Mehrez; Baghdadi, Nicolas; Mougenot, Bernard; Boulet, Gilles; Lili-Chabaane, Zohra

2015-04-01

Mapping surface soil moisture with meter-scale spatial resolution is appropriate for multi- domains particularly hydrology and agronomy. It allows water resources and irrigation management decisions, drought monitoring and validation of multi-hydrological water balance models. In the last years, various studies have demonstrated the large potential of radar remote sensing data, mainly from C frequency band, to retrieve soil moisture. However, the accuracy of the soil moisture estimation, by inversing backscattering radar coefficients (σ°), is affected by the influence of surface roughness and vegetation biomass contributions. In recent years, different empirical, semi empirical and physical approaches are developed for bare soil conditions, to estimate accurately spatial soil moisture variability. In this study, we propose an approach based on the change detection method for the retrieval of surface soil moisture at a higher spatial resolution. The proposal algorithm combines multi-temporal X-band SAR images (TerraSAR-X) with different continuous thetaprobe measurements. Seven thetaprobe stations are installed at different depths over the central semi arid region of Tunisia (9°23' - 10°17' E, 35° 1'-35°55' N). They cover approximately the entire of our study site and provide regional scale information. Ground data were collected over agricultural bare soil fields simultaneously to various TerraSAR-X data acquired during 2013-2014 and 2014-2015. More than fourteen test fields were selected for each spatial acquisition campaign, with variations in soil texture and in surface soil roughness. For each date, we considered the volumetric water content with thetaprobe instrument and gravimetric sampling; we measured also the roughness parameters with pin profilor. To retrieve soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our analyses are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of the measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: On the first one, we applied the change detection approach between successive radar images (∆σ°) assuming unchanged soil roughness effects. Our soil moisture retrieval algorithm was validated on the basis of comparisons between estimated and in situ soil moisture measurements over test fields. Using this option, results have shown an accuracy (RMSE) of about 4.8 %. Secondly, we corrected the sensitivity of the radar backscatter images to the surface roughness variability. Results have shown a reduction of the difference between the retrieved soil moisture and ground measurements with an RMSE about 3.7%.

Assimilation of SMOS Brightness Temperatures or Soil Moisture Retrievals into a Land Surface Model

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J. M.; Reichle, Rolf H.

2016-01-01

Three different data products from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated separately into the Goddard Earth Observing System Model, version 5 (GEOS-5) to improve estimates of surface and root-zone soil moisture. The first product consists of multi-angle, dual-polarization brightness temperature (Tb) observations at the bottom of the atmosphere extracted from Level 1 data. The second product is a derived SMOS Tb product that mimics the data at a 40 degree incidence angle from the Soil Moisture Active Passive (SMAP) mission. The third product is the operational SMOS Level 2 surface soil moisture (SM) retrieval product. The assimilation system uses a spatially distributed ensemble Kalman filter (EnKF) with seasonally varying climatological bias mitigation for Tb assimilation, whereas a time-invariant cumulative density function matching is used for SM retrieval assimilation. All assimilation experiments improve the soil moisture estimates compared to model-only simulations in terms of unbiased root-mean-square differences and anomaly correlations during the period from 1 July 2010 to 1 May 2015 and for 187 sites across the US. Especially in areas where the satellite data are most sensitive to surface soil moisture, large skill improvements (e.g., an increase in the anomaly correlation by 0.1) are found in the surface soil moisture. The domain-average surface and root-zone skill metrics are similar among the various assimilation experiments, but large differences in skill are found locally. The observation-minus-forecast residuals and analysis increments reveal large differences in how the observations add value in the Tb and SM retrieval assimilation systems. The distinct patterns of these diagnostics in the two systems reflect observation and model errors patterns that are not well captured in the assigned EnKF error parameters. Consequently, a localized optimization of the EnKF error parameters is needed to further improve Tb or SM retrieval assimilation.

Estimation of surface soil moisture and roughness from multi-angular ASAR imagery in the Watershed Allied Telemetry Experimental Research (WATER)

NASA Astrophysics Data System (ADS)

Wang, S. G.; Li, X.; Han, X. J.; Jin, R.

2011-05-01

Radar remote sensing has demonstrated its applicability to the retrieval of basin-scale soil moisture. The mechanism of radar backscattering from soils is complicated and strongly influenced by surface roughness. Additionally, retrieval of soil moisture using AIEM (advanced integrated equation model)-like models is a classic example of underdetermined problem due to a lack of credible known soil roughness distributions at a regional scale. Characterization of this roughness is therefore crucial for an accurate derivation of soil moisture based on backscattering models. This study aims to simultaneously obtain surface roughness parameters (standard deviation of surface height σ and correlation length cl) along with soil moisture from multi-angular ASAR images by using a two-step retrieval scheme based on the AIEM. The method firstly used a semi-empirical relationship that relates the roughness slope, Zs (Zs = σ2/cl) and the difference in backscattering coefficient (Δσ) from two ASAR images acquired with different incidence angles. Meanwhile, by using an experimental statistical relationship between σ and cl, both these parameters can be estimated. Then, the deduced roughness parameters were used for the retrieval of soil moisture in association with the AIEM. An evaluation of the proposed method was performed in an experimental area in the middle stream of the Heihe River Basin, where the Watershed Allied Telemetry Experimental Research (WATER) was taken place. It is demonstrated that the proposed method is feasible to achieve reliable estimation of soil water content. The key challenge is the presence of vegetation cover, which significantly impacts the estimates of surface roughness and soil moisture.

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.

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...

Aquarius/SAC-D soil moisture product using V3.0 observations

USDA-ARS?s Scientific Manuscript database

Although Aquarius was designed for ocean salinity mapping, our objective in this investigation is to exploit the large amount of land observations that Aquarius acquires and extend the mission scope to include the retrieval of surface soil moisture. The soil moisture retrieval algorithm development ...

Assimilation of Passive and Active Microwave Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Draper, C. S.; Reichle, R. H.; DeLannoy, G. J. M.; Liu, Q.

2012-01-01

Root-zone soil moisture is an important control over the partition of land surface energy and moisture, and the assimilation of remotely sensed near-surface soil moisture has been shown to improve model profile soil moisture [1]. To date, efforts to assimilate remotely sensed near-surface soil moisture at large scales have focused on soil moisture derived from the passive microwave Advanced Microwave Scanning Radiometer (AMSR-E) and the active Advanced Scatterometer (ASCAT; together with its predecessor on the European Remote Sensing satellites (ERS. The assimilation of passive and active microwave soil moisture observations has not yet been directly compared, and so this study compares the impact of assimilating ASCAT and AMSR-E soil moisture data, both separately and together. Since the soil moisture retrieval skill from active and passive microwave data is thought to differ according to surface characteristics [2], the impact of each assimilation on the model soil moisture skill is assessed according to land cover type, by comparison to in situ soil moisture observations.

Global Soil Moisture Estimation from L-Band Satellite Data: The Impact of Radiative Transfer Modeling in Assimilation and Retrieval Systems

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle; Reichle, Rolf; Gruber, Alexander; Bechtold, Michel; Quets, Jan; Vrugt, Jasper; Wigneron, Jean-Pierre

2018-01-01

The SMOS and SMAP missions have collected a wealth of global L-band Brightness temperature (Tb) observations. The retrieval of surface Soil moisture estimates, and the estimation of other geophysical Variables, such as root-zone soil moisture and temperature, via data Assimilation into land surface models largely depends on accurate Radiative transfer modeling (RTM). This presentation will focus on various configuration aspects of the RTM (i) for the inversion of SMOS Tb to surface soil moisture, and (ii) for the forward modeling as part of a SMOS Tb data assimilation System to estimate a consistent set of geophysical land surface Variables, using the GEOS-5 Catchment Land Surface Model.

Retrieval of an available water-based soil moisture proxy from thermal infrared remote sensing. Part I: Methodology and validation

USDA-ARS?s Scientific Manuscript database

A retrieval of soil moisture is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere-Land-Exchange-Inversion (ALEXI) model. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be l...

Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

NASA Astrophysics Data System (ADS)

Zhang, Sibo; Calvet, Jean-Christophe; Darrozes, José; Roussel, Nicolas; Frappart, Frédéric; Bouhours, Gilles

2018-03-01

This work assesses the estimation of surface volumetric soil moisture (VSM) using the global navigation satellite system interferometric reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 and 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show good agreement (R2 = 0.86 and RMSE = 0.04 m3 m-3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 and 5 cm, especially during light rainfall events.

A Data-Driven Approach for Daily Real-Time Estimates and Forecasts of Near-Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Reichle, Rolf H.; Mahanama, Sarith P. P.

2017-01-01

NASAs Soil Moisture Active Passive (SMAP) mission provides global surface soil moisture retrievals with a revisit time of 2-3 days and a latency of 24 hours. Here, to enhance the utility of the SMAP data, we present an approach for improving real-time soil moisture estimates (nowcasts) and for forecasting soil moisture several days into the future. The approach, which involves using an estimate of loss processes (evaporation and drainage) and precipitation to evolve the most recent SMAP retrieval forward in time, is evaluated against subsequent SMAP retrievals themselves. The nowcast accuracy over the continental United States (CONUS) is shown to be markedly higher than that achieved with the simple yet common persistence approach. The accuracy of soil moisture forecasts, which rely on precipitation forecasts rather than on precipitation measurements, is reduced relative to nowcast accuracy but is still significantly higher than that obtained through persistence.

Soil moisture retrieval by active/passive microwave remote sensing data

NASA Astrophysics Data System (ADS)

Wu, Shengli; Yang, Lijuan

2012-09-01

This study develops a new algorithm for estimating bare surface soil moisture using combined active / passive microwave remote sensing on the basis of TRMM (Tropical Rainfall Measuring Mission). Tropical Rainfall Measurement Mission was jointly launched by NASA and NASDA in 1997, whose main task was to observe the precipitation of the area in 40 ° N-40 ° S. It was equipped with active microwave radar sensors (PR) and passive sensor microwave imager (TMI). To accurately estimate bare surface soil moisture, precipitation radar (PR) and microwave imager (TMI) are simultaneously used for observation. According to the frequency and incident angle setting of PR and TMI, we first need to establish a database which includes a large range of surface conditions; and then we use Advanced Integral Equation Model (AIEM) to calculate the backscattering coefficient and emissivity. Meanwhile, under the accuracy of resolution, we use a simplified theoretical model (GO model) and the semi-empirical physical model (Qp Model) to redescribe the process of scattering and radiation. There are quite a lot of parameters effecting backscattering coefficient and emissivity, including soil moisture, surface root mean square height, correlation length, and the correlation function etc. Radar backscattering is strongly affected by the surface roughness, which includes the surface root mean square roughness height, surface correlation length and the correlation function we use. And emissivity is differently affected by the root mean square slope under different polarizations. In general, emissivity decreases with the root mean square slope increases in V polarization, and increases with the root mean square slope increases in H polarization. For the GO model, we found that the backscattering coefficient is only related to the root mean square slope and soil moisture when the incident angle is fixed. And for Qp Model, through the analysis, we found that there is a quite good relationship between Qpparameter and root mean square slope. So here, root mean square slope is a parameter that both models shared. Because of its big influence to backscattering and emissivity, we need to throw it out during the process of the combination of GO model and Qp model. The result we obtain from the combined model is the Fresnel reflection coefficient in the normal direction gama(0). It has a good relationship with the soil dielectric constant. In Dobson Model, there is a detailed description about Fresnel reflection coefficient and soil moisture. With the help of Dobson model and gama(0) that we have obtained, we can get the soil moisture that we want. The backscattering coefficient and emissivity data used in combined model is from TRMM/PR, TMI; with this data, we can obtain gama(0); further, we get the soil moisture by the relationship of the two parameters-- gama(0) and soil moisture. To validate the accuracy of the retrieval soil moisture, there is an experiment conducted in Tibet. The soil moisture data which is used to validate the retrieval algorithm is from GAME-Tibet IOP98 Soil Moisture and Temperature Measuring System (SMTMS). There are 9 observing sites in SMTMS to validate soil moisture. Meanwhile, we use the SMTMS soil moisture data obtained by Time Domain Reflectometer (TDR) to do the validation. And the result shows the comparison of retrieval and measured results is very good. Through the analysis, we can see that the retrieval and measured results in D66 is nearly close; and in MS3608, the measured result is a little higher than retrieval result; in MS3637, the retrieval result is a little higher than measured result. According to the analysis of the simulation results, we found that this combined active and passive approach to retrieve the soil moisture improves the retrieval accuracy.

An intercomparison of available soil moisture estimates from thermal-infrared and passive microwave remote sensing and land-surface modeling

USDA-ARS?s Scientific Manuscript database

Remotely-sensed soil moisture studies have mainly focused on retrievals using active and passive microwave (MW) sensors whose measurements provided a direct relationship to soil moisture (SM). MW sensors present obvious advantages such as the ability to retrieve through non-precipitating cloud cover...

Assimilation of SMOS (and SMAP) Retrieved Soil Moisture into the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Zavodsky, Bradley; Case, Jonathan; Stano, Geoffrey

2016-01-01

Goal: Accurate, high-resolution (approx.3 km) soil moisture in near-real time. Situational awareness (drought assessment, flood and fire threat). Local modeling applications (to improve sfc-PBL exchanges) Method: Assimilate satellite soil moisture retrievals into a land surface model. Combines high-resolution geophysical model data with latest satellite observations.

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation

NASA Technical Reports Server (NTRS)

Akbar, Ruzbeh; Cosh, Michael H.; O'Neill, Peggy E.; Entekhabi, Dara; Moghaddam, Mahta

2017-01-01

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 radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithms performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

Combined Radar-Radiometer Surface Soil Moisture and Roughness Estimation.

PubMed

Akbar, Ruzbeh; Cosh, Michael H; O'Neill, Peggy E; Entekhabi, Dara; Moghaddam, Mahta

2017-07-01

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 radar and radiometer observations simultaneously. A data-driven and noise-dependent regularization term has also been developed to automatically regularize and balance corresponding radar and radiometer contributions to achieve optimal soil moisture retrievals. It is shown that in order to compensate for measurement and observation noise, as well as forward model inaccuracies, in combined radar-radiometer estimation surface roughness can be considered a free parameter. Extensive Monte-Carlo numerical simulations and assessment using field data have been performed to both evaluate the algorithm's performance and to demonstrate soil moisture estimation. Unbiased root mean squared errors (RMSE) range from 0.18 to 0.03 cm3/cm3 for two different land cover types of corn and soybean. In summary, in the context of soil moisture retrieval, the importance of consistent forward emission and scattering development is discussed and presented.

Evaluation of the effects of varying moisture contents on microwave thermal emissions from agriculture fields

NASA Technical Reports Server (NTRS)

Burke, H. H. K.

1980-01-01

Three tasks related to soil moisture sensing at microwave wavelengths were undertaken: (1) analysis of data at L, X and K sub 21 band wavelengths over bare and vegetated fields from the 1975 NASA sponsored flight experiment over Phoenix, Arizona; (2) modeling of vegetation canopy at microwave wavelengths taking into consideration both absorption and volume scattering effects; and (3) investigation of overall atmospheric effects at microwave wavelengths that can affect soil moisture retrieval. Data for both bare and vegetated fields are found to agree well with theoretical estimates. It is observed that the retrieval of surface and near surface soil moisture information is feasible through multi-spectral and multi-temporal analysis. It is also established that at long wavelengths, which are optimal for surface sensing, atmospheric effects are generally minimal. At shorter wavelengths, which are optimal for atmosheric retrieval, the background surface properties are also established.

Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

NASA Technical Reports Server (NTRS)

Srivastava, Prashant K.; Han, Dawei; Rico-Ramirez, Miguel A.; O'Neill, Peggy; Islam, Tanvir; Gupta, Manika

2014-01-01

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale.

Assessing seasonal backscatter variations with respect to uncertainties in soil moisture retrieval in Siberian tundra regions

NASA Astrophysics Data System (ADS)

Högström, Elin; Trofaier, Anna Maria; Gouttevin, Isabella; Bartsch, Annett

2015-04-01

Data from the Advanced Scatterometer (ASCAT) instrument provide the basis of a near real-time, coarse scale, global soil moisture product. Numerous studies have shown the applicability of this product, including recent operational use for numerical weather forecasts. Soil moisture is a key element in the global cycles of water, energy and carbon. Among many application areas, it is essential for the understanding of permafrost development in a future climate change scenario. Dramatic climate changes are expected in the Arctic, where ca 25% of the land is underlain by permafrost, and it is to a large extent remote and inaccessible. The availability and applicability of satellite derived land-surface data relevant for permafrost studies, such as surface soil moisture, is thus crucial to landscape-scale analyses of climate-induced change. However, there are challenges in the soil moisture retrieval that are specific to the Arctic. This study investigates backscatter variability unrelated to soil moisture variations in order to understand the possible impact on the soil moisture retrieval. The focus is on tundra lakes, which are a common feature in the Arctic and are expected to affect the retrieval. ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath (120 m) data are used to resolve lakes and later understand and quantify their impacts on Metop ASCAT (25 km) soil moisture retrieval during the snow free period. Sites of interest are chosen according to high or low agreement between output from the land surface model ORCHIDEE and ASCAT derived SSM. The results show that in most cases low model agreement is related to high water fraction. The water fraction correlates with backscatter deviations (relative to a smooth water surface reference image) within the ASCAT footprint areas (R = 0.91-0.97). Backscatter deviations of up to 5 dB can occur in areas with less than 50% water fraction and an assumed soil moisture related range (sensitivity) of 7 dB in the ASCAT data. The study demonstrates that the usage of higher spatial resolution data than currently available for SSM is required in lowland permafrost environments. Furthermore, the results show that in the flat and open Arctic tundra areas, wind likely affects the soil moisture retrieval procedure rather than rain or remaining ice cover on the water surface. Therefore, the potential of a wind correction method is explored for sites where meteorological field data are available.

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...

Estimating the soil moisture profile by assimilating near-surface observations with the ensemble Kalman filter (EnKF)

NASA Astrophysics Data System (ADS)

Zhang, Shuwen; Li, Haorui; Zhang, Weidong; Qiu, Chongjian; Li, Xin

2005-11-01

The paper investigates the ability to retrieve the true soil moisture profile by assimilating near-surface soil moisture into a soil moisture model with an ensemble Kaiman filter (EnKF) assimilation scheme, including the effect of ensemble size, update interval and nonlinearities in the profile retrieval, the required time for full retrieval of the soil moisture profiles, and the possible influence of the depth of the soil moisture observation. These questions are addressed by a desktop study using synthetic data. The “true” soil moisture profiles are generated from the soil moisture model under the boundary condition of 0.5 cm d-1 evaporation. To test the assimilation schemes, the model is initialized with a poor initial guess of the soil moisture profile, and different ensemble sizes are tested showing that an ensemble of 40 members is enough to represent the covariance of the model forecasts. Also compared are the results with those from the direct insertion assimilation scheme, showing that the EnKF is superior to the direct insertion assimilation scheme, for hourly observations, with retrieval of the soil moisture profile being achieved in 16 h as compared to 12 days or more. For daily observations, the true soil moisture profile is achieved in about 15 days with the EnKF, but it is impossible to approximate the true moisture within 18 days by using direct insertion. It is also found that observation depth does not have a significant effect on profile retrieval time for the EnKF. The nonlinearities have some negative influence on the optimal estimates of soil moisture profile but not very seriously.

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.

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.

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.

Why is SMOS Drier than the South Fork In-situ Soil Moisture Network?

NASA Astrophysics Data System (ADS)

Walker, V. A.; Hornbuckle, B. K.; Cosh, M. H.

2014-12-01

Global maps of near-surface soil moisture are currently being produced by the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) satellite mission at 40 km. Within the next few months NASA's Soil Moisture Active Passive (SMAP) satellite mission will begin producing observations of near-surface soil moisture at 10 km. Near-surface soil moisture is the water content of the first 3 to 5 cm of the soil. Observations of near-surface soil moisture are expected to improve weather and climate forecasts. These satellite observations must be validated. We define validation as determining the space/time statistical characteristics of the uncertainty. A standard that has been used for satellite validation is in-situ measurements of near-surface soil moisture made with a network of sensors spanning the extent of a satellite footprint. Such a network of sensors has been established in the South Fork of the Iowa River in Central Iowa by the USDA ARS. Our analysis of data in 2013 indicates that SMOS has a dry bias: SMOS near-surface soil moisture is between 0.05 to 0.10 m^3m^{-3} lower than what is observed by the South Fork network. A dry bias in SMOS observations has also been observed in other regions of North America. There are many possible explanations for this difference: underestimation of vegetation, or soil surface roughness; undetected radio frequency interference (RFI); a retrieval model that is not appropriate for agricultural areas; or the use of an incorrect surface temperature in the retrieval process. We will begin our investigation by testing this last possibility: that SMOS is using a surface temperature that is too low which results in a drier soil moisture that compensates for this error. We will present a comparison of surface temperatures from the European Center for Medium-range Weather Forecasting (ECMWF) used to retrieve near-surface soil moisture from SMOS measurements of brightness temperature, and surface temperatures in the South Fork obtained from both tower and in-situ sensors. We will also use a long-term data set of tower and in-situ sensors collected in agricultural fields to develop a relationship between air temperature and the surface temperature relevant to the terrestrial microwave emission that is detected by SMOS.

A Methodology for Surface Soil Moisture and Vegetation Optical Depth Retrieval Using the Microwave Polarization Difference Index

NASA Technical Reports Server (NTRS)

Owe, Manfred; deJeu, Richard; Walker, Jeffrey; Zukor, Dorothy J. (Technical Monitor)

2001-01-01

A methodology for retrieving surface soil moisture and vegetation optical depth from satellite microwave radiometer data is presented. The procedure is tested with historical 6.6 GHz brightness temperature observations from the Scanning Multichannel Microwave Radiometer over several test sites in Illinois. Results using only nighttime data are presented at this time, due to the greater stability of nighttime surface temperature estimation. The methodology uses a radiative transfer model to solve for surface soil moisture and vegetation optical depth simultaneously using a non-linear iterative optimization procedure. It assumes known constant values for the scattering albedo and roughness. Surface temperature is derived by a procedure using high frequency vertically polarized brightness temperatures. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and is totally independent of wavelength. Results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors.

The error structure of the SMAP single and dual channel soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

Knowledge of the temporal error structure for remotely-sensed surface soil moisture retrievals can improve our ability to exploit them for hydrology and climate studies. This study employs a triple collocation type analysis to investigate both the total variance and temporal auto-correlation of erro...

Land surface dynamics monitoring using microwave passive satellite sensors

NASA Astrophysics Data System (ADS)

Guijarro, Lizbeth Noemi

Soil moisture, surface temperature and vegetation are variables that play an important role in our environment. There is growing demand for accurate estimation of these geophysical parameters for the research of global climate models (GCMs), weather, hydrological and flooding models, and for the application to agricultural assessment, land cover change, and a wide variety of other uses that meet the needs for the study of our environment. The different studies covered in this dissertation evaluate the capabilities and limitations of microwave passive sensors to monitor land surface dynamics. The first study evaluates the 19 GHz channel of the SSM/I instrument with a radiative transfer model and in situ datasets from the Illinois stations and the Oklahoma Mesonet to retrieve land surface temperature and surface soil moisture. The surface temperatures were retrieved with an average error of 5 K and the soil moisture with an average error of 6%. The results show that the 19 GHz channel can be used to qualitatively predict the spatial and temporal variability of surface soil moisture and surface temperature at regional scales. In the second study, in situ observations were compared with sensor observations to evaluate aspects of low and high spatial resolution at multiple frequencies with data collected from the Southern Great Plains Experiment (SGP99). The results showed that the sensitivity to soil moisture at each frequency is a function of wavelength and amount of vegetation. The results confirmed that L-band is more optimal for soil moisture, but each sensor can provide soil moisture information if the vegetation water content is low. The spatial variability of the emissivities reveals that resolution suffers considerably at higher frequencies. The third study evaluates C- and X-bands of the AMSR-E instrument. In situ datasets from the Soil Moisture Experiments (SMEX03) in South Central Georgia were utilized to validate the AMSR-E soil moisture product and to derive surface soil moisture with a radiative transfer model. The soil moisture was retrieved with an average error of 2.7% at X-band and 6.7% at C-band. The AMSR-E demonstrated its ability to successfully infer soil moisture during the SMEX03 experiment.

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).

Evaluation of a Soil Moisture Data Assimilation System Over West Africa

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Crow, W.; Zhan, X.; Jackson, T.; Reynolds, C.

2009-05-01

A crucial requirement of global crop yield forecasts by the U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and resulting soil moisture, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. IPAD estimates global soil moisture using daily estimates of minimum and maximum temperature and precipitation applied to a modified Palmer two-layer soil moisture model which calculates the daily amount of soil moisture withdrawn by evapotranspiration and replenished by precipitation. We attempt to improve upon the existing system by 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 soil moisture model. This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9°N- 20°N; 20°W-20°E). This region contains many key agricultural areas and has a high agro- meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline 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.

Application of neural network to remote sensing of soil moisture using theoretical polarimetric backscattering coefficients

NASA Technical Reports Server (NTRS)

Wang, L.; Shin, R. T.; Kong, J. A.; Yueh, S. H.

1993-01-01

This paper investigates the potential application of neural network to inversion of soil moisture using polarimetric remote sensing data. The neural network used for the inversion of soil parameters is multi-layer perceptron trained with the back-propagation algorithm. The training data include the polarimetric backscattering coefficients obtained from theoretical surface scattering models together with an assumed nominal range of soil parameters which are comprised of the soil permittivity and surface roughness parameters. Soil permittivity is calculated from the soil moisture and the assumed soil texture based on an empirical formula at C-, L-, and P-bands. The rough surface parameters for the soil surface, which is described by the Gaussian random process, are the root-mean-square (rms) height and correlation length. For the rough surface scattering, small perturbation method is used for the L-band frequency, and Kirchhoff approximation is used for the C-band frequency to obtain the corresponding backscattering coefficients. During the training, the backscattering coefficients are the inputs to the neural net and the output from the net are compared with the desired soil parameters to adjust the interconnecting weights. The process is repeated for each input-output data entry and then for the entire training data until convergence is reached. After training, the backscattering coefficients are applied to the trained neural net to retrieve the soil parameters which are compared with the desired soil parameters to verify the effectiveness of this technique. Several cases are examined. First, for simplicity, the correlation length and rms height of the soil surface are fixed while soil moisture is varied. Soil moisture obtained using the neural networks with either L-band or C-band backscattering coefficients for the HH and VV polarizations as inputs is in good agreement with the desired soil moisture. The neural net output matches the desired output for the soil moisture range of 16 to 60 percent for the C-band case. The next case investigated is to vary both soil moisture and rms height while keeping the correlation length fixed. For this case, C-band backscattering coefficients are not sufficient for retrieving two parameters because the Kirchhoff approximation gives the same HH and VV backscattering coefficients. Therefore, the backscattering coefficients at two different frequency bands are necessary to find both the soil moisture and rms height. Finally, the neural nets are also applied to simultaneously invert soil moisture, rms height, and correlation length. Overall, the soil moisture retrieved from the neural network agrees very well with the desired soil moisture. This suggests that the neural network shows potential for retrieval of soil parameters from remote sensing data.

SMOS first results over land

NASA Astrophysics Data System (ADS)

Kerr, Yann; Waldteufel, Philippe; Cabot, François; Richaume, Philippe; Jacquette, Elsa; Bitar, Ahmad Al; Mamhoodi, Ali; Delwart, Steven; Wigneron, Jean-Pierre

2010-05-01

The Soil Moisture and Ocean Salinity (SMOS) mission is ESA's (European Space Agency ) second Earth Explorer Opportunity mission, launched in November 2009. It is a joint programme between ESA CNES (Centre National d'Etudes Spatiales) and CDTI (Centro para el Desarrollo Tecnologico Industrial). SMOS carries a single payload, an L-band 2D interferometric radiometer in the 1400-1427 MHz protected band. This wavelength penetrates well through the atmosphere and hence the instrument probes the Earth surface emissivity. Surface emissivity can then be related to the moisture content in the first few centimeters of soil, and, after some surface roughness and temperature corrections, to the sea surface salinity over ocean. In order to prepare the data use and dissemination, the ground segment will produce level 1 and 2 data. Level 1 consists mainly of angular brightness temperatures while level 2 consists of geophysical products. In this context, a group of institutes prepared the soil moisture and ocean salinity Algorithm Theoretical Basis documents (ATBD) to be used to produce the operational algorithm. The principle of the soil moisture retrieval algorithm is based on an iterative approach which aims at minimizing a cost function given by the sum of the squared weighted differences between measured and modelled brightness temperature (TB) data, for a variety of incidence angles. This is achieved by finding the best suited set of the parameters which drive the direct TB model, e.g. soil moisture (SM) and vegetation characteristics. Despite the simplicity of this principle, the main reason for the complexity of the algorithm is that SMOS "pixels" can correspond to rather large, inhomogeneous surface areas whose contribution to the radiometric signal is difficult to model. Moreover, the exact description of pixels, given by a weighting function which expresses the directional pattern of the SMOS interferometric radiometer, depends on the incidence angle. The goal is to retrieve soil moisture over fairly large and thus inhomogeneous areas. The retrieval is carried out at nodes of a fixed Earth surface grid. To achieve this purpose, after checking input data quality and ingesting auxiliary data, the retrieval process per se can be initiated. This cannot be done blindly as the direct model will be dependent upon surface characteristics. It is thus necessary to first assess what is the dominant land use of a node. For this, an average weighing function (MEAN_WEF) which takes into account the "antenna"pattern is run over the high resolution land use map to assess the dominant cover type. This is used to drive the decision tree which, step by step, selects the type of model to be used as per surface conditions. All this being said and done the retrieval procedure starts if all the conditions are satisfied, ideally to retrieve 3 parameters over the dominant class (the so-called rich retrieval). If the algorithm does not converge satisfactorily, a new trial is made with less floating parameters ("poorer retrieval") until either results are satisfactory or the algorithm is considered to fail. The retrieval algorithm also delivers whenever possible a dielectric constant parameter (using the-so called cardioid approach). Finally, once the retrieval converged, it is possible to compute the brightness temperature at a given fixed angle (42.5°) using the selected forward models applied to the set of parameters obtained at the end of the retrieval process. So the output product of the level 2 soil moisture algorithm should be node position, soil moisture, dielectric constants, computed brightness temperature at 42.5°, flags and quality indices. During the presentation we will describe in more details the algorithm and accompanying work in particular decision tree principle and characteristics, the auxiliary data used and the special and "exotic"cases. We will also be more explicit on the algorithm validation and verification through the data collected during the commissioning phase. The main hurdle being working in spite of spurious signals (RFI) on some areas of the globe.

Improving long-term global precipitation dataset using multi-sensor surface soil moisture retrievals and the soil moisture analysis rainfall tool (SMART)

USDA-ARS?s Scientific Manuscript database

Using multiple historical satellite surface soil moisture products, the Kalman Filtering-based Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain g...

A Proposed Extension to the Soil Moisture and Ocean Salinity Level 2 Algorithm for Mixed Forest and Moderate Vegetation Pixels

NASA Technical Reports Server (NTRS)

Panciera, Rocco; Walker, Jeffrey P.; Kalma, Jetse; Kim, Edward

2011-01-01

The Soil Moisture and Ocean Salinity (SMOS)mission, launched in November 2009, provides global maps of soil moisture and ocean salinity by measuring the L-band (1.4 GHz) emission of the Earth's surface with a spatial resolution of 40-50 km.Uncertainty in the retrieval of soilmoisture over large heterogeneous areas such as SMOS pixels is expected, due to the non-linearity of the relationship between soil moisture and the microwave emission. The current baseline soilmoisture retrieval algorithm adopted by SMOS and implemented in the SMOS Level 2 (SMOS L2) processor partially accounts for the sub-pixel heterogeneity of the land surface, by modelling the individual contributions of different pixel fractions to the overall pixel emission. This retrieval approach is tested in this study using airborne L-band data over an area the size of a SMOS pixel characterised by a mix Eucalypt forest and moderate vegetation types (grassland and crops),with the objective of assessing its ability to correct for the soil moisture retrieval error induced by the land surface heterogeneity. A preliminary analysis using a traditional uniform pixel retrieval approach shows that the sub-pixel heterogeneity of land cover type causes significant errors in soil moisture retrieval (7.7%v/v RMSE, 2%v/v bias) in pixels characterised by a significant amount of forest (40-60%). Although the retrieval approach adopted by SMOS partially reduces this error, it is affected by errors beyond the SMOS target accuracy, presenting in particular a strong dry bias when a fraction of the pixel is occupied by forest (4.1%v/v RMSE,-3.1%v/v bias). An extension to the SMOS approach is proposed that accounts for the heterogeneity of vegetation optical depth within the SMOS pixel. The proposed approach is shown to significantly reduce the error in retrieved soil moisture (2.8%v/v RMSE, -0.3%v/v bias) in pixels characterised by a critical amount of forest (40-60%), at the limited cost of only a crude estimate of the optical depth of the forested area (better than 35% uncertainty). This study makes use of an unprecedented data set of airborne L-band observations and ground supporting data from the National Airborne Field Experiment 2005 (NAFE'05), which allowed accurate characterisation of the land surface heterogeneity over an area equivalent in size to a SMOS pixel.

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.

Towards soil property retrieval from space: Proof of concept using in situ observations

NASA Astrophysics Data System (ADS)

Bandara, Ranmalee; Walker, Jeffrey P.; Rüdiger, Christoph

2014-05-01

Soil moisture is a key variable that controls the exchange of water and energy fluxes between the land surface and the atmosphere. However, the temporal evolution of soil moisture is neither easy to measure nor monitor at large scales because of its high spatial variability. This is mainly a result of the local variation in soil properties and vegetation cover. Thus, land surface models are normally used to predict the evolution of soil moisture and yet, despite their importance, these models are based on low-resolution soil property information or typical values. Therefore, the availability of more accurate and detailed soil parameter data than are currently available is vital, if regional or global soil moisture predictions are to be made with the accuracy required for environmental applications. The proposed solution is to estimate the soil hydraulic properties via model calibration to remotely sensed soil moisture observation, with in situ observations used as a proxy in this proof of concept study. Consequently, the feasibility is assessed, and the level of accuracy that can be expected determined, for soil hydraulic property estimation of duplex soil profiles in a semi-arid environment using near-surface soil moisture observations under naturally occurring conditions. The retrieved soil hydraulic parameters were then assessed by their reliability to predict the root zone soil moisture using the Joint UK Land Environment Simulator model. When using parameters that were retrieved using soil moisture observations, the root zone soil moisture was predicted to within an accuracy of 0.04 m3/m3, which is an improvement of ∼0.025 m3/m3 on predictions that used published values or pedo-transfer functions.

Kalman filters for assimilating near-surface observations in the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

NASA Astrophysics Data System (ADS)

Medina, H.; Romano, N.; Chirico, G. B.

2012-12-01

We present a dual Kalman Filter (KF) approach for retrieving states and parameters controlling soil water dynamics in a homogenous soil column by using near-surface state observations. The dual Kalman filter couples a standard KF algorithm for retrieving the states and an unscented KF algorithm for retrieving the parameters. We examine the performance of the dual Kalman Filter applied to two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil matric pressure head (h). We use a synthetic time-series series of true states and noise corrupted observations and a synthetic time-series of meteorological forcing. The performance analyses account for the effect of the input parameters, the observation depth and the assimilation frequency as well as the relationship between the retrieved states and the assimilated variables. We show that the identifiability of the parameters is strongly conditioned by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. State identifiability is instead efficient even with a relatively coarse time-resolution of the assimilated observation. The accuracy of the retrieved states exhibits limited sensitivity to the observation depth and the assimilation frequency.

Examination of Soil Moisture Retrieval Using SIR-C Radar Data and a Distributed Hydrological Model

NASA Technical Reports Server (NTRS)

Hsu, A. Y.; ONeill, P. E.; Wood, E. F.; Zion, M.

1997-01-01

A major objective of soil moisture-related hydrological-research during NASA's SIR-C/X-SAR mission was to determine and compare soil moisture patterns within humid watersheds using SAR data, ground-based measurements, and hydrologic modeling. Currently available soil moisture-inversion methods using active microwave data are only accurate when applied to bare and slightly vegetated surfaces. Moreover, as the surface dries down, the number of pixels that can provide estimated soil moisture by these radar inversion methods decreases, leading to less accuracy and, confidence in the retrieved soil moisture fields at the watershed scale. The impact of these errors in microwave- derived soil moisture on hydrological modeling of vegetated watersheds has yet to be addressed. In this study a coupled water and energy balance model operating within a topographic framework is used to predict surface soil moisture for both bare and vegetated areas. In the first model run, the hydrological model is initialized using a standard baseflow approach, while in the second model run, soil moisture values derived from SIR-C radar data are used for initialization. The results, which compare favorably with ground measurements, demonstrate the utility of combining radar-derived surface soil moisture information with basin-scale hydrological modeling.

PALS (Passive Active L-band System) Radiometer-Based Soil Moisture Retrieval for the SMAP Validation Experiment 2012 (SMAPVEX12)

NASA Astrophysics Data System (ADS)

Colliander, A.; Jackson, T. J.; Chan, S.; Bindlish, R.; O'Neill, P. E.; Chazanoff, S. L.; McNairn, H.; Bullock, P.; Powers, J.; Wiseman, G.; Berg, A. A.; Magagi, R.; Njoku, E. G.

2014-12-01

NASA's (National Aeronautics and Space Administration) Soil Moisture Active Passive (SMAP) mission is scheduled for launch in early January 2015. For pre-launch soil moisture algorithm development and validation, the SMAP project and NASA coordinated a SMAP Validation Experiment 2012 (SMAPVEX12) together with Agriculture and Agri-Food Canada in the vicinity of Winnipeg, Canada in June 7-July 19, 2012. Coincident active and passive airborne L-band data were acquired using the Passive Active L-band System (PALS) on 17 days during the experiment. Simultaneously with the PALS measurements, soil moisture ground truth data were collected manually. The vegetation and surface roughness were sampled on non-flight days. The SMAP mission will produce surface (top 5 cm) soil moisture products a) using a combination of its L-band radiometer and SAR (Synthetic Aperture Radar) measurements, b) using the radiometer measurement only, and c) using the SAR measurements only. The SMAPVEX12 data are being utilized for the development and testing of the algorithms applied for generating these soil moisture products. This talk will focus on presenting results of retrieving surface soil moisture using the PALS radiometer. The issues that this retrieval faces are very similar to those faced by the global algorithm using the SMAP radiometer. However, the different spatial resolution of the two observations has to be accounted for in the analysis. The PALS 3 dB footprint in the experiment was on the order of 1 km, whereas the SMAP radiometer has a footprint of about 40 km. In this talk forward modeled brightness temperature over the manually sampled fields and the retrieved soil moisture over the entire experiment domain are presented and discussed. In order to provide a retrieval product similar to that of the SMAP passive algorithm, various ancillary information had to be obtained for the SMAPVEX12 domain. In many cases there are multiple options on how to choose and reprocess these data. The derivation of these data elements and their impact on the retrieval and the spatial scales of the different observations are also discussed. In particular, land cover and soil type heterogeneity have a dramatic impact on parameterization of the algorithm when going from finer to coarser spatial resolutions.

Modelling the passive microwave signature from land surfaces: a review of recent results and application to the SMOS & SMAP soil moisture retrieval algorithms

USDA-ARS?s Scientific Manuscript database

Two passive microwave missions are currently operating at L-band to monitor surface soil moisture (SM) over continental surfaces. The SMOS sensor, based on an innovative interferometric technology enabling multi-angular signatures of surfaces to be measured, was launched in November 2009....

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.

Assimilation of Spatially Sparse In Situ Soil Moisture Networks into a Continuous Model Domain

NASA Astrophysics Data System (ADS)

Gruber, A.; Crow, W. T.; Dorigo, W. A.

2018-02-01

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ignorance concerning the spatial structure of error afflicting ground and model-based soil moisture estimates. Here we apply newly developed triple collocation techniques to provide the spatial error information required to fully parameterize a two-dimensional (2-D) data assimilation system designed to assimilate spatially sparse observations acquired from existing ground-based soil moisture networks into a spatially continuous Antecedent Precipitation Index (API) model for operational agricultural drought monitoring. Over the contiguous United States (CONUS), the posterior uncertainty of surface soil moisture estimates associated with this 2-D system is compared to that obtained from the 1-D assimilation of remote sensing retrievals to assess the value of ground-based observations to constrain a surface soil moisture analysis. Results demonstrate that a fourfold increase in existing CONUS ground station density is needed for ground network observations to provide a level of skill comparable to that provided by existing satellite-based surface soil moisture retrievals.

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.

Using SMOS observations in the development of the SMAP level 4 surface and root-zone soil moisture project

USDA-ARS?s Scientific Manuscript database

The Soil Moisture and Ocean Salinity (SMOS; [1]) mission was launched by ESA in November 2009 and has since been observing L-band (1.4 GHz) upwelling passive microwaves. Along with these brightness temperature observations, ESA also disseminates retrievals of surface soil moisture that are derived ...

Separating vegetation and soil temperature using airborne multiangular remote sensing image data

NASA Astrophysics Data System (ADS)

Liu, Qiang; Yan, Chunyan; Xiao, Qing; Yan, Guangjian; Fang, Li

2012-07-01

Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

Operational Soil Moisture Retrieval Techniques: Theoretical Comparisons in the Context of Improving the NASA Standard Approach

NASA Astrophysics Data System (ADS)

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

2012-12-01

We are currently evaluating potential improvements to the standard NASA global soil moisture product derived using observations acquired from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). A major component of this effort is a thorough review of the theoretical basis of available passive-based soil moisture retrieval algorithms suitable for operational implementation. Several agencies provide routine soil moisture products. Our research focuses on five well-establish techniques that are capable of carrying out global retrieval using the same AMSR-E data set as the NASA approach (i.e. X-band brightness temperature data). In general, most passive-based algorithms include two major components: radiative transfer modeling, which provides the smooth surface reflectivity properties of the soil surface, and a complex dielectric constant model of the soil-water mixture. These two components are related through the Fresnel reflectivity equations. Furthermore, the land surface temperature, vegetation, roughness and soil properties need to be adequately accounted for in the radiative transfer and dielectric modeling. All of the available approaches we have examined follow the general data processing flow described above, however, the actual solutions as well as the final products can be very different. This is primarily a result of the assumptions, number of sensor variables utilized, the selected ancillary data sets and approaches used to account for the effect of the additional geophysical variables impacting the measured signal. The operational NASA AMSR-E-based retrievals have been shown to have a dampened temporal response and sensitivity range. Two possible approaches to addressing these issues are being evaluated: enhancing the theoretical basis of the existing algorithm, if feasible, or directly adjusting the dynamic range of the final soil moisture product. Both of these aspects are being actively investigated and will be discussed in our talk. Improving the quality and reliability of the global soil moisture product would result in greater acceptance and utilization in the related applications. USDA is an equal opportunity provider and employer.

Assimilation of SMOS Retrieved Soil Moisture into the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.

2014-01-01

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation and surface heat fluxes. It is also of critical importance for drought and flood monitoring and prediction and for public health applications such as monitoring vector-borne diseases. Land surface modeling benefits greatly from regular updates with soil moisture observations via data assimilation. Satellite remote sensing is the only practical observation type for this purpose in most areas due to its worldwide coverage. The newest operational satellite sensor for soil moisture is the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument aboard the Soil Moisture and Ocean Salinity (SMOS) satellite. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented the assimilation of SMOS soil moisture observations into the NASA Land Information System (LIS), an integrated modeling and data assimilation software platform. We present results from assimilating SMOS observations into the Noah 3.2 land surface model within LIS. The SMOS MIRAS is an L-band radiometer launched by the European Space Agency in 2009, from which we assimilate Level 2 retrievals [1] into LIS-Noah. The measurements are sensitive to soil moisture concentration in roughly the top 2.5 cm of soil. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Sensitivity is reduced where precipitation, snowcover, frozen soil, or dense vegetation is present. Due to the satellite's polar orbit, the instrument achieves global coverage twice daily at most mid- and low-latitude locations, with only small gaps between swaths.

Efficacy of Radiative Transfer Model Across Space, Time and Hydro-climates

NASA Astrophysics Data System (ADS)

Mohanty, B.; Neelam, M.

2017-12-01

The efficiency of radiative transfer model for better soil moisture retrievals is not yet clearly understood over natural systems with great variability and heterogeneity with respect to soil, land cover, topography, precipitation etc. However, this knowledge is important to direct and strategize future research direction and field campaigns. In this work, we present global sensitivity analysis (GSA) technique to study the influence of heterogeneity and uncertainties on radiative transfer model (RTM) and to quantify climate-soil-vegetation interactions. A framework is proposed to understand soil moisture mechanisms underlying these interactions, and influence of these interactions on soil moisture retrieval accuracy. Soil moisture dynamics is observed to play a key role in variability of these interactions, i.e., it enhances both mean and variance of soil-vegetation coupling. The analysis is conducted for different support scales (Point Scale, 800 m, 1.6 km, 3.2 km, 6.4 km, 12.8 km, and 36 km), seasonality (time), hydro-climates, aggregation (scaling) methods and across Level I and Level II ecoregions of contiguous USA (CONUS). For undisturbed natural environments such as SGP'97 (Oklahoma, USA) and SMEX04 (Arizona, USA), the sensitivity of TB to land surface variables remain nearly uniform and are not influenced by extent, support scales or averaging method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 (Iowa, USA) and SMAPVEX12 (Winnipeg, Canada), the sensitivity to variables are highly influenced by the distribution of land surface heterogeneity and upscaling methods. The climate-soil-vegetation interactions analyzed across all ecoregions are presented through a probability distribution function (PDF). The intensity of these interactions are categorized accordingly to yield "hotspots", where the RTM model fails to retrieve soil moisture. A ecoregion specific scaling function is proposed for these hotspots to rectify RTM for retrieving soil moisture.

Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

NASA Technical Reports Server (NTRS)

Hun, Eunjin; Crow, Wade T.; Holmes, Thomas; Bolten, John

2014-01-01

Despite considerable interest in the application of land surface data assimilation systems (LDAS) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model and a sequential data assimilation filter) against a series of linear models which perform the same function (i.e., have the same basic inputoutput structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross-correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moistureNDVI correlations obtained using individual LDAS components versus their linear analogs reveal the degree to which non-linearities andor complexities contained within each component actually contribute to the performance of the LDAS system as a whole. Here, a particular system based on surface soil moisture retrievals from the Land Parameter Retrieval Model (LPRM), a two-layer Palmer soil water balance model and an Ensemble Kalman filter (EnKF) is benchmarked. Results suggest significant room for improvement in each component of the system.

Kalman filters for assimilating near-surface observations into the Richards equation - Part 2: A dual filter approach for simultaneous retrieval of states and parameters

NASA Astrophysics Data System (ADS)

Medina, H.; Romano, N.; Chirico, G. B.

2014-07-01

This study presents a dual Kalman filter (DSUKF - dual standard-unscented Kalman filter) for retrieving states and parameters controlling the soil water dynamics in a homogeneous soil column, by assimilating near-surface state observations. The DSUKF couples a standard Kalman filter for retrieving the states of a linear solver of the Richards equation, and an unscented Kalman filter for retrieving the parameters of the soil hydraulic functions, which are defined according to the van Genuchten-Mualem closed-form model. The accuracy and the computational expense of the DSUKF are compared with those of the dual ensemble Kalman filter (DEnKF) implemented with a nonlinear solver of the Richards equation. Both the DSUKF and the DEnKF are applied with two alternative state-space formulations of the Richards equation, respectively differentiated by the type of variable employed for representing the states: either the soil water content (θ) or the soil water matric pressure head (h). The comparison analyses are conducted with reference to synthetic time series of the true states, noise corrupted observations, and synthetic time series of the meteorological forcing. The performance of the retrieval algorithms are examined accounting for the effects exerted on the output by the input parameters, the observation depth and assimilation frequency, as well as by the relationship between retrieved states and assimilated variables. The uncertainty of the states retrieved with DSUKF is considerably reduced, for any initial wrong parameterization, with similar accuracy but less computational effort than the DEnKF, when this is implemented with ensembles of 25 members. For ensemble sizes of the same order of those involved in the DSUKF, the DEnKF fails to provide reliable posterior estimates of states and parameters. The retrieval performance of the soil hydraulic parameters is strongly affected by several factors, such as the initial guess of the unknown parameters, the wet or dry range of the retrieved states, the boundary conditions, as well as the form (h-based or θ-based) of the state-space formulation. Several analyses are reported to show that the identifiability of the saturated hydraulic conductivity is hindered by the strong correlation with other parameters of the soil hydraulic functions defined according to the van Genuchten-Mualem closed-form model.

A quasi-global approach to improve day-time satellite surface soil moisture anomalies through land surface temperature input

USDA-ARS?s Scientific Manuscript database

Passive microwave observations from various space borne sensors have been linked to soil moisture of the Earth’s surface layer. The new generation passive microwave sensors are dedicated to retrieving this variable and make observations in the single, theoretically optimal L-band frequency (1-2 GHz)...

The international soil moisture network: A data hosting facility for global in situ soil moisture measurements

USDA-ARS?s Scientific Manuscript database

In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land co...

Upscaling sparse ground-based soil moisture observations for the validation of satellite surface soil moisture products

USDA-ARS?s Scientific Manuscript database

The contrast between the point-scale nature of current ground-based soil moisture instrumentation and the footprint resolution (typically >100 square kilometers) of satellites used to retrieve soil moisture poses a significant challenge for the validation of data products from satellite missions suc...

Validation of SMAP surface soil moisture products with core validation sites

USDA-ARS?s Scientific Manuscript database

The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diver...

Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets

USDA-ARS?s Scientific Manuscript database

Two satellites are currently monitoring surface soil moisture (SM) from L-band observations: SMOS (Soil Moisture and Ocean Salinity), a European Space Agency (ESA) satellite that was launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration...

Microwave Soil Moisture Retrieval Under Trees Using a Modified Tau-Omega Model

USDA-ARS?s Scientific Manuscript database

IPAD is to provide timely and accurate estimates of global crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these global crop yield forecasts is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogen...

SMOS Soil Moisture Data Assimilation in the NASA Land Information System: Impact on LSM Initialization and NWP Forecasts

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Case, Jonathan L.; Zavodsky, Bradley

2015-01-01

Land surface models are important components of numerical weather prediction (NWP) models, partitioning incoming energy into latent and sensitive heat fluxes that affect boundary layer growth and destabilization. During warm-season months, diurnal heating and convective initiation depend strongly on evapotranspiration and available boundary layer moisture, which are substantially affected by soil moisture content. Therefore, to properly simulate warm-season processes in NWP models, an accurate initialization of the land surface state is important for accurately depicting the exchange of heat and moisture between the surface and boundary layer. In this study, soil moisture retrievals from the Soil Moisture and Ocean Salinity (SMOS) satellite radiometer are assimilated into the Noah Land Surface Model via an Ensemble Kalman Filter embedded within the NASA Land Information System (LIS) software framework. The output from LIS-Noah is subsequently used to initialize runs of the Weather Research and Forecasting (WRF) NWP model. The impact of assimilating SMOS retrievals is assessed by initializing the WRF model with LIS-Noah output obtained with and without SMOS data assimilation. The southeastern United States is used as the domain for a preliminary case study. During the summer months, there is extensive irrigation in the lower Mississippi Valley for rice and other crops. The irrigation is not represented in the meteorological forcing used to drive the LIS-Noah integration, but the irrigated areas show up clearly in the SMOS soil moisture retrievals, resulting in a case with a large difference in initial soil moisture conditions. The impact of SMOS data assimilation on both Noah soil moisture fields and on short-term (0-48 hour) WRF weather forecasts will be presented.

The soil moisture active passive experiments (SMAPEx): Towards soil moisture retrieval from the SMAP mission

USDA-ARS?s Scientific Manuscript database

NASA’s Soil Moisture Active Passive (SMAP) mission, scheduled for launch in 2014, will carry the first combined L-band radar and radiometer system with the objective of mapping near surface soil moisture and freeze/thaw state globally at near-daily time step (2-3 days). SMAP will provide three soil ...

Application of triple collocation in ground-based validation of soil moisture active/passive (SMAP) level 2 data products

USDA-ARS?s Scientific Manuscript database

The validation of the soil moisture retrievals from the recently-launched NASA Soil Moisture Active/Passive (SMAP) satellite is important prior to their full public release. Uncertainty in attempts to characterize footprint-scale surface-layer soil moisture using point-scale ground observations has ...

Field-scale surface soil moisture retrieval using L-band synthetic aperture radar data: shrublands examples

NASA Astrophysics Data System (ADS)

Kim, S.; Arii, M.; Jackson, T. J.

2017-12-01

L-band airborne synthetic aperture radar (SAR) observations at 7-m spatial resolution were made over California shrublands to better understand the effects of soil and vegetation parameters on backscattering coefficient (σ0). Temporal changes in σ0 of up to 3 dB were highly correlated to surface soil moisture but not to vegetation, even though vegetation water content (VWC) varied seasonally by a factor of two. HH was always greater than VV, suggesting the importance of double-bounce scattering by the woody parts. However, the geometric and dielectric properties of the woody parts did not vary significantly over time. Instead the changes in VWC occurred primarily in thin leaves that may not meaningfully influence absorption and scattering. A physically-based model for single scattering by discrete elements of plants successfully simulated the magnitude of the temporal variations in HH, VV, and HH/VV with a difference of less than 0.9 dB. In order to simulate the observations, the VWC input of the plant to the model was formulated as a function of plant's dielectric property (water fraction) while the plant geometry remains static in time. In comparison, when the VWC input was characterized by the geometry of a growing plant, the model performed poorly in describing the observed patterns in the σ0 changes. The modeling results offer explanation of the observation that soil moisture correlated highly with σ0: the dominant mechanisms for HH and VV are double-bounce scattering by trunk, and soil surface scattering, respectively. The time-series inversion of the physical model was able to retrieve soil moisture with the difference of -0.037 m3/m3 (mean), 0.025 m3/m3 (standard deviation), and 0.89 (correlation). Together with the previous results over croplands using the SAR data offering 0.05 m3/m3 retrieval accuracy, we will demonstrate the efficacy of the model-based time-series soil moisture retrieval at field scales.

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.

Surface reflectance retrieval from imaging spectrometer data using three atmospheric codes

NASA Astrophysics Data System (ADS)

Staenz, Karl; Williams, Daniel J.; Fedosejevs, Gunar; Teillet, Phil M.

1994-12-01

Surface reflectance retrieval from imaging spectrometer data has become important for quantitative information extraction in many application areas. In order to calculate surface reflectance from remotely measured radiance, radiative transfer codes play an important role for removal of the scattering and gaseous absorption effects of the atmosphere. The present study evaluates surface reflectances retrieved from airborne visible/infrared imaging spectrometer (AVIRIS) data using three radiative transfer codes: modified 5S (M5S), 6S, and MODTRAN2. Comparisons of the retrieved surface reflectance with ground-based reflectance were made for different target types such as asphalt, gravel, grass/soil mixture (soccer field), and water (Sooke Lake). The results indicate that the estimation of the atmospheric water vapor content is important for an accurate surface reflectance retrieval regardless of the radiative transfer code used. For the present atmospheric conditions, a difference of 0.1 in aerosol optical depth had little impact on the retrieved surface reflectance. The performance of MODTRAN2 is superior in the gas absorption regions compared to M5S and 6S.

Modelling the Passive Microwave Signature from Land Surfaces: A Review of Recent Results and Application to the L-Band SMOS SMAP Soil Moisture Retrieval Algorithms

NASA Technical Reports Server (NTRS)

Wigneron, J.-P.; Jackson, T. J.; O'Neill, P.; De Lannoy, G.; De Rosnay, P.; Walker, J. P.; Ferrazzoli, P.; Mironov, V.; Bircher, S.; Grant, J. P.;

Calibration of Noah soil hydraulic property parameters using surface soil moisture from SMOS and basin-wide in situ observations

USDA-ARS?s Scientific Manuscript database

Soil hydraulic properties can be retrieved from physical sampling of soil, via surveys, but this is time consuming and only as accurate as the scale of the sample. Remote sensing provides an opportunity to get pertinent soil properties at large scales, which is very useful for large scale modeling....

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS

PubMed Central

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data. PMID:26090852

Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

PubMed

Wang, De-Cai; Zhang, Gan-Lin; Zhao, Ming-Song; Pan, Xian-Zhang; Zhao, Yu-Guo; Li, De-Cheng; Macmillan, Bob

2015-01-01

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

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.

A Methodology for Soil Moisture Retrieval from Land Surface Temperature, Vegetation Index, Topography and Soil Type

NASA Astrophysics Data System (ADS)

Pradhan, N. R.

2015-12-01

Soil moisture conditions have an impact upon hydrological processes, biological and biogeochemical processes, eco-hydrology, floods and droughts due to changing climate, near-surface atmospheric conditions and the partition of incoming solar and long-wave radiation between sensible and latent heat fluxes. Hence, soil moisture conditions virtually effect on all aspects of engineering / military engineering activities such as operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, peaking factor analysis in dam design etc. Like other natural systems, soil moisture pattern can vary from completely disorganized (disordered, random) to highly organized. To understand this varying soil moisture pattern, this research utilized topographic wetness index from digital elevation models (DEM) along with vegetation index from remotely sensed measurements in red and near-infrared bands, as well as land surface temperature (LST) in the thermal infrared bands. This research developed a methodology to relate a combined index from DEM, LST and vegetation index with the physical soil moisture properties of soil types and the degree of saturation. The advantage in using this relationship is twofold: first it retrieves soil moisture content at the scale of soil data resolution even though the derived indexes are in a coarse resolution, and secondly the derived soil moisture distribution represents both organized and disorganized patterns of actual soil moisture. The derived soil moisture is used in driving the hydrological model simulations of runoff, sediment and nutrients.

Advances in Assimilation of Satellite-Based Passive Microwave Observations for Soil-Moisture Estimation

NASA Technical Reports Server (NTRS)

De Lannoy, Gabrielle J. M.; Pauwels, Valentijn; Reichle, Rolf H.; Draper, Clara; Koster, Randy; Liu, Qing

2012-01-01

Satellite-based microwave measurements have long shown potential to provide global information about soil moisture. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS, [1]) mission as well as the future National Aeronautics and Space Administration (NASA) Soil Moisture Active and Passive (SMAP, [2]) mission measure passive microwave emission at L-band frequencies, at a relatively coarse (40 km) spatial resolution. In addition, SMAP will measure active microwave signals at a higher spatial resolution (3 km). These new L-band missions have a greater sensing depth (of -5cm) compared with past and present C- and X-band microwave sensors. ESA currently also disseminates retrievals of SMOS surface soil moisture that are derived from SMOS brightness temperature observations and ancillary data. In this research, we address two major challenges with the assimilation of recent/future satellite-based microwave measurements: (i) assimilation of soil moisture retrievals versus brightness temperatures for surface and root-zone soil moisture estimation and (ii) scale-mismatches between satellite observations, models and in situ validation data.

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.

Retrieval of Aerosol Microphysical Properties Based on the Optimal Estimation Method: Information Content Analysis for Satellite Polarimetric Remote Sensing Measurements

NASA Astrophysics Data System (ADS)

Hou, W. Z.; Li, Z. Q.; Zheng, F. X.; Qie, L. L.

2018-04-01

This paper evaluates the information content for the retrieval of key aerosol microphysical and surface properties for multispectral single-viewing satellite polarimetric measurements cantered at 410, 443, 555, 670, 865, 1610 and 2250 nm over bright land. To conduct the information content analysis, the synthetic data are simulated by the Unified Linearized Vector Radiative Transfer Model (UNLVTM) with the intensity and polarization together over bare soil surface for various scenarios. Following the optimal estimation theory, a principal component analysis method is employed to reconstruct the multispectral surface reflectance from 410 nm to 2250 nm, and then integrated with a linear one-parametric BPDF model to represent the contribution of polarized surface reflectance, thus further to decouple the surface-atmosphere contribution from the TOA measurements. Focusing on two different aerosol models with the aerosol optical depth equal to 0.8 at 550 nm, the total DFS and DFS component of each retrieval aerosol and surface parameter are analysed. The DFS results show that the key aerosol microphysical properties, such as the fine- and coarse-mode columnar volume concentration, the effective radius and the real part of complex refractive index at 550 nm, could be well retrieved with the surface parameters simultaneously over bare soil surface type. The findings of this study can provide the guidance to the inversion algorithm development over bright surface land by taking full use of the single-viewing satellite polarimetric measurements.

The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

NASA Astrophysics Data System (ADS)

Dong, Jianzhi; Crow, Wade T.; Bindlish, Rajat

2018-01-01

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal autocorrelation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically polarized brightness temperature-based single-channel algorithm (SCA-V, the current baseline SMAP algorithm) and the dual-channel algorithm (DCA). A key assumption made in SCA-V is that real-time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that while SCA-V generally outperforms DCA, SCA-V can produce larger total errors when this assumption is significantly violated by interannual variability in vegetation health and biomass. Furthermore, larger autocorrelated errors in SCA-V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the autocorrelated error in SCA-V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA-V algorithm would reduce the magnitude of autocorrelated soil moisture retrieval errors.

Using Remotely-Sensed Estimates of Soil Moisture to Infer Soil Texture and Hydraulic Properties across a Semi-arid Watershed

NASA Technical Reports Server (NTRS)

Santanello, Joseph A.; Peters-Lidard, Christa D.; Garcia, Matthew E.; Mocko, David M.; Tischler, Michael A.; Moran, M. Susan; Thoma, D. P.

2007-01-01

Near-surface soil moisture is a critical component of land surface energy and water balance studies encompassing a wide range of disciplines. However, the processes of infiltration, runoff, and evapotranspiration in the vadose zone of the soil are not easy to quantify or predict because of the difficulty in accurately representing soil texture and hydraulic properties in land surface models. This study approaches the problem of parameterizing soils from a unique perspective based on components originally developed for operational estimation of soil moisture for mobility assessments. Estimates of near-surface soil moisture derived from passive (L-band) microwave remote sensing were acquired on six dates during the Monsoon '90 experiment in southeastern Arizona, and used to calibrate hydraulic properties in an offline land surface model and infer information on the soil conditions of the region. Specifically, a robust parameter estimation tool (PEST) was used to calibrate the Noah land surface model and run at very high spatial resolution across the Walnut Gulch Experimental Watershed. Errors in simulated versus observed soil moisture were minimized by adjusting the soil texture, which in turn controls the hydraulic properties through the use of pedotransfer functions. By estimating a continuous range of widely applicable soil properties such as sand, silt, and clay percentages rather than applying rigid soil texture classes, lookup tables, or large parameter sets as in previous studies, the physical accuracy and consistency of the resulting soils could then be assessed. In addition, the sensitivity of this calibration method to the number and timing of microwave retrievals is determined in relation to the temporal patterns in precipitation and soil drying. The resultant soil properties were applied to an extended time period demonstrating the improvement in simulated soil moisture over that using default or county-level soil parameters. The methodology is also applied to an independent case at Walnut Gulch using a new soil moisture product from active (C-band) radar imagery with much lower spatial and temporal resolution. Overall, results demonstrate the potential to gain physically meaningful soils information using simple parameter estimation with few but appropriately timed remote sensing retrievals.

Assimilation of SMOS Retrievals in the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.; Crosson, William L.

2016-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm(sub 3 cm(sub -3). These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve.

Assimilation of SMOS Retrievals in the Land Information System

PubMed Central

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.; Crosson, William L.

2018-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite provides retrievals of soil moisture in the upper 5 cm with a 30-50 km resolution and a mission accuracy requirement of 0.04 cm3 cm−3. These observations can be used to improve land surface model soil moisture states through data assimilation. In this paper, SMOS soil moisture retrievals are assimilated into the Noah land surface model via an Ensemble Kalman Filter within the NASA Land Information System. Bias correction is implemented using Cumulative Distribution Function (CDF) matching, with points aggregated by either land cover or soil type to reduce sampling error in generating the CDFs. An experiment was run for the warm season of 2011 to test SMOS data assimilation and to compare assimilation methods. Verification of soil moisture analyses in the 0-10 cm upper layer and root zone (0-1 m) was conducted using in situ measurements from several observing networks in the central and southeastern United States. This experiment showed that SMOS data assimilation significantly increased the anomaly correlation of Noah soil moisture with station measurements from 0.45 to 0.57 in the 0-10 cm layer. Time series at specific stations demonstrate the ability of SMOS DA to increase the dynamic range of soil moisture in a manner consistent with station measurements. Among the bias correction methods, the correction based on soil type performed best at bias reduction but also reduced correlations. The vegetation-based correction did not produce any significant differences compared to using a simple uniform correction curve. PMID:29367795

Innovative approach to retrieve land surface emissivity and land surface temperature in areas of highly dynamic emissivity changes by using thermal infrared data

NASA Astrophysics Data System (ADS)

Heinemann, S.

2015-12-01

The land surface temperature (LST) is an extremely significant parameter in order to understand the processes of energetic interactions between Earth's surface and atmosphere. This knowledge is significant for various environmental research questions, particularly with regard to the recent climate change. This study shows an innovative approach to retrieve land surface emissivity (LSE) and LST by using thermal infrared (TIR) data from satellite sensors, such as SEVIRI and AATSR. So far there are no methods to derive LSE/LST particularly in areas of highly dynamic emissivity changes. Therefore especially for regions with large surface temperature amplitude in the diurnal cycle such as bare and uneven soil surfaces but also for regions with seasonal changes in vegetation cover including various surface areas such as grassland, mixed forests or agricultural land different methods were investigated to identify the most appropriate one. The LSE is retrieved by using the day/night Temperature-Independent Spectral Indices (TISI) method, and the Generalised Split-Window (GSW) method is used to retrieve the LST. Nevertheless different GSW algorithms show that equal LSEs lead to large LST differences. Additionally LSE is also measured using a NDVI-based threshold method (NDVITHM) to distinguish between soil, dense vegetation cover and pixel composed of soil and vegetation. The data used for this analysis were derived from MODIS TIR. The analysis is implemented with IDL and an intercomparison is performed to determine the most effective methods. To compensate temperature differences between derived and ground truth data appropriate correction terms by comparing derived LSE/LST data with ground-based measurements are developed. One way to calibrate LST retrievals is by comparing the canopy leaf temperature of conifers derived from TIR data with the surrounding air temperature (e.g. from synoptic stations). Prospectively, the derived LSE/LST data become validated with near infrared data obtained from an UVA with a TIR camera (TIRC) onboard, and also compared with ground-based measurements. This study aims to generate an appropriate method by integrating developed correction terms to eventually obtain a high correlation between all, LSE/LST, TIRC and ground truth data.

Southern U.S. Soil Moisture Map

NASA Image and Video Library

2015-05-19

Southern U.S. NASA's SMAP soil moisture retrievals from April 27, 2015, when severe storms were affecting Texas. Top: radiometer data alone. Bottom: combined radar and radiometer data with a resolution of 5.6 miles (9 kilometers). The combined product reveals more detailed surface soil moisture features. http://photojournal.jpl.nasa.gov/catalog/PIA19338

Soil moisture retrieval in forest biomes: field experiment focus for SMAP 2018-2020 and beyond

USDA-ARS?s Scientific Manuscript database

The Soil Moisture Active Passive (SMAP) project has made excellent progress in addressing the requirements and science goals of the primary mission. The primary mission baseline requirement is estimates of global surface soil moisture with an error of no greater than 4% volumetric (one sigma) exclud...

Enhancing begetation productivity forecasting using remotely-sensed surface soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

With the onset of data availability from the ESA Soil Moisture and Ocean Salinity (SMOS) mission (Kerr and Levine, 2008) and the expected 2015 launch of the NASA Soil Moisture Active and Passive (SMAP) mission (Entekhabi et al., 2010), the next five years should see a significant expansion in our ab...

A review of the applications of ASCAT soil moisture products

USDA-ARS?s Scientific Manuscript database

Remote sensing of soil moisture has reached a level of good maturity and accuracy for which the retrieved products are ready to use in real-world applications. Due to the importance of soil moisture in the partitioning of the water and energy fluxes between the land surface and the atmosphere, a wid...

Application of Terrestrial Microwave Remote Sensing to Agricultural Drought Monitoring

NASA Astrophysics Data System (ADS)

Crow, W. T.; Bolten, J. D.

2014-12-01

Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Such systems, however, are prone to random error associated with: incorrect process model physics, poor parameter choices and noisy meteorological inputs. The presentation will describe attempts to remediate these sources of error via the assimilation of remotely-sensed surface soil moisture retrievals from satellite-based passive microwave sensors into a global soil water balance model. Results demonstrate the ability of satellite-based soil moisture retrieval products to significantly improve the global characterization of root-zone soil moisture - particularly in data-poor regions lacking adequate ground-based rain gage instrumentation. This success has lead to an on-going effort to implement an operational land data assimilation system at the United States Department of Agriculture's Foreign Agricultural Service (USDA FAS) to globally monitor variations in root-zone soil moisture availability via the integration of satellite-based precipitation and soil moisture information. Prospects for improving the performance of the USDA FAS system via the simultaneous assimilation of both passive and active-based soil moisture retrievals derived from the upcoming NASA Soil Moisture Active/Passive mission will also be discussed.

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.

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.

Soil Moisture Retrieval from Aquarius

USDA-ARS?s Scientific Manuscript database

Aquarius observations over land offer an unprecedented opportunity to provide a value-added product, land surface soil moisture, which will contribute to a better understanding of the Earth’s climate and water cycle. Additionally, Aquarius will provide the first spaceborne data that can be used to a...

Soil Moisture Data Assimilation in the NASA Land Information System for Local Modeling Applications and Improved Situational Awareness

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Blakenship, Clay B.; Zavodsky, Bradley T.

2014-01-01

As part of the NASA Soil Moisture Active Passive (SMAP) Early Adopter (EA) program, the NASA Shortterm Prediction Research and Transition (SPoRT) Center has implemented a data assimilation (DA) routine into the NASA Land Information System (LIS) for soil moisture retrievals from the European Space Agency's Soil Moisture Ocean Salinity (SMOS) satellite. The SMAP EA program promotes application-driven research to provide a fundamental understanding of how SMAP data products will be used to improve decision-making at operational agencies. SPoRT has partnered with select NOAA/NWS Weather Forecast Offices (WFOs) that use output from a real-time regional configuration of LIS, without soil moisture DA, to initialize local numerical weather prediction (NWP) models and enhance situational awareness. Improvements to local NWP with the current LIS have been demonstrated; however, a better representation of the land surface through assimilation of SMOS (and eventually SMAP) retrievals is expected to lead to further model improvement, particularly during warm-season months. SPoRT will collaborate with select WFOs to assess the impact of soil moisture DA on operational forecast situations. Assimilation of the legacy SMOS instrument data provides an opportunity to develop expertise in preparation for using SMAP data products shortly after the scheduled launch on 5 November 2014. SMOS contains a passive L-band radiometer that is used to retrieve surface soil moisture at 35-km resolution with an accuracy of 0.04 cu cm cm (exp -3). SMAP will feature a comparable passive L-band instrument in conjunction with a 3-km resolution active radar component of slightly degraded accuracy. A combined radar-radiometer product will offer unprecedented global coverage of soil moisture at high spatial resolution (9 km) for hydrometeorological applications, balancing the resolution and accuracy of the active and passive instruments, respectively. The LIS software framework manages land surface model (LSM) simulations and includes an Ensemble Kalman Filter for conducting land surface DA. SPoRT has added a module to read, quality-control and bias-correct swaths of Level II SMOS soil moisture retrievals prior to assimilation within LIS. The impact of SMOS DA is being tested using the Noah LSM. Experiments are being conducted to examine the impacts of SMOS soil moisture DA on the resulting LISNoah fields and subsequent NWP simulations using the Weather Research and Forecasting (WRF) model initialized with LIS-Noah output. LIS-Noah soil moisture will be validated against in situ observations from Texas A&M's North American Soil Moisture Database to reveal the impact and possible improvement in soil moisture trends through DA. WRF model NWP case studies will test the impacts of DA on the simulated near-surface and boundary-layer environments, and precipitation during both quiescent and disturbed weather scenarios. Emphasis will be placed on cases with large analysis increments, especially due to contributions from regional irrigation patterns that are not represented by precipitation input in the baseline LIS-Noah run. This poster presentation will describe the soil moisture DA methodology and highlight LIS-Noah and WRF simulation results with and without assimilation.

The Impact of Dielectric Constant Model and Surface Reference on Differences Between SMOS and Aquarius Sea Surface Salinity

NASA Technical Reports Server (NTRS)

Dinnat, E. P.; Boutin, J.; Yin, X.; LeVine, D. M.

2014-01-01

Two ongoing space missions share the scientific objective of mapping the global Sea Surface Salinity (SSS), yet their observations show significant discrepancies. ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Aquarius use L-band (1.4 GHz) radiometers to measure emission from the sea surface and retrieve SSS. Significant differences in SSS retrieved by both sensors are observed, with SMOS SSS being generally lower than Aquarius SSS, except for very cold waters where SMOS SSS is the highest overall. Figure 1 is an example of the difference between the SSS retrieved by SMOS and Aquarius averaged over one month and 1 degree in longitude and latitude. Differences are mostly between -1 psu and +1 psu (psu, practical salinity unit), with a significant regional and latitudinal dependence. We investigate the impact of the vicarious calibration and retrieval algorithm used by both mission on these differences.

Optimizing available water capacity using microwave satellite data for improving irrigation management

NASA Astrophysics Data System (ADS)

Gupta, Manika; Bolten, John; Lakshmi, Venkat

2015-04-01

This work addresses the improvement of available water capacity by developing a technique for estimating soil hydraulic parameters through the utilization of satellite-retrieved near surface soil moisture. The prototype involves the usage of Monte Carlo analysis to assimilate historical remote sensing soil moisture data available from the Advanced Microwave Scanning Radiometer (AMSR-E) within the hydrological model. The main hypothesis used in this study is that near-surface soil moisture data contain useful information that can describe the effective hydrological conditions of the basin such that when appropriately In the method followed in this study the hydraulic parameters are derived directly from information on the soil moisture state at the AMSR-E footprint scale and the available water capacity is derived for the root zone by coupling of AMSR-E soil moisture with the physically-based hydrological model. The available capacity water, which refers to difference between the field capacity and wilting point of the soil and represent the soil moisture content at 0.33 bar and 15 bar respectively is estimated from the soil hydraulic parameters using the van Genuchten equation. The initial ranges of soil hydraulic parameters are taken in correspondence with the values available from the literature based on Soil Survey Geographic (SSURGO) database within the particular AMSR-E footprint. Using the Monte Carlo simulation, the ranges are narrowed in the region where simulation shows a good match between predicted and near-surface soil moisture from AMSR-E. In this study, the uncertainties in accurately determining the parameters of the nonlinear soil water retention function for large-scale hydrological modeling is the focus of the development of the Bayesian framework. Thus, the model forecasting has been combined with the observational information to optimize the model state and the soil hydraulic parameters simultaneously. The optimization process is divided into two steps during one time interval: the state variable is optimized through the state filter and the optimal parameter values are then transferred for retrieving soil moisture. However, soil moisture from sensors such as AMSR-E can only be retrieved for the top few centimeters of soil. So, for the present study, a homogeneous soil system has been considered. By assimilating this information into the model, the accuracy of model structure in relating surface moisture dynamics to deeper soil profiles can be ascertained. 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 alongwith the available water capacity, the root mean square error (RMSE) and Mean Bias error (MBE) are used to measure the performance of the soil moisture simulations. The optimized parameters as compared to the pedo-transfer based parameters were found to reduce the RMSE from 0.14 to 0.04 and 0.15 to 0.07 in surface layer and root zone respectively.

Considering Combined or Separated Roughness and Vegetation Effects in Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Parrens, Marie; Wigernon, Jean-Pierre; Richaume, Philippe; Al Bitar, Ahmad; Mialon, Arnaud; Fernandez-Moran, Roberto; Al-Yarri, Amen; O'Neill, Peggy; Kerr, Yann

2016-01-01

For more than six years, the Soil Moisture and Ocean Salinity (SMOS) mission has provided multi angular and full-polarization brightness temperature (TB) measurements at L-band. Geophysical products such as soil moisture (SM) and vegetation optical depth at nadir (tau(sub nad)) are retrieved by an operational algorithm using TB observations at different angles of incidence and polarizations. However, the quality of the retrievals depends on several surface effects, such as vegetation, soil roughness and texture, etc. In the microwave forward emission model used in the retrievals (L-band Microwave Emission Model, L-MEB),soil roughness is modeled with a semi-empirical equation using four main parameters (Q(sub r), H(sub r), N(sub rp), with p = H or V polarizations). At present, these parameters are calibrated with data provided by airborne studies and in situ measurements made at a local scale that is not necessarily representative of the large SMOS footprints (43 km on average) at global scale. In this study, we evaluate the impact of the calibrated values of N(sub rp) and H(sub r) on the SM and tau(sub nad) retrievals based on SMOS TB measurements (SMOS Level 3 product) over the Soil Climate Analysis Network (SCAN) network located in North America over five years (2011-2015). In this study, Qr was set equal to zero and we assumed that N(sub rH)= N(sub rV). The retrievals were performed by varying N(sub rp) from -1 to 2 by steps of 1 and H(sub r) from 0 to 0.6 by steps of 0.1. At satellite scale, the results show that combining vegetation and roughness effects in a single parameter provides the best results in terms of soil moisture retrievals, as evaluated against the in situ SM data. Even though our retrieval approach was very simplified, as we did not account for pixel heterogeneity, the accuracy we obtained in the SM retrievals was almost systematically better than those of the Level 3 product. Improved results were also obtained in terms of optical depth retrievals. These new results may have key consequences in terms of calibration of roughness effects within the algorithms of the SMOS (ESA) and the SMAP (NASA) space missions.

Characterizing a New Surface-Based Shortwave Cloud Retrieval Technique, Based on Transmitted Radiance for Soil and Vegetated Surface Types

NASA Technical Reports Server (NTRS)

Coddington, Odele; Pilewskie, Peter; Schmidt, K. Sebastian; McBride, Patrick J.; Vukicevic, Tomislava

2013-01-01

This paper presents an approach using the GEneralized Nonlinear Retrieval Analysis (GENRA) tool and general inverse theory diagnostics including the maximum likelihood solution and the Shannon information content to investigate the performance of a new spectral technique for the retrieval of cloud optical properties from surface based transmittance measurements. The cumulative retrieval information over broad ranges in cloud optical thickness (tau), droplet effective radius (r(sub e)), and overhead sun angles is quantified under two conditions known to impact transmitted radiation; the variability in land surface albedo and atmospheric water vapor content. Our conclusions are: (1) the retrieved cloud properties are more sensitive to the natural variability in land surface albedo than to water vapor content; (2) the new spectral technique is more accurate (but still imprecise) than a standard approach, in particular for tau between 5 and 60 and r(sub e) less than approximately 20 nm; and (3) the retrieved cloud properties are dependent on sun angle for clouds of tau from 5 to 10 and r(sub e) less than 10 nm, with maximum sensitivity obtained for an overhead sun.

SMAP Global Map of Surface Soil Moisture Aug. 25-27, 2015

NASA Image and Video Library

2015-09-02

A three-day composite global map of surface soil moisture as retrieved from NASA SMAP radiometer instrument between Aug. 25-27, 2015. Dry areas appear yellow/orange, such as the Sahara Desert, western Australia and the western U.S. Wet areas appear blue, representing the impacts of localized storms. White areas indicate snow, ice or frozen ground. http://photojournal.jpl.nasa.gov/catalog/PIA19877

Synergy of the SimSphere land surface process model with ASTER imagery for the retrieval of spatially distributed estimates of surface turbulent heat fluxes and soil moisture content

NASA Astrophysics Data System (ADS)

Petropoulos, George; Wooster, Martin J.; Carlson, Toby N.; Drake, Nick

2010-05-01

Accurate information on spatially explicit distributed estimates of key land-atmosphere fluxes and related land surface parameters is of key importance in a range of disciplines including hydrology, meteorology, agriculture and ecology. Estimation of those parameters from remote sensing frequently employs the integration of such data with mathematical representations of the transfers of energy, mass and radiation between soil, vegetation and atmosphere continuum, known as Soil Vegetation Atmosphere Transfer (SVAT) models. The ability of one such inversion modelling scheme to resolve for key surface energy fluxes and of soil surface moisture content is examined here using data from a multispectral high spatial resolution imaging instrument, the Advanced Spaceborne Thermal Emission and Reflection Scanning Radiometer (ASTER) and SimSphere one-dimensional SVAT model. Accuracy of the investigated methodology, so-called as the "triangle" method, is verified using validated ground observations obtained from selected days collected from nine CARBOEUROPE IP sites representing a variety of climatic, topographic and environmental conditions. Subsequently, a new framework is suggested for the retrieval of two additional parameters by the investigated method, namely the Evaporative (EF) and the Non-Evaporative (NEF) Fractions. Results indicated a close agreement between the inverted surface fluxes and surface moisture availability maps as well as of the EF and NEF parameters with the observations both spatially and temporally with accuracies comparable to those obtained in similar experiments with high spatial resolution data. Inspection of the inverted surface fluxes maps regionally, showed an explainable distribution in the range of the inverted parameters in relation with the surface heterogeneity. Overall performance of the "triangle" inversion methodology was found to be affected predominantly by the SVAT model "correct" initialisation representative of the test site environment, most importantly the atmospheric conditions required in the SVAT model initial conditions. This study represents the first comprehensive evaluation of the performance of this particular methodological implementation at a European setting using the SimSphere SVAT with the ASTER data. The present work is also very timely in that, a variation of this specific inversion methodology has been proposed for the operational retrieval of the soil surface moisture content by National Polar-orbiting Operational Environmental Satellite System (NPOESS), in a series of satellite platforms that are due to be launched in the next 12 years starting from 2012. KEYWORDS: micrometeorology, surface heat fluxes, soil moisture content, ASTER, triangle method, SimSphere, CarboEurope IP

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.

Surface roughness retrieval by inversion of the Hapke model: A multiscale approach

NASA Astrophysics Data System (ADS)

Labarre, S.; Ferrari, C.; Jacquemoud, S.

2017-07-01

Surface roughness is a key property of soils that controls many surface processes and influences the scattering of incident electromagnetic waves at a wide range of scales. Hapke (2012b) designed a photometric model providing an approximate analytical solution of the Bidirectional Reflectance Distribution Function (BRDF) of a particulate medium: he introduced the effect of surface roughness as a correction factor of the BRDF of a smooth surface. This photometric roughness is defined as the mean slope angle of the facets composing the surface, integrated over all scales from the grain size to the local topography. Yet its physical meaning is still a question at issue, as the scale at which it occurs is not clearly defined. This work aims at better understanding the relative influence of roughness scales on soil BRDF and to test the ability of the Hapke model to retrieve a roughness that depicts effectively the ground truth. We apply a wavelet transform on millimeter digital terrain models (DTM) acquired over volcanic terrains. This method allows splitting the frequency band of a signal in several sub-bands, each corresponding to a spatial scale. We demonstrate that sub-centimeter surface features dominate both the integrated roughness and the BRDF shape. We investigate the suitability of the Hapke model for surface roughness retrieval by inversion on optical data. A global sensitivity analysis of the model shows that soil BRDF is very sensitive to surface roughness, nearly as much as the single scattering albedo according to the phase angle, but also that these two parameters are strongly correlated. Based on these results, a simplified two-parameter model depending on surface albedo and roughness is proposed. Inversion of this model on BRDF data simulated by a ray-tracing code over natural targets shows a good estimation of surface roughness when the assumptions of the model are verified, with a priori knowledge on surface albedo.

Retrieval of aerosol optical depth over bare soil surfaces using time series of MODIS imagery

NASA Astrophysics Data System (ADS)

Yuan, Zhengwu; Yuan, Ranyin; Zhong, Bo

2014-11-01

Aerosol Optical Depth (AOD) is one of the key parameters which can not only reflect the characterization of atmospheric turbidity, but also identify the climate effects of aerosol. The current MODIS aerosol estimation algorithm over land is based on the "dark-target" approach which works only over densely vegetated surfaces. For non-densely vegetated surfaces (such as snow/ice, desert, and bare soil surfaces), this method will be failed. In this study, we develop an algorithm to derive AOD over the bare soil surfaces. Firstly, this method uses the time series of MODIS imagery to detect the " clearest" observations during the non-growing season in multiple years for each pixel. Secondly, the "clearest" observations after suitable atmospheric correction are used to fit the bare soil's bidirectional reflectance distribution function (BRDF) using Kernel model. As long as the bare soil's BRDF is established, the surface reflectance of "hazy" observations can be simulated. Eventually, the AOD over the bare soil surfaces are derived. Preliminary validation results by comparing with the ground measurements from AERONET at Xianghe sites show a good agreement.

Soil moisture retrieval from Sentinel-1 satellite data

NASA Astrophysics Data System (ADS)

Benninga, Harm-Jan; van der Velde, Rogier; Su, Zhongbo

2016-04-01

Reliable up-to-date information on the current water availability and models to evaluate management scenarios are indispensable for skilful water management. The Sentinel-1 radar satellite programme provides an opportunity to monitor water availability (as surface soil moisture) from space on an operational basis at unprecedented fine spatial and temporal resolutions. However, the influences of soil roughness and vegetation cover complicate the retrieval of soil moisture states from radar data. In this contribution, we investigate the sensitivity of Sentinel-1 radar backscatter to soil moisture states and vegetation conditions. The analyses are based on 105 Sentinel-1 images in the period from October 2014 to January 2016 covering the Twente region in the Netherlands. This area is almost flat and has a heterogeneous landscape, including agricultural (mainly grass, cereal and corn), forested and urban land covers. In-situ measurements at 5 cm depth collected from the Twente soil moisture monitoring network are used as reference. This network consists of twenty measurement stations (most of them at agricultural fields) distributed across an area of 50 km × 40 km. The Normalized Difference Vegetation Index (NDVI) derived from optical images is adopted as proxy to represent seasonal variability in vegetation conditions. The results from this sensitivity study provide insight into the potential capability of Sentinel-1 data for the estimation of soil moisture states and they will facilitate the further development of operational retrieval methods. An operationally applicable soil moisture retrieval method requires an algorithm that is usable without the need for area specific model calibration with detailed field information (regarding roughness and vegetation). Because it is not yet clear which method provides the most reliable soil moisture retrievals from Sentinel-1 data, multiple soil moisture retrieval methods will be studied in which the fine spatiotemporal resolution and the dual-polarized information of Sentinel-1 are utilized. Three candidate algorithms are presented at the conference, which are a data-driven algorithm, inversion of a radar scattering model and downscaling of coarser resolution soil moisture products. The research is part of the OWAS1S project (Optimizing Water Availability with Sentinel-1 Satellites), which stands for integration of the freely available global Sentinel-1 data and local knowledge on soil physical processes, to optimize water management of regional water systems and to develop value-added products for agriculture.

Using Multi-Dimensional Microwave Remote Sensing Information for the Retrieval of Soil Surface Roughness

NASA Astrophysics Data System (ADS)

Marzahn, P.; Ludwig, R.

2016-06-01

In this Paper the potential of multi parametric polarimetric SAR (PolSAR) data for soil surface roughness estimation is investigated and its potential for hydrological modeling is evaluated. The study utilizes microwave backscatter collected from the Demmin testsite in the North-East Germany during AgriSAR 2006 campaign using fully polarimetric L-Band airborne SAR data. For ground truthing extensive soil surface roughness in addition to various other soil physical properties measurements were carried out using photogrammetric image matching techniques. The correlation between ground truth roughness indices and three well established polarimetric roughness estimators showed only good results for Re[ρRRLL] and the RMS Height s. Results in form of multitemporal roughness maps showed only satisfying results due to the fact that the presence and development of particular plants affected the derivation. However roughness derivation for bare soil surfaces showed promising results.

Comparison of SMOS and Aquarius Sea Surface Salinity and Analysis of Possible Causes for the Differences

NASA Technical Reports Server (NTRS)

Dinnat, E. P.; Boutin, J.; Yin, X.; Le Vine, D. M.; Waldteufel, P.; Vergely, J. -L.

2014-01-01

Two ongoing space missions share the scientific objective of mapping the global Sea Surface Salinity (SSS), yet their observations show significant discrepancies. ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Aquarius use L-band (1.4 GHz) radiometers to measure emission from the sea surface and retrieve SSS. Significant differences in SSS retrieved by both sensors are observed, with SMOS SSS being generally lower than Aquarius SSS, except for very cold waters where SMOS SSS is the highest overall. Figure 1 is an example of the difference between the SSS retrieved by SMOS and Aquarius averaged over one month and 1 degree in longitude and latitude. Differences are mostly between -1 psu and +1 psu (psu, practical salinity unit), with a significant regional and latitudinal dependence. We investigate the impact of the vicarious calibration and some components of the retrieval algorithm used by both mission on these differences.

MODIS-based spatiotemporal patterns of soil moisture and evapotranspiration interactions in Tampa Bay urban watershed

NASA Astrophysics Data System (ADS)

Chang, Ni-Bin; Xuan, Zhemin; Wimberly, Brent

2011-09-01

Soil moisture and evapotranspiration (ET) is affected by both water and energy balances in the soilvegetation- atmosphere system, it involves many complex processes in the nexus of water and thermal cycles at the surface of the Earth. These impacts may affect the recharge of the upper Floridian aquifer. The advent of urban hydrology and remote sensing technologies opens new and innovative means to undertake eventbased assessment of ecohydrological effects in urban regions. For assessing these landfalls, the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing images can be used for the estimation of such soil moisture change in connection with two other MODIS products - Enhanced Vegetation Index (EVI), Land Surface Temperature (LST). Supervised classification for soil moisture retrieval was performed for Tampa Bay area on the 2 kmx2km grid with MODIS images. Machine learning with genetic programming model for soil moisture estimation shows advances in image processing, feature extraction, and change detection of soil moisture. ET data that were derived by Geostationary Operational Environmental Satellite (GOES) data and hydrologic models can be retrieved from the USGS web site directly. Overall, the derived soil moisture in comparison with ET time series changes on a seasonal basis shows that spatial and temporal variations of soil moisture and ET that are confined within a defined region for each type of surfaces, showing clustered patterns and featuring space scatter plot in association with the land use and cover map. These concomitant soil moisture patterns and ET fluctuations vary among patches, plant species, and, especially, location on the urban gradient. Time series plots of LST in association with ET, soil moisture and EVI reveals unique ecohydrological trends. Such ecohydrological assessment can be applied for supporting the urban landscape management in hurricane-stricken regions.

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.

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...

The impact of acquisition times on the accuracy of microwave soil moisture retrievals over the contiguous U.S.

USDA-ARS?s Scientific Manuscript database

Satellite-derived soil moisture products have become an important data source for the study of land surface processes and related applications. For satellites with sun-synchronous orbits, these products are typically derived separately for ascending and descending overpasses with different local acq...

Data Assimilation to Extract Soil Moisture Information From SMAP Observations

NASA Technical Reports Server (NTRS)

Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.

2017-01-01

Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, they can be used to reduce the need for localized bias correction techniques typically implemented in data assimilation (DA) systems that tend to remove some of the independent information provided by satellite observations. Here, we use a statistical neural network (NN) algorithm to retrieve SMAP (Soil Moisture Active Passive) surface soil moisture estimates in the climatology of the NASA Catchment land surface model. Assimilating these estimates without additional bias correction is found to significantly reduce the model error and increase the temporal correlation against SMAP CalVal in situ observations over the contiguous United States. A comparison with assimilation experiments using traditional bias correction techniques shows that the NN approach better retains the independent information provided by the SMAP observations and thus leads to larger model skill improvements during the assimilation. A comparison with the SMAP Level 4 product shows that the NN approach is able to provide comparable skill improvements and thus represents a viable assimilation approach.

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.

Evaluation of the predicted error of the soil moisture retrieval from C-band SAR by comparison against modelled soil moisture estimates over Australia

PubMed Central

Doubková, Marcela; Van Dijk, Albert I.J.M.; Sabel, Daniel; Wagner, Wolfgang; Blöschl, Günter

2012-01-01

The Sentinel-1 will carry onboard a C-band radar instrument that will map the European continent once every four days and the global land surface at least once every twelve days with finest 5 × 20 m spatial resolution. The high temporal sampling rate and operational configuration make Sentinel-1 of interest for operational soil moisture monitoring. Currently, updated soil moisture data are made available at 1 km spatial resolution as a demonstration service using Global Mode (GM) measurements from the Advanced Synthetic Aperture Radar (ASAR) onboard ENVISAT. The service demonstrates the potential of the C-band observations to monitor variations in soil moisture. Importantly, a retrieval error estimate is also available; these are needed to assimilate observations into models. The retrieval error is estimated by propagating sensor errors through the retrieval model. In this work, the existing ASAR GM retrieval error product is evaluated using independent top soil moisture estimates produced by the grid-based landscape hydrological model (AWRA-L) developed within the Australian Water Resources Assessment system (AWRA). The ASAR GM retrieval error estimate, an assumed prior AWRA-L error estimate and the variance in the respective datasets were used to spatially predict the root mean square error (RMSE) and the Pearson's correlation coefficient R between the two datasets. These were compared with the RMSE calculated directly from the two datasets. The predicted and computed RMSE showed a very high level of agreement in spatial patterns as well as good quantitative agreement; the RMSE was predicted within accuracy of 4% of saturated soil moisture over 89% of the Australian land mass. Predicted and calculated R maps corresponded within accuracy of 10% over 61% of the continent. The strong correspondence between the predicted and calculated RMSE and R builds confidence in the retrieval error model and derived ASAR GM error estimates. The ASAR GM and Sentinel-1 have the same basic physical measurement characteristics, and therefore very similar retrieval error estimation method can be applied. Because of the expected improvements in radiometric resolution of the Sentinel-1 backscatter measurements, soil moisture estimation errors can be expected to be an order of magnitude less than those for ASAR GM. This opens the possibility for operationally available medium resolution soil moisture estimates with very well-specified errors that can be assimilated into hydrological or crop yield models, with potentially large benefits for land-atmosphere fluxes, crop growth, and water balance monitoring and modelling. PMID:23483015

Kalman filters for assimilating near-surface observations into the Richards equation - Part 1: Retrieving state profiles with linear and nonlinear numerical schemes

NASA Astrophysics Data System (ADS)

Chirico, G. B.; Medina, H.; Romano, N.

2014-07-01

This paper examines the potential of different algorithms, based on the Kalman filtering approach, for assimilating near-surface observations into a one-dimensional Richards equation governing soil water flow in soil. Our specific objectives are: (i) to compare the efficiency of different Kalman filter algorithms in retrieving matric pressure head profiles when they are implemented with different numerical schemes of the Richards equation; (ii) to evaluate the performance of these algorithms when nonlinearities arise from the nonlinearity of the observation equation, i.e. when surface soil water content observations are assimilated to retrieve matric pressure head values. The study is based on a synthetic simulation of an evaporation process from a homogeneous soil column. Our first objective is achieved by implementing a Standard Kalman Filter (SKF) algorithm with both an explicit finite difference scheme (EX) and a Crank-Nicolson (CN) linear finite difference scheme of the Richards equation. The Unscented (UKF) and Ensemble Kalman Filters (EnKF) are applied to handle the nonlinearity of a backward Euler finite difference scheme. To accomplish the second objective, an analogous framework is applied, with the exception of replacing SKF with the Extended Kalman Filter (EKF) in combination with a CN numerical scheme, so as to handle the nonlinearity of the observation equation. While the EX scheme is computationally too inefficient to be implemented in an operational assimilation scheme, the retrieval algorithm implemented with a CN scheme is found to be computationally more feasible and accurate than those implemented with the backward Euler scheme, at least for the examined one-dimensional problem. The UKF appears to be as feasible as the EnKF when one has to handle nonlinear numerical schemes or additional nonlinearities arising from the observation equation, at least for systems of small dimensionality as the one examined in this study.

Enhancing SMAP Soil Moisture Retrievals via Superresolution Techniques

NASA Astrophysics Data System (ADS)

Beale, K. D.; Ebtehaj, A. M.; Romberg, J. K.; Bras, R. L.

2017-12-01

Soil moisture is a key state variable that modulates land-atmosphere interactions and its high-resolution global scale estimates are essential for improved weather forecasting, drought prediction, crop management, and the safety of troop mobility. Currently, NASA's Soil Moisture Active/Passive (SMAP) satellite provides a global picture of soil moisture variability at a resolution of 36 km, which is prohibitive for some hydrologic applications. The goal of this research is to enhance the resolution of SMAP passive microwave retrievals by a factor of 2 to 4 using modern superresolution techniques that rely on the knowledge of high-resolution land surface models. In this work, we explore several super-resolution techniques including an empirical dictionary method, a learned dictionary method, and a three-layer convolutional neural network. Using a year of global high-resolution land surface model simulations as training set, we found that we are able to produce high-resolution soil moisture maps that outperform the original low-resolution observations both qualitatively and quantitatively. In particular, on a patch-by-patch basis we are able to produce estimates of high-resolution soil moisture maps that improve on the original low-resolution patches by on average 6% in terms of mean-squared error, and 14% in terms of the structural similarity index.

Soil Moisture Retrieval During a Corn Growth Cycle using L-band (1.6 GHz) Radar Observations

NASA Technical Reports Server (NTRS)

Joseph, Alicia T.; vanderVelde, Rogier; O'Neill, Peggy E.; Lang, Roger; Gish, Tim

2007-01-01

New opportunities for large-scale soil moisture monitoring will emerge with the launch of two low frequency (L-band 1.4 GHz) radiometers: the Aquarius mission in 2009 and the Soil Moisture and Ocean Salinity (SMOS) mission in 2008. Soil moisture is an important land surface variable affecting water and heat exchanges between atmosphere, land surface and deeper ground water reservoirs. The data products from these sensors provide valuable information in a range of climate and hydrologic applications (e.g., numecal weather prediction, drought monitoring, flood forecasting, water resources management, etc.). This paper describes a unique data set that was collected during a field campaign at OPE^ (Optimizing Production Inputs for Economic and Environmental Enhancements) site in Beltsville, Maryland throughout the eompj2ete corn growing in 2002. This investigation describes a simple methodology to correct active microwave observations for vegetation effects, which could potentially be implemented in a global soil moisture monitoring algorithm. The methodology has been applied to radar observation collected during the entire corn growth season and validation against ground measurements showed that the top 5-cm soil moisture can be retrieved with an accuracy up to 0.033 [cu cm/cu cm] depending on the sensing configuration.

Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors

NASA Astrophysics Data System (ADS)

McJannet, David; Hawdon, Aaron; Baker, Brett; Renzullo, Luigi; Searle, Ross

2017-12-01

Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.

Soil moisture status estimation over Three Gorges area with Landsat TM data based on temperature vegetation dryness index

NASA Astrophysics Data System (ADS)

Xu, Lina; Niu, Ruiqing; Li, Jiong; Dong, Yanfang

2011-12-01

Soil moisture is the important indicator of climate, hydrology, ecology, agriculture and other parameters of the land surface and atmospheric interface. Soil moisture plays an important role on the water and energy exchange at the land surface/atmosphere interface. Remote sensing can provide information on large area quickly and easily, so it is significant to do research on how to monitor soil moisture by remote sensing. This paper presents a method to assess soil moisture status using Landsat TM data over Three Gorges area in China based on TVDI. The potential of Temperature- Vegetation Dryness Index (TVDI) from Landsat TM data in assessing soil moisture was investigated in this region. After retrieving land surface temperature and vegetation index a TVDI model based on the features of Ts-NDVI space is established. And finally, soil moisture status is estimated according to TVDI. It shows that TVDI has the advantages of stability and high accuracy to estimating the soil moisture status.

Compact polarimetric synthetic aperture radar for monitoring soil moisture condition

NASA Astrophysics Data System (ADS)

Merzouki, A.; McNairn, H.; Powers, J.; Friesen, M.

2017-12-01

Coarse resolution soil moisture maps are currently operationally delivered by ESA's SMOS and NASA's SMAP passive microwaves sensors. Despite this evolution, operational soil moisture monitoring at the field scale remains challenging. A number of factors contribute to this challenge including the complexity of the retrieval that requires advanced SAR systems with enhanced temporal revisit capabilities. Since the launch of RADARSAT-2 in 2007, Agriculture and Agri-Food Canada (AAFC) has been evaluating the accuracy of these data for estimating surface soil moisture. Thus, a hybrid (multi-angle/multi-polarization) retrieval approach was found well suited for the planned RADARSAT Constellation Mission (RCM) considering the more frequent relook expected with the three satellite configuration. The purpose of this study is to evaluate the capability of C-band CP data to estimate soil moisture over agricultural fields, in anticipation of the launch of RCM. In this research we introduce a new CP approach based on the IEM and simulated RCM CP mode intensities from RADARSAT-2 images acquired at different dates. The accuracy of soil moisture retrieval from the proposed multi-polarization and hybrid methods will be contrasted with that from a more conventional quad-pol approach, and validated against in situ measurements by pooling data collected over AAFC test sites in Ontario, Manitoba and Saskatchewan, Canada.

Bridging the Global Precipitation and Soil Moisture Active Passive Missions: Variability of Microwave Surface Emissivity from In situ and Remote Sensing Perspectives

NASA Astrophysics Data System (ADS)

Zheng, Y.; Kirstetter, P.; Hong, Y.; Turk, J.

2016-12-01

The overland precipitation retrievals from satellite passive microwave (PMW) sensors such as the Global Precipitation Mission (GPM) microwave imager (GMI) are impacted by the land surface emissivity. The estimation of PMW emissivity faces challenges because it is highly variable under the influence of surface properties such as soil moisture, surface roughness and vegetation. This study proposes an improved quantitative understanding of the relationship between the emissivity and surface parameters. Surface parameter information is obtained through (i) in-situ measurements from the International Soil Moisture Network and (ii) satellite measurements from the Soil Moisture Active and Passive mission (SMAP) which provides global scale soil moisture estimates. The variation of emissivity is quantified with soil moisture, surface temperature and vegetation at various frequencies/polarization and over different types of land surfaces to sheds light into the processes governing the emission of the land. This analysis is used to estimate the emissivity under rainy conditions. The framework built with in-situ measurements serves as a benchmark for satellite-based analyses, which paves a way toward global scale emissivity estimates using SMAP.

Surface soil moisture retrievals from remote sensing: Current status, products & future trends

NASA Astrophysics Data System (ADS)

Petropoulos, George P.; Ireland, Gareth; Barrett, Brian

Advances in Earth Observation (EO) technology, particularly over the last two decades, have shown that soil moisture content (SMC) can be measured to some degree or other by all regions of the electromagnetic spectrum, and a variety of techniques have been proposed to facilitate this purpose. In this review we provide a synthesis of the efforts made during the last 20 years or so towards the estimation of surface SMC exploiting EO imagery, with a particular emphasis on retrievals from microwave sensors. Rather than replicating previous overview works, we provide a comprehensive and critical exploration of all the major approaches employed for retrieving SMC in a range of different global ecosystems. In this framework, we consider the newest techniques developed within optical and thermal infrared remote sensing, active and passive microwave domains, as well as assimilation or synergistic approaches. Future trends and prospects of EO for the accurate determination of SMC from space are subject to key challenges, some of which are identified and discussed within. It is evident from this review that there is potential for more accurate estimation of SMC exploiting EO technology, particularly so, by exploring the use of synergistic approaches between a variety of EO instruments. Given the importance of SMC in Earth's land surface interactions and to a large range of applications, one can appreciate that its accurate estimation is critical in addressing key scientific and practical challenges in today's world such as food security, sustainable planning and management of water resources. The launch of new, more sophisticated satellites strengthens the development of innovative research approaches and scientific inventions that will result in a range of pioneering and ground-breaking advancements in the retrievals of soil moisture from space.

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.

SMOS after 2 YEARS and a half in orbit

NASA Astrophysics Data System (ADS)

Kerr, Y.; Richaume, P.; Wigneron, J.-P.; Waldteufel, P.; Mecklenburg, S.; Cabot, F.; Boutin, J.; Font, J.; Reul, N.

2012-04-01

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) and ocean salinity. These two 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 in particular in improving models forecasts. The purpose of this communication is to present the mission results after more than two years in orbit as well as some outstanding results already obtained. A special attention will be devoted to level 2 products. Modeling multi-angular brightness temperatures is not straightforward. The radiative model transfer model L-MEB (L-band Microwave Emission) is used over land while different models with different approaches as to the modeling of sea surface roughness are used over ocean surfaces. Over land the approach is based on semi-empirical relationships, adapted to different type of surface. The model computes a dielectric constant leading to surface emissivity. Surface features (roughness, vegetation) are also considered in the models. However, considering SMOS spatial resolution a wide area is seen by the instrument with strong heterogeneity. The L2 soil moisture retrieval scheme takes this into account. Brightness temperatures are computed for every classes composing a working area. A weighted function is applied for the incidence angle and the antenna beam. Once the brightness temperature is computed for the entire working area, the minimizing process starts. If no soil moisture is derived (not attempted or process failed) a dielectric constant is still derived from an simplified modeled (the cardioid model). SMOS data enabled very quickly to infer Sea surface salinity fields. As salinity retrieval is quite challenging, retrieving it enable to assess very finely the characteristics of the complete system in terms of stability, drift etc. Some anomalies such as the ascending descending temperature differences, temporal drifts or land sea contamination were used to infer issues and improve data quality. The modeling has to account for several perturbing factors 'galactic reflection, sea state, atmospheric path and Faraday rotation etc…as the useful signal is quite small when compared to the perturbing factors impact as well as the instrument sensitivity. Over sea ice several studies showed that it was possible to infer thin ice (first year ice, 50 cm or less) from SMOS measurements. Other studies focused on the Antarctic plateau with also very interesting new results. This presentation will show in detail the SMOS in flight results. The retrieval schemes have been developed to reach science requirements, that is to derive the surface soil moisture over continental surface with an accuracy better than 0,04m3/m3. Over the ocean the goals are not yet satisfied but results are already getting close to the requirements.

Round Robin evaluation of soil moisture retrieval models for the MetOp-A ASCAT Instrument

NASA Astrophysics Data System (ADS)

Gruber, Alexander; Paloscia, Simonetta; Santi, Emanuele; Notarnicola, Claudia; Pasolli, Luca; Smolander, Tuomo; Pulliainen, Jouni; Mittelbach, Heidi; Dorigo, Wouter; Wagner, Wolfgang

2014-05-01

Global soil moisture observations are crucial to understand hydrologic processes, earth-atmosphere interactions and climate variability. ESA's Climate Change Initiative (CCI) project aims to create a global consistent long-term soil moisture data set based on the merging of the best available active and passive satellite-based microwave sensors and retrieval algorithms. Within the CCI, a Round Robin evaluation of existing retrieval algorithms for both active and passive instruments was carried out. In this study we present the comparison of five different retrieval algorithms covering three different modelling principles applied to active MetOp-A ASCAT L1 backscatter data. These models include statistical models (Bayesian Regression and Support Vector Regression, provided by the Institute for Applied Remote Sensing, Eurac Research Viale Druso, Italy, and an Artificial Neural Network, provided by the Institute of Applied Physics, CNR-IFAC, Italy), a semi-empirical model (provided by the Finnish Meteorological Institute), and a change detection model (provided by the Vienna University of Technology). The algorithms were applied on L1 backscatter data within the period of 2007-2011, resampled to a 12.5 km grid. The evaluation was performed over 75 globally distributed, quality controlled in situ stations drawn from the International Soil Moisture Network (ISMN) using surface soil moisture data from the Global Land Data Assimilation System (GLDAS-) Noah land surface model as second independent reference. The temporal correlation between the data sets was analyzed and random errors of the the different algorithms were estimated using the triple collocation method. Absolute soil moisture values as well as soil moisture anomalies were considered including both long-term anomalies from the mean seasonal cycle and short-term anomalies from a five weeks moving average window. Results show a very high agreement between all five algorithms for most stations. A slight vegetation dependency of the errors and a spatial decorrelation of the performance patterns of the different algorithms was found. We conclude that future research should focus on understanding, combining and exploiting the advantages of all available modelling approaches rather than trying to optimize one approach to fit every possible condition.

A Vegetation Correction Methodology for Time Series Based Soil Moisture Retrieval From C-band Radar Observations

NASA Technical Reports Server (NTRS)

Joseph, Alicia T.; O'Neil, P. E.; vanderVelde, R.; Gish, T.

2008-01-01

A methodology is presented to correct backscatter (sigma(sup 0)) observations for the effect of vegetation. The proposed methodology is based on the concept that the ratio of the surface scattering over the total amount of scattering (sigma(sup 0)(sub soil)/sigma(sup 0)) is only affected by the vegetation and can be described as a function of the vegetation water content. Backscatter observations sigma(sup 0) from the soil are not influenced by vegetation. Under bare soil conditions (sigma(sup 0)(sub soil)/sigma(sup 0)) equals 1. Under low to moderate biomass and soil moisture conditions, vegetation affects the observed sigma(sup 0) through absorption of the surface scattering and contribution of direct scattering by the vegetation itself. Therefore, the contribution of the surface scattering is smaller than the observed total amount of scattering and decreases as the biomass increases. For dense canopies scattering interactions between the soil surface and vegetation elements (e.g. leaves and stems) also become significant. Because these higher order scattering mechanisms are influenced by the soil surface, an increase in (sigma(sup 0)(sub soil)/sigma(sup 0)) may be observed as the biomass increases under densely vegetated conditions. This methodology is applied within the framework of time series based approach for the retrieval of soil moisture. The data set used for this investigation has been collected during a campaign conducted at USDA's Optimizing Production Inputs for Economic and Environmental Enhancement OPE-3) experimental site in Beltsville, Maryland (USA). This campaign took place during the corn growth cycle from May 10th to 0ctober 2nd, 2002. In this period the corn crops reached a vegetation water content of 5.1 kg m(exp -2) at peak biomass and a soil moisture range varying between 0.00 to 0.26 cubic cm/cubic cm. One of the deployed microwave instruments operated was a multi-frequency (C-band (4.75 GHz) and L-band (1.6 GHz)) quad-polarized (HH, HV, VV, VH) radar which was mounted on a 20 meter long boom. In the OPE-3 field campaign, radar observations were collected once a week at nominal times of 8 am, 10 am, 12 noon and 2 pm. During each data run the radar acquired sixty independent measurements within an azimuth of 120 degrees from a boom height of 12.2 m and at three different incidence angles (15,35, and 55 degrees). The sixty observations were averaged to provide one backscatter value for the study area and its accuracy is estimated to be 51.0 dB. For this investigation the C-band observations have been used. Application of the proposed methodology to the selected data set showed a well-defined relationship between (sigma(sup 0)(sub soil)/sigma(sup 0)) and the vegetation water content. It is found that this relationship can be described with two experimentally determined parameters, which depend on the sensing configuration (e.g. incidence angle and polarization). Through application of the proposed vegetation correction methodology and the obtained parameterizations, the soil moisture retrieval accuracy within the framework of a time series based approach is improved from 0.033 to 0.032 cubic cm/cubic cm, from 0.049 to 0.033 cubic cm/cubic cm and from 0.079 to 0.047 cubic cm/cubic cm for incidence angles of 15,35 and 55 degrees, respectively. Improvement in soil moisture retrieval due to vegetation correction is greater at larger incidence angles (due to the increased path length and larger vegetation effects on the surface signal at the larger angles).

Passive Microwave Remote Sensing of Soil Moisture

NASA Technical Reports Server (NTRS)

Njoku, Eni G.; Entekhabi, Dara

1996-01-01

Microwave remote sensing provides a unique capability for direct observation of soil moisture. Remote measurements from space afford the possibility of obtaining frequent, global sampling of soil moisture over a large fraction of the Earth's land surface. Microwave measurements have the benefit of being largely unaffected by cloud cover and variable surface solar illumination, but accurate soil moisture estimates are limited to regions that have either bare soil or low to moderate amounts of vegetation cover. A particular advantage of passive microwave sensors is that in the absence of significant vegetation cover soil moisture is the dominant effect on the received signal. The spatial resolutions of passive Microwave soil moisture sensors currently considered for space operation are in the range 10-20 km. The most useful frequency range for soil moisture sensing is 1-5 GHz. System design considerations include optimum choice of frequencies, polarizations, and scanning configurations, based on trade-offs between requirements for high vegetation penetration capability, freedom from electromagnetic interference, manageable antenna size and complexity, and the requirement that a sufficient number of information channels be available to correct for perturbing geophysical effects. This paper outlines the basic principles of the passive microwave technique for soil moisture sensing, and reviews briefly the status of current retrieval methods. Particularly promising are methods for optimally assimilating passive microwave data into hydrologic models. Further studies are needed to investigate the effects on microwave observations of within-footprint spatial heterogeneity of vegetation cover and subsurface soil characteristics, and to assess the limitations imposed by heterogeneity on the retrievability of large-scale soil moisture information from remote observations.

Retrieval of BRDF/Albedo by the Angular and Spectral Kernel Driven Model with Global Soil and Leaf Optical Database

NASA Astrophysics Data System (ADS)

Zhang, Y.; Wen, J.; Xiao, Q.; You, D.

2016-12-01

Operational algorithms for land surface BRDF/Albedo products are mainly developed from kernel-driven model, combining atmospherically corrected, multidate, multiband surface reflectance to extract BRDF parameters. The Angular and Spectral Kernel Driven model (ASK model), which incorporates the component spectra as a priori knowledge, provides a potential way to make full use of the multi-sensor data with multispectral information and accumulated observations. However, the ASK model is still not feasible for global BRDF/Albedo inversions due to the lack of sufficient field measurements of component spectra at the large scale. This research outlines a parameterization scheme on the component spectra for global scale BRDF/Albedo inversions in the frame of ASK. The parameter γ(λ) can be derived from the ratio of the leaf reflectance and soil reflectance, supported by globally distributed soil spectral library, ANGERS and LOPEX leaf optical properties database. To consider the intrinsic variability in both the land cover and spectral dimension, the mean and standard deviation of γ(λ) for 28 soil units and 4 leaf types in seven MODIS bands were calculated, with a world soil map used for global BRDF/Albedo products retrieval. Compared to the retrievals from BRF datasets simulated by the PROSAIL model, ASK model shows an acceptable accuracy on the parameterization strategy, with the RMSE 0.007 higher at most than inversion by true component spectra. The results indicate that the classification on ratio contributed to capture the spectral characteristics in BBRDF/Albedo retrieval, whereas the ratio range should be controlled within 8% in each band. Ground-based measurements in Heihe river basin were used to validate the accuracy of the improved ASK model, and the generated broadband albedo products shows good agreement with in situ data, which suggests that the improvement of the component spectra on the ASK model has potential for global scale BRDF/Albedo inversions.

Soil water content spatial pattern estimated by thermal inertia from air-borne sensors

NASA Astrophysics Data System (ADS)

Coppola, Antonio; Basile, Angelo; Esposito, Marco; Menenti, Massimo; Buonanno, Maurizio

2010-05-01

Remote sensing of soil water content from air- or space-borne platforms offer the possibility to provide large spatial coverage and temporal continuity. The water content can be actually monitored in a thin soil layer, usually up to a depth of 0.05m below the soil surface. To the contrary, difficulties arise in the estimation of the water content storage along the soil profile and its spatial (horizontal) distribution, which are closely connected to soil hydraulic properties and their spatial distribution. A promising approach for estimating soil water contents profiles is the integration of remote sensing of surface water content and hydrological modeling. A major goal of the scientific group is to develop a practical and robust procedure for estimating water contents throughout the soil profile from surface water content. As a first step, in this work, we will show some preliminary results from aircraft images analysis and their validation by field campaigns data. The data extracted from the airborne sensors provided the opportunity of retrieving land surface temperatures with a very high spatial resolution. The surface water content pattern, as deduced by the thermal inertia estimations, was compared to the surface water contents maps measured in situ by time domain reflectometry-based probes.

Microwave Brightness Of Land Surfaces From Outer Space

NASA Technical Reports Server (NTRS)

Kerr, Yann H.; Njoku, Eni G.

1991-01-01

Mathematical model approximates microwave radiation emitted by land surfaces traveling to microwave radiometer in outer space. Applied to measurements made by Scanning Multichannel Microwave Radiometer (SMMR). Developed for interpretation of microwave imagery of Earth to obtain distributions of various chemical, physical, and biological characteristics across its surface. Intended primarily for use in mapping moisture content of soil and fraction of Earth covered by vegetation. Advanced Very-High-Resolution Radiometer (AVHRR), provides additional information on vegetative cover, thereby making possible retrieval of soil-moisture values from SMMR measurements. Possible to monitor changes of land surface during intervals of 5 to 10 years, providing significant data for mathematical models of evolution of climate.

Inter-Comparison of SMOS and Aquarius Sea Surface Salinity: Effects of the Dielectric Constant and Vicarious Calibration

NASA Technical Reports Server (NTRS)

Dinnat, Emmanuel P.; Boutin, Jacqueline; Yin, Xiaobin; Le Vine, David M.

2014-01-01

Two spaceborne instruments share the scientific objective of mapping the global Sea Surface Salinity (SSS). ESA's Soil Moisture and Ocean Salinity (SMOS) and NASA's Aquarius use L-band (1.4 GHz) radiometry to retrieve SSS. We find that SSS retrieved by SMOS is generally lower than SSS retrieved by Aquarius, except for very cold waters where SMOS SSS is higher overall. The spatial distribution of the differences in SSS is similar to the distribution of sea surface temperature. There are several differences in the retrieval algorithm that could explain the observed SSS differences. We assess the impact of the dielectric constant model and the ancillary sea surface salinity used by both missions for calibrating the radiometers and retrieving SSS. The differences in dielectric constant model produce differences in SSS of the order of 0.3 psu and exhibit a dependence on latitude and temperature. We use comparisons with the Argo in situ data to assess the performances of the model in various regions of the globe. Finally, the differences in the ancillary sea surface salinity products used to perform the vicarious calibration of both instruments are relatively small (0.1 psu), but not negligible considering the requirements for spaceborne remote sensing of SSS.

Enhancing USDA's Retrospective Analog Year Analyses Using NASA Satellite Precipitation and Soil Moisture Data

NASA Astrophysics Data System (ADS)

Teng, W. L.; Shannon, H. D.

2013-12-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted, including maps, charts, and time series of recent weather, climate, and crop observations; numerical output from weather and crop models; and reports from the press, USDA attachés, and foreign governments. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. Because both the amount and timing of precipitation significantly affect crop yields, WAOB has often, as part of its operational process, used historical time series of surface-based precipitation observations to visually identify growing seasons with similar (analog) weather patterns as, and help estimate crop yields for, the current growing season. As part of a larger effort to improve WAOB estimates by integrating NASA remote sensing observations and research results into WAOB's decision-making environment, a more rigorous, statistical method for identifying analog years was developed. This method, termed the analog index (AI), is based on the Nash-Sutcliffe model efficiency coefficient. The AI was computed for five study areas and six growing seasons of data analyzed (2003-2007 as potential analog years and 2008 as the target year). Previously reported results compared the performance of AI for time series derived from surface-based observations vs. satellite-retrieved precipitation data. Those results showed that, for all five areas, crop yield estimates derived from satellite-retrieved precipitation data are closer to measured yields than are estimates derived from surface-based precipitation observations. Subsequent work has compared the relative performance of AI for time series derived from satellite-retrieved surface soil moisture data and from root zone soil moisture derived from the assimilation of surface soil moisture data into a land surface model. These results, which also showed the potential benefits of satellite data for analog year analyses, will be presented.

A new calibration of the effective scattering albedo and soil roughness parameters in the SMOS SM retrieval algorithm

NASA Astrophysics Data System (ADS)

Fernandez-Moran, R.; Wigneron, J.-P.; De Lannoy, G.; Lopez-Baeza, E.; Parrens, M.; Mialon, A.; Mahmoodi, A.; Al-Yaari, A.; Bircher, S.; Al Bitar, A.; Richaume, P.; Kerr, Y.

2017-10-01

This study focuses on the calibration of the effective vegetation scattering albedo (ω) and surface soil roughness parameters (HR, and NRp, p = H,V) in the Soil Moisture (SM) retrieval from L-band passive microwave observations using the L-band Microwave Emission of the Biosphere (L-MEB) model. In the current Soil Moisture and Ocean Salinity (SMOS) Level 2 (L2), v620, and Level 3 (L3), v300, SM retrieval algorithms, low vegetated areas are parameterized by ω = 0 and HR = 0.1, whereas values of ω = 0.06 - 0.08 and HR = 0.3 are used for forests. Several parameterizations of the vegetation and soil roughness parameters (ω, HR and NRp, p = H,V) were tested in this study, treating SMOS SM retrievals as homogeneous over each pixel instead of retrieving SM over a representative fraction of the pixel, as implemented in the operational SMOS L2 and L3 algorithms. Globally-constant values of ω = 0.10, HR = 0.4 and NRp = -1 (p = H,V) were found to yield SM retrievals that compared best with in situ SM data measured at many sites worldwide from the International Soil Moisture Network (ISMN). The calibration was repeated for collections of in situ sites classified in different land cover categories based on the International Geosphere-Biosphere Programme (IGBP) scheme. Depending on the IGBP land cover class, values of ω and HR varied, respectively, in the range 0.08-0.12 and 0.1-0.5. A validation exercise based on in situ measurements confirmed that using either a global or an IGBP-based calibration, there was an improvement in the accuracy of the SM retrievals compared to the SMOS L3 SM product considering all statistical metrics (R = 0.61, bias = -0.019 m3 m-3, ubRMSE = 0.062 m3 m-3 for the IGBP-based calibration; against R = 0.54, bias = -0.034 m3 m-3 and ubRMSE = 0.070 m3 m-3 for the SMOS L3 SM product). This result is a key step in the calibration of the roughness and vegetation parameters in the operational SMOS retrieval algorithm. The approach presented here is the core of a new forthcoming SMOS optimized SM product.

Soil Moisture Estimate under Forest using a Semi-empirical Model at P-Band

NASA Astrophysics Data System (ADS)

Truong-Loi, M.; Saatchi, S.; Jaruwatanadilok, S.

2013-12-01

In this paper we show the potential of a semi-empirical algorithm to retrieve soil moisture under forests using P-band polarimetric SAR data. In past decades, several remote sensing techniques have been developed to estimate the surface soil moisture. In most studies associated with radar sensing of soil moisture, the proposed algorithms are focused on bare or sparsely vegetated surfaces where the effect of vegetation can be ignored. At long wavelengths such as L-band, empirical or physical models such as the Small Perturbation Model (SPM) provide reasonable estimates of surface soil moisture at depths of 0-5cm. However for densely covered vegetated surfaces such as forests, the problem becomes more challenging because the vegetation canopy is a complex scattering environment. For this reason there have been only few studies focusing on retrieving soil moisture under vegetation canopy in the literature. Moghaddam et al. developed an algorithm to estimate soil moisture under a boreal forest using L- and P-band SAR data. For their studied area, double-bounce between trunks and ground appear to be the most important scattering mechanism. Thereby, they implemented parametric models of radar backscatter for double-bounce using simulations of a numerical forest scattering model. Hajnsek et al. showed the potential of estimating the soil moisture under agricultural vegetation using L-band polarimetric SAR data and using polarimetric-decomposition techniques to remove the vegetation layer. Here we use an approach based on physical formulation of dominant scattering mechanisms and three parameters that integrates the vegetation and soil effects at long wavelengths. The algorithm is a simplification of a 3-D coherent model of forest canopy based on the Distorted Born Approximation (DBA). The simplified model has three equations and three unknowns, preserving the three dominant scattering mechanisms of volume, double-bounce and surface for three polarized backscattering coefficients: σHH, σVV and σHV. The inversion process, which is not an ill-posed problem, uses the non-linear optimization method of Levenberg-Marquardt and estimates the three model parameters: vegetation aboveground biomass, average soil moisture and surface roughness. The model analytical formulation will be first recalled and sensitivity analyses will be shown. Then some results obtained with real SAR data will be presented and compared to ground estimates.

Evaluation of Remote Sensing and Hydrological Model Based Soil Moisture Datasets in Drought Perspective

NASA Astrophysics Data System (ADS)

Hüsami Afşar, M.; Bulut, B.; Yilmaz, M. T.

2017-12-01

Soil moisture is one of the fundamental parameters of the environment that plays a major role in carbon, energy, and water cycles. Spatial distribution and temporal changes of soil moisture is one of the important components in climatic, ecological and natural hazards at global, regional and local levels scales. Therefore retrieval of soil moisture datasets has a great importance in these studies. Given soil moisture can be retrieved through different platforms (i.e., in-situ measurements, numerical modeling, and remote sensing) for the same location and time period, it is often desirable to evaluate these different datasets to assign the most accurate estimates for different purposes. During last decades, efforts have been given to provide evaluations about different soil moisture products based on various statistical analysis of the soil moisture time series (i.e., comparison of correlation, bias, and their error standard deviation). On the other hand, there is still need for the comparisons of the soil moisture products in drought analysis context. In this study, LPRM and NOAH Land Surface Model soil moisture datasets are investigated in drought analysis context using station-based watershed average datasets obtained over four USDA ARS watersheds as ground truth. Here, the drought analysis are performed using the standardized soil moisture datasets (i.e., zero mean and one standard deviation) while the droughts are defined as consecutive negative anomalies less than -1 for longer than 3 months duration. Accordingly, the drought characteristics (duration and severity) and false alarm and hit/miss ratios of LPRM and NOAH datasets are validated using station-based datasets as ground truth. Results showed that although the NOAH soil moisture products have better correlations, LPRM based soil moisture retrievals show better consistency in drought analysis. This project is supported by TUBITAK Project number 114Y676.

A Parameterized Inversion Model for Soil Moisture and Biomass from Polarimetric Backscattering Coefficients

NASA Technical Reports Server (NTRS)

Truong-Loi, My-Linh; Saatchi, Sassan; Jaruwatanadilok, Sermsak

2012-01-01

A semi-empirical algorithm for the retrieval of soil moisture, root mean square (RMS) height and biomass from polarimetric SAR data is explained and analyzed in this paper. The algorithm is a simplification of the distorted Born model. It takes into account the physical scattering phenomenon and has three major components: volume, double-bounce and surface. This simplified model uses the three backscattering coefficients ( sigma HH, sigma HV and sigma vv) at low-frequency (P-band). The inversion process uses the Levenberg-Marquardt non-linear least-squares method to estimate the structural parameters. The estimation process is entirely explained in this paper, from initialization of the unknowns to retrievals. A sensitivity analysis is also done where the initial values in the inversion process are varying randomly. The results show that the inversion process is not really sensitive to initial values and a major part of the retrievals has a root-mean-square error lower than 5% for soil moisture, 24 Mg/ha for biomass and 0.49 cm for roughness, considering a soil moisture of 40%, roughness equal to 3cm and biomass varying from 0 to 500 Mg/ha with a mean of 161 Mg/ha

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).

Ground-Based Passive Microwave Remote Sensing Observations of Soil Moisture at S and L Band with Insight into Measurement Accuracy

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Crosson, William L.; Jackson, Thomas J.; Manu, Andrew; Tsegaye, Teferi D.; Soman, V.; Arnold, James E. (Technical Monitor)

2001-01-01

Accurate estimates of spatially heterogeneous algorithm variables and parameters are required in determining the spatial distribution of soil moisture using radiometer data from aircraft and satellites. A ground-based experiment in passive microwave remote sensing of soil moisture was conducted in Huntsville, Alabama from July 1-14, 1996 to study retrieval algorithms and their sensitivity to variable and parameter specification. With high temporal frequency observations at S and L band, we were able to observe large scale moisture changes following irrigation and rainfall events, as well as diurnal behavior of surface moisture among three plots, one bare, one covered with short grass and another covered with alfalfa. The L band emitting depth was determined to be on the order of 0-3 or 0-5 cm below 0.30 cubic centimeter/cubic centimeter with an indication of a shallower emitting depth at higher moisture values. Surface moisture behavior was less apparent on the vegetated plots than it was on the bare plot because there was less moisture gradient and because of difficulty in determining vegetation water content and estimating the vegetation b parameter. Discrepancies between remotely sensed and gravimetric, soil moisture estimates on the vegetated plots point to an incomplete understanding of the requirements needed to correct for the effects of vegetation attenuation. Quantifying the uncertainty in moisture estimates is vital if applications are to utilize remotely-sensed soil moisture data. Computations based only on the real part of the complex dielectric constant and/or an alternative dielectric mixing model contribute a relatively insignificant amount of uncertainty to estimates of soil moisture. Rather, the retrieval algorithm is much more sensitive to soil properties, surface roughness and biomass.

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.

Synergistic estimation of surface parameters from jointly using optical and microwave observations in EOLDAS

NASA Astrophysics Data System (ADS)

Timmermans, Joris; Gomez-Dans, Jose; Lewis, Philip; Loew, Alexander; Schlenz, Florian

2017-04-01

The large amount of remote sensing data nowadays available provides a huge potential for monitoring crop development, drought conditions and water efficiency. This potential however not been realized yet because algorithms for land surface parameter retrieval mostly use data from only a single sensor. Consequently products that combine different low-level observations from different sensors are hard to find. The lack of synergistic retrieval is caused because it is easier to focus on single sensor types/footprints and temporal observation times, than to find a way to compensate for differences. Different sensor types (microwave/optical) require different radiative transfer (RT) models and also require consistency between the models to have any impact on the retrieval of soil moisture by a microwave instrument. Varying spatial footprints require first proper collocation of the data before one can scale between different resolutions. Considering these problems, merging optical and microwave observations have not been performed yet. The goal of this research was to investigate the potential of integrating optical and microwave RT models within the Earth Observation Land Data Assimilation System (EOLDAS) synergistically to derive biophysical parameters. This system uses a Bayesian data assimilation approach together with observation operators such as the PROSAIL model to estimate land surface parameters. For the purpose of enabling the system to integrate passive microwave radiation (from an ELBARRA II passive microwave radiometer), the Community Microwave Emission Model (CMEM) RT-model, was integrated within the EOLDAS system. In order to quantify the potential, a variety of land surface parameters was chosen to be retrieved from the system, in particular variables that a) impact only optical RT (such as leaf water content and leaf dry matter), b) only impact the microwave RT (such as soil moisture and soil temperature), and c) Leaf Area Index (LAI) that impacts both optical and microwave RT. The results show a high potential when both optical and microwave are used independently. Using only RapidEye only with SAIL RT model, LAI was estimated with R=0.68 with p=0.09, although estimating leaf water content and dry matter showed lower correlations |R|<0.4. The results for retrieving soil temperature and leaf area index retrievals using only (passive microwave) Elbarra-II observations were good with respectively R=[0.85, 0.79], P=[0.0, 0.0], when focusing on dry-spells (of at least 9 days) only the results respectively [R=0.73, and P=0.0], and R=0.89 and R=0.77 for respectively the trend and anomalies. Synergistically using optical and microwave shows also a good potential. This scenario shows that absolute errors improved (with RMSE=1.22 and S=0.89), but with degrading correlations (R=0.59 and P=0.04); the sparse optical observations only improved part of the temporal domain. However in general the synergistic retrieval showed good potential; microwave data provides better information concerning the overall trend of the retrieved LAI due to the regular acquisitions, while optical data provides better information concerning the absolute values of the LAI.

Multi-Frequency Investigation into Scattering from Vegetation over the Growth Cycle

NASA Technical Reports Server (NTRS)

Lang, R. H.; Kurum, M.; O'Neill, P. E.; Joseph, A. T.; Deshpande, M. D.; Cosh, M. H.

2016-01-01

In this investigation, we aim to collect and use time-series multi-frequency microwave data over winter wheat during entire growth cycle to characterize vegetation dynamics and to quantify its effects on soil moisture retrievals. We plan to incorporate C-band radar and VHF receiver within the existing L-band radarradiometer system called ComRAD (SMAPs ground based simulator). With C-bands ability to sense vegetation details and VHFs root-zone soil moisture within ComRADs footprint, we will be able to test our discrete scatterer vegetation models and parameters at various surface conditions. The purpose of this study is to determine optical depth and effective scattering albedo of vegetation of a given type (i.e. winter wheat) at various stages of growth that are need to refine soil moisture retrieval algorithms being developed for the SMAP mission.

Applications of the Atmosphere-Land Exchange Inverse (ALEXI) Model and Highlights of Current Projects

NASA Astrophysics Data System (ADS)

Hain, C.; Mecikalski, J. R.; Schultz, L. A.

2009-12-01

The Atmosphere-Land Exchange Inverse (ALEXI) model was developed as an auxiliary means for estimating surface fluxes over large regions primarily using remote-sensing data. The model is unique in that no information regarding antecedent precipitation or moisture storage capacity is required - the surface moisture status is deduced from a radiometric temperature change signal. ALEXI uses the available water fraction (fAW) as a proxy for soil moisture conditions. Combining fAW with ALEXI’s ability to provide valuable information about the partitioning of the surface energy budget, which can dictated largely by soil moisture conditions, accommodates the retrieval of an average fAW from the surface to the rooting depth of the active vegetation. Using this approach has many advantages over traditional energy flux and soil moisture measurements (towers with limited range and large monetary/personnel costs) or approximation methods (parametrization of the relationship between available water and soil moisture) in that data is available both spatially and temporal over a large, non-homogeneous, sometimes densely vegetated area. Being satellite based, the model can be run anywhere thermal infrared satellite information is available. The current ALEXI climatology dates back to March 2000 and covers the continental U.S. Examples of projects underway using the ALEXI soil moisture retrieval tools include the Southern Florida Water Management Project; NASA’s Project Nile, which proposes to acquire hydrological information for the water management in the Nile River basin; and a USDA pro ject to expand the ALEXI framework to include Europe and parts of northern Africa using data from the European geostationary satellites, specifically the Meteosat Second Generation (MSG) Series.

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.

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.

Drought monitoring with soil moisture active passive (SMAP) measurements

NASA Astrophysics Data System (ADS)

Mishra, Ashok; Vu, Tue; Veettil, Anoop Valiya; Entekhabi, Dara

2017-09-01

Recent launch of space-borne systems to estimate surface soil moisture may expand the capability to map soil moisture deficit and drought with global coverage. In this study, we use Soil Moisture Active Passive (SMAP) soil moisture geophysical retrieval products from passive L-band radiometer to evaluate its applicability to forming agricultural drought indices. Agricultural drought is quantified using the Soil Water Deficit Index (SWDI) based on SMAP and soil properties (field capacity and available water content) information. The soil properties are computed using pedo-transfer function with soil characteristics derived from Harmonized World Soil Database. The SMAP soil moisture product needs to be rescaled to be compatible with the soil parameters derived from the in situ stations. In most locations, the rescaled SMAP information captured the dynamics of in situ soil moisture well and shows the expected lag between accumulations of precipitation and delayed increased in surface soil moisture. However, the SMAP soil moisture itself does not reveal the drought information. Therefore, the SMAP based SWDI (SMAP_SWDI) was computed to improve agriculture drought monitoring by using the latest soil moisture retrieval satellite technology. The formulation of SWDI does not depend on longer data and it will overcome the limited (short) length of SMAP data for agricultural drought studies. The SMAP_SWDI is further compared with in situ Atmospheric Water Deficit (AWD) Index. The comparison shows close agreement between SMAP_SWDI and AWD in drought monitoring over Contiguous United States (CONUS), especially in terms of drought characteristics. The SMAP_SWDI was used to construct drought maps for CONUS and compared with well-known drought indices, such as, AWD, Palmer Z-Index, sc-PDSI and SPEI. Overall the SMAP_SWDI is an effective agricultural drought indicator and it provides continuity and introduces new spatial mapping capability for drought monitoring. As an agricultural drought index, SMAP_SWDI has potential to capture short term moisture information similar to AWD and related drought indices.

Connecting Satellite Observations with Water Cycle Variables Through Land Data Assimilation: Examples Using the NASA GEOS-5 LDAS

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; De Lannoy, Gabrielle J. M.; Forman, Barton A.; Draper, Clara S.; Liu, Qing

2013-01-01

A land data assimilation system (LDAS) can merge satellite observations (or retrievals) of land surface hydrological conditions, including soil moisture, snow, and terrestrial water storage (TWS), into a numerical model of land surface processes. In theory, the output from such a system is superior to estimates based on the observations or the model alone, thereby enhancing our ability to understand, monitor, and predict key elements of the terrestrial water cycle. In practice, however, satellite observations do not correspond directly to the water cycle variables of interest. The present paper addresses various aspects of this seeming mismatch using examples drawn from recent research with the ensemble-based NASA GEOS-5 LDAS. These aspects include (1) the assimilation of coarse-scale observations into higher-resolution land surface models, (2) the partitioning of satellite observations (such as TWS retrievals) into their constituent water cycle components, (3) the forward modeling of microwave brightness temperatures over land for radiance-based soil moisture and snow assimilation, and (4) the selection of the most relevant types of observations for the analysis of a specific water cycle variable that is not observed (such as root zone soil moisture). The solution to these challenges involves the careful construction of an observation operator that maps from the land surface model variables of interest to the space of the assimilated observations.

Dual Assimilation of Microwave and Thermal-Infrared Satellite Observations of Soil Moisture into NLDAS for Improved Drought Monitoring

NASA Astrophysics Data System (ADS)

Hain, C.; Crow, W. T.; Anderson, M. C.; Zhan, X.; Wardlow, B.; Svoboda, M. D.; Mecikalski, J. R.

2011-12-01

Our research group is currently developing an operational data assimilation (DA) system for the optimal assimilation of thermal infrared (TIR) and microwave (MV) soil moisture (SM) and insertion of near real-time green vegetation fraction (GVF) into the Noah land-surface model component of the National Land Data Assimilation System (NLDAS). NLDAS produces the hydrologic products (e.g. soil moisture, evapotranspiration, and runoff) used by NCEP for operational drought monitoring, but these products are sensitive to model input errors in soil texture (affecting infiltration rates) and prescribed precipitation rates. Periodic updates of SM state variables in LSMs achieved by assimilating diagnostic moisture information retrieved using satellite remote sensing have been shown to compensate for model errors and result in improved hydrologic output. The work proposed here will build on a project currently funded under the Climate Test Bed Program entitled "A GOES Thermal-Based Drought Early Warning Index for NIDIS", which is developing an operational TIR SM index (Evaporative Stress Index; ESI) based on maps of the ratio of actual to potential ET (fPET) generated with the Atmosphere-Land Exchange Inverse (ALEXI) surface energy balance algorithm. The research team has demonstrated that diagnostic information about SM and evapotranspiration (ET) from MW and TIR remote sensing can significantly reduce SM drifts in LSMs such as Noah. The two different SM retrievals have been shown to be quite complementary: TIR provides relatively high spatial (down to 100 m) and low temporal resolution (due to cloud cover) retrievals over a wide range of GVF, while MW provides relatively low spatial (25 to 60 km) and high temporal resolution (can retrieve through cloud cover), but only over areas with low GVF. Furthermore, MW retrievals are sensitive to SM only in the first few centimeters of the soil profile, while TIR provides information about SM conditions integrated over the full root-zone, reflected in the observed canopy temperature. The added value of TIR over MW alone is most significant in areas of moderate to dense vegetation cover where MW retrievals have very little sensitivity to SM at any depth. Finally, climatological estimates of GVF currently used in the operational NLDAS are not always representative of observed seasonal and intra-seasonal GVF conditions, especially in regions experiencing drought conditions. A detailed methodology of the assimilation system will be presented along with an analysis of initial results, with an emphasis on comparisons with in-situ SM observations and standard drought metrics.

Retrieve sea surface salinity using principal component regression model based on SMOS satellite data

NASA Astrophysics Data System (ADS)

Zhao, Hong; Li, Changjun; Li, Hongping; Lv, Kebo; Zhao, Qinghui

2016-06-01

The sea surface salinity (SSS) is a key parameter in monitoring ocean states. Observing SSS can promote the understanding of global water cycle. This paper provides a new approach for retrieving sea surface salinity from Soil Moisture and Ocean Salinity (SMOS) satellite data. Based on the principal component regression (PCR) model, SSS can also be retrieved from the brightness temperature data of SMOS L2 measurements and Auxiliary data. 26 pair matchup data is used in model validation for the South China Sea (in the area of 4°-25°N, 105°-125°E). The RMSE value of PCR model retrieved SSS reaches 0.37 psu (practical salinity units) and the RMSE of SMOS SSS1 is 1.65 psu when compared with in-situ SSS. The corresponding Argo daily salinity data during April to June 2013 is also used in our validation with RMSE value 0.46 psu compared to 1.82 psu for daily averaged SMOS L2 products. This indicates that the PCR model is valid and may provide us with a good approach for retrieving SSS from SMOS satellite data.

Microwave Soil Moisture Retrieval Under Trees

NASA Technical Reports Server (NTRS)

O'Neill, P.; Lang, R.; Kurum, M.; Joseph, A.; Jackson, T.; Cosh, M.

2008-01-01

Soil moisture is recognized as an important component of the water, energy, and carbon cycles at the interface between the Earth's surface and atmosphere. Current baseline soil moisture retrieval algorithms for microwave space missions have been developed and validated only over grasslands, agricultural crops, and generally light to moderate vegetation. Tree areas have commonly been excluded from operational soil moisture retrieval plans due to the large expected impact of trees on masking the microwave response to the underlying soil moisture. Our understanding of the microwave properties of trees of various sizes and their effect on soil moisture retrieval algorithms at L band is presently limited, although research efforts are ongoing in Europe, the United States, and elsewhere to remedy this situation. As part of this research, a coordinated sequence of field measurements involving the ComRAD (for Combined Radar/Radiometer) active/passive microwave truck instrument system has been undertaken. Jointly developed and operated by NASA Goddard Space Flight Center and George Washington University, ComRAD consists of dual-polarized 1.4 GHz total-power radiometers (LH, LV) and a quad-polarized 1.25 GHz L band radar sharing a single parabolic dish antenna with a novel broadband stacked patch dual-polarized feed, a quad-polarized 4.75 GHz C band radar, and a single channel 10 GHz XHH radar. The instruments are deployed on a mobile truck with an 19-m hydraulic boom and share common control software; real-time calibrated signals, and the capability for automated data collection for unattended operation. Most microwave soil moisture retrieval algorithms developed for use at L band frequencies are based on the tau-omega model, a simplified zero-order radiative transfer approach where scattering is largely ignored and vegetation canopies are generally treated as a bulk attenuating layer. In this approach, vegetation effects are parameterized by tau and omega, the microwave vegetation opacity and single scattering albedo. One goal of our current research is to determine whether the tau-omega model can work for tree canopies given the increased scatter from trees compared to grasses and crops, and. if so, what are effective values for tau and omega for trees.

Utilization of Ancillary Data Sets for Conceptual SMAP Mission Algorithm Development and Product Generation

NASA Technical Reports Server (NTRS)

O'Neill, P.; Podest, E.

2011-01-01

The planned Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey, Earth Science and Applications from Space: National Imperatives for the Next Decade and Beyond [1]. Scheduled to launch late in 2014, the proposed SMAP mission would provide high resolution and frequent revisit global mapping of soil moisture and freeze/thaw state, utilizing enhanced Radio Frequency Interference (RFI) mitigation approaches to collect new measurements of the hydrological condition of the Earth's surface. The SMAP instrument design incorporates an L-band radar (3 km) and an L band radiometer (40 km) sharing a single 6-meter rotating mesh antenna to provide measurements of soil moisture and landscape freeze/thaw state [2]. These observations would (1) improve our understanding of linkages between the Earth's water, energy, and carbon cycles, (2) benefit many application areas including numerical weather and climate prediction, flood and drought monitoring, agricultural productivity, human health, and national security, (3) help to address priority questions on climate change, and (4) potentially provide continuity with brightness temperature and soil moisture measurements from ESA's SMOS (Soil Moisture Ocean Salinity) and NASA's Aquarius missions. In the planned SMAP mission prelaunch time frame, baseline algorithms are being developed for generating (1) soil moisture products both from radiometer measurements on a 36 km grid and from combined radar/radiometer measurements on a 9 km grid, and (2) freeze/thaw products from radar measurements on a 3 km grid. These retrieval algorithms need a variety of global ancillary data, both static and dynamic, to run the retrieval models, constrain the retrievals, and provide flags for indicating retrieval quality. The choice of which ancillary dataset to use for a particular SMAP product would be based on a number of factors, including its availability and ease of use, its inherent error and resulting impact on the overall soil moisture or freeze/thaw retrieval accuracy, and its compatibility with similar choices made by the SMOS mission. All decisions regarding SMAP ancillary data sources would be fully documented by the SMAP Project and made available to the user community.

A Round Robin evaluation of AMSR-E soil moisture retrievals

NASA Astrophysics Data System (ADS)

Mittelbach, Heidi; Hirschi, Martin; Nicolai-Shaw, Nadine; Gruber, Alexander; Dorigo, Wouter; de Jeu, Richard; Parinussa, Robert; Jones, Lucas A.; Wagner, Wolfgang; Seneviratne, Sonia I.

2014-05-01

Large-scale and long-term soil moisture observations based on remote sensing are promising data sets to investigate and understand various processes of the climate system including the water and biochemical cycles. Currently, the ESA Climate Change Initiative for soil moisture develops and evaluates a consistent global long-term soil moisture data set, which is based on merging passive and active remotely sensed soil moisture. Within this project an inter-comparison of algorithms for AMSR-E and ASCAT Level 2 products was conducted separately to assess the performance of different retrieval algorithms. Here we present the inter-comparison of AMSR-E Level 2 soil moisture products. These include the public data sets from University of Montana (UMT), Japan Aerospace and Space Exploration Agency (JAXA), VU University of Amsterdam (VUA; two algorithms) and National Aeronautics and Space Administration (NASA). All participating algorithms are applied to the same AMSR-E Level 1 data set. Ascending and descending paths of scaled surface soil moisture are considered and evaluated separately in daily and monthly resolution over the 2007-2011 time period. Absolute values of soil moisture as well as their long-term anomalies (i.e. removing the mean seasonal cycle) and short-term anomalies (i.e. removing a five weeks moving average) are evaluated. The evaluation is based on conventional measures like correlation and unbiased root-mean-square differences as well as on the application of the triple collocation method. As reference data set, surface soil moisture of 75 quality controlled soil moisture sites from the International Soil Moisture Network (ISMN) are used, which cover a wide range of vegetation density and climate conditions. For the application of the triple collocation method, surface soil moisture estimates from the Global Land Data Assimilation System are used as third independent data set. We find that the participating algorithms generally display a better performance for the descending compared to the ascending paths. A first classification of the sites defined by geographical locations show that the algorithms have a very similar average performance. Further classifications of the sites by land cover types and climate regions will be conducted which might result in a more diverse performance of the algorithms.

Smap Soil Moisture Data Assimilation for the Continental United States and Eastern Africa

NASA Astrophysics Data System (ADS)

Blankenship, C. B.; Case, J.; Zavodsky, B.; Crosson, W. L.

2016-12-01

The NASA Short-Term Prediction Research and Transition (SPoRT) Center at Marshall Space Flight Center manages near-real-time runs of the Noah Land Surface Model within the NASA Land Information System (LIS) over Continental U.S. (CONUS) and Eastern Africa domains. Soil moisture products from the CONUS model run are used by several NOAA/National Weather Service Weather Forecast Offices for flood and drought situational awareness. The baseline LIS configuration is the Noah model driven by atmospheric and combined radar/gauge precipitation analyses, and input satellite-derived real-time green vegetation fraction on a 3-km grid for the CONUS. This configuration is being enhanced by adding the assimilation of Level 2 Soil Moisture Active/Passive (SMAP) soil moisture retrievals in a parallel run beginning on 1 April 2015. Our implementation of SMAP assimilation includes a cumulative distribution function (CDF) matching approach that aggregates points with similar soil types. This method allows creation of robust CDFs with a short data record, and also permits the correction of local anomalies that may arise from poor forcing data (e.g., quality-control problems with rain gauges). Validation results using in situ soil monitoring networks in the CONUS are shown, with comparisons to the baseline SPoRT-LIS run. Initial results are also presented from a modeling run in eastern Africa, forced by Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation data. Strategies for spatial downscaling and for dealing with effective depth of the retrieval product are also discussed.

Soil Moisture Monitoring Using GNSS-R Signals; First Experimental Results with the SAM Sensor

NASA Astrophysics Data System (ADS)

Egido, A.; Martin-Puig, C.; Felip, D.; Garcia, M.; Caparrini, M.; Farres, E.; Ruffini, G.

2009-04-01

Observing the Earth surface with Global Navigation Satellite Systems (GNSS) reflected signals has become a noteworthy remote sensing technique for the scientific community. The growing interest in GNSS as a remote sensing tool is due to its global availability and the carrier frequencies used. In fact, L-band, in which all current and next-future Global Navigation Satellite Systems emit, is a portion of the electromagnetic spectrum that highly interacts with the natural medium and for this reason, the possible applications exploiting these signals are numerous. In addition, the large number of GNSS signals in space, and their steadily increasing quantity and quality predicts a promising future for this remote sensing technique. Among a wide variety of applications, soil moisture (SM) monitoring represents an important niche for GNSS-R. SM is a prime parameter for the surface hydrologic cycle since it drives the evapotranspiration and the heat storage capability of the soil, as well as determines the possibility of surface runoff after rainfalls. Despite the recognised environmental and commercial relevance of SM, providing such parameter over global and large scales remains a significant challenge. Sensors based on GNSS-R offer a suitable and efficient solution to this issue. The basis for the retrieval of SM with GNSS-R systems lays in the variability of the ground dielectric properties associated to water content. The higher the concentration of water in the soil, the higher the dielectric constant and reflectivity, which affects signals that reflect from the Earth surface by increasing their peak power. Previous investigations, [1,2] demonstrated the capability of GPS bistatic scatterometers to sense small changes in surface reflectivity, becoming a precedent for this promising research line. GNSS-R present various advantages with respect to others methods currently used to retrieve soil moisture. Firstly, as already mentioned, GNSS signals lie in L band, which is the most sensitive band to soil volumetric water content, i.e. soil moisture. Secondly, variations on thermal background do not contaminate GNSS-R signals as they do for other remote sensing techniques, such as radiometry. Finally, GNSS scatterometry from space has a potential higher spatial resolution than microwave radiometry, due to the highly stable carrier and code modulations of the incident signals which enables the use of Delay Doppler Mapping. However, in order to be able to obtain accurate SM estimates there are several effects that need to be taken into consideration. Some of those are mainly due to diffuse scattering effects over the soil surface, for instance effects due to surface roughness, vegetation canopy, and noise. This paper reviews the theoretical approach for SM retrieval using GNSS-R, and focuses on the description of the development of an innovative GNSS-R system for soil moisture retrieval (named SAM). The validation campaigns performed with the SAM sensor, together with the results obtained are presented in the paper, which is finalized with the conclusions achieved and the ideas for future work on GNSS-R based sensors. AKNOWLEDGEMENTS The authors would like to thank the European Space Agency for partially funding the SAM system development in the framework of the GSTP program, and Tragsatec for their collaboration in the acquisition of ground truth data and satellite imagery processing used for the SAM instrument validation. REFERENCES [1] A. Kavak, G. Xu, W.J. Vogel, GPS Multipath Fade Meassurements to Determine L-Band Ground Reflectivity Properties, University of Texas, Austin, 1998. [2] D. Masters, V. Zavorotny, S. Katzberg, W. Emery GPS Signal Scattering from Land for Moisture Content Determination IGARSS Proceedings, July 24-28, 2000.

SMAP Data Assimilation at NASA SPoRT

NASA Technical Reports Server (NTRS)

Blankenship, Clay B.; Case, Jonathan L.; Zavodsky, Bradley T.

2016-01-01

The NASA Short-Term Prediction Research and Transition (SPoRT) Center maintains a near-real- time run of the Noah Land Surface Model within the Land Information System (LIS) at 3-km resolution. Soil moisture products from this model are used by several NOAA/National Weather Service Weather Forecast Offices for flood and drought situational awareness. We have implemented assimilation of soil moisture retrievals from the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active/ Passive (SMAP) satellites, and are now evaluating the SMAP assimilation. The SMAP-enhanced LIS product is planned for public release by October 2016.

Survival, dispersal, and potential soil-mediated suppression of Phytophthora ramorum in a California redwood-tanoak forest.

PubMed

Fichtner, E J; Lynch, S C; Rizzo, D M

2009-05-01

Because the role of soil inoculum of Phytophthora ramorum in the sudden oak death disease cycle is not well understood, this work addresses survival, chlamydospore production, pathogen suppression, and splash dispersal of the pathogen in infested forest soils. Colonized rhododendron and bay laurel leaf disks were placed in mesh sachets before transfer to the field in January 2005 and 2006. Sachets were placed under tanoak, bay laurel, and redwood at three vertical locations: leaf litter surface, litter-soil interface, and below the soil surface. Sachets were retrieved after 4, 8, 20, and 49 weeks. Pathogen survival was higher in rhododendron leaf tissue than in bay tissue, with >80% survival observed in rhododendron tissue after 49 weeks in the field. Chlamydospore production was determined by clearing infected tissue in KOH. Moist redwood-associated soils suppressed chlamydospore production. Rain events splashed inoculum as high as 30 cm from the soil surface, inciting aerial infection of bay laurel and tanoak. Leaf litter may provide an incomplete barrier to splash dispersal. This 2-year study illustrates annual P. ramorum survival in soil and the suppressive nature of redwood-associated soils to chlamydospore production. Infested soil may serve as primary inoculum for foliar infections by splash dispersal during rain events.

Photopolarimetric Retrievals of Snow Properties

NASA Technical Reports Server (NTRS)

Ottaviani, M.; van Diedenhoven, B.; Cairns, B.

2015-01-01

Polarimetric observations of snow surfaces, obtained in the 410-2264 nm range with the Research Scanning Polarimeter onboard the NASA ER-2 high-altitude aircraft, are analyzed and presented. These novel measurements are of interest to the remote sensing community because the overwhelming brightness of snow plagues aerosol and cloud retrievals based on airborne and spaceborne total reflection measurements. The spectral signatures of the polarized reflectance of snow are therefore worthwhile investigating in order to provide guidance for the adaptation of algorithms currently employed for the retrieval of aerosol properties over soil and vegetated surfaces. At the same time, the increased information content of polarimetric measurements allows for a meaningful characterization of the snow medium. In our case, the grains are modeled as hexagonal prisms of variable aspect ratios and microscale roughness, yielding retrievals of the grains' scattering asymmetry parameter, shape and size. The results agree with our previous findings based on a more limited data set, with the majority of retrievals leading to moderately rough crystals of extreme aspect ratios, for each scene corresponding to a single value of the asymmetry parameter.

Model-based surface soil moisture (SSM) retrieval algorithm using multi-temporal RISAT-1 C-band SAR data

NASA Astrophysics Data System (ADS)

Pandey, Dharmendra K.; Maity, Saroj; Bhattacharya, Bimal; Misra, Arundhati

2016-05-01

Accurate measurement of surface soil moisture of bare and vegetation covered soil over agricultural field and monitoring the changes in surface soil moisture is vital for estimation for managing and mitigating risk to agricultural crop, which requires information and knowledge to assess risk potential and implement risk reduction strategies and deliver essential responses. The empirical and semi-empirical model-based soil moisture inversion approach developed in the past are either sensor or region specific, vegetation type specific or have limited validity range, and have limited scope to explain physical scattering processes. Hence, there is need for more robust, physical polarimetric radar backscatter model-based retrieval methods, which are sensor and location independent and have wide range of validity over soil properties. In the present study, Integral Equation Model (IEM) and Vector Radiative Transfer (VRT) model were used to simulate averaged backscatter coefficients in various soil moisture (dry, moist and wet soil), soil roughness (smooth to very rough) and crop conditions (low to high vegetation water contents) over selected regions of Gujarat state of India and the results were compared with multi-temporal Radar Imaging Satellite-1 (RISAT-1) C-band Synthetic Aperture Radar (SAR) data in σ°HH and σ°HV polarizations, in sync with on field measured soil and crop conditions. High correlations were observed between RISAT-1 HH and HV with model simulated σ°HH & σ°HV based on field measured soil with the coefficient of determination R2 varying from 0.84 to 0.77 and RMSE varying from 0.94 dB to 2.1 dB for bare soil. Whereas in case of winter wheat crop, coefficient of determination R2 varying from 0.84 to 0.79 and RMSE varying from 0.87 dB to 1.34 dB, corresponding to with vegetation water content values up to 3.4 kg/m2. Artificial Neural Network (ANN) methods were adopted for model-based soil moisture inversion. The training datasets for the NNs were obtained from theoretical forward-scattering models with controlled parameters, thus allowing the control of wide range of soil and crop parameters with which the network was trained. A preliminary performance analysis showed good results with estimation of soil moisture with RMSE better than 6%.

The Soil Moisture Dependence of TRMM Microwave Imager Rainfall Estimates

NASA Astrophysics Data System (ADS)

Seyyedi, H.; Anagnostou, E. N.

2011-12-01

This study presents an in-depth analysis of the dependence of overland rainfall estimates from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) on the soil moisture conditions at the land surface. TMI retrievals are verified against rainfall fields derived from a high resolution rain-gauge network (MESONET) covering Oklahoma. Soil moisture (SOM) patterns are extracted based on recorded data from 2000-2007 with 30 minutes temporal resolution. The area is divided into wet and dry regions based on normalized SOM (Nsom) values. Statistical comparison between two groups is conducted based on recorded ground station measurements and the corresponding passive microwave retrievals from TMI overpasses at the respective MESONET station location and time. The zero order error statistics show that the Probability of Detection (POD) for the wet regions (higher Nsom values) is higher than the dry regions. The Falls Alarm Ratio (FAR) and volumetric FAR is lower for the wet regions. The volumetric missed rain for the wet region is lower than dry region. Analysis of the MESONET-to-TMI ratio values shows that TMI tends to overestimate for surface rainfall intensities less than 12 (mm/h), however the magnitude of the overestimation over the wet regions is lower than the dry regions.

Analysis of ASAR Wide Swath Mode time series for the retrieval of soil moisture in mountainous areas

NASA Astrophysics Data System (ADS)

Greifeneder, Felix; Notarnicola, Claudia; Cuozzo, Giovanni; Spindler, Nadine; Bertoldi, Giacomo; Della Chiesa, Stefano; Niedrist, Georg; Stamenkovic, Jelena; Wagner, Wolgang

2014-05-01

Soil moisture is a key element in the global cycles of water, energy, and carbon. Knowledge on the spatial and temporal distribution of the soil moisture content (SMC) is therefore essential for a number of hydrological applications as well as earth sciences like meteorology or climatology (Heathman et al., 2003). In the last few years there has been an increasing interest towards the estimation of SMC at local scales using active microwave sensors (Barret et al., 2009). Compared to passive microwave sensors, SAR offers the potential to provide data at high spatial resolution (modern sensors can acquire images with up to approximately 1 m), which is particularly important in mountainous areas. So far, these areas have been considered only marginally in research and only pioneer studies can be found in the literature (Brocca et al., 2012; Bertoldi et al. 2013). In this work we analyzed the temporal and spatial dynamics of the surface SMC (0 - 5 cm depth) on the basis of ground data collected by fixed meteorological stations located in the emerging Long-Term Ecological Research (LTER) site Mazia Valley (Province of Bolzano, South Tyrol, Italy), SAR data from ENVISATs ASAR sensor, wide swath (WS) mode (acquired between 2005 and 2012), and SMC estimates from the hydrological model GEOtop (Endrizzi et al., 2013). The SMC retrieval process was based on the support vector regression (SVR) method introduced by Pasolli et al. (2011). The training of the algorithm was based on data acquired in 2010. Furthermore, the SAR backscatter and derived SMC have been compared with time-series derived from the distributed hydrological model GEOtop. The differences in terms of temporal and spatial dynamic have been analyzed. The main goal of this work is to evaluate the spatial and temporal patterns of SAR derived SMC at field scale and to correlate them with ground information. This is a preparatory study to establish a methodology for the retrieval of SMC with high spatial and temporal sampling and to improve retrieval accuracies by integrating temporal information from different sources of ancillary data and from SAR time-series. It was found that the dynamics of both, temporal and spatial SMC patterns obtained from various data sources (ASAR, GEOtop and meteorological stations), show a similar general temporal behaviour that indicates the robustness of the retrieval algorithm with ASAR WS. However, depending on land cover, soil type and local topographic conditions different spatial patters can be found between SMC estimations coming from ASAR and from the GEOtop model. Introducing information on the temporal behaviour of the SAR signal proves to be a promising method for increasing the confidence and accuracy in estimating SMC, complementing hydrological model predictions. Following steps were identified as critical for the retrieval process: the topographic correction and geocoding of SAR data and the calibration of the meteorological stations. Both factors can have significant influence on the quality of SMC estimation. The accuracy of meteorological input and soil parameterization were identified as the most crucial challenges for SMC derived from hydrological modeling. References Barrett, B. W., E. Dwyer, and P. Whelan. "Soil moisture retrieval from active spaceborne microwave observations: An evaluation of current techniques." Remote Sensing 1, no. 3 (2009): 210-242. Bertoldi, G., S. Della Chiesa, C. Notarnicola, L. Pasolli, G. Niedrist, and U. Tappeiner. "Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT 2 images and hydrological modeling." Journal of Hydrology (2014). under revision. Brocca, L., A. Tarpanelli, T. Moramarco, F. Melone, S. M. Ratto, M. Cauduro, S. Ferraris et al. "Soil Moisture Estimation in Alpine Catchments through Modeling and Satellite Observations." Vadose Zone Journal (2013). Endrizzi, S., S. Gruber, M. Dall'Amico, and R. Rigon. "GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects." Geoscientific Model Development Discussions 6, no. 4 (2013): 6279-6341. Heathman, G. C., P. J. Starks, L. R. Ahuja, and T. J. Jackson. "Assimilation of surface soil moisture to estimate profile soil water content." Journal of Hydrology 279, no. 1 (2003): 1-17. Pasolli, L., C. Notarnicola, L. Bruzzone, G. Bertoldi, S. Della Chiesa, V. Hell, G. Niedrist. "Estimation of Soil Moisture in an Alpine catchment with RADARSAT2 Images." Applied and Environmental Soil Science 2011 (2011)

Probabilistic Assessment of Soil Moisture using C-band Quad-polarized Remote Sensing Data from RISAT1

NASA Astrophysics Data System (ADS)

Pal, Manali; Suman, Mayank; Das, Sarit Kumar; Maity, Rajib

2017-04-01

Information on spatio-temporal distribution of surface Soil Moisture Content (SMC) is essential in several hydrological, meteorological and agricultural applications. There has been increasing importance of microwave active remote sensing data for large-scale estimation of surface SMC because of its ability to monitor spatial and temporal variation of surface SMC at regional, continental and global scale at a reasonably fine spatial and temporal resolution. The use of Synthetic Aperture Radar (SAR) is highly potential for catchment-scale applications due to high spatial resolution (˜10-20 m) both for vegetated and bare soil surface as well as because of its all-weather and day and night characteristics. However, one prime disadvantage of SAR is that their signal is subjective to SMC along with Land Use Land Cover (LULC) and surface roughness conditions, making the retrieval of SMC from SAR data an "ill-posed" problem. Moreover, the quantification of uncertainty due to inappropriate surface roughness characterization, soil texture, inversion techniques etc. even in the latest established retrieval methods, is little explored. This paper reports a recently developed method to estimate the surface SMC with probabilistic assessment of uncertainty associated with the estimation (Pal et al., 2016). Quad-polarized SAR data from Radar Imaging Satellite1 (RISAT1), launched in 2012 by Indian Space Research Organization (ISRO) and information on LULC regarding bareland and vegetated land (<30 cm height) are used in estimation using the potential of multivariate probabilistic assessment through copulas. The salient features of the study are: 1) development of a combined index to understand the role of all the quad-polarized backscattering coefficients and soil texture information in SMC estimation; 2) applicability of the model for different incidence angles using normalized incidence angle theory proposed by Zibri et al. (2005); and 3) assessment of uncertainty range of the estimated SMC. Supervised Principal Component Analysis (SPCA) is used for development of combined index and Frank copula is found to be the best-fit copula. The developed model is validated with the field soil moisture values over 334 monitoring points within the study area and used for development of a soil moisture map. While the performance is promising, the model is applicable only for bare and vegetated land. References: Pal, M., Maity, R., Suman, M., Das, S.K., Patel, P., and Srivastava, H.S., (2016). "Satellite-Based Probabilistic Assessment of Soil Moisture Using C-Band Quad-Polarized RISAT1 Data." IEEE Transactions on Geoscience and Remote Sensing, In Press, doi:10.1109/TGRS.2016.2623378. Zribi, M., Baghdadi, N., Holah, N., and Fafin, O., (2005)."New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion." Remote Sensing of Environment, vol. 96, nos. 3-4, pp. 485-496.

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.

Exploiting the synergy between SMAP and SMOS to improve brightness temperature simulations and soil moisture retrievals in arid regions

NASA Astrophysics Data System (ADS)

Ebrahimi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Neysani Samany, Najmeh; Wigneron, Jean-Pierre

2018-02-01

The objective of this study was to exploit the synergy between SMOS and SMAP based on vegetation optical depth (VOD) to improve brightness temperature (TB) simulations and land surface soil moisture (SM) retrievals in arid regions of the world. In the current operational algorithm of SMAP (level 2), vegetation water content (VWC) is considered as a proxy to compute VOD which is calculated by an empirical conversion function of NDVI. Avoiding the empirical estimation of VOD, the SMOS algorithm is used to retrieve simultaneously SM and VOD from TB observations. The present study attempted to improve SMAP TB simulations and SM retrievals by benefiting from the advantages of the SMOS (L-MEB) algorithm. This was achieved by using a synergy method based on replacing the default value of SMAP VOD with the retrieved value of VOD from the SMOS multi angular and bi-polarization observations of TB. The insitu SM measurements, used as reference SM in this study, were obtained from the International Soil Moisture Network (ISMN) over 180 stations located in arid regions of the world. Furthermore, four stations were randomly selected to analyze the temporal variations in VOD and SM. Results of the synergy method showed that the accuracy of the TB simulations and SM retrievals was respectively improved at 144 and 124 stations (out of a total of 180 stations) in terms of coefficient of determination (R2) and unbiased root mean squared error (UbRMSE). Analyzing the temporal variations in VOD showed that the SMOS VOD, conversely to the SMAP VOD, can better illustrate the presence of herbaceous plants and may be a better indicator of the seasonal changes in the vegetation density and biomass over the year.

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.

Retrieval of Soil Moisture and Roughness from the Polarimetric Radar Response

NASA Technical Reports Server (NTRS)

Sarabandi, Kamal; Ulaby, Fawwaz T.

1997-01-01

The main objective of this investigation was the characterization of soil moisture using imaging radars. In order to accomplish this task, a number of intermediate steps had to be undertaken. In this proposal, the theoretical, numerical, and experimental aspects of electromagnetic scattering from natural surfaces was considered with emphasis on remote sensing of soil moisture. In the general case, the microwave backscatter from natural surfaces is mainly influenced by three major factors: (1) the roughness statistics of the soil surface, (2) soil moisture content, and (3) soil surface cover. First the scattering problem from bare-soil surfaces was considered and a hybrid model that relates the radar backscattering coefficient to soil moisture and surface roughness was developed. This model is based on extensive experimental measurements of the radar polarimetric backscatter response of bare soil surfaces at microwave frequencies over a wide range of moisture conditions and roughness scales in conjunction with existing theoretical surface scattering models in limiting cases (small perturbation, physical optics, and geometrical optics models). Also a simple inversion algorithm capable of providing accurate estimates of soil moisture content and surface rms height from single-frequency multi-polarization radar observations was developed. The accuracy of the model and its inversion algorithm is demonstrated using independent data sets. Next the hybrid model for bare-soil surfaces is made fully polarimetric by incorporating the parameters of the co- and cross-polarized phase difference into the model. Experimental data in conjunction with numerical simulations are used to relate the soil moisture content and surface roughness to the phase difference statistics. For this purpose, a novel numerical scattering simulation for inhomogeneous dielectric random surfaces was developed. Finally the scattering problem of short vegetation cover above a rough soil surface was considered. A general scattering model for grass-blades of arbitrary cross section was developed and incorporated in a first order random media model. The vegetation model and the bare-soil model are combined and the accuracy of the combined model is evaluated against experimental observations from a wheat field over the entire growing season. A complete set of ground-truth data and polarimetric backscatter data were collected. Also an inversion algorithm for estimating soil moisture and surface roughness from multi-polarized multi-frequency observations of vegetation-covered ground is developed.

An analysis of soil moisture and vegetation conditions during a period of rapid subseasonal oscillations between drought and pluvials over Texas during 2015

NASA Astrophysics Data System (ADS)

Hunt, E. D.; Otkin, J.; Zhong, Y.

2017-12-01

Flash drought, characterized by the rapid onset of abnormally warm and dry weather conditions that leads to the rapid depletion of soil moisture and rapid deteriorations in vegetation health. Flash recovery, on the other hand, is characterized by a period(s) of intense precipitation where drought conditions are quickly eradicated and may be replaced by saturated soils and flooding. Both flash drought and flash recovery are closely tied to the rapid depletion or recharge of root zone soil moisture; therefore, soil moisture observations are very useful for monitoring their evolution. However, in-situ soil moisture observations tend to be concentrated over small regions and thus other methods are needed to provide a spatially continuous depiction of soil moisture conditions. One option is to use top soil moisture retrievals from the Soil Moisture Active Passive (SMAP) sensor. SMAP provides routine coverage of surface soil moisture (0-5 cm) over most of the globe, including the timespan (2015) and region of interest (Texas) that are the focus of our study. This region had an unusual sequence of flash recovery-flash drought-flash recovery during an six-month period during 2015 that provides a valuable case study of rapid transitions between extreme soil moisture conditions. During this project, SMAP soil moisture retrievals are being used in combination with in-situ soil moisture observations and assimilated into the Land Information System (LIS) to provide information about soil moisture content. LIS also provides greenness vegetation fraction data over large regions. The relationship between soil moisture and vegetation conditions and the response of the vegetation to the rapidly changing conditions are also assessed using the satellite thermal infrared based Evaporative Stress Index (ESI) that depicts anomalies in evapotranspiration, along with other vegetation datasets (leaf area index, greenness fraction) derived using MODIS observations. Preliminary results with the Noah land surface model (inside of LIS) shows that it broadly captured the soil moisture evolution during the 2015 sequence but tended to underestimate the magnitude of soil moisture anomalies. The ESI also showed negative anomalies during the drought. These and other results will be presented at the annual meeting.

Passive Microwave Soil Moisture Retrieval through Combined Radar/Radiometer Ground Based Simulator with Special Reference to Dielectric Schemes

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K., ,, Dr.; O'Neill, Peggy, ,, Dr.

2014-05-01

Soil moisture is an important element for weather and climate prediction, hydrological sciences, and applications. Hence, measurements of this hydrologic variable are required to improve our understanding of hydrological processes, ecosystem functions, and the linkages between the Earth's water, energy, and carbon cycles (Srivastava et al. 2013). The retrieval of soil moisture depends not only on parameterizations in the retrieval algorithm but also on the soil dielectric mixing models used (Behari 2005). Although a number of soil dielectric mixing models have been developed, testing these models for soil moisture retrieval has still not been fully explored, especially with SMAP-like simulators. The main objective of this work focuses on testing different dielectric models for soil moisture retrieval using the Combined Radar/Radiometer (ComRAD) ground-based L-band simulator developed jointly by NASA/GSFC and George Washington University (O'Neill et al., 2006). The ComRAD system was deployed during a field experiment in 2012 in order to provide long active/passive measurements of two crops under controlled conditions during an entire growing season. L-band passive data were acquired at a look angle of 40 degree from nadir at both horizontal & vertical polarization. Currently, there are many dielectric models available for soil moisture retrieval; however, four dielectric models (Mironov, Dobson, Wang & Schmugge and Hallikainen) were tested here and found to be promising for soil moisture retrieval (some with higher performances). All the above-mentioned dielectric models were integrated with Single Channel Algorithms using H (SCA-H) and V (SCA-V) polarizations for the soil moisture retrievals. All the ground-based observations were collected from test site-United States Department of Agriculture (USDA) OPE3, located a few miles away from NASA GSFC. Ground truth data were collected using a theta probe and in situ sensors which were then used for validation. Analysis indicated a higher performance in terms of soil moisture retrieval accuracy for the Mironov dielectric model (RMSE of 0.035 m3/m3), followed by Dobson, Wang & Schmugge, and Hallikainen. This analysis indicates that Mironov dielectric model is promising for passive-only microwave soil moisture retrieval and could be a useful choice for SMAP satellite soil moisture retrieval. Keywords: Dielectric models; Single Channel Algorithm, Combined Radar/Radiometer, Soil moisture; L band References: Behari, J. (2005). Dielectric Behavior of Soil (pp. 22-40). Springer Netherlands O'Neill, P. E., Lang, R. H., Kurum, M., Utku, C., & Carver, K. R. (2006), Multi-Sensor Microwave Soil Moisture Remote Sensing: NASA's Combined Radar/Radiometer (ComRAD) System. In IEEE MicroRad, 2006 (pp. 50-54). IEEE. Srivastava, P. K., Han, D., Rico Ramirez, M. A., & Islam, T. (2013), Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology, 498, 292-304. USDA OPE3 web site at http://www.ars.usda.gov/Research/.

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.

Uncertainty Assessment of Space-Borne Passive Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Quets, Jan; De Lannoy, Gabrielle; Reichle, Rolf; Cosh, Michael; van der Schalie, Robin; Wigneron, Jean-Pierre

2017-01-01

The uncertainty associated with passive soil moisture retrieval is hard to quantify, and known to be underlain by various, diverse, and complex causes. Factors affecting space-borne retrieved soil moisture estimation include: (i) the optimization or inversion method applied to the radiative transfer model (RTM), such as e.g. the Single Channel Algorithm (SCA), or the Land Parameter Retrieval Model (LPRM), (ii) the selection of the observed brightness temperatures (Tbs), e.g. polarization and incidence angle, (iii) the definition of the cost function and the impact of prior information in it, and (iv) the RTM parameterization (e.g. parameterizations officially used by the SMOS L2 and SMAP L2 retrieval products, ECMWF-based SMOS assimilation product, SMAP L4 assimilation product, and perturbations from those configurations). This study aims at disentangling the relative importance of the above-mentioned sources of uncertainty, by carrying out soil moisture retrieval experiments, using SMOS Tb observations in different settings, of which some are mentioned above. The ensemble uncertainties are evaluated at 11 reference CalVal sites, over a time period of more than 5 years. These experimental retrievals were inter-compared, and further confronted with in situ soil moisture measurements and operational SMOS L2 retrievals, using commonly used skill metrics to quantify the temporal uncertainty in the retrievals.

Center pivot mounted infrared sensors: Retrieval of ET and interface with satellite systems

USDA-ARS?s Scientific Manuscript database

Infrared sensors mounted aboard cener pivot irrigation systems can remotely sense the surface temperatures of the crops and soils, which provides important information on crop water status. This can be used for irrigation management and irrigation automation, which can increase crop water productivi...

The MVACS Robotic Arm Camera

NASA Astrophysics Data System (ADS)

Keller, H. U.; Hartwig, H.; Kramm, R.; Koschny, D.; Markiewicz, W. J.; Thomas, N.; Fernades, M.; Smith, P. H.; Reynolds, R.; Lemmon, M. T.; Weinberg, J.; Marcialis, R.; Tanner, R.; Boss, B. J.; Oquest, C.; Paige, D. A.

2001-08-01

The Robotic Arm Camera (RAC) is one of the key instruments newly developed for the Mars Volatiles and Climate Surveyor payload of the Mars Polar Lander. This lightweight instrument employs a front lens with variable focus range and takes images at distances from 11 mm (image scale 1:1) to infinity. Color images with a resolution of better than 50 μm can be obtained to characterize the Martian soil. Spectral information of nearby objects is retrieved through illumination with blue, green, and red lamp sets. The design and performance of the camera are described in relation to the science objectives and operation. The RAC uses the same CCD detector array as the Surface Stereo Imager and shares the readout electronics with this camera. The RAC is mounted at the wrist of the Robotic Arm and can characterize the contents of the scoop, the samples of soil fed to the Thermal Evolved Gas Analyzer, the Martian surface in the vicinity of the lander, and the interior of trenches dug out by the Robotic Arm. It can also be used to take panoramic images and to retrieve stereo information with an effective baseline surpassing that of the Surface Stereo Imager by about a factor of 3.

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.

Actual daily evapotranspiration estimated from MERIS and AATSR data over the Chinese Loess Plateau

NASA Astrophysics Data System (ADS)

Liu, R.; Wen, J.; Wang, X.; Wang, L.; Tian, H.; Zhang, T. T.; Shi, X. K.; Zhang, J. H.; Lu, Sh. N.

2009-02-01

The Loess Plateau is located in north of China and has a significant impact on the climate and ecosystem evolvement over the East Asian continent. Based on the land surface energy balance theory, the potential of using Medium Resolution Imaging Spectrometer (onboard sensor of the Environmental Satellite) remote sensing data on 7, 11 and 27 June 2005 is explored. The "split-window" algorithm is used to retrieve surface temperature from the Advanced the Along-Track Scanning Radiometer, another onboard senor of the Environmental Satellite. Then the near surface net radiation, sensible heat flux and soil heat flux are estimated by using the developed algorithm. We introduce a simple algorithm to predict the heat flux partitioning between the soil and vegetation. Combining the sunshine hours, air temperature, sunshine duration and wind speed measured by weather stations, a model for estimating daily ET is proposed. The instantaneous ET is also converted to daily value. Comparison of latent heats flux retrieved by remote sensing data with ground observation from eddy covariance flux system during Loess Plateau land surface process field Experiment, the maximum and minimum error of this approach are 10.96% and 4.80% respectively, the cause of the bias is also explored and discussed.

A data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission

NASA Astrophysics Data System (ADS)

Entekhabi, D.; Jagdhuber, T.; Das, N. N.; Baur, M.; Link, M.; Piles, M.; Akbar, R.; Konings, A. G.; Mccoll, K. A.; Alemohammad, S. H.; Montzka, C.; Kunstmann, H.

2016-12-01

The active-passive soil moisture retrieval algorithm of NASA's SMAP mission depends on robust statistical estimation of active-passive covariation (β) and vegetation structure (Γ) parameters in order to provide reliable global measurements of soil moisture on an intermediate level (9km) compared to the native resolution of the radiometer (36km) and radar (3km) instruments. These parameters apply to the SMAP radiometer-radar combination over the period of record that was cut short with the end of the SMAP radar transmission. They also apply to the current SMAP radiometer and Sentinel 1A/B radar combination for high-resolution surface soil moisture mapping. However, the performance of the statistically-based approach is directly dependent on the selection of a representative time frame in which these parameters can be estimated assuming dynamic soil moisture and stationary soil roughness and vegetation cover. Here, we propose a novel, data-driven and physics-based single-pass retrieval of active-passive microwave covariation and vegetation parameters for the SMAP mission. The algorithm does not depend on time series analyses and can be applied using minimum one pair of an active-passive acquisition. The algorithm stems from the physical link between microwave emission and scattering via conservation of energy. The formulation of the emission radiative transfer is combined with the Distorted Born Approximation of radar scattering for vegetated land surfaces. The two formulations are simultaneously solved for the covariation and vegetation structure parameters. Preliminary results from SMAP active-passive observations (April 13th to July 7th 2015) compare well with the time-series statistical approach and confirms the capability of this method to estimate these parameters. Moreover, the method is not restricted to a given frequency (applies to both L-band and C-band combinations for the radar) or incidence angle (all angles and not just the fixed 40° incidence). Therefore, the approach is applicable to the combination of SMAP and Sentinel-1A/B data for active-passive and high-resolution soil moisture estimation.

SMAP Validation Experiment 2015 (SMAPVEX15)

NASA Astrophysics Data System (ADS)

Colliander, A.; Jackson, T. J.; Cosh, M. H.; Misra, S.; Crow, W. T.; Chae, C. S.; Moghaddam, M.; O'Neill, P. E.; Entekhabi, D.; Yueh, S. H.

2015-12-01

NASA's (National Aeronautics and Space Administration) Soil Moisture Active Passive (SMAP) mission was launched in January 2015. The objective of the mission is global mapping of soil moisture and freeze/thaw state. For soil moisture algorithm validation, the SMAP project and NASA coordinated SMAPVEX15 around the Walnut Gulch Experimental Watershed (WGEW) in Tombstone, Arizona on August 1-19, 2015. The main goals of SMAPVEX15 are to understand the effects and contribution of heterogeneity on the soil moisture retrievals, evaluate the impact of known RFI sources on retrieval, and analyze the brightness temperature product calibration and heterogeneity effects. Additionally, the campaign aims to contribute to the validation of GPM (Global Precipitation Mission) data products. The campaign will feature three airborne microwave instruments: PALS (Passive Active L-band System), UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar) and AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface). PALS has L-band radiometer and radar, and UAVSAR and AirMOSS have L- and P-band synthetic aperture radars, respectively. The PALS instrument will map the area on seven days coincident with SMAP overpasses; UAVSAR and AirMOSS on four days. WGEW was selected as the experiment site due to the rainfall patterns in August and existing dense networks of precipitation gages and soil moisture sensors. An additional temporary network of approximately 80 soil moisture stations was deployed in the region. Rainfall observations were supplemented with two X-band mobile scanning radars, approximately 25 tipping bucket rain gauges, three laser disdrometers, and three vertically-profiling K-band radars. Teams were on the field to take soil moisture samples for gravimetric soil moisture, bulk density and rock fraction determination as well as to measure surface roughness and vegetation water content. In this talk we will present preliminary results from the experiment including comparisons between SMAP and PALS soil moisture retrievals with respect to the in situ measurements. Acknowledgement: This work was carried out in part at Jet Propulsion Laboratory, California Institute of Technology under contract with National Aeronautics and Space Administration.

Using semi-variogram analysis for providing spatially distributed information on soil surface condition for land surface modeling

NASA Astrophysics Data System (ADS)

Croft, Holly; Anderson, Karen; Kuhn, Nikolaus J.

2010-05-01

The ability to quantitatively and spatially assess soil surface roughness is important in geomorphology and land degradation studies. Soils can experience rapid structural degradation in response to land cover changes, resulting in increased susceptibility to erosion and a loss of Soil Organic Matter (SOM). Changes in soil surface condition can also alter sediment detachment, transport and deposition processes, infiltration rates and surface runoff characteristics. Deriving spatially distributed quantitative information on soil surface condition for inclusion in hydrological and soil erosion models is therefore paramount. However, due to the time and resources involved in using traditional field sampling techniques, there is a lack of spatially distributed information on soil surface condition. Laser techniques can provide data for a rapid three dimensional representation of the soil surface at a fine spatial resolution. This provides the ability to capture changes at the soil surface associated with aggregate breakdown, flow routing, erosion and sediment re-distribution. Semi-variogram analysis of the laser data can be used to represent spatial dependence within the dataset; providing information about the spatial character of soil surface structure. This experiment details the ability of semi-variogram analysis to spatially describe changes in soil surface condition. Soil for three soil types (silt, silt loam and silty clay) was sieved to produce aggregates between 1 mm and 16 mm in size and placed evenly in sample trays (25 x 20 x 2 cm). Soil samples for each soil type were exposed to five different durations of artificial rainfall, to produce progressively structurally degraded soil states. A calibrated laser profiling instrument was used to measure surface roughness over a central 10 x 10 cm plot of each soil state, at 2 mm sample spacing. The laser data were analysed within a geostatistical framework, where semi-variogram analysis quantitatively represented the change in soil surface structure during crusting. The laser data were also used to create digital surface models (DSM) of the soil states for visual comparison. This research has shown that aggregate breakdown and soil crusting can be shown quantitatively by a decrease in sill variance (silt soil: 11.67 (control) to 1.08 (after 90 mins rainfall)). Features present within semi-variograms were spatially linked to features at the soil surface, such as soil cracks, tillage lines and areas of deposition. Directional semi-variograms were used to provide a spatially orientated component, where the directional sill variance associated with a soil crack was shown to increase from 7.95 to 19.33. Periodicity within semi-variogram was also shown to quantify the spatial scale of soil cracking networks and potentially surface flowpaths; an average distance between soil cracks of 37 mm closely corresponded to the distance of 38 mm shown in the semi-variogram. The results provide a strong basis for the future retrieval of spatio-temporal variations in soil surface condition. Furthermore, the presence of process-based information on hydrological pathways within semi-variograms may work towards an inclusion of geostatisically-derived information in land surface models and the understanding of complex surface processes at different spatial scales.

SMOS/SMAP Synergy for SMAP Level 2 Soil Moisture Algorithm Evaluation

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Zhao, Tianjie; Cosh, Michael; Chan, Steven; O'Neill, Peggy; Njoku, Eni; Colliander, Andreas; Kerr, Yann

2011-01-01

Soil Moisture Active Passive (SMAP) satellite has been proposed to provide global measurements of soil moisture and land freeze/thaw state at 10 km and 3 km resolutions, respectively. SMAP would also provide a radiometer-only soil moisture product at 40-km spatial resolution. This product and the supporting brightness temperature observations are common to both SMAP and European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. As a result, there are opportunities for synergies between the two missions. These include exploiting the data for calibration and validation and establishing longer term L-band brightness temperature and derived soil moisture products. In this investigation we will be using SMOS brightness temperature, ancillary data, and soil moisture products to develop and evaluate a candidate SMAP L2 passive soil moisture retrieval algorithm. This work will begin with evaluations based on the SMOS product grids and ancillary data sets and transition to those that will be used by SMAP. An important step in this analysis is reprocessing the multiple incidence angle observations provided by SMOS to a global brightness temperature product that simulates the constant 40 degree incidence angle observations that SMAP will provide. The reprocessed brightness temperature data provide a basis for evaluating different SMAP algorithm alternatives. Several algorithms are being considered for the SMAP radiometer-only soil moisture retrieval. In this first phase, we utilized only the Single Channel Algorithm (SCA), which is based on the radiative transfer equation and uses the channel that is most sensitive to soil moisture (H-pol). Brightness temperature is corrected sequentially for the effects of temperature, vegetation, roughness (dynamic ancillary data sets) and soil texture (static ancillary data set). European Centre for Medium-Range Weather Forecasts (ECMWF) estimates of soil temperature for the top layer (as provided as part of the SMOS ancillary data) were used to correct for surface temperature effects and to derive microwave emissivity. ECMWF data were also used for precipitation forecasts, presence of snow, and frozen ground. Vegetation options are described below. One year of soil moisture observations from a set of four watersheds in the U.S. were used to evaluate four different retrieval methodologies: (1) SMOS soil moisture estimates (version 400), (2) SeA soil moisture estimates using the SMOS/SMAP data with SMOS estimated vegetation optical depth, which is part of the SMOS level 2 product, (3) SeA soil moisture estimates using the SMOS/SMAP data and the MODIS-based vegetation climatology data, and (4) SeA soil moisture estimates using the SMOS/SMAP data and actual MODIS observations. The use of SMOS real-world global microwave observations and the analyses described here will help in the development and selection of different land surface parameters and ancillary observations needed for the SMAP soil moisture algorithms. These investigations will greatly improve the quality and reliability of this SMAP product at launch.

Impact of implementation of spaceborne lidar-retrieved canopy height in the WRF model

NASA Astrophysics Data System (ADS)

Lee, Junhong; Hong, Jinkyu

2017-04-01

Canopy height is closely related to biomass and aerodynamic properties, which regulate turbulent transfer of energy and mass at the soil-vegetation-atmosphere continuum. However, this key information has been prescribed as a constant value in a fixed plant functional type in atmospheric models. This presentation reports impacts of using realistic forest canopy height, retrieved from spaceborne LiDAR, on regional climate simulation in the Weather Research and Forecasting (WRF) model's land surface model. Numerical simulations were conducted over the Amazon Basin and East Asia during summer season. Over these regions, the LiDAR-retrieved canopy heights were higher than the default values used in the WRF,which are dependent only on plant functional type. By modifying roughness length and zero-plane displacement height, the change of canopy height resulted in changes in surface energy balance by regulating aerodynamic conductances and vertical temperature gradient, thus modifying the lifting condensation level and equivalent potential temperature in the atmospheric boundary layer. Our analysis also showed that the WRF model better reproduced the observed precipitation when LiDAR-retrieved canopy height was used over the Amazon Basin.

Implementation of spaceborne lidar-retrieved canopy height in the WRF model

NASA Astrophysics Data System (ADS)

Lee, Junhong; Hong, Jinkyu

2016-06-01

Canopy height is closely related to biomass and aerodynamic properties, which regulate turbulent transfer of energy and mass at the soil-vegetation-atmosphere continuum. However, this key information has been prescribed as a constant value in a fixed plant functional type in atmospheric models. This paper is the first to report impacts of using realistic forest canopy height, retrieved from spaceborne lidar, on regional climate simulation by using the canopy height data in the Weather Research and Forecasting (WRF) model's land surface model. Numerical simulations were conducted over the Amazon Basin during summer season. Over this region, the lidar-retrieved canopy heights were higher than the default values used in the WRF, which are dependent only on plant functional type. By modifying roughness length and zero-plane displacement height, the change of canopy height resulted in changes in surface energy balance by regulating aerodynamic conductances and vertical temperature gradient, thus modifying the lifting condensation level and equivalent potential temperature in the atmospheric boundary layer. Our analysis also showed that the WRF model better reproduced the observed precipitation when lidar-retrieved canopy height was used over the Amazon Basin.

Sensitivity of Rayleigh wave ellipticity and implications for surface wave inversion

NASA Astrophysics Data System (ADS)

Cercato, Michele

2018-04-01

The use of Rayleigh wave ellipticity has gained increasing popularity in recent years for investigating earth structures, especially for near-surface soil characterization. In spite of its widespread application, the sensitivity of the ellipticity function to the soil structure has been rarely explored in a comprehensive and systematic manner. To this end, a new analytical method is presented for computing the sensitivity of Rayleigh wave ellipticity with respect to the structural parameters of a layered elastic half-space. This method takes advantage of the minor decomposition of the surface wave eigenproblem and is numerically stable at high frequency. This numerical procedure allowed to retrieve the sensitivity for typical near surface and crustal geological scenarios, pointing out the key parameters for ellipticity interpretation under different circumstances. On this basis, a thorough analysis is performed to assess how ellipticity data can efficiently complement surface wave dispersion information in a joint inversion algorithm. The results of synthetic and real-world examples are illustrated to analyse quantitatively the diagnostic potential of the ellipticity data with respect to the soil structure, focusing on the possible sources of misinterpretation in data inversion.

The NASA Soil Moisture Active Passive (SMAP) Mission - Science and Data Product Development Status

NASA Technical Reports Server (NTRS)

Nloku, E.; Entekhabi, D.; O'Neill, P.

2012-01-01

The Soil Moisture Active Passive (SMAP) mission, planned for launch in late 2014, has the objective of frequent, global mapping of near-surface soil moisture and its freeze-thaw state. The SMAP measurement system utilizes an L-band radar and radiometer sharing a rotating 6-meter mesh reflector antenna. The instruments will operate on a spacecraft in a 685 km polar orbit with 6am/6pm nodal crossings, viewing the surface at a constant 40-degree incidence angle with a 1000-km swath width, providing 3-day global coverage. Data from the instruments will yield global maps of soil moisture and freeze/thaw state at 10 km and 3 km resolutions, respectively, every two to three days. The 10-km soil moisture product will be generated using a combined radar and radiometer retrieval algorithm. SMAP will also provide a radiometer-only soil moisture product at 40-km spatial resolution and a radar-only soil moisture product at 3-km resolution. The relative accuracies of these products will vary regionally and will depend on surface characteristics such as vegetation water content, vegetation type, surface roughness, and landscape heterogeneity. The SMAP soil moisture and freeze/thaw measurements will enable significantly improved estimates of the fluxes of water, energy and carbon between the land and atmosphere. Soil moisture and freeze/thaw controls of these fluxes are key factors in the performance of models used for weather and climate predictions and for quantifYing the global carbon balance. Soil moisture measurements are also of importance in modeling and predicting extreme events such as floods and droughts. The algorithms and data products for SMAP are being developed in the SMAP Science Data System (SDS) Testbed. In the Testbed algorithms are developed and evaluated using simulated SMAP observations as well as observational data from current airborne and spaceborne L-band sensors including data from the SMOS and Aquarius missions. We report here on the development status of the SMAP data products. The Testbed simulations are designed to capture various sources of errors in the products including environment effects, instrument effects (nonideal aspects of the measurement system), and retrieval algorithm errors. The SMAP project has developed a Calibration and Validation (Cal/Val) Plan that is designed to support algorithm development (pre-launch) and data product validation (post-launch). A key component of the Cal/Val Plan is the identification, characterization, and instrumentation of sites that can be used to calibrate and validate the sensor data (Level l) and derived geophysical products (Level 2 and higher).

The Australian National Airborne Field Experiment 2005: Soil Moisture Remote Sensing at 60 Meter Resolution and Up

NASA Technical Reports Server (NTRS)

Kim, E. J.; Walker, J. P.; Panciera, R.; Kalma, J. D.

2006-01-01

Spatially-distributed soil moisture observations have applications spanning a wide range of spatial resolutions from the very local needs of individual farmers to the progressively larger areas of interest to weather forecasters, water resource managers, and global climate modelers. To date, the most promising approach for space-based remote sensing of soil moisture makes use of passive microwave emission radiometers at L-band frequencies (1-2 GHz). Several soil moisture-sensing satellites have been proposed in recent years, with the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission scheduled to be launched first in a couple years. While such a microwave-based approach has the advantage of essentially allweather operation, satellite size limits spatial resolution to 10's of km. Whether used at this native resolution or in conjunction with some type of downscaling technique to generate soil moisture estimates on a finer-scale grid, the effects of subpixel spatial variability play a critical role. The soil moisture variability is typically affected by factors such as vegetation, topography, surface roughness, and soil texture. Understanding and these factors is the key to achieving accurate soil moisture retrievals at any scale. Indeed, the ability to compensate for these factors ultimately limits the achievable spatial resolution and/or accuracy of the retrieval. Over the last 20 years, a series of airborne campaigns in the USA have supported the development of algorithms for spaceborne soil moisture retrieval. The most important observations involved imagery from passive microwave radiometers. The early campaigns proved that the retrieval worked for larger and larger footprints, up to satellite-scale footprints. These provided the solid basis for proposing the satellite missions. More recent campaigns have explored other aspects such as retrieval performance through greater amounts of vegetation. All of these campaigns featured extensive ground truth collection over a range of grid spacings, to provide a basis for examining the effects of subpixel variability. However, the native footprint size of the airborne L-band radiometers was always a few hundred meters. During the recently completed (November, 2005) National Airborne Field Experiment (NAFE) campaign in Australia, a compact L-band radiometer was deployed on a small aircraft. This new combination permitted routine observations at native resolutions as high as 60 meters, substantially finer than in previous airborne soil moisture campaigns, as well as satellite footprint areal coverage. The radiometer, the Polarimetric L-band Microwave Radiometer (PLMR) performed extremely well and operations included extensive calibration-related observations. Thus, along with the extensive fine-scale ground truth, the NAFE dataset includes all the ingredients for the first scaling studies involving very-high-native resolution soil moisture observations and the effects of vegetation, roughness, etc. A brief overview of the NAFE will be presented, then examples of the airborne observations with resolutions from 60 m to 1 km will be shown, and early results from scaling studies will be discussed.

The global SMOS Level 3 daily soil moisture and brightness temperature maps

NASA Astrophysics Data System (ADS)

Bitar, Ahmad Al; Mialon, Arnaud; Kerr, Yann H.; Cabot, François; Richaume, Philippe; Jacquette, Elsa; Quesney, Arnaud; Mahmoodi, Ali; Tarot, Stéphane; Parrens, Marie; Al-Yaari, Amen; Pellarin, Thierry; Rodriguez-Fernandez, Nemesio; Wigneron, Jean-Pierre

2017-06-01

The objective of this paper is to present the multi-orbit (MO) surface soil moisture (SM) and angle-binned brightness temperature (TB) products for the SMOS (Soil Moisture and Ocean Salinity) mission based on a new multi-orbit algorithm. The Level 3 algorithm at CATDS (Centre Aval de Traitement des Données SMOS) makes use of MO retrieval to enhance the robustness and quality of SM retrievals. The motivation of the approach is to make use of the longer temporal autocorrelation length of the vegetation optical depth (VOD) compared to the corresponding SM autocorrelation in order to enhance the retrievals when an acquisition occurs at the border of the swath. The retrieval algorithm is implemented in a unique operational processor delivering multiple parameters (e.g. SM and VOD) using multi-angular dual-polarisation TB from MO. A subsidiary angle-binned TB product is provided. In this study the Level 3 TB V310 product is showcased and compared to SMAP (Soil Moisture Active Passive) TB. The Level 3 SM V300 product is compared to the single-orbit (SO) retrievals from the Level 2 SM processor from ESA with aligned configuration. The advantages and drawbacks of the Level 3 SM product (L3SM) are discussed. The comparison is done on a global scale between the two datasets and on the local scale with respect to in situ data from AMMA-CATCH and USDA ARS Watershed networks. The results obtained from the global analysis show that the MO implementation enhances the number of retrievals: up to 9 % over certain areas. The comparison with the in situ data shows that the increase in the number of retrievals does not come with a decrease in quality, but rather at the expense of an increased time lag in product availability from 6 h to 3.5 days, which can be a limiting factor for applications like flood forecast but reasonable for drought monitoring and climate change studies. The SMOS L3 soil moisture and L3 brightness temperature products are delivered using an open licence and free of charge using a web application (https://www.catds.fr/sipad/). The RE04 products, versions 300 and 310, used in this paper are also available at ftp://ext-catds-cpdc:catds2010@ftp.ifremer.fr/Land_products/GRIDDED/L3SM/RE04/.

Precipitation estimation using L-Band and C-Band soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterome...

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.

A review of spatial downscaling of satellite remotely sensed soil moisture

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.

2017-06-01

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

Validation of SMAP Radar Vegetation Data Cubes from Agricultural Field Measurements

NASA Astrophysics Data System (ADS)

Tsang, L.; Xu, X.; Liao, T.; Kim, S.; Njoku, E. G.

2012-12-01

The NASA Soil Moisture Active/Passive (SMAP) Mission will be launched in October 2014. The objective of the SMAP mission is to provide global measurements of soil moisture and its freeze/thaw state. These measurements will be used to enhance understanding of processes that link the water, energy and carbon cycles, and to extend the capabilities of weather and climate prediction models. In the active algorithm, the retrieval is performed based on the backscattering data cube, which are characterized by two surface parameters, which are soil moisture and soil surface rms height, and one vegetation parameter, the vegetation water content. We have developed a physical-based forward scattering model to generate the data cube for agricultural fields. To represent the agricultural crops, we include a layer of cylinders and disks on top of the rough surface. The scattering cross section of the vegetation layer and its interaction with the underground soil surface were calculated by the distorted Born approximation, which give explicitly three scattering mechanisms. A) The direct volume scattering B) The double bounce effect as, and C) The double bouncing effects. The direct volume scattering is calculated by using the Body of Revolution code. The double bounce effects, exhibited by the interaction of rough surface with the vegetation layer is considered by modifying the rough surface reflectivity using the coherent wave as computed by Numerical solution of Maxwell equations of 3 Dimensional simulations (NMM3D) of bare soil scattering. The rough surface scattering of the soil was calculated by NMM3D. We have compared the physical scattering models with field measurements. In the field campaign, the measurements were made on soil moisture, rough surface rms heights and vegetation water content as well as geometric parameters of vegetation. The three main crops lands are grassland, cornfield and soybean fields. The corresponding data cubes are validated using SGP99, SMEX02 and SMEX 08 field experiments.

Development of a SMAP-Based Drought Monitoring Product

NASA Astrophysics Data System (ADS)

Sadri, S.; Wood, E. F.; Pan, M.; Lettenmaier, D. P.

2016-12-01

Agricultural drought is defined as a deficit in the amount of soil moisture over a prolonged period of time. Soil moisture information over time and space provides critical insight for agricultural management, including both water availability for crops and moisture conditions that affect management practices such as fertilizer, pesticide applications, and their impact as non-point pollution runoff. Since April of 2015, NASA's Soil Moisture Active Passive (SMAP) mission has retrieved soil moisture using L-band passive radiometric measurements at a 8 day repeat orbit with a swath of 1000 km that maps the Earth in 2-3 days depending on locations. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP soil moisture in terms of probability percentiles for dry (drought) or wet (pluvial) conditions. SMAP observations do result in retrievals that are spatially and temporally discontinuous. Additionally, the short SMAP record length provides a statistical challenge in estimating a drought index and thus drought risk evaluations. In this presentation, we describe a SMAP drought index for the CONUS region based on near-surface soil moisture percentiles. Because the length of the SMAP data record is limited, we use a Bayesian conditional probability approach to extend the SMAP record back to 1979 based on simulated soil moisture of the same period from the Variable Infiltration Capacity (VIC) Land Surface Model (LSM), simulated by Princeton University. This is feasible because the VIC top soil layer (10 cm) is highly correlated with the SMAP 36 km passive microwave during 2015-2016, with more than half the CONUS grids having a cross-correlation greater than 0.6, and over 0.9 in many regions. Given the extended SMAP record, we construct an empirical probability distribution of near-surface soil moisture drought index showing severities similar to those used by the U.S. Drought Monitor (from D0-D4), for a specific SMAP observation. The analysis is done for each of the 8,150 SMAP grids covering the CONUS domain. Comparisons between the SMAP drought index and that from the VIC LSM are presented for selected recent drought events. Issues such as seasonality, robustness of the fitting, regions of poor SMAP-VIC correlations, and extensions to other areas will be discussed.

Full wave two-dimensional modeling of scattering and inverse scattering for layered rough surfaces with buried objects

NASA Astrophysics Data System (ADS)

Kuo, Chih-Hao

Efficient and accurate modeling of electromagnetic scattering from layered rough surfaces with buried objects finds applications ranging from detection of landmines to remote sensing of subsurface soil moisture. The formulation of a hybrid numerical/analytical solution to electromagnetic scattering from layered rough surfaces is first presented in this dissertation. The solution to scattering from each rough interface is sought independently based on the extended boundary condition method (EBCM), where the scattered fields of each rough interface are expressed as a summation of plane waves and then cast into reflection/transmission matrices. To account for interactions between multiple rough boundaries, the scattering matrix method (SMM) is applied to recursively cascade reflection and transmission matrices of each rough interface and obtain the composite reflection matrix from the overall scattering medium. The validation of this method against the Method of Moments (MoM) and Small Perturbation Method (SPM) is addressed and the numerical results which investigate the potential of low frequency radar systems in estimating deep soil moisture are presented. Computational efficiency of the proposed method is also discussed. In order to demonstrate the capability of this method in modeling coherent multiple scattering phenomena, the proposed method has been employed to analyze backscattering enhancement and satellite peaks due to surface plasmon waves from layered rough surfaces. Numerical results which show the appearance of enhanced backscattered peaks and satellite peaks are presented. Following the development of the EBCM/SMM technique, a technique which incorporates a buried object in layered rough surfaces by employing the T-matrix method and the cylindrical-to-spatial harmonics transformation is proposed. Validation and numerical results are provided. Finally, a multi-frequency polarimetric inversion algorithm for the retrieval of subsurface soil properties using VHF/UHF band radar measurements is devised. The top soil dielectric constant is first determined using an L-band inversion algorithm. For the retrieval of subsurface properties, a time-domain inversion technique is employed together with a parameter optimization for the pulse shape of time delay echoes from VHF/UHF band radar observations. Numerical studies to investigate the accuracy of the proposed inversion technique in presence of errors are addressed.

High-resolution Mapping of Permafrost and Soil Freeze/thaw Dynamics in the Tibetan Plateau Based on Multi-sensor Satellite Observations

NASA Astrophysics Data System (ADS)

Zhang, W.; Yi, Y.; Yang, K.; Kimball, J. S.

2016-12-01

The Tibetan Plateau (TP) is underlain by the world's largest extent of alpine permafrost ( 2.5×106 km2), dominated by sporadic and discontinuous permafrost with strong sensitivity to climate warming. Detailed permafrost distributions and patterns in most of the TP region are still unknown due to extremely sparse in-situ observations in this region characterized by heterogeneous land cover and large temporal dynamics in surface soil moisture conditions. Therefore, satellite-based temperature and moisture observations are essential for high-resolution mapping of permafrost distribution and soil active layer changes in the TP region. In this study, we quantify the TP regional permafrost distribution at 1-km resolution using a detailed satellite data-driven soil thermal process model (GIPL2). The soil thermal model is calibrated and validated using in-situ soil temperature/moisture observations from the CAMP/Tibet field campaign (9 sites: 0-300 cm soil depth sampling from 1997-2007), a multi-scale soil moisture and temperature monitoring network in the central TP (CTP-SMTMN, 57 sites: 5-40 cm, 2010-2014) and across the whole plateau (China Meteorology Administration, 98 sites: 0-320 cm, 2000-2015). Our preliminary results using the CAMP/Tibet and CTP-SMTMN network observations indicate strong controls of surface thermal and soil moisture conditions on soil freeze/thaw dynamics, which vary greatly with underlying topography, soil texture and vegetation cover. For regional mapping of soil freeze/thaw and permafrost dynamics, we use the most recent soil moisture retrievals from the NASA SMAP (Soil Moisture Active Passive) sensor to account for the effects of temporal soil moisture dynamics on soil thermal heat transfer, with surface thermal conditions defined by MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature records. Our study provides the first 1-km map of spatial patterns and recent changes of permafrost conditions in the TP.

Forest canopy height from Multiangle Imaging SpectroRadiometer (MISR) assessed with high resolution discrete return lidar

Treesearch

Mark Chopping; Anne Nolin; Gretchen G. Moisen; John V. Martonchik; Michael Bull

2009-01-01

In this study retrievals of forest canopy height were obtained through adjustment of a simple geometricoptical (GO) model against red band surface bidirectional reflectance estimates from NASA's Multiangle Imaging SpectroRadiometer (MISR), mapped to a 250 m grid. The soil-understory background contribution was partly isolated prior to inversion using regression...

Bias Reduction in Short Records of Satellite Soil Moisture

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Koster, Randal D.

2004-01-01

Although surface soil moisture data from different sources (satellite retrievals, ground measurements, and land model integrations of observed meteorological forcing data) have been shown to contain consistent and useful information in their seasonal cycle and anomaly signals, they typically exhibit very different mean values and variability. These biases pose a severe obstacle to exploiting the useful information contained in satellite retrievals through data assimilation. A simple method of bias removal is to match the cumulative distribution functions (cdf) of the satellite and model data. However, accurate cdf estimation typically requires a long record of satellite data. We demonstrate here that by wing spatial sampling with a 2 degree moving window we can obtain local statistics based on a one-year satellite record that are a good approximation to those that would be derived from a much longer time series. This result should increase the usefulness of relatively short satellite data records.

Effects of varying soil moisture contents and vegetation canopies on microwave emissions

NASA Technical Reports Server (NTRS)

Burke, H.-H. K.; Schmugge, T. J.

1982-01-01

Results of NASA airborne passive microwave scans of bare and vegetated fields for comparison with ground truth tests are discussed and a model for atmospheric scattering of radiation by vegetation is detailed. On-board radiometers obtained data at 21, 2.8, and 1.67 cm during three passes over each of 46 fields, 28 of which were bare and the others having wheat or alfalfa. Ground-based sampling included moisture in five layers down to 15 cm in addition to soil temperature. The relationships among the brightness temperature and soil moisture, as well as the surface roughness and the vegetation canopy were examined. A model was developed for the dielectric coefficient and volume scattering for a vegetation medium. L- to C-band data were found useful for retrieving soil information directly. A surface moisture content of 5-35% yielded an emissivity of 0.9-0.7. The data agreed well with a combined multilayer radiative transfer model with simple roughness correction.

Review of crop growth and soil moisture monitoring from a ground-based instrument implementing the Interference Pattern GNSS-R Technique

NASA Astrophysics Data System (ADS)

Rodriguez-Alvarez, N.; Bosch-Lluis, X.; Camps, A.; Aguasca, A.; Vall-Llossera, M.; Valencia, E.; Ramos-Perez, I.; Park, H.

2011-12-01

Reflectometry using Global Navigation Satellite Systems signals (GNSSR) has been the focus of many studies during the past few years for a number of applications over different scenarios as land, ocean or snow and ice surfaces. In the past decade, its potential has increased yearly, with improved receivers and signal processors, from generic GNSS receivers whose signals were recorded in magnetic tapes to instruments that measure full Delay Doppler Maps (the power distribution of the reflected GNSS signal over the 2-D space of delay offsets and Doppler shifts) in real time. At present, these techniques are considered to be promising tools to retrieve geophysical parameters such as soil moisture, vegetation height, topography, altimetry, sea state and ice and snow thickness, among others. This paper focuses on the land geophysical retrievals (topography, vegetation height and soil moisture) performed from a ground-based instrument using the Interference Pattern Technique (IPT). This technique consists of the measurement of the power fluctuations of the interference signal resulting from the simultaneous reception of the direct and the reflected GNSS signals. The latest experiment performed using this technique over a maize field is shown in this paper. After a review of the previous results, this paper presents the latest experiment performed using this technique over a maize field. This new study provides a deeper analysis on the soil moisture retrieval by observing three irrigation-drying cycles and comparing them to different depths soil moisture probes. Furthermore, the height of the maize, almost 300 cm, has allowed testing the capabilities of the technique over dense and packed vegetation layers, with high vegetation water content.

Recent advances in the salinity retrieval algorithms for Aquarius and Soil Moisture Active Passive (SMAP)

NASA Astrophysics Data System (ADS)

Meissner, Thomas; Wentz, Frank; Lee, Tong

2017-04-01

Our presentation discusses the latest improvements in the salinity retrievals both for Aquarius and Soil Moisture Active-Passive (SMAP) since the last releases. The Aquarius V4.0 was released in June 2015. The final V5.0 release is planned for late 2017. SMAP V 2.0 has been released in September 2016. We will present validation results for both Aquarius V5.0 pre-release and SMAP V2.0 salinity comparing with near-surface salinity measurements from Argo floats. We show that salty biases at higher northern latitudes in Aquarius V4.0 can be explained by inaccuracy in the model used in correcting for the absorption by atmospheric oxygen. These biases will be mitigated in V5.0 by fine-tuning the parameters in the oxygen absorption model. The full 360-degree look capability of SMAP makes it possible to take observations from the forward and backward looking direction at the same instance of time. This two-look capability aids the salinity retrievals. One of the largest spurious contaminations in the salinity retrievals is caused by the galactic reflection from the ocean surface. Because in most instances the reflected galaxy appears only in either the forward or the backward look, it is possible to determine its contribution by taking the difference of the measured SMAP brightness temperatures between the two looks. Our result suggests that the surface roughness that is used in the galactic correction needs to be increased and also the estimated strength of some of the galactic sources need to be slightly adjusted. The improved galaxy correction has been implemented in SMAP V2.0 retrieval and will be included in Aquarius V5.0 as well. It helps the mitigation of residual zonal and temporal biases that were present in both products. Another major cause of the observed zonal biases in SMAP is the emissive SMAP mesh antenna. In order to correct for it, an accurate knowledge of the emissivity of the antenna and its physical temperature are required. We discuss the improvements in the correction for the emissive SMAP antenna in SMAP V2.0 over V1.0.

From ASCAT to Sentinel-1: Soil Moisture Monitoring using European C-Band Radars

NASA Astrophysics Data System (ADS)

Wagner, Wolfgang; Bauer-Marschallinger, Bernhard; Hochstöger, Simon

2016-04-01

The Advanced Scatterometer (ASCAT) is a C-Band radar instrument flown on board of the series of three METOP satellites. Albeit not operating in one of the more favorable longer wavelength ranges (S, L or P-band) as the dedicated Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions, it is one of main microwave sensors used for monitoring of soil moisture on a global scale. Its attractiveness for soil moisture monitoring applications stems from its operational status, high radiometric accuracy and stability, short revisit time, multiple viewing directions and long heritage (Wagner et al. 2013). From an application perspective, its main limitation is its spatial resolution of about 25 km, which does not allow resolving soil moisture patterns driven by smaller-scale hydrometeorological processes (e.g. convective precipitation, runoff patterns, etc.) that are themselves related to highly variable land surface characteristics (soil characteristics, topography, vegetation cover, etc.). Fortunately, the technique of aperture synthesis allows to significantly improve the spatial resolution of spaceborne radar instruments up to the meter scale. Yet, past Synthetic Aperture Radar (SAR) missions had not yet been designed to achieve a short revisit time required for soil moisture monitoring. This has only changed recently with the development and launch of SMAP (Entekhabi et al. 2010) and Sentinel-1 (Hornacek et al. 2012). Unfortunately, the SMAP radar failed only after a few months of operations, which leaves Sentinel-1 as the only currently operational SAR mission capable of delivering high-resolution radar observations with a revisit time of about three days for Europe, about weekly for most crop growing regions worldwide, and about bi-weekly to monthly over the rest of the land surface area. Like ASCAT, Sentinel-1 acquires C-band backscatter data in VV polarization over land. Therefore, for the interpretation of both ASCAT and Sentinel-1 backscatter observation, the same physical processes and geophysical variables (e.g. vegetation optical depth, surface roughness, soil volume scattering, etc.) need to be considered. The difference lies mainly in the scaling, i.e. how prominently the different variables influence the C-band data at the different spatial (25 km versus 20 m) and temporal (daily versus 3-30 days repeat coverage) scales. Therefore, while the general properties of soil moisture retrievals schemes used for ASCAT and Sentinel-1 can be the same, the details of the algorithm and parameterization will be different. This presentation will review similarities and differences of soil moisture retrieval approaches used for ASCAT and Sentinel-1, with a focus on the change detection method developed by TU Wien. Some first comparisons of ASCAT and Sentinel-1 soil moisture data over Europe will also be shown. REFERENCES Entekhabi, D., Njoku, E.G., O'Neill, P.E., Kellog, K.H., Crow, W.T., Edelstein, W.N., Entin, J.K., Goodman, S.D., Jackson, T.J., Johnson, J., Kimball, J., Piepmeier, J.R., Koster, R., Martin, N., McDonald, K.C., Moghaddam, M., Moran, S., Reichle, R., Shi, J.C., Spencer, M.W., Thurman, S.W., Tsang, L., & Van Zyl, J. (2010). The Soil Moisture Active Passive (SMAP) mission. Proceedings of the IEEE, 98, 704-716 Hornacek, M., Wagner, W., Sabel, D., Truong, H.L., Snoeij, P., Hahmann, T., Diedrich, E., & Doubkova, M. (2012). Potential for High Resolution Systematic Global Surface Soil Moisture Retrieval via Change Detection Using Sentinel-1. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, 1303-1311 Wagner, W., Hahn, S., Kidd, R., Melzer, T., Bartalis, Z., Hasenauer, S., Figa-Saldana, J., De Rosnay, P., Jann, A., Schneider, S., Komma, J., Kubu, G., Brugger, K., Aubrecht, C., Züger, C., Gangkofer, U., Kienberger, S., Brocca, L., Wang, Y., Blöschl, G., Eitzinger, J., Steinnocher, K., Zeil, P., & Rubel, F. (2013). The ASCAT soil moisture product: A review of its specifications, validation results, and emerging applications. Meteorologische Zeitschrift, 22, 5-33

Surface Roughness Retrieval By Inversion Of Hapke Model: A Multi-scale Approach

NASA Astrophysics Data System (ADS)

Labarre, S.; Ferrari, C. C.; Jacquemoud, S.

2015-12-01

Surface roughness is a key property of soils that affects the various processes involved in their evolution such as solar absorption, erosion or moisture, both on Earth and other Solar System surfaces. In the 80's, B.Hapke provided an approximate analytic solution for the bidirectional reflectance distribution function (BRDF) of a particulate medium and, later on, included the effect of surface roughness as a correction factor for the BRDF of a smooth surface. The effect of roughness on the BRDF is modeled as a shadowing function of the so-called roughness parameter, which is the mean slope angle of the facets composing the surface integrated over all scales from the sub-millimeter to the kilometer scales. Hapke model is widely used in planetary sciences to retrieve the roughness parameter from observed BRDFs. Yet the physical meaning of the retrieved roughness is not clear as the scale at which it happens is not defined. This work aims at understanding the relative impact of the roughness defined at each scale to the BRDF in order to test the ability of the singly retrieved roughness parameter at describing the ground truth. We propose to perform a wavelet analysis on meter-sized digital elevation models (DEM) generated from various volcanic and sedimentary terrains at high-mm-scale spatial resolution. It consists in splitting the DEM in several spatial frequencies and in simulating the BRDF at each scale with a ray-tracing code. Also the global BRDF is simulated so that the relative contribution of each scale can be studied. Then the Hapke model is fitted to the global BRDF to retrieve the roughness parameter. We will expose and discuss the results of this study. Figure: BRDF of a'a lava DEM simulated at varying azimut (φi) and incidence angles (i), in the principal plan. The direction of the light source is given by the colored squares. Mean slope angle of the surface is 36°.

Evaluation of dielectric mixing models for microwave soil moisture retrieval using data from the Combined Radar/Radiometer (ComRAD) ground-based SMAP simulator

USDA-ARS?s Scientific Manuscript database

Soil moisture measurements are required to improve our understanding of hydrological processes, ecosystem functions, and linkages between the Earth’s water, energy, and carbon cycles. The efficient retrieval of soil moisture depends on various factors in which soil dielectric mixing models are consi...

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

The herbicide glyphosate and its metabolite AMPA in the Lavaux vineyard area, western Switzerland: proof of widespread export to surface waters. Part II: the role of infiltration and surface runoff.

PubMed

Daouk, Silwan; De Alencastro, Luiz F; Pfeifer, Hans-Rudolf

2013-01-01

Two parcels of the Lavaux vineyard area, western Switzerland, were studied to assess to which extent the widely used herbicide, glyphosate, and its metabolite aminomethylphosphonic acid (AMPA) were retained in the soil or exported to surface waters. They were equipped at their bottom with porous ceramic cups and runoff collectors, which allowed retrieving water samples for the growing seasons 2010 and 2011. The role of slope, soil properties and rainfall regime in their export was examined and the surface runoff/throughflows ratio was determined with a mass balance. Our results revealed elevated glyphosate and AMPA concentrations at 60 and 80 cm depth at parcel bottoms, suggesting their infiltration in the upper parts of the parcels and the presence of preferential flows in the studied parcels. Indeed, the succession of rainy days induced the gradual saturation of the soil porosity, leading to rapid infiltration through macropores, as well as surface runoff formation. Furthermore, the presence of more impervious weathered marls at 100 cm depth induced throughflows, the importance of which in the lateral transport of the herbicide molecules was determined by the slope steepness. Mobility of glyphosate and AMPA into the unsaturated zone was thus likely driven by precipitation regime and soil characteristics, such as slope, porosity structure and layer permeability discrepancy. Important rainfall events (>10 mm/day) were clearly exporting molecules from the soil top layer, as indicated by important concentrations in runoff samples. The mass balance showed that total loss (10-20%) mainly occurred through surface runoff (96%) and, to a minor extent, by throughflows in soils (4%), with subsequent exfiltration to surface waters.

Intercomparison of four remote-sensing-based energy balance methods to retrieve surface evapotranspiration and water stress of irrigated fields in semi-arid climate

NASA Astrophysics Data System (ADS)

Chirouze, J.; Boulet, G.; Jarlan, L.; Fieuzal, R.; Rodriguez, J. C.; Ezzahar, J.; Er-Raki, S.; Bigeard, G.; Merlin, O.; Garatuza-Payan, J.; Watts, C.; Chehbouni, G.

2014-03-01

Instantaneous evapotranspiration rates and surface water stress levels can be deduced from remotely sensed surface temperature data through the surface energy budget. Two families of methods can be defined: the contextual methods, where stress levels are scaled on a given image between hot/dry and cool/wet pixels for a particular vegetation cover, and single-pixel methods, which evaluate latent heat as the residual of the surface energy balance for one pixel independently from the others. Four models, two contextual (S-SEBI and a modified triangle method, named VIT) and two single-pixel (TSEB, SEBS) are applied over one growing season (December-May) for a 4 km × 4 km irrigated agricultural area in the semi-arid northern Mexico. Their performance, both at local and spatial standpoints, are compared relatively to energy balance data acquired at seven locations within the area, as well as an uncalibrated soil-vegetation-atmosphere transfer (SVAT) model forced with local in situ data including observed irrigation and rainfall amounts. Stress levels are not always well retrieved by most models, but S-SEBI as well as TSEB, although slightly biased, show good performance. The drop in model performance is observed for all models when vegetation is senescent, mostly due to a poor partitioning both between turbulent fluxes and between the soil/plant components of the latent heat flux and the available energy. As expected, contextual methods perform well when contrasted soil moisture and vegetation conditions are encountered in the same image (therefore, especially in spring and early summer) while they tend to exaggerate the spread in water status in more homogeneous conditions (especially in winter). Surface energy balance models run with available remotely sensed products prove to be nearly as accurate as the uncalibrated SVAT model forced with in situ data.

Mapping of bare soil surface parameters from TerraSAR-X radar images over a semi-arid region

NASA Astrophysics Data System (ADS)

Gorrab, A.; Zribi, M.; Baghdadi, N.; Lili Chabaane, Z.

2015-10-01

The goal of this paper is to analyze the sensitivity of X-band SAR (TerraSAR-X) signals as a function of different physical bare soil parameters (soil moisture, soil roughness), and to demonstrate that it is possible to estimate of both soil moisture and texture from the same experimental campaign, using a single radar signal configuration (one incidence angle, one polarization). Firstly, we analyzed statistically the relationships between X-band SAR (TerraSAR-X) backscattering signals function of soil moisture and different roughness parameters (the root mean square height Hrms, the Zs parameter and the Zg parameter) at HH polarization and for an incidence angle about 36°, over a semi-arid site in Tunisia (North Africa). Results have shown a high sensitivity of real radar data to the two soil parameters: roughness and moisture. A linear relationship is obtained between volumetric soil moisture and radar signal. A logarithmic correlation is observed between backscattering coefficient and all roughness parameters. The highest dynamic sensitivity is obtained with Zg parameter. Then, we proposed to retrieve of both soil moisture and texture using these multi-temporal X-band SAR images. Our approach is based on the change detection method and combines the seven radar images with different continuous thetaprobe measurements. To estimate soil moisture from X-band SAR data, we analyzed statistically the sensitivity between radar measurements and ground soil moisture derived from permanent thetaprobe stations. Our approaches are applied over bare soil class identified from an optical image SPOT / HRV acquired in the same period of measurements. Results have shown linear relationship for the radar signals as a function of volumetric soil moisture with high sensitivity about 0.21 dB/vol%. For estimation of change in soil moisture, we considered two options: (1) roughness variations during the three-month radar acquisition campaigns were not accounted for; (2) a simple correction for temporal variations in roughness was included. The results reveal a small improvement in the estimation of soil moisture when a correction for temporal variations in roughness is introduced. Finally, by considering the estimated temporal dynamics of soil moisture, a methodology is proposed for the retrieval of clay and sand content (expressed as percentages) in soil. Two empirical relationships were established between the mean moisture values retrieved from the seven acquired radar images and the two soil texture components over 36 test fields. Validation of the proposed approach was carried out over a second set of 34 fields, showing that highly accurate clay estimations can be achieved.

Coincident Retrieval of Ocean Surface Roughness and Salinity Using Airborne and Satellite Microwave Radiometry and Reflectometry Measurements during the Carolina Offshore (Caro) Experiment.

NASA Astrophysics Data System (ADS)

Burrage, D. M.; Wesson, J. C.; Wang, D. W.; Garrison, J. L.; Zhang, H.

2017-12-01

The launch of the Cyclone Global Navigation Satellite System (CYGNSS) constellation of 8 microsats carrying GPS L-band reflectometers on 15 Dec., 2016, and continued operation of the L-band radiometer on the European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite, allow these complementary technologies to coincidentally retrieve Ocean surface roughness (Mean Square Slope, MSS), Surface Wind speed (WSP), and Sea Surface Salinity (SSS). The Carolina Offshore (Caro) airborne experiment was conducted jointly by NRL SSC and Purdue University from 7-11 May, 2017 with the goal of under-flying CYGNSS and SMOS and overflying NOAA buoys, to obtain high-resolution reflectometer and radiometer data for combined retrieval of MSS, SSS and WSP on the continental shelf. Airborne instruments included NRL's Salinity Temperature and Roughness Remote Scanner (STARRS) L-, C- and IR-band radiometer system, and a 4-channel dual-pol L-band (GPS) and S-band (XM radio) reflectometer, built by Purdue University. Flights either crossed NOAA buoys on various headings, or intersected with specular point ground tracks at predicted CYGNSS overpass times. Prevailing winds during Caro were light to moderate (1-8 m/s), so specular returns dominated the reflectometer Delay Doppler Maps (DDMs), and MSS was generally low. In contrast, stronger winds (1-12 m/s) and rougher seas (wave heights 1-5 m) were experienced during the preceding Maine Offshore (Maineo) experiment in March, 2016. Several DDM observables were used to retrieve MSS and WSP, and radiometer brightness temperatures produced Sea Surface Temperature (SST), SSS and also WSP estimates. The complementary relationship of Kirchoff's formula e+r=1, between radiometric emissivity, e, and reflectivity, r, was exploited to seek consistent estimates of MSS, and use it to correct the SSS retrievals for sea surface roughness effects. The relative performance and utility of the various airborne and satellite retrieval algorithms were assessed, and the coincident buoy, aircraft and satellite retrievals of MSS, WSP and SSS were compared. During Caro WSP from the different instruments generally agreed. Some anomalously high wind retrievals found here and elsewhere in current CYGNSS Level 2 data may yield to the science team's recent L1 calibration revision.

An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian penisula

USDA-ARS?s Scientific Manuscript database

Soil moisture (SM) can be retrieved from active microwave (AM)-, passive microwave (PM)- and thermal infrared (TIR)-observations, each having their unique spatial- and temporal-coverage. A limitation of TIR-based SM retrievals is its dependency on cloud-free conditions, while microwave retrievals ar...

Exploring the Validity Range of the Polarimetric Two-Scale Two-Component Model for Soil Moisture Retrieval by Using AGRISAR Data

NASA Astrophysics Data System (ADS)

Di Martino, Gerardo; Iodice, Antonio; Natale, Antonio; Riccio, Daniele; Ruello, Giuseppe

2015-04-01

The recently proposed polarimetric two-scale two- component model (PTSTCM) in principle allows us obtaining a reasonable estimation of the soil moisture even in moderately vegetated areas, where the volumetric scattering contribution is non-negligible, provided that the surface component is dominant and the double-bounce component is negligible. Here we test the PTSTCM validity range by applying it to polarimetric SAR data acquired on areas for which, at the same times of SAR acquisitions, ground measurements of soil moisture were performed. In particular, we employ the AGRISAR'06 database, which includes data from several fields covering a period that spans all the phases of vegetation growth.

Soil Moisture Remote Sensing with GNSS-R at the Valencia Anchor Station. The SOMOSTA (Soil Moisture Station) Experiment

NASA Astrophysics Data System (ADS)

Lopez-Baeza, Ernesto

2016-07-01

In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on soil moisture monitoring byGlobal Navigation Satellite System Reflected signals(GNSS-R) at the Valencia Anchor Station is introduced. L-band microwaves have very good advantages in soil moisture remote sensing, for being unaffected by clouds and the atmosphere, and for the ability to penetrate vegetation. During this experimental campaign, the ESA GNSS-R Oceanpal antenna was installed on the same tower as the ESA ELBARA-II passive microwave radiometer, both measuring instruments having similar field of view. This experiment is fruitfully framed within the ESA - China Programme of Collaboration on GNSS-R. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and two down-looking antennas for receiving LHCP (left-hand circular polarisation) and RHCP (right-hand circular polarisation) reflected signals from the soil surface. We could collect data from the three different antennas through the two channels of Oceanpal and, in addition, calibration could be performed to reduce the impact from the differing channels. Reflectivity was thus measured and soil moisture could be retrieved by the L- MEB (L-band Microwave Emission of the Biosphere) model considering the effect of vegetation optical thickness and soil roughness. By contrasting GNSS-R and ELBARA-II radiometer data, a negative correlation existed between reflectivity measured by GNSS-R and brightness temperature measured by the radiometer. The two parameters represent reflection and absorption of the soil. Soil moisture retrieved by both L-band remote sensing methods shows good agreement. In addition, correspondence with in-situ measurements and rainfall is also good.

Towards SMOS: The 2006 National Airborne Field Experiment Plan

NASA Astrophysics Data System (ADS)

Walker, J. P.; Merlin, O.; Panciera, R.; Kalma, J. D.

2006-05-01

The 2006 National Airborne Field Experiment (NAFE) is the second in a series of two intensive experiments to be conducted in different parts of Australia. The NAFE'05 experiment was undertaken in the Goulburn River catchment during November 2005, with the objective to provide high resolution data for process level understanding of soil moisture retrieval, scaling and data assimilation. The NAFE'06 experiment will be undertaken in the Murrumbidgee catchment during November 2006, with the objective to provide data for SMOS (Soil Moisture and Ocean Salinity) level soil moisture retrieval, downscaling and data assimilation. To meet this objective, PLMR (Polarimetric L-band Multibeam Radiometer) and supporting instruments (TIR and NDVI) will be flown at an altitude of 10,000 ft AGL to provide 1km resolution passive microwave data (and 20m TIR) across a 50km x 50km area every 2-3 days. This will both simulate a SMOS pixel and provide the 1km soil moisture data required for downscale verification, allowing downscaling and near-surface soil moisture assimilation techniques to be tested with remote sensing data which is consistent with that from current (MODIS) and planned (SMOS) satellite sensors.. Additionally, two transects will be flown across the area to provide both 1km multi-angular passive microwave data for SMOS algorithm development, and on the same day, 50m resolution passive microwave data for algorithm verification. The study area contains a total of 13 soil moisture profile and rainfall monitoring sites for assimilation verification, and the transect fight lines are planned to go through 5 of these. Ground monitoring of surface soil moisture and vegetation for algorithm verification will be targeted at these 5 focus farms, with soil moisture measurements made at 250m spacing for 1km resolution flights and 50m spacing for 50m resolution flights. While this experiment has a particular emphasis on the remote sensing of soil moisture, it is open for collaboration from interested scientists from all disciplines of environmental remote sensing and its application. See www.nafe.unimelb.edu.au for more detailed information on these experiments.

Soil Moisture Retrieval with Airborne PALS Instrument over Agricultural Areas in SMAPVEX16

NASA Technical Reports Server (NTRS)

Colliander, Andreas; Jackson, Thomas J.; Cosh, Mike; Misra, Sidharth; Bindlish, Rajat; Powers, Jarrett; McNairn, Heather; Bullock, P.; Berg, A.; Magagi, A.;

1km Soil Moisture from Downsampled Sentinel-1 SAR Data: Harnessing Assets and Overcoming Obstacles.

NASA Astrophysics Data System (ADS)

Bauer-Marschallinger, Bernhard; Cao, Senmao; Schaufler, Stefan; Paulik, Christoph; Naeimi, Vahid; Wagner, Wolfgang

2017-04-01

Radars onboard Earth observing satellites allow estimating Surface Soil Moisture (SSM) regularly and globally. The use of coarse-scale measurements from active or passive radars for SSM retrieval is well established and in operational use. Thanks to the Sentinel-1 mission, launched in 2014 and deploying Synthetic Aperture Radars (SAR), high-resolution radar imagery is routinely available at the scale of 20 meters, with a high revisit frequency of 3-6 days and with unprecedented radiometric accuracy. However, the direct exploitation of high-resolution SAR data for SSM retrieval is complicated by several problems: Small-scaled contributions to the radar backscatter from individual ground features often obscure the soil moisture signal, rendering common algorithms insensitive to SSM. Furthermore, the influence of vegetation dynamics on the radar signal is less understood than in the coarse-scale case, leading to biases during the vegetation period. Finally, the large data volumes of high-resolution remote sensing data present a great load on hardware systems. Consequently, a spatial resampling of the high-resolution SAR data to a 500 meters sampling is done, allowing the exploitation of information at 10 meter sampling, but reducing effectively the inherent uncertainties. The thereof retrieved 1km SSM product aims to describe the soil moisture dynamics at medium scale with high quality. We adopted the TU-Wien Change Detection algorithm to the Sentinel-1 data, which was already successfully used for retrieving SSM from ERS-1/2 and Envisat-ASAR observations. The adoption entails a new method for SAR image resampling, including a masking for pixels that do not carry soil moisture signals, preventing them to spread during downsampling. Furthermore, the observation angle between the radar sensors and the ground is treated in a different way, as Sentinel-1 sensors observe from fixed orbit paths (in contrast to other radar sensors). Here, a regression model is developed that successfully estimates the dependency of radar backscatter to observation angle with statistical parameters from the Sentinel-1 SAR time series archive. We present the Sentinel-1 1km-SSM product generated by the adopted change detection algorithm. The dataset covers the European continent and holds data from October 2014 ongoing. In addition to a validation of the SSM product, the statistical SAR parameters used during SSM retrieval are examined.

Assessment of Version 4 of the SMAP Passive Soil Moisture Standard Product

NASA Technical Reports Server (NTRS)

O'neill, P. O.; Chan, S.; Bindlish, R.; Jackson, T.; Colliander, A.; Dunbar, R.; Chen, F.; Piepmeier, Jeffrey R.; Yueh, S.; Entekhabi, D.;

Distributed Soil Moisture Estimation in a Mountainous Semiarid Basin: Constraining Soil Parameter Uncertainty through Field Studies

NASA Astrophysics Data System (ADS)

Yatheendradas, S.; Vivoni, E.

2007-12-01

A common practice in distributed hydrological modeling is to assign soil hydraulic properties based on coarse textural datasets. For semiarid regions with poor soil information, the performance of a model can be severely constrained due to the high model sensitivity to near-surface soil characteristics. Neglecting the uncertainty in soil hydraulic properties, their spatial variation and their naturally-occurring horizonation can potentially affect the modeled hydrological response. In this study, we investigate such effects using the TIN-based Real-time Integrated Basin Simulator (tRIBS) applied to the mid-sized (100 km2) Sierra Los Locos watershed in northern Sonora, Mexico. The Sierra Los Locos basin is characterized by complex mountainous terrain leading to topographic organization of soil characteristics and ecosystem distributions. We focus on simulations during the 2004 North American Monsoon Experiment (NAME) when intensive soil moisture measurements and aircraft- based soil moisture retrievals are available in the basin. Our experiments focus on soil moisture comparisons at the point, topographic transect and basin scales using a range of different soil characterizations. We compare the distributed soil moisture estimates obtained using (1) a deterministic simulation based on soil texture from coarse soil maps, (2) a set of ensemble simulations that capture soil parameter uncertainty and their spatial distribution, and (3) a set of simulations that conditions the ensemble on recent soil profile measurements. Uncertainties considered in near-surface soil characterization provide insights into their influence on the modeled uncertainty, into the value of soil profile observations, and into effective use of on-going field observations for constraining the soil moisture response uncertainty.

Evaluating Soil Moisture Retrievals from ESA's SMOS and NASA's SMAP Brightness Temperature Datasets

NASA Technical Reports Server (NTRS)

Al-Yaari, A.; Wigernon, J.-P.; Kerr, Y.; Rodriguez-Fernandez, N.; O'Neill, P. E.; Jackson, T. J.; De Lannoy, G. J. M.; Al Bitar, A.; Mialon, A.; Richaume, P.;

Evaluating soil moisture retrievals from ESA’s SMOS and NASA’s SMAP brightness temperature datasets

PubMed Central

Al-Yaari, A.; Wigneron, J.-P.; Kerr, Y.; Rodriguez-Fernandez, N.; O’Neill, P. E.; Jackson, T. J.; De Lannoy, G.J.M.; Al Bitar, A; Mialon, A.; Richaume, P.; Walker, JP; Mahmoodi, A.; Yueh, S.

2018-01-01

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d’études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013 – 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R > 0.80) over the Oznet sites and worst (R = 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R= 0.51 and R= 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets. PMID:29743730

Evaluating soil moisture retrievals from ESA's SMOS and NASA's SMAP brightness temperature datasets.

PubMed

Al-Yaari, A; Wigneron, J-P; Kerr, Y; Rodriguez-Fernandez, N; O'Neill, P E; Jackson, T J; De Lannoy, G J M; Al Bitar, A; Mialon, A; Richaume, P; Walker, J P; Mahmoodi, A; Yueh, S

2017-05-01

Two satellites are currently monitoring surface soil moisture (SM) using L-band observations: SMOS (Soil Moisture and Ocean Salinity), a joint ESA (European Space Agency), CNES (Centre national d'études spatiales), and CDTI (the Spanish government agency with responsibility for space) satellite launched on November 2, 2009 and SMAP (Soil Moisture Active Passive), a National Aeronautics and Space Administration (NASA) satellite successfully launched in January 2015. In this study, we used a multilinear regression approach to retrieve SM from SMAP data to create a global dataset of SM, which is consistent with SM data retrieved from SMOS. This was achieved by calibrating coefficients of the regression model using the CATDS (Centre Aval de Traitement des Données) SMOS Level 3 SM and the horizontally and vertically polarized brightness temperatures (TB) at 40° incidence angle, over the 2013 - 2014 period. Next, this model was applied to SMAP L3 TB data from Apr 2015 to Jul 2016. The retrieved SM from SMAP (referred to here as SMAP_Reg) was compared to: (i) the operational SMAP L3 SM (SMAP_SCA), retrieved using the baseline Single Channel retrieval Algorithm (SCA); and (ii) the operational SMOSL3 SM, derived from the multiangular inversion of the L-MEB model (L-MEB algorithm) (SMOSL3). This inter-comparison was made against in situ soil moisture measurements from more than 400 sites spread over the globe, which are used here as a reference soil moisture dataset. The in situ observations were obtained from the International Soil Moisture Network (ISMN; https://ismn.geo.tuwien.ac.at/) in North of America (PBO_H2O, SCAN, SNOTEL, iRON, and USCRN), in Australia (Oznet), Africa (DAHRA), and in Europe (REMEDHUS, SMOSMANIA, FMI, and RSMN). The agreement was analyzed in terms of four classical statistical criteria: Root Mean Squared Error (RMSE), Bias, Unbiased RMSE (UnbRMSE), and correlation coefficient (R). Results of the comparison of these various products with in situ observations show that the performance of both SMAP products i.e. SMAP_SCA and SMAP_Reg is similar and marginally better to that of the SMOSL3 product particularly over the PBO_H2O, SCAN, and USCRN sites. However, SMOSL3 SM was closer to the in situ observations over the DAHRA and Oznet sites. We found that the correlation between all three datasets and in situ measurements is best (R > 0.80) over the Oznet sites and worst (R = 0.58) over the SNOTEL sites for SMAP_SCA and over the DAHRA and SMOSMANIA sites (R= 0.51 and R= 0.45 for SMAP_Reg and SMOSL3, respectively). The Bias values showed that all products are generally dry, except over RSMN, DAHRA, and Oznet (and FMI for SMAP_SCA). Finally, our analysis provided interesting insights that can be useful to improve the consistency between SMAP and SMOS datasets.

Phosphorus mitigation during springtime runoff by amendments applied to grassed soil.

PubMed

Uusi-Kämppä, J; Turtola, E; Närvänen, A; Jauhiainen, L; Uusitalo, R

2012-01-01

Permanent grass vegetation on sloping soils is an option to protect fields from erosion, but decaying grass may liberate considerable amounts of dissolved reactive P (DRP) in springtime runoff. We studied the effects of freezing and thawing of grassed soil on surface runoff P concentrations by indoor rainfall simulations and tested whether the peak P concentrations could be reduced by amending the soil with P-binding materials containing Ca or Fe. Forty grass-vegetated soil blocks (surface area 0.045 m, depth 0.07 m) were retrieved from two permanent buffer zones on a clay and loam soil in southwest Finland. Four replicates were amended with either: (i) gypsum from phosphoric acid processing (CaSO × 2HO, 6 t ha), (ii) chalk powder (CaCO, 3.3 t ha), (iii) Fe-gypsum (6 t ha) from TiO processing, or (iv) granulated ferric sulfate (Fe[SO], 0.7 t ha), with four replicates serving as untreated controls. Rainfall (3.3 h × 5 mm h) was applied on presaturated samples set at a slope of 5% and the surface runoff was analyzed for DRP, total dissolved P (TDP), total P (TP), and suspended solids. Rainfall simulation was repeated twice after the samples were frozen. Freezing and thawing of the samples increased the surface runoff DRP concentration of the control treatment from 0.19 to 0.46 mg L, up to 2.6-3.7 mg L, with DRP being the main P form in surface runoff. Compared with the controls, surface runoff from soils amended with Fe compounds had 57 to 80% and 47 to 72% lower concentrations of DRP and TP, respectively, but the gypsum and chalk powder did not affect the P concentrations. Thus, amendments containing Fe might be an option to improve DRP retention in, e.g., buffer zones. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Temporal and spatial patterns in vegetation and atmospheric properties from AVIRIS

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

Roberts, D.A.; Green, R.O.; Adams, J.B.

1997-12-01

Little research has focused on the use of imaging spectrometry for change detection. In this paper, the authors apply Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data to the monitoring of seasonal changes in atmospheric water vapor, liquid water, and surface cover in the vicinity of the Jasper Ridge, CA, for three dates in 1992. Apparent surface reflectance was retrieved and water vapor and liquid water mapped by using a radiative-transfer-based inversion that accounts for spatially variable atmospheres. Spectral mixture analysis (SMA) was used to model reflectance data as mixtures of green vegetation (GV), nonphotosynthetic vegetation (NPV), soil, and shade. Temporal andmore » spatial patterns in endmember fractions and liquid water were compared to the normalized difference vegetation index (NDVI). The reflectance retrieval algorithm was tested by using a temporally invariant target.« less

Satellite Sea-surface Salinity Retrieval Dependencies

NASA Astrophysics Data System (ADS)

Bayler, E. J.; Ren, L.

2016-02-01

Comparing satellite sea-surface salinity (SSS) measurements and in situ observations reveals large-scale differences. What causes these differences? In this study, five boxes, sampling various oceanic regimes of the global ocean, provide insights on the relative performance of satellite SSS retrievals with respect to the influences of SST, precipitation and wind speed. The regions sampled are: the Inter-tropical Convergence Zone (ITCZ), the South Pacific Convergence Zone (SPCZ), NASA's Salinity Processes of the Upper-ocean Regional Study (SPURS) area, the North Pacific subarctic region, and the southern Indian Ocean. This study examines satellite SSS data from NASA's Aquarius Mission and ESA's Soil Moisture - Ocean Salinity (SMOS) mission, specifically: Aquarius official Aquarius Data Processing System (ADPS) Level-2 data, experimental Aquarius Combined Active-Passive (CAP) Level-2 SSS data developed by NASA's Jet Propulsion Laboratory (JPL), and SMOS Level-2 data.

Estimating effective roughness parameters of the L-MEB model for soil moisture retrieval using passive microwave observations from SMAPVEX12

USDA-ARS?s Scientific Manuscript database

Although there have been efforts to improve existing soil moisture retrieval algorithms, the ability to estimate soil moisture from passive microwave observations is still hampered by problems in accurately modeling the observed microwave signal. This paper focuses on the estimation of effective sur...

The Hydrosphere State (Hydros) Satellite Mission: An Earth System Pathfinder for Global Mapping of Soil Moisture and Land Freeze/Thaw

NASA Technical Reports Server (NTRS)

Entekhabi, D.; Njoku, E. G.; Spencer, M.; Kim, Y.; Smith, J.; McDonald, K. C.; vanZyl, J.; Houser, P.; Dorion, T.; Koster, R.;

Model Evaluation with Multi-wavelength Satellite Observations Using a Neural Network

NASA Astrophysics Data System (ADS)

Kolassa, Jana; Jimenez, Carlos; Aires, Filipe

2013-04-01

A methodology has been developed to evaluate fields of modelled parameters against a set of satellite observations. The method employs a Neural Network (NN) to construct a statistical model capturing the relationship between the satellite observations and the parameter from a land surface model, in this case the Soil Moisture (SM). This statistical model is then used to estimate the parameter of interest from the set of satellite observations. These estimates are compared to the modelled parameter in order to detect local deviations indicating a possible problem in the model or in the satellite observations. Several synthetic tests, during which an artificial error was added to the"true" soil moisture fields, showed that the methodology is able to correct the errors (Jimenez et al., submitted, 2012). This evaluation technique is very general and can be applied to any modelled parameter for which sensitive satellite observations are available. The use of NNs simplifies the evaluation of the model against satellite observations and is particularly well-suited to utilize the synergy from the observations at different wavelengths (Aires et al., 2005, 2012). In this study the proposed methodology has been applied to evaluate SM fields from a number of land surface models against a synergy of satellite observations from passive and active microwave, infrared and visible sensors. In an inter-comparison of the performance of several land surface models (ORCHIDEE (de Rosnay et al., 2002), HTESSEL (Balsamo et al., 2009), JULES (Best et al., 2011) ) it was found that the soil moisture fields from JULES, HTESSEL and ORCHIDEE are very consistent with the observations, but ORCHIDEE soil moisture shows larger local deviations close to some river basins (Kolassa et al., in press, 2012; Jimenez et al., submitted, 2012). Differences between all models and the observations could also be observed in the Eastern US and over mountainous regions, however, the errors here are more likely linked to the retrieved SM uncertainties. The proposed methodology can also be used to evaluate the quality of the model forcings: two soil moisture fields from ORCHIDEE using WATCH (Weedon et al., 2011) and ERA-interim (Balsamo et al., 2010) forcings were analysed. It was shown that the WATCH forcing data are more optimal, underlining the importance of forcing data for the accuracy of model predictions (Kolassa et al., in press, 2012). References Aires, F., Prigent, C., and Rossow, W.B. (2005), Sensitivity of satellite microwave and infrared observations to soil moisture at a global scale: 2. Global statistical relationships, J. Geophys. Res., 110, D11103, doi:10.1029/2004JD005094. Aires, F., O. Aznay, C. Prigent, M. Paul, F. Bernardo, Synergetic multi-wavelegnth remote sensing versus a posteriori combination of retrieved products: Application for the retrieval of atmospheric profiles using MetOp measurements, J. Geophys. Res., 2011 Balsamo, G., Viterbo, P., Beljaars, A., van den Hurk, B., Hirschi, M., Betts, A. and Scipa,l K. (2009) A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrol., 10, 623-643 Balsamo, G., Boussetta, S., Lopez, P., and Ferranti, L. (2010), Evaluation of ERA-Interim and ERA- Interim-GPCP-rescaled precipitation over the U.S.A., ERA-Report Series, 5, pp. 10. Best, M. J., M. Pryor, D. B. Clark, G. G. Rooney, R .L. H. Essery, C. B. Ménard, J. M. Edwards, M. A. Hendry, A. Porson, N. Gedney, L. M. Mercado, S. Sitch, E. Blyth, O. Boucher, P. M. Cox, C. S. B. Grimmond, and R. J. Harding (2011), The Joint UK Land Environment Simulator (JULES), model description - Part 1: Energy and water fluxes, Geosci. Model Dev., 4 Jimenez, C., Clark, D., Kolassa, J., Aires, F., Prigent, C., and Blyth, E. (2012), A joint analysis of modeled soil moisture fields and satellite observations (2012), J. Geophys. Res., Kolassa, J., Aires, F., Polcher, J., Prigent, C., and Pereira, J. (2012), Soil moisture Retrieval from Multi-instrument Observations: Information Content Analysis and Retrieval Methodology (2012), J. Geophys. Res., de Rosnay, P., Polcher, J., Bruen, M., Laval, K. (2002), Impact of a physically based soil water flow and soil-plant interaction representation for modeling large-scale land surface processes, J. Geophys. Res., 107, D11, 4118, 10.1029/2001JD000634 Weedon, G.P., Gomes, S., Viterbo, P., Shuttleworth, W.J., Blyth, E., O ̈sterle, H., Adam, J.C., Bellouin, N., Boucher, O., and Best, M. (2011), Creation of the WATCH Forcing Data and its use to assess global and regional reference crop evaporation over land during the twentieth century, J. Hydrometeorology,12, 5, pp. 823-848.

Urban heat island

NASA Technical Reports Server (NTRS)

Kim, Hongsuk H.

1991-01-01

The phenomenon of urban heat island was investigated by the use of LANDSAT Thematic Mapper data sets collected over the metropolitan area of Washington DC (U.S.). By combining the retrieved spectral albedos and temperatures, urban modification on radiation budgets of five surface categories were analyzed. The surface radiation budget imagery of the area show that urban heating is attributable to a large heat flux from the rapidly heating surfaces of asphalt, bare soil and short grass. In summer, symptoms of diurnal heating begin to appear by mid morning and can be about 10 degrees warmer than nearby woodlands in summer.

The Value of GRACE Data in Improving, Assessing and Evaluating Land Surface and Climate Models

NASA Astrophysics Data System (ADS)

Yang, Z.

2011-12-01

I will review how the Gravity Recovery and Climate Experiment (GRACE) satellite measurements have improved land surface models that are developed for weather, climate, and hydrological studies. GRACE-derived terrestrial water storage (TWS) changes have been successfully used to assess and evaluate the improved representations of land-surface hydrological processes such as groundwater-soil moisture interaction, frozen soil and infiltration, and the topographic control on runoff production, as evident in the simulations from the latest Noah-MP, the Community Land Model, and the Community Climate System Model. GRACE data sets have made it possible to estimate key terrestrial water storage components (snow mass, surface water, groundwater or water table depth), biomass, and surface water fluxes (evapotranspiration, solid precipitation, melt of snow/ice). Many of the examples will draw from my Land, Environment and Atmosphere Dynamics group's work on land surface model developments, snow mass retrieval, and multi-sensor snow data assimilation using the ensemble Karman filter and the ensemble Karman smoother. Finally, I will briefly outline some future directions in using GRACE in land surface modeling.

Multi-sensor data processing method for improved satellite retrievals

NASA Astrophysics Data System (ADS)

Fan, Xingwang

2017-04-01

Satellite remote sensing has provided massive data that improve the overall accuracy and extend the time series of environmental studies. In reflective solar bands, satellite data are related to land surface properties via radiative transfer (RT) equations. These equations generally include sensor-related (calibration coefficients), atmosphere-related (aerosol optical thickness) and surface-related (surface reflectance) parameters. It is an ill-posed problem to solve three parameters with only one RT equation. Even if there are two RT equations (dual-sensor data), the problem is still unsolvable. However, a robust solution can be obtained when any two parameters are known. If surface and atmosphere are known, sensor intercalibration can be performed. For example, the Advanced Very High Resolution Radiometer (AVHRR) was calibrated to the MODerate-resolution Imaging Spectroradiometer (MODIS) in Fan and Liu (2014) [Fan, X., and Liu, Y. (2014). Quantifying the relationship between intersensor images in solar reflective bands: Implications for intercalibration. IEEE Transactions on Geoscience and Remote Sensing, 52(12), 7727-7737.]. If sensor and surface are known, atmospheric data can be retrieved. For example, aerosol data were retrieved using tandem TERRA and AQUA MODIS images in Fan and Liu (2016a) [Fan, X., and Liu, Y. (2016a). Exploiting TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. Remote Sensing, 8(12), 996.]. If sensor and atmosphere are known, data consistency can be obtained. For example, Normalized Difference Vegetation Index (NDVI) data were intercalibrated among coarse-resolution sensors in Fan and Liu (2016b) [Fan, X., and Liu, Y. (2016b). A global study of NDVI difference among moderate-resolution satellite sensors. ISPRS Journal of Photogrammetry and Remote Sensing, 121, 177-191.], and among fine-resolution sensors in Fan and Liu (2017) [Fan, X., and Liu, Y. (2017). A generalized model for intersensor NDVI calibration and its comparison with regression approaches. IEEE Transactions on Geoscience and Remote Sensing, 55(3), doi: 10.1109/TGRS.2016.2635802.]. These studies demonstrate the success of multi-sensor data and novel methods in the research domain of geoscience. These data will benefit remote sensing of terrestrial parameters in decadal timescales, such as soil salinity content in Fan et al. (2016) [Fan, X., Weng, Y., and Tao, J. (2016). Towards decadal soil salinity mapping using Landsat time series data. International Journal of Applied Earth Observation and Geoinformation, 52, 32-41.].

Estimating Sea Surface Salinity and Wind Using Combined Passive and Active L-Band Microwave Observations

NASA Technical Reports Server (NTRS)

Yueh, Simon H.; Chaubell, Mario J.

2012-01-01

Several L-band microwave radiometer and radar missions have been, or will be, operating in space for land and ocean observations. These include the NASA Aquarius mission and the Soil Moisture Active Passive (SMAP) mission, both of which use combined passive/ active L-band instruments. Aquarius s passive/active L-band microwave sensor has been designed to map the salinity field at the surface of the ocean from space. SMAP s primary objectives are for soil moisture and freeze/thaw detection, but it will operate continuously over the ocean, and hence will have significant potential for ocean surface research. In this innovation, an algorithm has been developed to retrieve simultaneously ocean surface salinity and wind from combined passive/active L-band microwave observations of sea surfaces. The algorithm takes advantage of the differing response of brightness temperatures and radar backscatter to salinity, wind speed, and direction, thus minimizing the least squares error (LSE) measure, which signifies the difference between measurements and model functions of brightness temperatures and radar backscatter. The algorithm uses the conjugate gradient method to search for the local minima of the LSE. Three LSE measures with different measurement combinations have been tested. The first LSE measure uses passive microwave data only with retrieval errors reaching 1 to 2 psu (practical salinity units) for salinity, and 1 to 2 m/s for wind speed. The second LSE measure uses both passive and active microwave data for vertical and horizontal polarizations. The addition of active microwave data significantly improves the retrieval accuracy by about a factor of five. To mitigate the impact of Faraday rotation on satellite observations, the third LSE measure uses measurement combinations invariant under the Faraday rotation. For Aquarius, the expected RMS SSS (sea surface salinity) error will be less than about 0.2 psu for low winds, and increases to 0.3 psu at 25 m/s wind speed for warm waters (25 C). To achieve the required 0.2 psu accuracy, the impact of sea surface roughness (e.g. wind-generated ripples) on the observed brightness temperature has to be corrected to better than one tenth of a degree Kelvin. With this algorithm, the accuracy of retrieved wind speed will be high, varying from a few tenths to 0.6 m/s. The expected direction accuracy is also excellent (less than 10 ) for mid to high winds, but degrades for lower speeds (less than 7 m/s).

Monitoring and Modeling Crop Health and Water Use via in-situ, Airborne and Space-based Platforms

NASA Astrophysics Data System (ADS)

McCabe, M. F.

2014-12-01

The accurate retrieval of plant water use, health and function together with soil state and condition, represent key objectives in the management and monitoring of large-scale agricultural production. In regions of water shortage or stress, understanding the sustainable use of available water supplies is critical. Unfortunately, this need is all too often limited by a lack of reliable observations. Techniques that balance the demand for reliable ground-based data with the rapid retrieval of spatially distributed crop characteristics represent a needed line of research. Data from in-situ monitoring coupled with advances in satellite retrievals of key land surface variables, provide the information necessary to characterize many crop health and water use features, including evaporation, leaf-chlorophyll and other common vegetation indices. With developments in UAV and quadcopter solutions, the opportunity to bridge the spatio-temporal gap between satellite and ground based sensing now exists, along with the capacity for customized retrievals of crop information. While there remain challenges in the routine application of autonomous airborne systems, the state of current technology and sensor developments provide the capacity to explore the operational potential. While this presentation will focus on the multi-scale estimation of crop-water use and crop-health characteristics from satellite-based sensors, the retrieval of high resolution spatially distributed information from near-surface airborne and ground-based systems will also be examined.

[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.

The Sample Handling System for the Mars Icebreaker Life Mission: from Dirt to Data

NASA Technical Reports Server (NTRS)

Dave, Arwen; Thompson, Sarah J.; McKay, Christopher P.; Stoker, Carol R.; Zacny, Kris; Paulsen, Gale; Mellerowicz, Bolek; Glass, Brian J.; Wilson, David; Bonaccorsi, Rosalba;

Converting Soil Moisture Observations to Effective Values for Improved Validation of Remotely Sensed Soil Moisture

NASA Technical Reports Server (NTRS)

Laymon, Charles A.; Crosson, William L.; Limaye, Ashutosh; Manu, Andrew; Archer, Frank

2005-01-01

We compare soil moisture retrieved with an inverse algorithm with observations of mean moisture in the 0-6 cm soil layer. A significant discrepancy is noted between the retrieved and observed moisture. Using emitting depth functions as weighting functions to convert the observed mean moisture to observed effective moisture removes nearly one-half of the discrepancy noted. This result has important implications in remote sensing validation studies.

First and Higher Order Effects on Zero Order Radiative Transfer Model

NASA Astrophysics Data System (ADS)

Neelam, M.; Mohanty, B.

2014-12-01

Microwave radiative transfer model are valuable tool in understanding the complex land surface interactions. Past literature has largely focused on local sensitivity analysis for factor priotization and ignoring the interactions between the variables and uncertainties around them. Since land surface interactions are largely nonlinear, there always exist uncertainties, heterogeneities and interactions thus it is important to quantify them to draw accurate conclusions. In this effort, we used global sensitivity analysis to address the issues of variable uncertainty, higher order interactions, factor priotization and factor fixing for zero-order radiative transfer (ZRT) model. With the to-be-launched Soil Moisture Active Passive (SMAP) mission of NASA, it is very important to have a complete understanding of ZRT for soil moisture retrieval to direct future research and cal/val field campaigns. This is a first attempt to use GSA technique to quantify first order and higher order effects on brightness temperature from ZRT model. Our analyses reflect conditions observed during the growing agricultural season for corn and soybeans in two different regions in - Iowa, U.S.A and Winnipeg, Canada. We found that for corn fields in Iowa, there exist significant second order interactions between soil moisture, surface roughness parameters (RMS height and correlation length) and vegetation parameters (vegetation water content, structure and scattering albedo), whereas in Winnipeg, second order interactions are mainly due to soil moisture and vegetation parameters. But for soybean fields in both Iowa and Winnipeg, we found significant interactions only to exist between soil moisture and surface roughness parameters.

Characterizing soil moisture and snow cover effects on boreal-arctic soil freeze/thaw dynamics and cold-season carbon emissions

NASA Astrophysics Data System (ADS)

Yi, Y.; Kimball, J. S.; Moghaddam, M.; Chen, R. H.; Reichle, R. H.; Oechel, W. C.; Zona, D.

2017-12-01

The contribution of cold season respiration to boreal-arctic carbon cycle and its potential feedbacks to climate change remain poorly quantified. Here, we developed an integrated modeling framework combining airborne low frequency (L+P-band) airborne radar retrievals and landscape level (≥1km) environmental observations from satellite optical and microwave sensors with a detailed permafrost carbon model to investigate underlying processes controlling soil freeze/thaw (FT) dynamics and cold season carbon emissions. The permafrost carbon model simulates the snow and soil thermal dynamics with soil water phase change included and accounts for soil carbon decomposition up to 3m below surface. Local-scale ( 50m) radar retrievals of active layer thickness (ALT), soil moisture and freeze/thaw (FT) status from NASA airborne UAVSAR and AirMOSS sensors are used to inform the model parameterizations of soil moisture effects on soil FT dynamics, and scaling properties of active layer processes. Both tower observed land-atmosphere fluxes and atmospheric CO2 measurements are used to evaluate the model processes controlling cold season carbon respiration, particularly the effects of snow cover and soil moisture on deep soil carbon emissions during the early cold season. Initial comparisons showed that the model can well capture the seasonality of cold season respiration in both tundra and boreal forest areas, with large emissions in late fall and early winter and gradually diminishing throughout the winter. Model sensitivity analyses are used to clarify how changes in soil thermodynamics at depth control the magnitude and seasonality of cold season respiration, and how a deeper unfrozen active layer with warming may contribute to changes in cold season respiration. Model outputs include ALT and regional carbon fluxes at 1-km resolution spanning recent satellite era (2001-present) across Alaska. These results will be used to quantify cold season respiration contributions to the annual carbon cycle and help close the boreal-arctic annual carbon budget.

ESA's Soil Moisture dnd Ocean Salinity Mission - Contributing to Water Resource Management

NASA Astrophysics Data System (ADS)

Mecklenburg, S.; Kerr, Y. H.

2015-12-01

The Soil Moisture and Ocean Salinity (SMOS) mission, launched in November 2009, is the European Space Agency's (ESA) second Earth Explorer Opportunity mission. The scientific objectives of the SMOS mission directly respond to the need for global observations of soil moisture and ocean salinity, two key variables used in predictive hydrological, oceanographic and atmospheric models. SMOS observations also provide information on the characterisation of ice and snow covered surfaces and the sea ice effect on ocean-atmosphere heat fluxes and dynamics, which affects large-scale processes of the Earth's climate system. The focus of this paper will be on SMOS's contribution to support water resource management: SMOS surface soil moisture provides the input to derive root-zone soil moisture, which in turn provides the input for the drought index, an important monitoring prediction tool for plant available water. In addition to surface soil moisture, SMOS also provides observations on vegetation optical depth. Both parameters aid agricultural applications such as crop growth, yield forecasting and drought monitoring, and provide input for carbon and land surface modelling. SMOS data products are used in data assimilation and forecasting systems. Over land, assimilating SMOS derived information has shown to have a positive impact on applications such as NWP, stream flow forecasting and the analysis of net ecosystem exchange. Over ocean, both sea surface salinity and severe wind speed have the potential to increase the predictive skill on the seasonal and short- to medium-range forecast range. Operational users in particular in Numerical Weather Prediction and operational hydrology have put forward a requirement for soil moisture data to be available in near-real time (NRT). This has been addressed by developing a fast retrieval for a NRT level 2 soil moisture product based on Neural Networks, which will be available by autumn 2015. This paper will focus on presenting the above applications and used SMOS data products.

An integrated GIS application system for soil moisture data assimilation

NASA Astrophysics Data System (ADS)

Wang, Di; Shen, Runping; Huang, Xiaolong; Shi, Chunxiang

2014-11-01

The gaps in knowledge and existing challenges in precisely describing the land surface process make it critical to represent the massive soil moisture data visually and mine the data for further research.This article introduces a comprehensive soil moisture assimilation data analysis system, which is instructed by tools of C#, IDL, ArcSDE, Visual Studio 2008 and SQL Server 2005. The system provides integrated service, management of efficient graphics visualization and analysis of land surface data assimilation. The system is not only able to improve the efficiency of data assimilation management, but also comprehensively integrate the data processing and analysis tools into GIS development environment. So analyzing the soil moisture assimilation data and accomplishing GIS spatial analysis can be realized in the same system. This system provides basic GIS map functions, massive data process and soil moisture products analysis etc. Besides,it takes full advantage of a spatial data engine called ArcSDE to effeciently manage, retrieve and store all kinds of data. In the system, characteristics of temporal and spatial pattern of soil moiture will be plotted. By analyzing the soil moisture impact factors, it is possible to acquire the correlation coefficients between soil moisture value and its every single impact factor. Daily and monthly comparative analysis of soil moisture products among observations, simulation results and assimilations can be made in this system to display the different trends of these products. Furthermore, soil moisture map production function is realized for business application.

AirMOSS P-Band Radar Retrieval of Subcanopy Soil Moisture Profile

NASA Astrophysics Data System (ADS)

Tabatabaeenejad, A.; Burgin, M. S.; Duan, X.; Moghaddam, M.

2013-12-01

Knowledge of soil moisture, as a key variable of the Earth system, plays an important role in our under-standing of the global water, energy, and carbon cycles. The importance of such knowledge has led NASA to fund missions such as Soil Moisture Active and Passive (SMAP) and Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS). The AirMOSS mission seeks to improve the estimates of the North American Net Ecosystem Exchange (NEE) by providing high-resolution observations of the root zone soil moisture (RZSM) over regions representative of the major North American biomes. AirMOSS flies a P-band SAR to penetrate vegetation and into the root zone to provide estimates of RZSM. The flights cover areas containing flux tower sites in regions from the boreal forests in Saskatchewan, Canada, to the tropical forests in La Selva, Costa Rica. The radar snapshots are used to generate estimates of RZSM via inversion of a scattering model of vegetation overlying soils with variable moisture profiles. These retrievals will be used to generate a time record of RZSM, which will be integrated with an ecosystem demography model in order to estimate the respiration and photosynthesis carbon fluxes. The aim of this work is the retrieval of the moisture profile over AirMOSS sites using the collected P-band radar data. We have integrated layered-soil scattering models into a forest scattering model; for the backscattering from ground and for the trunk-ground double-bounce mechanism, we have used a layered small perturbation method and a coherent scattering model of layered soil, respectively. To estimate the soil moisture profile, we represent it as a second-order polynomial in the form of az2 + bz + c, where z is the depth and a, b, and c are the coefficients to be retrieved from radar measurements. When retrieved, these coefficients give us the soil moisture up to a prescribed depth of validity. To estimate the unknown coefficients of the polynomial, we use simulated annealing to minimize a cost function. Considering the required accuracy and reasonableness of the computational cost, and guided by in-situ field observations from several sites and prior field campaigns, the inversion algorithm parameters are chosen judiciously after extensive simulations using synthetic and real radar data. The ancillary data necessary to characterize a pixel are readily available. For example, the slope of each pixel is included in the radar data delivered by JPL. For land cover type within the continental United States, we use the National Land Cover Database (NLCD). Soil texture data are available from the Soil Survey Geographic (SSURGO) database for the United States. The handling and processing of the ancillary data is an involved and detailed process that will be briefly presented at the talk. We apply the retrieval method to the data acquired over several AirMOSS sites, and validate the results using in-situ soil moisture measurements. Retrieved profiles from several specific pixels at each site, the retrieval errors, and the retrieved moisture maps of the 100 km by 25 km imaged domains will be reported at the talk.

Burial increases seed longevity of two Artemisia tridentata (Asteraceae) subspecies

USGS Publications Warehouse

Wijayratne, Upekala C.; Pyke, David A.

2012-01-01

Premise of the study: Seed longevity and persistence in soil seed banks may be especially important for population persistence in ecosystems where opportunities for seedling establishment and disturbance are unpredictable. The fire regime, an important driver of population dynamics in sagebrush steppe ecosystems, has been altered by exotic annual grass invasion. Soil seed banks may play an active role in postfire recovery of the foundation shrub Artemisia tridentata, yet conditions under which seeds persist are largely unknown. Methods: We investigated seed longevity of two Artemisia tridentata subspecies in situ by retrieving seed bags that were placed at varying depths over a 2 yr period. We also sampled naturally dispersed seeds in litter and soil immediately after seed dispersal and before flowering in subsequent seasons to estimate seed persistence. Key results: After 24 mo, seeds buried at least 3 cm below the soil surface retained 30–40% viability whereas viability of seeds on the surface and under litter declined to 0 and Artemisia tridentata has the potential to form a short-term soil seed bank that persists longer than has been commonly assumed, and that burial is necessary for seed longevity. Use of seeding techniques that promote burial of some seeds to aid in formation of a soil seed bank may increase restoration potential.

Differential distribution patterns of ammonia-oxidizing archaea and bacteria in acidic soils of Nanling National Nature Reserve forests in subtropical China.

PubMed

Gan, Xian-Hua; Zhang, Fang-Qiu; Gu, Ji-Dong; Guo, Yue-Dong; Li, Zhao-Qing; Zhang, Wei-Qiang; Xu, Xiu-Yu; Zhou, Yi; Wen, Xiao-Ying; Xie, Guo-Guang; Wang, Yong-Feng

2016-02-01

In addition to ammonia-oxidizing bacteria (AOB) the more recently discovered ammonia-oxidizing archaea (AOA) can also oxidize ammonia, but little is known about AOA community structure and abundance in subtropical forest soils. In this study, both AOA and AOB were investigated with molecular techniques in eight types of forests at surface soils (0-2 cm) and deep layers (18-20 cm) in Nanling National Nature Reserve in subtropical China. The results showed that the forest soils, all acidic (pH 4.24-5.10), harbored a wide range of AOA phylotypes, including the genera Nitrosotalea, Nitrososphaera, and another 6 clusters, one of which was reported for the first time. For AOB, only members of Nitrosospira were retrieved. Moreover, the abundance of the ammonia monooxygenase gene (amoA) from AOA dominated over AOB in most soil samples (13/16). Soil depth, rather than forest type, was an important factor shaping the community structure of AOA and AOB. The distribution patterns of AOA and AOB in soil layers were reversed: AOA diversity and abundances in the deep layers were higher than those in the surface layers; on the contrary, AOB diversity and abundances in the deep layers were lower than those in the surface layers. Interestingly, the diversity of AOA was positively correlated with pH, but negatively correlated with organic carbon, total nitrogen and total phosphorus, and the abundance of AOA was negatively correlated with available phosphorus. Our results demonstrated that AOA and AOB were differentially distributed in acidic soils in subtropical forests and affected differently by soil characteristics.

Physically Accurate Soil Freeze-Thaw Processes in a Global Land Surface Scheme

NASA Astrophysics Data System (ADS)

Cuntz, Matthias; Haverd, Vanessa

2018-01-01

The model Soil-Litter-Iso (SLI) calculates coupled heat and water transport in soil. It was recently implemented into the Australian land surface model CABLE, which is the land component of the Australian Community Climate and Earth System Simulator (ACCESS). Here we extended SLI to include accurate freeze-thaw processes in the soil and snow. SLI provides thence an implicit solution of the energy and water balances of soil and snow as a standalone model and within CABLE. The enhanced SLI was tested extensively against theoretical formulations, laboratory experiments, field data, and satellite retrievals. The model performed well for all experiments at wide-ranging temporal and spatial scales. SLI melts snow faster at the end of the cold season compared to observations though because there is no subgrid variability within SLI given by the implicit, coupled solution of energy and water. Combined CABLE-SLI shows very realistic dynamics and extent of permafrost on the Northern hemisphere. It illustrated, however, also the limits of possible comparisons between large-scale land surface models and local permafrost observations. CABLE-SLI exhibits the same patterns of snow depth and snow water equivalent on the Northern hemisphere compared to satellite-derived observations but quantitative comparisons depend largely on the given meteorological input fields. Further extension of CABLE-SLI with depth-dependence of soil carbon will allow realistic projections of the development of permafrost and frozen carbon stocks in a changing climate.

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

NASA Astrophysics Data System (ADS)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn't involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

Soil moisture mapping for aquarius

USDA-ARS?s Scientific Manuscript database

Aquarius is the first satellite to provide both passive and active L-band observations of the Earth. In addition, the instruments on Satelite de Aplicaciones Cientificas-D (SAC-D) provide complementary information for analysis and retrieval algorithms. Our research focuses on the retrieval of soil m...

Hyperspectral analysis of clay minerals

NASA Astrophysics Data System (ADS)

Janaki Rama Suresh, G.; Sreenivas, K.; Sivasamy, R.

2014-11-01

A study was carried out by collecting soil samples from parts of Gwalior and Shivpuri district, Madhya Pradesh in order to assess the dominant clay mineral of these soils using hyperspectral data, as 0.4 to 2.5 μm spectral range provides abundant and unique information about many important earth-surface minerals. Understanding the spectral response along with the soil chemical properties can provide important clues for retrieval of mineralogical soil properties. The soil samples were collected based on stratified random sampling approach and dominant clay minerals were identified through XRD analysis. The absorption feature parameters like depth, width, area and asymmetry of the absorption peaks were derived from spectral profile of soil samples through DISPEC tool. The derived absorption feature parameters were used as inputs for modelling the dominant soil clay mineral present in the unknown samples using Random forest approach which resulted in kappa accuracy of 0.795. Besides, an attempt was made to classify the Hyperion data using Spectral Angle Mapper (SAM) algorithm with an overall accuracy of 68.43 %. Results showed that kaolinite was the dominant mineral present in the soils followed by montmorillonite in the study area.

Multiangular L-band Datasets for Soil Moisture and Sea Surface Salinity Retrieval Measured by Airborne HUT-2D Synthetic Aperture Radiometer

NASA Astrophysics Data System (ADS)

Kainulainen, J.; Rautiainen, K.; Seppänen, J.; Hallikainen, M.

2009-04-01

SMOS is the European Space Agency's next Earth Explorer satellite due for launch in 2009. It aims for global monitoring of soil moisture and ocean salinity utilizing a new technology concept for remote sensing: two-dimensional aperture synthesis radiometry. The payload of SMOS is Microwave Imaging Radiometer by Aperture Synthesis, or MIRAS. It is a passive instrument that uses 72 individual L-band receivers for measuring the brightness temperature of the Earth. From each acquisition, i.e. integration time or snapshot, MIRAS provides two-dimensional brightness temperature of the scene in the instrument's field of view. Thus, consecutive snapshots provide multiangular measurements of the target once the instrument passes over it. Depending on the position of the target in instrument's swath, the brightness temperature of the target at incidence angles from zero up to 50 degrees can be measured with one overpass. To support the development MIRAS instrument, its calibration, and soil moisture and sea surface salinity retrieval algorithm development, Helsinki University of Technology (TKK) has designed, manufactured and tested a radiometer which operates at L-band and utilizes the same two-dimensional methodology of interferometery and aperture synthesis as MIRAS does. This airborne instrument, called HUT-2D, was designed to be used on board the University's research aircraft. It provides multiangular measurements of the target in its field of view, which spans up to 30 degrees off the boresight of the instrument, which is pointed to the nadir. The number of independent measurements of each target point depends on the flight speed and altitude. In addition to the Spanish Airborne MIRAS demonstrator (AMIRAS), HUT-2D is the only European airborne synthetic aperture radiometer. This paper presents the datasets and measurement campaigns, which have been carried out using the HUT-2D radiometer and are available for the scientific community. In April 2007 HUT-2D participated in to the first scientific measurement campaign. This campaign consisted of a single flight over the Gulf of Finland simultaneously with R/V Aranda's (Finnish Marine Research Institute) ground truth collection. The vessel measured e.g. sea surface salinity and sea temperature along the test lines measured with the radiometer system. During the autumn of 2007 HUT-2D participated in the CoSMOS-2007 campaign, in which three datasets from the Finnish coastal area were measured in order to demonstrate sea salinity retrieval. The campaign consisted of two two-hour measurement flights over an expected salinity gradient with HUT-2D and the Danish conventional radiometer EMIRAD. For the reference data, sea surface temperature and salinity were measured along the gradient line from a vessel. The third flight included different maneuvers, such as wing-wags, circles, and clover leafs, over the Gulf of Finland. During the same autumn, HUT-2D was used to measure datasets in northern Finland for soil moisture retrieval purposes. The flight consisted of measurement flights over test areas in Sodankylä, and Pallas. These test sites were equipped with weather stations of Finnish Meteorological Institute. Also soil moisture samples were collected at the sites. During the transition flights (approx. 800 km) from southern Finland to these test sites HUT-2D measured continuously, however, ground reference data for soil moisture was not collected beyond a few weather stations overpassed. Land classification maps for the transit flights are available. The most significant measurement campaign of HUT-2D so far was carried out during the spring of 2008. This 6-week campaign consisted of measurements of soil moisture test sites in Germany (Danube Catchment Area, DCA) and Spain (Valencia Anchor Station, VAS). The campaign at the DCA site consisted of four two-hour flights over the selected test lines in the Danube river catchment area, which is actively used for soil moisture studies. The VAC site consisted of 10 x 10 kilometers area also used for soil moisture studies. This area was mapped with HUT-2D in four different days.

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.

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.

Estimation of Land Surface Energy Balance Using Satellite Data of Spatial Reduced Resolution

NASA Astrophysics Data System (ADS)

Vintila, Ruxandra; Radnea, Cristina; Savin, Elena; Poenaru, Violeta

2010-12-01

The paper presents preliminary results concerning the monitoring at national level of several geo-biophysical variables retrieved by remote sensing, in particular those related to drought or aridisation. The study, which is in progress, represents also an exercise for to the implementation of a Land Monitoring Core Service for Romania, according to the Kopernikus Program and in compliance with the INSPIRE Directive. The SEBS model has been used to retrieve land surface energy balance variables, such as turbulent heat fluxes, evaporative fraction and daily evaporation, based on three information types: (1) surface albedo, emissivity, temperature, fraction of vegetation cover (fCover), leaf area index (LAI) and vegetation height; (2) air pressure, temperature, humidity and wind speed at the planetary boundary layer (PBL) height; (3) downward solar radiation and downward longwave radiation. AATSR and MERIS archived reprocessed images have provided several types of information. Thus, surface albedo, emissivity, and land surface temperature have been retrieved from AATSR, while LAI and fCover have been estimated from MERIS. The vegetation height has been derived from CORINE Land Cover and PELCOM Land Use databases, while the meteorological information at the height of PBL have been estimated from the measurements provided by the national weather station network. Other sources of data used during this study have been the GETASSE30 digital elevation model with 30" spatial resolution, used for satellite image orthorectification, and the SIGSTAR-200 geographical information system of soil resources of Romania, used for water deficit characterisation. The study will continue by processing other AATSR and MERIS archived images, complemented by the validation of SEBS results with ground data collected on the most important biomes for Romania at various phenological stages, and the transformation of evaporation / evapotranspiration into a drought index using the soil texture data. It is also foreseen to develop procedures for processing near-real time AATSR and MERIS images from the rolling archives, as well as procedures for dealing with SENTINEL 3 images in the future, for timely delivery of reliable information to authorities and planning for drought to reduce its effects on citizens.

Could we constrain some major properties of hot Super-Earths with NIRSPEC-JWT spectra?

NASA Astrophysics Data System (ADS)

Rouan, D.; Samuel, B.; Leconte, J.; Léger, A.

2014-03-01

CoRot-7b and Kepler-10b were the first super-earths with solid surfaces identified thanks to transits detection from space using ultra-precise photometry. At only a few stellar radii from their host stars, these two rocky planets are very hot. The current model (Leger et al., 2011) is that they are atmosphere-free, in a synchronous rotation state, receive strong stellar winds and fluxes and that they feature a lava ocean on their hot dayside. We show how observations with NIRSPEC-JWST could further confirm and constrain, or reject the atmosphere-free lava ocean planet model for very hot super earths. Taking CoRoT-7b as a baseline, we explore the consequences on the phase-curve of a non tidal-locked rotation, of the presence/absence of an atmosphere, and of different values of the surface albedo. Simulated observations of the reflected light and thermal emission using NIRSPEC-JWST are used to look for detectable signatures of those peculiar conditions. We also study how to retrieve the temperature map of the surface. We demonstrate that thanks to the broad range of wavelengths accessible with JWST, we should be able to constrain several parameters: i) the Bond albedo is retrieved to within ±0.03 in most cases; ii) the lag effect allows to retrieve the rotation period of a non phaselocked planet to within 3 hours; iii) the shortest rotation period compatible with an actually phase-locked planet is in the range 30 - 800 h depending on the thermal properties of the soil; iv) the presence of a thick atmosphere with a pressure of one bar, and an specific opacity higher than 10-5m-2kg-1 is detectable; v) The latitudinal temperature profile can be retrieved to within 30 K for a signal to noise ratio of 7.5. We conclude that it should thus be possible to distinguish the situation of a lava ocean with phase-locking and no atmosphere from other cases. In addition, obtaining the surface temperature map and the albedo will bring important constraints on the nature or the physical state of the soil of hot super-earths. We examine the extension of this method to other cases of super-earths.

Stand-alone error characterisation of microwave satellite soil moisture using a Fourier method

USDA-ARS?s Scientific Manuscript database

Error characterisation of satellite-retrieved soil moisture (SM) is crucial for maximizing their utility in research and applications in hydro-meteorology and climatology. Error characteristics can provide insights for retrieval development and validation, and inform suitable strategies for data fus...

Triple collocation based merging of satellite soil moisture retrievals

USDA-ARS?s Scientific Manuscript database

We propose a method for merging soil moisture retrievals from space borne active and passive microwave instruments based on weighted averaging taking into account the error characteristics of the individual data sets. The merging scheme is parameterized using error variance estimates obtained from u...

Associating Land Surface Temperature Retrieved From Satellite and Unmanned Aerial Vehicle Data With Urban Cover and Topography in Aburrá Valley

NASA Astrophysics Data System (ADS)

Guzmán, G.; Hoyos Ortiz, C. D.

2017-12-01

Urban heat island effect commonly refers to temperature differences between urban areas and their countrysides due to urbanization. These temperature differences are evident at surface, and within the canopy and the boundary layer. This effect is heterogeneous within the city, and responds to urban morphology, prevailing materials, amount of vegetation, among others, which are also important in the urban balance of energy. In order to study the relationship between land surface temperature (LST) and urban coverage over Aburrá Valley, which is a narrow valley locate at tropical Andes in northern South America, Landsat 8 mission products of LST, density of vegetation (normalized difference vegetation index, NDVI), and a proxy of soil humidity are derived and used. The results are analyzed from the point of view of dominant urban form and settlement density at scale of neighborhoods, and also from potential downward solar radiation received at the surface. Besides, specific sites were chosen to obtain LST from thermal imaging using an unmanned aerial vehicle to characterize micro-scale patterns and to validate Landast retrievals. Direct relationships between LST, NDVI, soil humidity, and duration of insolation are found, showing the impact of the current spatial distribution of land uses on surface temperature over Aburrá Valley. In general, the highest temperatures correspond to neighborhoods with large, flat-topped buildings in commercial and industrial areas, and low-rise building in residential areas with scarce vegetation, all on the valley bottom. Landsat images are in the morning for the Aburrá Valley, for that reason the coldest temperatures are prevalent at certain orientation of the hillslope, according with the amount of radiation received from sunrise to time of data.

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.

Devastating Carolina Floods Viewed by NASA SMAP

NASA Image and Video Library

2015-10-08

Surface soil moisture in the Southeastern United States as retrieved from NASA's Soil Moisture Active Passive (SMAP) satellite observatory at around 6 a.m. on Oct. 5, 2015. Large parts of South Carolina appear blue, representing the impact of heavy localized rains and flooding. Regions in blue indicate areas with saturated soil conditions and possible standing water. Large-scale flooding was experienced all over South Carolina on Oct. 5-6, 2015. As of Oct. 7, 17 deaths had been attributed to these floods, with heavy economic losses. In some regions, the intensity of these floods was described as a 1,000-year storm (1-in-1,000 chance of happening in any given year). At least 14 dams have already failed as a result of these floods. http://photojournal.jpl.nasa.gov/catalog/PIA20001

High-Resolution Soil Moisture Retrieval using SMAP-L Band Radiometer and RISAT-C band Radar Data for the Indian Subcontinent

NASA Astrophysics Data System (ADS)

Singh, G.; Das, N. N.; Panda, R. K.; Mohanty, B.; Entekhabi, D.; Bhattacharya, B. K.

2016-12-01

Soil moisture status at high resolution (1-10 km) is vital for hydrological, agricultural and hydro-metrological applications. The NASA Soil Moisture Active Passive (SMAP) mission had potential to provide reliable soil moisture estimate at finer spatial resolutions (3 km and 9 km) at the global extent, but suffered a malfunction of its radar, consequently making the SMAP mission observations only from radiometer that are of coarse spatial resolution. At present, the availability of high-resolution soil moisture product is limited, especially in developing countries like India, which greatly depends on agriculture for sustaining a huge population. Therefore, an attempt has been made in the reported study to combine the C-band synthetic aperture radar (SAR) data from Radar Imaging Satellite (RISAT) of the Indian Space Research Organization (ISRO) with the SMAP mission L-band radiometer data to obtain high-resolution (1 km and 3 km) soil moisture estimates. In this study, a downscaling approach (Active-Passive Algorithm) implemented for the SMAP mission was used to disaggregate the SMAP radiometer brightness temperature (Tb) using the fine resolution SAR backscatter (σ0) from RISAT. The downscaled high-resolution Tb was then subjected to tau-omega model in conjunction with high-resolution ancillary data to retrieve soil moisture at 1 and 3 km scale. The retrieved high-resolution soil moisture estimates were then validated with ground based soil moisture measurement under different hydro-climatic regions of India. Initial results show tremendous potential and reasonable accuracy for the retrieved soil moisture at 1 km and 3 km. It is expected that ISRO will implement this approach to produce high-resolution soil moisture estimates for the Indian subcontinent.

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.

[Bare Soil Moisture Inversion Model Based on Visible-Shortwave Infrared Reflectance].

PubMed

Zheng, Xiao-po; Sun, Yue-jun; Qin, Qi-ming; Ren, Hua-zhong; Gao, Zhong-ling; Wu, Ling; Meng, Qing-ye; Wang, Jin-liang; Wang, Jian-hua

2015-08-01

Soil is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, moisture, microbes, et al. Among its complex compositions, soil moisture varies greatly. Therefore, the fast and accurate inversion of soil moisture by using remote sensing is very crucial. In order to reduce the influence of soil type on the retrieval of soil moisture, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate soil moisture. The modeling of the new index contains several key steps: Firstly, soil samples with different moisture level were artificially prepared, and soil reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the moisture absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different soil at different moisture conditions. Then advantages of the two features at reducing soil types' effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and soil moisture was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in soil moisture extraction. It can weaken the influences caused by soil types at different moisture levels and improve the bare soil moisture inversion accuracy.

Unified Science Information Model for SoilSCAPE using the Mercury Metadata Search System

NASA Astrophysics Data System (ADS)

Devarakonda, Ranjeet; Lu, Kefa; Palanisamy, Giri; Cook, Robert; Santhana Vannan, Suresh; Moghaddam, Mahta Clewley, Dan; Silva, Agnelo; Akbar, Ruzbeh

2013-12-01

SoilSCAPE (Soil moisture Sensing Controller And oPtimal Estimator) introduces a new concept for a smart wireless sensor web technology for optimal measurements of surface-to-depth profiles of soil moisture using in-situ sensors. The objective is to enable a guided and adaptive sampling strategy for the in-situ sensor network to meet the measurement validation objectives of spaceborne soil moisture sensors such as the Soil Moisture Active Passive (SMAP) mission. This work is being carried out at the University of Michigan, the Massachusetts Institute of Technology, University of Southern California, and Oak Ridge National Laboratory. At Oak Ridge National Laboratory we are using Mercury metadata search system [1] for building a Unified Information System for the SoilSCAPE project. This unified portal primarily comprises three key pieces: Distributed Search/Discovery; Data Collections and Integration; and Data Dissemination. Mercury, a Federally funded software for metadata harvesting, indexing, and searching would be used for this module. Soil moisture data sources identified as part of this activity such as SoilSCAPE and FLUXNET (in-situ sensors), AirMOSS (airborne retrieval), SMAP (spaceborne retrieval), and are being indexed and maintained by Mercury. Mercury would be the central repository of data sources for cal/val for soil moisture studies and would provide a mechanism to identify additional data sources. Relevant metadata from existing inventories such as ORNL DAAC, USGS Clearinghouse, ARM, NASA ECHO, GCMD etc. would be brought in to this soil-moisture data search/discovery module. The SoilSCAPE [2] metadata records will also be published in broader metadata repositories such as GCMD, data.gov. Mercury can be configured to provide a single portal to soil moisture information contained in disparate data management systems located anywhere on the Internet. Mercury is able to extract, metadata systematically from HTML pages or XML files using a variety of methods including OAI-PMH [3]. The Mercury search interface then allows users to perform simple, fielded, spatial and temporal searches across a central harmonized index of metadata. Mercury supports various metadata standards including FGDC, ISO-19115, DIF, Dublin-Core, Darwin-Core, and EML. This poster describes in detail how Mercury implements the Unified Science Information Model for Soil moisture data. References: [1]Devarakonda R., et al. Mercury: reusable metadata management, data discovery and access system. Earth Science Informatics (2010), 3(1): 87-94. [2]Devarakonda R., et al. Daymet: Single Pixel Data Extraction Tool. http://daymet.ornl.gov/singlepixel.html (2012). Last Accesses 10-01-2013 [3]Devarakonda R., et al. Data sharing and retrieval using OAI-PMH. Earth Science Informatics (2011), 4(1): 1-5.

Estimation of Land Surface Fluxes and Their Uncertainty via Variational Data Assimilation Approach

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2016-12-01

Accurate estimation of land surface heat and moisture fluxes as well as root zone soil moisture is crucial in various hydrological, meteorological, and agricultural applications. "In situ" measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state variables. In this work, we applied a novel approach based on the variational data assimilation (VDA) methodology to estimate land surface fluxes and soil moisture profile from the land surface states. This study accounts for the strong linkage between terrestrial water and energy cycles by coupling the dual source energy balance equation with the water balance equation through the mass flux of evapotranspiration (ET). Heat diffusion and moisture diffusion into the column of soil are adjoined to the cost function as constraints. This coupling results in more accurate prediction of land surface heat and moisture fluxes and consequently soil moisture at multiple depths with high temporal frequency as required in many hydrological, environmental and agricultural applications. One of the key limitations of VDA technique is its tendency to be ill-posed, meaning that a continuum of possibilities exists for different parameters that produce essentially identical measurement-model misfit errors. On the other hand, the value of heat and moisture flux estimation to decision-making processes is limited if reasonable estimates of the corresponding uncertainty are not provided. In order to address these issues, in this research uncertainty analysis will be performed to estimate the uncertainty of retrieved fluxes and root zone soil moisture. The assimilation algorithm is tested with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. We demonstrate the VDA performance by comparing the (synthetic) true measurements (including profile of soil moisture and temperature, land surface water and heat fluxes, and root water uptake) with VDA estimates. In addition, the feasibility of extending the proposed approach to use remote sensing observations is tested by limiting the number of LST observations and soil moisture observations.

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.

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.

Improved water balance component estimates through joint assimilation of GRACE water storage and SMOS soil moisture retrievals

NASA Astrophysics Data System (ADS)

Tian, Siyuan; Tregoning, Paul; Renzullo, Luigi J.; van Dijk, Albert I. J. M.; Walker, Jeffrey P.; Pauwels, Valentijn R. N.; Allgeyer, Sébastien

2017-03-01

The accuracy of global water balance estimates is limited by the lack of observations at large scale and the uncertainties of model simulations. Global retrievals of terrestrial water storage (TWS) change and soil moisture (SM) from satellites provide an opportunity to improve model estimates through data assimilation. However, combining these two data sets is challenging due to the disparity in temporal and spatial resolution at both vertical and horizontal scale. For the first time, TWS observations from the Gravity Recovery and Climate Experiment (GRACE) and near-surface SM observations from the Soil Moisture and Ocean Salinity (SMOS) were jointly assimilated into a water balance model using the Ensemble Kalman Smoother from January 2010 to December 2013 for the Australian continent. The performance of joint assimilation was assessed against open-loop model simulations and the assimilation of either GRACE TWS anomalies or SMOS SM alone. The SMOS-only assimilation improved SM estimates but reduced the accuracy of groundwater and TWS estimates. The GRACE-only assimilation improved groundwater estimates but did not always produce accurate estimates of SM. The joint assimilation typically led to more accurate water storage profile estimates with improved surface SM, root-zone SM, and groundwater estimates against in situ observations. The assimilation successfully downscaled GRACE-derived integrated water storage horizontally and vertically into individual water stores at the same spatial scale as the model and SMOS, and partitioned monthly averaged TWS into daily estimates. These results demonstrate that satellite TWS and SM measurements can be jointly assimilated to produce improved water balance component estimates.

The influence of Vegetation Water Content (VWC) dynamics on microwave observations of a corn canopy during SMAPVEX16-IA

NASA Astrophysics Data System (ADS)

Steele-Dunne, Susan; Polo Bermejo, Jaime; Judge, Jasmeet; Bongiovanni, Tara; Chakrabarti, Subit; Liu, Pang-Wei; Bragdon, James; Hornbuckle, Brian

2017-04-01

Vegetation cover confounds soil moisture retrieval from both active and passive microwave remote sensing observations. Vegetation attenuates the signal from the soil as well as contributing to emission and scattering. The goal of this study was to characterize the vertical distribution of moisture within an agricultural canopy, to examine how this varies during the growing season and to determine the influence these changes have on emission and backscatter from the surface. To this end, an extensive campaign of destructive sampling was conducted in a rain-fed corn field at Buckeye, Iowa within the SMAPVEX16-IA study domain. The experiment duration extended from the beginning of IOP1 to the end of IOP2, i.e. from May 18 to August 16 2016. Destructive vegetation sampling was performed on most days upon which SMAP had both an ascending and a descending pass. On these days, destructive samples were collected at 6pm and 6pm unless the weather conditions were prohibitive. In addition to measuring the bulk vegetation water content for comparison to the SMAP retrieved VWC, the samples were split into leaves and stems. To study the vertical profiles, leaf moisture content was measured as a function of collar height and the stem was cut into 10cm sections. The influence of plant development on the bulk and profile VWC was clearly discernible in the observations. Diurnal variations in bulk VWC were relatively small due to moisture availability in the root zone. SMAP brightness temperatures, and tower-based observations from the University of Florida radiometer and radar systems were analyzed to investigate the impact of VWC variations on emission and backscatter. Dynamic variations in SMAP retrieved soil moisture were notably larger than those observed in-situ, particularly during the early growing season. This may be attributed to the difference between observed VWC and that used in the SMAP retrieval during the early growing season. Backscatter (and RVI) increased, as expected, in response to accumulating biomass, though retaining some sensitivity to soil moisture variations. Polarization-dependent diurnal differences of up to 2dB were observed in the backscatter from the fully grown corn canopy.

Improving Soil Moisture and Temperature Profile and Surface Turbulent Fluxes Estimations in Irrigated Field by Assimilating Multi-source Data into Land Surface Model

NASA Astrophysics Data System (ADS)

Chen, Weijing; Huang, Chunlin; Shen, Huanfeng; Wang, Weizhen

2016-04-01

The optimal estimation of hydrothermal conditions in irrigation field is restricted by the deficiency of accurate irrigation information (when and how much to irrigate). However, the accurate estimation of soil moisture and temperature profile and surface turbulent fluxes are crucial to agriculture and water management in irrigated field. In the framework of land surface model, soil temperature is a function of soil moisture - subsurface moisture influences the heat conductivity at the interface of layers and the heat storage in different layers. In addition, soil temperature determines the phase of soil water content with the transformation between frozen and unfrozen. Furthermore, surface temperature affects the partitioning of incoming radiant energy into ground (sensible and latent heat flux), as a consequence changes the delivery of soil moisture and temperature. Given the internal positive interaction lying in these variables, we attempt to retrieve the accurate estimation of soil moisture and temperature profile via assimilating the observations from the surface under unknown irrigation. To resolve the input uncertainty of imprecise irrigation quantity, original EnKS is implemented with inflation and localization (referred to as ESIL) aiming at solving the underestimation of the background error matrix and the extension of observation information from the top soil to the bottom. EnKS applied in this study includes the states in different time points which tightly connect with adjacent ones. However, this kind of relationship gradually vanishes along with the increase of time interval. Thus, the localization is also employed to readjust temporal scale impact between states and filter out redundant or invalid correlation. Considering the parameter uncertainty which easily causes the systematic deviation of model states, two parallel filters are designed to recursively estimate both states and parameters. The study area consists of irrigated farmland and is located in an artificial oasis in the semi-arid region of northwestern China. Land surface temperature (LST) and soil volumetric water content (SVW) at first layer measured at Daman station are taken as observations in the framework of data assimilation. The study demonstrates the feasibility of ESIL in improving the soil moisture and temperature profile under unknown irrigation. ESIL promotes the coefficient correlation with in-situ measurements for soil moisture and temperature at first layer from 0.3421 and 0.7027 (ensemble simulation) to 0.8767 and 0.8304 meanwhile all the RMSE of soil moisture and temperature in deeper layers dramatically decrease more than 40 percent in different degree. To verify the reliability of ESIL in practical application, thereby promoting the utilization of satellite data, we test ESIL with varying observation internal interval and standard deviation. As a consequence, ESIL shows stabilized and promising effectiveness in soil moisture and soil temperature estimation.

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.

Retrieving Land Surface Temperature and Emissivity from Multispectral and Hyperspectral Thermal Infrared Instruments

NASA Astrophysics Data System (ADS)

Hook, Simon; Hulley, Glynn; Nicholson, Kerry

2017-04-01

Land Surface Temperature and Emissivity (LST&E) data are critical variables for studying a variety of Earth surface processes and surface-atmosphere interactions such as evapotranspiration, surface energy balance and water vapor retrievals. LST&E have been identified as an important Earth System Data Record (ESDR) by NASA and many other international organizations Accurate knowledge of the LST&E is a key requirement for many energy balance models to estimate important surface biophysical variables such as evapotranspiration and plant-available soil moisture. LST&E products are currently generated from sensors in low earth orbit (LEO) such as the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites as well as from sensors in geostationary Earth orbit (GEO) such as the Geostationary Operational Environmental Satellites (GOES) and airborne sensors such as the Hyperspectral Thermal Emission Spectrometer (HyTES). LST&E products are generated with varying accuracies depending on the input data, including ancillary data such as atmospheric water vapor, as well as algorithmic approaches. NASA has identified the need to develop long-term, consistent, and calibrated data and products that are valid across multiple missions and satellite sensors. We will discuss the different approaches that can be used to retrieve surface temperature and emissivity from multispectral and hyperspectral thermal infrared sensors using examples from a variety of different sensors such as those mentioned, and planned new sensors like the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and the Hyperspectral Infrared Imager (HyspIRI). We will also discuss a project underway at NASA to develop a single unified product from some the individual sensor products and assess the errors associated with the product.

Surface Emissivity Effects on Thermodynamic Retrieval of IR Spectral Radiance

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Larar, Allen M.; Smith, William L.; Liu, Xu

2006-01-01

The surface emissivity effect on the thermodynamic parameters (e.g., the surface skin temperature, atmospheric temperature, and moisture) retrieved from satellite infrared (IR) spectral radiance is studied. Simulation analysis demonstrates that surface emissivity plays an important role in retrieval of surface skin temperature and terrestrial boundary layer (TBL) moisture. NAST-I ultraspectral data collected during the CLAMS field campaign are used to retrieve thermodynamic properties of the atmosphere and surface. The retrievals are then validated by coincident in-situ measurements, such as sea surface temperature, radiosonde temperature and moisture profiles. Retrieved surface emissivity is also validated by that computed from the observed radiance and calculated emissions based on the retrievals of surface temperature and atmospheric profiles. In addition, retrieved surface skin temperature and emissivity are validated together by radiance comparison between the observation and retrieval-based calculation in the window region where atmospheric contribution is minimized. Both simulation and validation results have lead to the conclusion that variable surface emissivity in the inversion process is needed to obtain accurate retrievals from satellite IR spectral radiance measurements. Retrieval examples are presented to reveal that surface emissivity plays a significant role in retrieving accurate surface skin temperature and TBL thermodynamic parameters.

Use of a flux-based field capacity criterion to identify effective hydraulic parameters of layered soil profiles subjected to synthetic drainage experiments

NASA Astrophysics Data System (ADS)

Nasta, Paolo; Romano, Nunzio

2016-01-01

This study explores the feasibility of identifying the effective soil hydraulic parameterization of a layered soil profile by using a conventional unsteady drainage experiment leading to field capacity. The flux-based field capacity criterion is attained by subjecting the soil profile to a synthetic drainage process implemented numerically in the Soil-Water-Atmosphere-Plant (SWAP) model. The effective hydraulic parameterization is associated to either aggregated or equivalent parameters, the former being determined by the geometrical scaling theory while the latter is obtained through the inverse modeling approach. Outcomes from both these methods depend on information that is sometimes difficult to retrieve at local scale and rather challenging or virtually impossible at larger scales. The only knowledge of topsoil hydraulic properties, for example, as retrieved by a near-surface field campaign or a data assimilation technique, is often exploited as a proxy to determine effective soil hydraulic parameterization at the largest spatial scales. Comparisons of the effective soil hydraulic characterization provided by these three methods are conducted by discussing the implications for their use and accounting for the trade-offs between required input information and model output reliability. To better highlight the epistemic errors associated to the different effective soil hydraulic properties and to provide some more practical guidance, the layered soil profiles are then grouped by using the FAO textural classes. For the moderately heterogeneous soil profiles available, all three approaches guarantee a general good predictability of the actual field capacity values and provide adequate identification of the effective hydraulic parameters. Conversely, worse performances are encountered for the highly variable vertical heterogeneity, especially when resorting to the "topsoil-only" information. In general, the best performances are guaranteed by the equivalent parameters, which might be considered a reference for comparisons with other techniques. As might be expected, the information content of the soil hydraulic properties pertaining only to the uppermost soil horizon is rather inefficient and also not capable to map out the hydrologic behavior of the real vertical soil heterogeneity since the drainage process is significantly affected by profile layering in almost all cases.

[A Method to Reconstruct Surface Reflectance Spectrum from Multispectral Image Based on Canopy Radiation Transfer Model].

PubMed

Zhao, Yong-guang; Ma, Ling-ling; Li, Chuan-rong; Zhu, Xiao-hua; Tang, Ling-li

2015-07-01

Due to the lack of enough spectral bands for multi-spectral sensor, it is difficult to reconstruct surface retlectance spectrum from finite spectral information acquired by multi-spectral instrument. Here, taking into full account of the heterogeneity of pixel from remote sensing image, a method is proposed to simulate hyperspectral data from multispectral data based on canopy radiation transfer model. This method first assumes the mixed pixels contain two types of land cover, i.e., vegetation and soil. The sensitive parameters of Soil-Leaf-Canopy (SLC) model and a soil ratio factor were retrieved from multi-spectral data based on Look-Up Table (LUT) technology. Then, by combined with a soil ratio factor, all the parameters were input into the SLC model to simulate the surface reflectance spectrum from 400 to 2 400 nm. Taking Landsat Enhanced Thematic Mapper Plus (ETM+) image as reference image, the surface reflectance spectrum was simulated. The simulated reflectance spectrum revealed different feature information of different surface types. To test the performance of this method, the simulated reflectance spectrum was convolved with the Landsat ETM + spectral response curves and Moderate Resolution Imaging Spectrometer (MODIS) spectral response curves to obtain the simulated Landsat ETM+ and MODIS image. Finally, the simulated Landsat ETM+ and MODIS images were compared with the observed Landsat ETM+ and MODIS images. The results generally showed high correction coefficients (Landsat: 0.90-0.99, MODIS: 0.74-0.85) between most simulated bands and observed bands and indicated that the simulated reflectance spectrum was well simulated and reliable.

Simulation of snow and soil water content as a basis for satellite retrievals

USDA-ARS?s Scientific Manuscript database

It is not yet possible to determine whether the snow has changed over time despite collection of passive microwave data for more than thirty years. Physically-based, but computationally simple snow and soil models have been coupled to form the basis of a data assimilation system for retrievals of sn...

Evaluation of the tau-omega model for passive microwave soil moisture retrieval using SMAPEx data sets

USDA-ARS?s Scientific Manuscript database

The parameters used for passive soil moisture retrieval algorithms reported in the literature encompass a wide range, leading to a large uncertainty in the applicability of those values. This paper presents an evaluation of the proposed parameterizations of the tau-omega model from 1) SMAP ATBD for ...

Vegetation water content of crops and woodlands for improving soil moisture retrievals from Coriolis WindSat

USDA-ARS?s Scientific Manuscript database

Estimation of vegetation water content (VWC) by shortwave infrared remote sensing improves soil moisture retrievals. The largest unknown for predicting VWC is stem water content; for woodlands, stem water content is expected to be proportional to stem height. Airborne imagery were acquired and photo...

Vegetation water content of crops and woodlands for improving soil moisture retrievals from Coriolis WindSat

USDA-ARS?s Scientific Manuscript database

Estimation of vegetation water content (VWC) by shortwave infrared remote sensing improves soil moisture retrievals. The largest unknown for predicting VWC is stem water content, which is assumed to be allometrically related to canopy water content. From forest science, stem volume is linearly relat...

Faraday Rotation for SMOS Retrievals of Ocean Salinity and Soil Moisture

NASA Technical Reports Server (NTRS)

El-Nimri, Salem; Le Vine, David M.

2016-01-01

Faraday rotation is a change in polarization as radiation propagates from the surface through the ionosphere to the sensor. At L-band (1.4 GHz) this change can be significant and can be important for the remote sensing of soil moisture and ocean salinity from space. Consequently, modern L-band radiometers (SMOS, Aquarius and SMOS) are polarimetric to measure Faraday rotation in situ so that a correction can be made. This is done using the ratio of the third and second Stokes parameters. In the case of SMOS this procedure has produced very noisy estimates. An alternate procedure is reported here in which the total electron content is estimated and averaged to reduce noise.

Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation

PubMed Central

Verstraeten, Willem W.; Veroustraete, Frank; Feyen, Jan

2008-01-01

The proper assessment of evapotranspiration and soil moisture content are fundamental in food security research, land management, pollution detection, nutrient flows, (wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc. This paper takes an extensive, though not exhaustive sample of international scientific literature to discuss different approaches to estimate land surface and ecosystem related evapotranspiration and soil moisture content. This review presents: (i)a summary of the generally accepted cohesion theory of plant water uptake and transport including a shortlist of meteorological and plant factors influencing plant transpiration;(ii)a summary on evapotranspiration assessment at different scales of observation (sap-flow, porometer, lysimeter, field and catchment water balance, Bowen ratio, scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii)a summary on data assimilation schemes conceived to estimate evapotranspiration using optical and thermal remote sensing; and(iv)for soil moisture content, a summary on soil moisture retrieval techniques at different spatial and temporal scales is presented. Concluding remarks on the best available approaches to assess evapotranspiration and soil moisture content with and emphasis on remote sensing data assimilation, are provided. PMID:27879697

Analysis of relationships between land surface temperature and land use changes in the Yellow River Delta

NASA Astrophysics Data System (ADS)

Ning, Jicai; Gao, Zhiqiang; Meng, Ran; Xu, Fuxiang; Gao, Meng

2018-06-01

This study analyzed land use and land cover changes and their impact on land surface temperature using Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager and Thermal Infrared Sensor imagery of the Yellow River Delta. Six Landsat images comprising two time series were used to calculate the land surface temperature and correlated vegetation indices. The Yellow River Delta area has expanded substantially because of the deposited sediment carried from upstream reaches of the river. Between 1986 and 2015, approximately 35% of the land use area of the Yellow River Delta has been transformed into salterns and aquaculture ponds. Overall, land use conversion has occurred primarily from poorly utilized land into highly utilized land. To analyze the variation of land surface temperature, a mono-window algorithm was applied to retrieve the regional land surface temperature. The results showed bilinear correlation between land surface temperature and the vegetation indices (i.e., Normalized Difference Vegetation Index, Adjusted-Normalized Vegetation Index, Soil-Adjusted Vegetation Index, and Modified Soil-Adjusted Vegetation Index). Generally, values of the vegetation indices greater than the inflection point mean the land surface temperature and the vegetation indices are correlated negatively, and vice versa. Land surface temperature in coastal areas is affected considerably by local seawater temperature and weather conditions.

Large-area Soil Moisture Surveys Using a Cosmic-ray Rover: Approaches and Results from Australia

NASA Astrophysics Data System (ADS)

Hawdon, A. A.; McJannet, D. L.; Renzullo, L. J.; Baker, B.; Searle, R.

2017-12-01

Recent improvements in satellite instrumentation has increased the resolution and frequency of soil moisture observations, and this in turn has supported the development of higher resolution land surface process models. Calibration and validation of these products is restricted by the mismatch of scales between remotely sensed and contemporary ground based observations. Although the cosmic ray neutron soil moisture probe can provide estimates soil moisture at a scale useful for the calibration and validation purposes, it is spatially limited to a single, fixed location. This scaling issue has been addressed with the development of mobile soil moisture monitoring systems that utilizes the cosmic ray neutron method, typically referred to as a `rover'. This manuscript describes a project designed to develop approaches for undertaking rover surveys to produce soil moisture estimates at scales comparable to satellite observations and land surface process models. A custom designed, trailer-mounted rover was used to conduct repeat surveys at two scales in the Mallee region of Victoria, Australia. A broad scale survey was conducted at 36 x 36 km covering an area of a standard SMAP pixel and an intensive scale survey was conducted over a 10 x 10 km portion of the broad scale survey, which is at a scale equivalent to that used for national water balance modelling. We will describe the design of the rover, the methods used for converting neutron counts into soil moisture and discuss factors controlling soil moisture variability. We found that the intensive scale rover surveys produced reliable soil moisture estimates at 1 km resolution and the broad scale at 9 km resolution. We conclude that these products are well suited for future analysis of satellite soil moisture retrievals and finer scale soil moisture models.

Vegetation Water Content Mapping for Agricultural Regions in SMAPVEX16

NASA Astrophysics Data System (ADS)

White, W. A.; Cosh, M. H.; McKee, L.; Berg, A. A.; McNairn, H.; Hornbuckle, B. K.; Colliander, A.; Jackson, T. J.

2017-12-01

Vegetation water content impacts the ability of L-band radiometers to measure surface soil moisture. Therefore it is necessary to quantify the amount of water held in surface vegetation for an accurate soil moisture remote sensing retrieval. A methodology is presented for generating agricultural vegetation water content maps using Landsat 8 scenes for agricultural fields of Iowa and Manitoba for the Soil Moisture Active Passive Validation Experiments in 2016 (SMAPVEX16). Manitoba has a variety of row crops across the region, and the study period encompasses the time frame from emergence to reproduction, as well as a forested region. The Iowa study site is dominated by corn and soybeans, presenting an easier challenge. Ground collection of vegetation biomass and water content were also collected to provide a ground truth data source. Errors for the resulting vegetation water content maps ranged depending upon crop type, but generally were less than 15% of the total plant water content per crop type. Interpolation is done between Landsat overpasses to produce daily vegetation water content maps for the summer of 2016 at a 30 meter resolution.

Estimating Evapotranspiration with Land Data Assimilation Systems

NASA Technical Reports Server (NTRS)

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

2011-01-01

Advancements in both land surface models (LSM) and land surface data assimilation, especially over the last decade, have substantially advanced the ability of land data assimilation systems (LDAS) to estimate evapotranspiration (ET). This article provides a historical perspective on international LSM intercomparison efforts and the development of LDAS systems, both of which have improved LSM ET skill. In addition, an assessment of ET estimates 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 (NLDAS-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.

SMOS sea surface salinity maps of the Arctic Ocean

NASA Astrophysics Data System (ADS)

Gabarro, Carolina; Olmedo, Estrella; Turiel, Antonio; Ballabrera-Poy, Joaquim; Martinez, Justino; Portabella, Marcos

2016-04-01

Salinity and temperature gradients drive the thermohaline circulation of the oceans, and play a key role in the ocean-atmosphere coupling. The strong and direct interactions between the ocean and the cryosphere (primarily through sea ice and ice shelves) is also a key ingredient of the thermohaline circulation. The ESA's Soil Moisture and Ocean Salinity (SMOS) mission, launched in 2009, has the objective measuring soil moisture over the continents and sea surface salinity over the oceans. Although the mission was originally conceived for hydrological and oceanographic studies [1], SMOS is also making inroads in the cryospheric monitoring. SMOS carries an innovative L-band (1.4 GHz, or 21-cm wavelength), passive interferometric radiometer (the so-called MIRAS) that measures the electromagnetic radiation emitted by the Earth's surface, at about 50 km spatial resolution wide swath (1200-km), and with a 3-day revisit time at the equator, but a more frequent one at the poles. Although the SMOS radiometer operating frequency offers almost the maximum sensitivity of the brightness temperature (TB) to sea surface salinity (SSS) variations, this is rather low, , i.e.,: 90% of ocean SSS values span a range of brightness temperatures of only 5K at L-band. This sensitivity is particularly low in cold waters. This implies that the SSS retrieval requires high radiometric performance. Since the SMOS launch, SSS Level 3 maps have been distributed by several expert laboratories including the Barcelona Expert Centre (BEC). However, since the TB sensitivity to SSS decreases with decreasing sea surface temperature (SST), large retrieval errors had been reported when retrieving salinity values at latitudes above 50⁰N. Two new processing algorithms, recently developed at BEC, have led to a considerable improvement of the SMOS data, allowing for the first time to derive SSS maps in cold waters. The first one is to empirically characterize and correct the systematic biases with six years of SMOS data acquisitions. The second is the modification of the filtering criterion to account for the statistical distributions of SSS at each ocean grid point. This allows retrieving a value of SSS which is less affected by outliers originated from RFI and other effects. We will provide an assessment of the quality of these new SSS products in the Arctic, as well as illustrate the potential of these maps to monitor the main river discharges to the Arctic Ocean. [1] Font, J.; Camps, A.; Borges, A.; Martín-Neira, M.; Boutin, J.; Reul, N.; Kerr, Y.; Hahne, A. & Mecklenburg, S. SMOS: The Challenging Sea Surface Salinity Measurement From Space Proceedings of the IEEE, 2010, 98, 649 -665

Inter-Comparison of Retrieved and Modelled Soil Moisture and Coherency of Remotely Sensed Hydrology Data

NASA Astrophysics Data System (ADS)

Kolassa, Jana; Aires, Filipe

2013-04-01

A neural network algorithm has been developed for the retrieval of Soil Moisture (SM) from global satellite observations. The algorithm estimates soil moisture from a synergy of passive and active microwave, infrared and visible satellite observations in order to capture the different SM variabilities that the individual sensors are sensitive to. The advantages and drawbacks of each satellite observation have been analysed and the information type and content carried by each observation have been determined. A global data set of monthly mean soil moisture for the 1993-2000 period has been computed with the neural network algorithm (Kolassa et al., in press, 2012). The resulting soil moisture retrieval product has then been used in an inter-comparison study including soil moisture from (1) the HTESSEL model (Balsamo et al., 2009), (2) the WACMOS satellite product (Liu et al., 2011), and (3) in situ measurements from the International Soil Moisture Network (Dorigo et al., 2011). The analysis showed that the satellite remote sensing products are well-suited to capture the spatial variability of the in situ data and even show the potential to improve the modelled soil moisture. Both satellite retrievals also display a good agreement with the temporal structures of the in situ data, however, HTESSEL appears to be more suitable for capturing the temporal variability (Kolassa et al., in press, 2012). The use of this type of neural network approach is currently being investigated as a retrieval option for the SMOS mission. Our soil moisture retrieval product has also been used in a coherence study with precipitation data from GPCP (Adler et al., 2003) and inundation estimates from GIEMS (Prigent et al., 2007). It was investigated on a global scale whether the three observation-based datasets are coherent with each other and show the expected behaviour. For most regions of the Earth, the datasets were consistent and the behaviour observed could be explained with the known hydrological processes. In addition, a regional analysis was conducted over several large river basins, including a detailed analysis of the time-lagged correlations between the three datasets and the spatial propagation of observed signals. Results appear consistent with the knowledge of the hydrological processes governing the individual basins. References Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S. Curtis, D. Bolvin, A. Gruber, J. Susskind, and P. Arkin (2003), The Version 2 Global Precipita- tion Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present).J. Hydrometeor., 4,1147-1167. Balsamo, G., Viterbo, P., Beljaars, A., van den Hurk, B., Hirschi, M., Betts, A. and Scipa,l K. (2009) A Revised Hydrology for the ECMWF Model: Verification from Field Site to Terrestrial Water Storage and Impact in the Integrated Forecast System, J. Hydrol., 10, 623-643 Dorigo, W. A., Wagner, W., Hohensinn, R., Hahn, S., Paulik, C., Xaver, A., Gruber, A., Drusch, M., Mecklenburg, S., van Oevelen, P., Robock, A., and Jackson, T. (2011), The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements, Hydrol. Earth Syst. Sci., 15, 1675-1698 Kolassa, J., Aires, F., Polcher, J., Prigent, C., and Pereira, J. (2012), Soil moisture Retrieval from Multi-instrument Observations: Information Content Analysis and Retrieval Methodology (2012), J. Geophys. Res., Liu, Y. Y., Parinussa, R. M., Dorigo, W. A., De Jeu, R. A. M., Wagner, W., van Dijk, A. I. J. M., McCabe, M. F., and Evans, J. P.(2011), Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals, Hydrol. Earth Syst. Sci., 15, 425-436. Prigent, C., F. Papa, F. Aires, W. B. Rossow, and E. Matthews (2007), Global inundation dy- namics inferred from multiple satellite observations, 1993-2000, J. Geophys. Res., 112, D12107, doi:10.1029/2006JD007847.

Monitoring soil moisture patterns in alpine meadows using ground sensor networks and remote sensing techniques

NASA Astrophysics Data System (ADS)

Bertoldi, Giacomo; Brenner, Johannes; Notarnicola, Claudia; Greifeneder, Felix; Nicolini, Irene; Della Chiesa, Stefano; Niedrist, Georg; Tappeiner, Ulrike

2015-04-01

Soil moisture content (SMC) is a key factor for numerous processes, including runoff generation, groundwater recharge, evapotranspiration, soil respiration, and biological productivity. Understanding the controls on the spatial and temporal variability of SMC in mountain catchments is an essential step towards improving quantitative predictions of catchment hydrological processes and related ecosystem services. The interacting influences of precipitation, soil properties, vegetation, and topography on SMC and the influence of SMC patterns on runoff generation processes have been extensively investigated (Vereecken et al., 2014). However, in mountain areas, obtaining reliable SMC estimations is still challenging, because of the high variability in topography, soil and vegetation properties. In the last few years, there has been an increasing interest in the estimation of surface SMC at local scales. On the one hand, low cost wireless sensor networks provide high-resolution SMC time series. On the other hand, active remote sensing microwave techniques, such as Synthetic Aperture Radars (SARs), show promising results (Bertoldi et al. 2014). As these data provide continuous coverage of large spatial extents with high spatial resolution (10-20 m), they are particularly in demand for mountain areas. However, there are still limitations related to the fact that the SAR signal can penetrate only a few centimeters in the soil. Moreover, the signal is strongly influenced by vegetation, surface roughness and topography. In this contribution, we analyse the spatial and temporal dynamics of surface and root-zone SMC (2.5 - 5 - 25 cm depth) of alpine meadows and pastures in the Long Term Ecological Research (LTER) Area Mazia Valley (South Tyrol - Italy) with different techniques: (I) a network of 18 stations; (II) field campaigns with mobile ground sensors; (III) 20-m resolution RADARSAT2 SAR images; (IV) numerical simulations using the GEOtop hydrological model (Rigon et al., 2006; Endrizzi et al., 2014). The objective of this work is to understand the physical controls of the observed SCM patterns. In particular, we want to investigate: • How the SMC signal propagates with depth, to understand the capability of SAR surface SMC observations to predict root-zone SMC. • The role of land management and vegetation properties with respect to soil and bedrock properties in determining SMC spatial variability and temporal patterns. In this context, we use the GEOtop model to understand if a relationship exists between the observed SMC patterns and the underlying runoff generation processes. Results show that meadows and pastures have different behaviours. Meadows are in general wetter because of irrigation and the presence of soils with higher organic content and higher water holding capacity. Moreover, surface and root depth SCM dynamics are correlated. In contrast, pastures are drier, with lower vegetation density and more compact soils due animal trampling. Because of shallow soils and impermeable bedrock, root zone SMC shows a different behaviour with respect to the surface, with occurrence of sub-surface saturation excess, as verified from numerical experiments performed with the hydrological model. Results suggest how SAR retrieved surface SMC can be used to extrapolate root zone SMC, when soil properties are homogenous and differences in vegetation density are properly accounted with a robust retrieval processes (Pasolli et al., in press 2015). However, in situations characterized by shallow subsurface saturation excess flow, a more sophisticated modelling approach is required to estimate root zone SMC using remote sensing observations. Bertoldi, G., Della, S., Notarnicola, C., Pasolli, L., Niedrist, G., & Tappeiner, U. (2014). Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling, 516, 245-257. doi:10.1016/j.jhydrol.2014.02.018 Endrizzi, S., Gruber, S., Dall'Amico, M., & Rigon, R. (2014). GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects. Geoscientific Model Development, 7(6), 2831-2857. doi:10.5194/gmd-7-2831-2014 Pasolli, L., Notarnicola, C., Bertoldi, G., Bruzzone, L., Remegaldo, R., Niedrist, G, Della Chiesa S., Tappeiner, U., Zebisch, M. (2014): Multi-scale assessment of soil moisture variability in mountain areas by using active radar images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, in press 2015. Rigon, R., Bertoldi, G., & Over, T. M. (2006). GEOtop: A Distributed Hydrological Model with Coupled Water and Energy Budgets. Journal of Hydrometeorology, 7, 371-388. Vereecken, H., Huisman, J. A., Pachepsky, Y., Montzka, C., van der Kruk, J., Bogena, H., … Vanderborght, J. (2014). On the spatio-temporal dynamics of soil moisture at the field scale. Journal of Hydrology. doi:http://dx.doi.org/10.1016/j.jhydrol.2013.11.061

Weekly Gridded Aquarius L-band Radiometer-Scatterometer Observations and Salinity Retrievals over the Polar Regions - Part 2: Initial Product Analysis

NASA Technical Reports Server (NTRS)

Brucker, L.; Dinnat, E. P.; Koenig, L. S.

2014-01-01

Following the development and availability of Aquarius weekly polar-gridded products, this study presents the spatial and temporal radiometer and scatterometer observations at L band (frequency1.4 GHz) over the cryosphere including the Greenland and Antarctic ice sheets, sea ice in both hemispheres, and over sub-Arctic land for monitoring the soil freeze-thaw state. We provide multiple examples of scientific applications for the L-band data over the cryosphere. For example, we show that over the Greenland Ice Sheet, the unusual 2012 melt event lead to an L-band brightness temperature (TB) sustained decrease of 5 K at horizontal polarization. Over the Antarctic ice sheet, normalized radar cross section (NRCS) observations recorded during ascending and descending orbits are significantly different, highlighting the anisotropy of the ice cover. Over sub-Arctic land, both passive and active observations show distinct values depending on the soil physical state (freeze-thaw). Aquarius sea surface salinity (SSS) retrievals in the polar waters are also presented. SSS variations could serve as an indicator of fresh water input to the ocean from the cryosphere, however the presence of sea ice often contaminates the SSS retrievals, hindering the analysis. The weekly grided Aquarius L-band products used a redistributed by the US Snow and Ice Data Center at http:nsidc.orgdataaquariusindex.html, and show potential for cryospheric studies.

Surface-induced brightness temperature variations and their effects on detecting thin cirrus clouds using IR emission channels in the 8-12 micrometer region

NASA Technical Reports Server (NTRS)

Gao, Bo-Cai; Wiscombe, W. J.

1993-01-01

A method for detecting cirrus clouds in terms of brightness temperature differences between narrow bands at 8, 11, and 12 mu m has been proposed by Ackerman et al. (1990). In this method, the variation of emissivity with wavelength for different surface targets was not taken into consideration. Based on state-of-the-art laboratory measurements of reflectance spectra of terrestrial materials by Salisbury and D'Aria (1992), we have found that the brightness temperature differences between the 8 and 11 mu m bands for soils, rocks and minerals, and dry vegetation can vary between approximately -8 K and +8 K due solely to surface emissivity variations. We conclude that although the method of Ackerman et al. is useful for detecting cirrus clouds over areas covered by green vegetation, water, and ice, it is less effective for detecting cirrus clouds over areas covered by bare soils, rocks and minerals, and dry vegetation. In addition, we recommend that in future the variation of surface emissivity with wavelength should be taken into account in algorithms for retrieving surface temperatures and low-level atmospheric temperature and water vapor profiles.

Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

NASA Astrophysics Data System (ADS)

Sreelash, K.; Buis, Samuel; Sekhar, M.; Ruiz, Laurent; Kumar Tomer, Sat; Guérif, Martine

2017-03-01

Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC components. These results show the potential of crop model inversion for estimating the AWC components of two-layered soils and may guide the sampling of representative years and fields to use this technique for mapping soil properties that are relevant for distributed hydrological modelling.

Integration of satellite-induced fluorescence and vegetation optical depth to improve the retrieval of land evaporation

NASA Astrophysics Data System (ADS)

Pagán, B. R.; Martens, B.; Maes, W. H.; Miralles, D. G.

2017-12-01

Global satellite-based data sets of land evaporation overcome limitations in coverage of in situ measurements while retaining some observational nature. Although their potential for real world applications are promising, their value during dry conditions is still poorly understood. Most evaporation retrieval algorithms are not directly sensitive to soil moisture. An exception is the Global Land Evaporation Amsterdam Model (GLEAM), which uses satellite surface soil moisture and precipitation to account for land water availability. The existing methodology may greatly benefit from the optimal integration of novel observations of the land surface. Microwave vegetation optical depth (VOD) and near-infrared solar-induced fluorescence (SIF) are expected to reflect different aspects of evaporative stress. While the former is considered to be a proxy of vegetation water content, the latter is indicative of the activity of photosynthetic machinery. As stomata regulate both photosynthesis and transpiration, we expect a relationship between SIF and transpiration. An important motivation to incorporate observations in land evaporation calculations is that plant transpiration - usually the largest component of the flux - is extremely challenging to model due to species-dependent responses to drought. Here we present an innovative integration of VOD and SIF into the GLEAM evaporative stress function. VOD is utilized as a measurement of isohydricity to improve the representation of species specific drought responses. SIF is used for transpiration modelling, a novel application, and standardized by incoming solar radiation to better account for radiation-limited periods. Results are validated with global FLUXNET and International Soil Moisture Network data and demonstrate that the incorporation of VOD and SIF can yield accurate estimates of transpiration over large-scales, which are essential to further understand ecosystem-atmosphere feedbacks and the response of terrestrial hydrology and ecology to meteorological drought. The resulting retrievals of land evaporation can be used to benchmark climate model representation of turbulent fluxes, at a time when these models still consider water stress rudimentarily, and typically assume the same sensitivity for all vegetation types to drought stress.

Assimilation of Remotely Sensed Evaporative Fraction for Improved Agricultural Irrigation Water Management

NASA Astrophysics Data System (ADS)

Lei, F.; Crow, W. T.; Kustas, W. P.; Yang, Y.; Anderson, M. C.

2017-12-01

Improving the water usage efficiency and maintaining water use sustainability is challenging under rapidly changed natural environments. For decades, extensive field investigations and conceptual/physical numerical modeling have been developed to quantify and track surface water and energy fluxes at different spatial and temporal scales. Meanwhile, with the development of satellite-based sensors, land surface eco-hydrological parameters can be retrieved remotely to supplement ground-based observations. However, both models and remote sensing retrievals contain various sources of errors and an accurate and spatio-temporally continuous simulation and forecasting system at the field-scale is crucial for the efficient water management in agriculture. Specifically, data assimilation technique can optimally integrate measurements acquired from various sources (including in-situ and remotely-sensed data) with numerical models through consideration of different types of uncertainties. In this presentation, we will focus on improving the estimation of water and energy fluxes over a vineyard in California, U.S. A high-resolution remotely-sensed Evaporative Fraction (EF) product from the Atmosphere-Land Exchange Inverse (ALEXI) model will be incorporated into a Soil Vegetation Atmosphere Transfer (SVAT) model via a 2-D data assimilation method. The results will show that both the accuracy and spatial variability of soil water content and evapotranspiration in SVAT model can be enhanced through the assimilation of EF data. Furthermore, we will demonstrate that by taking the optimized soil water flux as initial condition and combining it with weather forecasts, future field water status can be predicted under different irrigation scenarios. Finally, we will discuss the practical potential of these advances by leveraging our numerical experiment for the design of new irrigation strategies and water management techniques.

Soil moisture retrival from Sentinel-1 and Modis synergy

NASA Astrophysics Data System (ADS)

Gao, Qi; Zribi, Mehrez; Escorihuela, Maria Jose; Baghdadi, Nicolas

2017-04-01

This study presents two methodologies retrieving soil moisture from SAR remote sensing data. The study is based on Sentinel-1 data in the VV polarization, over a site in Urgell, Catalunya (Spain). In the two methodologies using change detection techniques, preprocessed radar data are combined with normalized difference vegetation index (NDVI) auxiliary data to estimate the mean soil moisture with a resolution of 1km. By modeling the relationship between the backscatter difference and NDVI, the soil moisture at a specific NDVI value is retrieved. The first algorithm is already developed on West Africa(Zribi et al., 2014) from ERS scatterometer data to estimate soil water status. In this study, it is adapted to Sentinel-1 data and take into account the high repetitiveness of data in optimizing the inversion approach. Another new method is developed based on the backscatter difference between two adjacent days of Sentinel-1 data w.r.t. NDVI, with smaller vegetation change, the backscatter difference is more sensitive to soil moisture. The proposed methodologies have been validated with the ground measurement in two demonstrative fields with RMS error about 0.05 (in volumetric moisture), and the coherence between soil moisture variations and rainfall events is observed. Soil moisture maps at 1km resolution are generated for the study area. The results demonstrate the potential of Sentinel-1 data for the retrieval of soil moisture at 1km or even better resolution.

Infusion of SMAP Data into Offline and Coupled Models: Evaluation, Calibration, and Assimilation

NASA Astrophysics Data System (ADS)

Lawston, P.; Santanello, J. A., Jr.; Dennis, E. J.; Kumar, S.

2017-12-01

The impact of the land surface on the water and energy cycle is modulated by its coupling to the planetary boundary layer (PBL), and begins at the local scale. A core component of the local land-atmosphere coupling (LoCo) effort requires understanding the `links in the chain' between soil moisture and precipitation, most notably through surface heat fluxes and PBL evolution. To date, broader (i.e. global) application of LoCo diagnostics has been limited by observational data requirements of the coupled system (and in particular, soil moisture) that are typically only met during localized, short-term field campaigns. SMAP offers, for the first time, the ability to map high quality, near-surface soil moisture globally every few days at a spatial resolution comparable to current modeling efforts. As a result, there are numerous potential avenues for SMAP model-data fusion that can be explored in the context of improving understanding of L-A interaction and NWP. In this study, we assess multiple points of intersection of SMAP products with offline and coupled models and evaluate impacts using process-level diagnostics. Results will inform upon the importance of high-resolution soil moisture mapping for improved coupled prediction and model development, as well as reconciling differences in modeled, retrieved, and measured soil moisture. Specifically, NASA model (LIS, NU-WRF) and observation (SMAP, NLDAS-2) products are combined with in-situ standard and IOP measurements (soil moisture, flux, and radiosonde) over the ARM-SGP. An array of land surface model spinups (via LIS-Noah) are performed with varying atmospheric forcing, greenness fraction, and soil layering permutations. Calibration of LIS-Noah soil hydraulic parameters is then performed using an array of in-situ soil moisture and flux and SMAP products. In addition, SMAP assimilation is performed in LIS-Noah both at the scale of the observation (36 and 9km) and the model grid (1km). The focus is on the consistency in calibrated parameters, impact of soil drydown dynamics and soil layers, and terrestrial (soil moisture-flux) coupling. The impacts of these various spinup runs and initialization of NU-WRF coupled forecasts then follows with a focus on weather (ambient, PBL, and precipitation) using LoCo metrics.

Combined evaluation of optical and microwave satellite dataset for soil moisture deficit estimation

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Singh, Sudhir Kumar; Gupta, Manika; Gupta, Dileep Kumar; Kumar, Pradeep

2016-04-01

Soil moisture is a key variable responsible for water and energy exchanges from land surface to the atmosphere (Srivastava et al., 2014). On the other hand, Soil Moisture Deficit (or SMD) can help regulating the proper use of water at specified time to avoid any agricultural losses (Srivastava et al., 2013b) and could help in preventing natural disasters, e.g. flood and drought (Srivastava et al., 2013a). In this study, evaluation of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) and soil moisture from Soil Moisture and Ocean Salinity (SMOS) satellites are attempted for prediction of Soil Moisture Deficit (SMD). Sophisticated algorithm like Adaptive Neuro Fuzzy Inference System (ANFIS) is used for prediction of SMD using the MODIS and SMOS dataset. The benchmark SMD estimated from Probability Distributed Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the validation. The performances are assessed in terms of Nash Sutcliffe Efficiency, Root Mean Square Error and the percentage of bias between ANFIS simulated SMD and the benchmark. The performance statistics revealed a good agreement between benchmark and the ANFIS estimated SMD using the MODIS dataset. The assessment of the products with respect to this peculiar evidence is an important step for successful development of hydro-meteorological model and forecasting system. The analysis of the satellite products (viz. SMOS soil moisture and MODIS LST) towards SMD prediction is a crucial step for successful hydrological modelling, agriculture and water resource management, and can provide important assistance in policy and decision making. Keywords: Land Surface Temperature, MODIS, SMOS, Soil Moisture Deficit, Fuzzy Logic System References: Srivastava, P.K., Han, D., Ramirez, M.A., Islam, T., 2013a. Appraisal of SMOS soil moisture at a catchment scale in a temperate maritime climate. Journal of Hydrology 498, 292-304. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., Al-Shrafany, D., Islam, T., 2013b. Data fusion techniques for improving soil moisture deficit using SMOS satellite and WRF-NOAH land surface model. Water Resources Management 27, 5069-5087. Srivastava, P.K., Han, D., Rico-Ramirez, M.A., O'Neill, P., Islam, T., Gupta, M., 2014. Assessment of SMOS soil moisture retrieval parameters using tau-omega algorithms for soil moisture deficit estimation. Journal of Hydrology 519, 574-587.

La-oxides as tracers for PuO{sub 2} to simulate contaminated aerosol behavior

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

Meyer, L.C.; Newton, G.J.; Cronenberg, A.W.

1994-04-01

An analytical and experimental study was performed on the use of lanthanide oxides (La-oxides) as surrogates for plutonium oxides (PuO{sub 2}) during simulated buried waste retrieval. This study determined how well the La-oxides move compared to PuO{sub 2} in aerosolized soils during retrieval scenarios. As part of the analytical study, physical properties of La-oxides and PuO{sub 2}, such as molecular diameter, diffusivity, density, and molecular weight are compared. In addition, an experimental study was performed in which Idaho National Engineering Laboratory (INEL) soil, INEL soil with lanthanides, and INEL soil with plutonium were aerosolized and collected in filters. Comparison ofmore » particle size distribution parameters from this experimental study show similarity between INEL soil, INEL soil with lanthanides, and INEL soil with plutonium.« less

Comparison of soil moisture retrieval algorithms based on the synergy between SMAP and SMOS-IC

NASA Astrophysics Data System (ADS)

Ebrahimi-Khusfi, Mohsen; Alavipanah, Seyed Kazem; Hamzeh, Saeid; Amiraslani, Farshad; Neysani Samany, Najmeh; Wigneron, Jean-Pierre

2018-05-01

This study was carried out to evaluate possible improvements of the soil moisture (SM) retrievals from the SMAP observations, based on the synergy between SMAP and SMOS. We assessed the impacts of the vegetation and soil roughness parameters on SM retrievals from SMAP observations. To do so, the effects of three key input parameters including the vegetation optical depth (VOD), effective scattering albedo (ω) and soil roughness (HR) parameters were assessed with the emphasis on the synergy with the VOD product derived from SMOS-IC, a new and simpler version of the SMOS algorithm, over two years of data (April 2015 to April 2017). First, a comprehensive comparison of seven SM retrieval algorithms was made to find the best one for SM retrievals from the SMAP observations. All results were evaluated against in situ measurements over 548 stations from the International Soil Moisture Network (ISMN) in terms of four statistical metrics: correlation coefficient (R), root mean square error (RMSE), bias and unbiased RMSE (UbRMSE). The comparison of seven SM retrieval algorithms showed that the dual channel algorithm based on the additional use of the SMOS-IC VOD product (selected algorithm) led to the best results of SM retrievals over 378, 399, 330 and 271 stations (out of a total of 548 stations) in terms of R, RMSE, UbRMSE and both R & UbRMSE, respectively. Moreover, comparing the measured and retrieved SM values showed that this synergy approach led to an increase in median R value from 0.6 to 0.65 and a decrease in median UbRMSE from 0.09 m3/m3 to 0.06 m3/m3. Second, using the algorithm selected in a first step and defined above, the ω and HR parameters were calibrated over 218 rather homogenous ISMN stations. 72 combinations of various values of ω and HR were used for the calibration over different land cover classes. In this calibration process, the optimal values of ω and HR were found for the different land cover classes. The obtained results indicated that the impact of the VOD parameter on SM retrievals is more considerable than the effects of HR and ω. Overall, the inclusion of the VOD parameter in the SMAP SM retrieval algorithm was found to be a very interesting approach and showed the large potential benefit of the synergy between SMAP and SMOS.

The GLORIE Campaign: Assessment of the Capabilities of Airborne GNSS-R for Land Remote Sensing.

NASA Astrophysics Data System (ADS)

Mangiarotti, S.; Motte, E.; Zribi, M., Sr.; Fanise, P., Sr.

2015-12-01

In June and July 2015 an intensive flight campaign was conducted over the south west of France to test the sensitivity of Global Navigation Satellite System Reflectometry (GNSS-R) to the geophysical parameters of continental surfaces. Namely, the parameters of interest were soil moisture, soil roughness, plant water content, forest biomass and level of inland water bodies and rivers. We used the GLORI polarimetric GNSS-R instrument, collecting raw 10MSPS 2-bit IQ direct (RHCP, zenith) and reflected (RHCP and LHCP, nadir) signals at GPS L1 frequency aboard the ATR-42 aircraft of the SAFIRE fleet. Simultaneous measurement of aircraft attitude and position were recorded. The flight plan included flyovers of several areas of interests, with collocated ground truth measurements of soil moisture, soil roughness, cultivated biomass, and forest biomass. Also flyovers of ponds, lakes and river were included for power calibration and altimetry retrievals. In total, 6 flights were performed between June 19th and July 6th, representing more than 15 hours of raw data. A conventional GNSS-R processing of the data was performed in order to compute the direct and reflected complex waveforms. A preliminary data analysis based on the variations of the ratio of reflected maximum correlation amplitude in the LHCP antenna to direct maximum correlated amplitude shows measurements sensitivity to soil type, land use and incidence angle. Also, first altimetric retrievals using phase-delay techniques shows very promising results over calm waters. Current work is ongoing in order to fit the observed polarimetric measurements with innovative bistatic scattering models capable of taking into account complex geometries and land use configurations.

Estimation of Physical Parameters of a Multilayered Multi-Scale Vegetated Surface

NASA Astrophysics Data System (ADS)

Hosni, I.; Bennaceur Farah, L.; Naceur, M. S.; Farah, I. R.

2016-06-01

Soil moisture is important to enable the growth of vegetation in the way that it also conditions the development of plant population. Additionally, its assessment is important in hydrology and agronomy, and is a warning parameter for desertification. Furthermore, the soil moisture content affects exchanges with the atmosphere via the energy balance at the soil surface; it is significant due to its impact on soil evaporation and transpiration. Therefore, it conditions the energy transfer between Earth and atmosphere. Many remote sensing methods were tested. For the soil moisture; the first methods relied on the optical domain (short wavelengths). Obviously, due to atmospheric effects and the presence of clouds and vegetation cover, this approach is doomed to fail in most cases. Therefore, the presence of vegetation canopy complicates the retrieval of soil moisture because the canopy contains moisture of its own. This paper presents a synergistic methodology of SAR and optical remote sensing data, and it's for simulation of statistical parameters of soil from C-band radar measurements. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. Backscattering coefficients were simulated using the established backscattering model. A two-dimensional multiscale SPM model has been employed to investigate the problem of electromagnetic scattering from an underlying soil. The water cloud model (WCM) is used to account for the effect of vegetation water content on radar backscatter data, whereof to eliminate the impact of vegetation layer and isolate the contributions of vegetation scattering and absorption from the total backscattering coefficient.

Assessment of Evapotranspiration and Soil Moisture Content Across Different Scales of Observation.

PubMed

Verstraeten, Willem W; Veroustraete, Frank; Feyen, Jan

2008-01-09

The proper assessment of evapotranspiration and soil moisture content arefundamental in food security research, land management, pollution detection, nutrient flows,(wild-) fire detection, (desert) locust, carbon balance as well as hydrological modelling; etc.This paper takes an extensive, though not exhaustive sample of international scientificliterature to discuss different approaches to estimate land surface and ecosystem relatedevapotranspiration and soil moisture content. This review presents:(i) a summary of the generally accepted cohesion theory of plant water uptake andtransport including a shortlist of meteorological and plant factors influencing planttranspiration;(ii) a summary on evapotranspiration assessment at different scales of observation (sapflow,porometer, lysimeter, field and catchment water balance, Bowen ratio,scintillometer, eddy correlation, Penman-Monteith and related approaches);(iii) a summary on data assimilation schemes conceived to estimate evapotranspirationusing optical and thermal remote sensing; and(iv) for soil moisture content, a summary on soil moisture retrieval techniques atdifferent spatial and temporal scales is presented.Concluding remarks on the best available approaches to assess evapotranspiration and soilmoisture content with and emphasis on remote sensing data assimilation, are provided.

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.

Modeling and Simulation of a Tethered Harpoon for Comet Sampling

NASA Technical Reports Server (NTRS)

Quadrelli, Marco B.

2014-01-01

This paper describes the development of a dynamic model and simulation results of a tethered harpoon for comet sampling. This model and simulation was done in order to carry out an initial sensitivity analysis for key design parameters of the tethered system. The harpoon would contain a canister which would collect a sample of soil from a cometary surface. Both a spring ejected canister and a tethered canister are considered. To arrive in close proximity of the spacecraft at the end of its trajectory so it could be captured, the free-flying canister would need to be ejected at the right time and with the proper impulse, while the tethered canister must be recovered by properly retrieving the tether at a rate that would avoid an excessive amplitude of oscillatory behavior during the retrieval. The paper describes the model of the tether dynamics and harpoon penetration physics. The simulations indicate that, without the tether, the canister would still reach the spacecraft for collection, that the tether retrieval of the canister would be achievable with reasonable fuel consumption, and that the canister amplitude upon retrieval would be insensitive to variations in vertical velocity dispersion.

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.

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.

A framework for combining multiple soil moisture retrievals based on maximizing temporal correlation

NASA Astrophysics Data System (ADS)

Kim, Seokhyeon; Parinussa, Robert M.; Liu, Yi. Y.; Johnson, Fiona M.; Sharma, Ashish

2015-08-01

A method for combining two microwave satellite soil moisture products by maximizing the temporal correlation with a reference data set has been developed. The method was applied to two global soil moisture data sets, Japan Aerospace Exploration Agency (JAXA) and Land Parameter Retrieval Model (LPRM), retrieved from the Advanced Microwave Scanning Radiometer 2 observations for the period 2012-2014. A global comparison revealed superior results of the combined product compared to the individual products against the reference data set of ERA-Interim volumetric water content. The global mean temporal correlation coefficient of the combined product with this reference was 0.52 which outperforms the individual JAXA (0.35) as well as the LPRM (0.45) product. Additionally, the performance was evaluated against in situ observations from the International Soil Moisture Network. The combined data set showed a significant improvement in temporal correlation coefficients in the validation compared to JAXA and minor improvements for the LPRM product.

Seasonal Parameterizations of the Tau-Omega Model Using the ComRAD Ground-Based SMAP Simulator

NASA Technical Reports Server (NTRS)

O'Neill, P.; Joseph, A.; Srivastava, P.; Cosh, M.; Lang, R.

2014-01-01

NASA's Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November 2014. In the prelaunch time frame, the SMAP team has focused on improving retrieval algorithms for the various SMAP baseline data products. The SMAP passive-only soil moisture product depends on accurate parameterization of the tau-omega model to achieve the required accuracy in soil moisture retrieval. During a field experiment (APEX12) conducted in the summer of 2012 under dry conditions in Maryland, the Combined Radar/Radiometer (ComRAD) truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40 degrees over corn and soybeans throughout the crop growth cycle. A similar experiment was conducted only over corn in 2002 under normal moist conditions. Data from these two experiments will be analyzed and compared to evaluate how changes in vegetation conditions throughout the growing season in both a drought and normal year can affect parameterizations in the tau-omega model for more accurate soil moisture retrieval.

Assimilation of SMOS Soil Moisture Retrievals in the Land Information System

NASA Technical Reports Server (NTRS)

Blankenship, Clay; Case, Jonathan L.; Zavodsky, Brad

2014-01-01

Soil moisture is a crucial variable for weather prediction because of its influence on evaporation. It is of critical importance for drought and flood monitoring and prediction and for public health applications. The NASA Short-term Prediction Research and Transition Center (SPoRT) has implemented a new module in the NASA Land Information System (LIS) to assimilate observations from the ESA's Soil Moisture and Ocean Salinity (SMOS) satellite. SMOS Level 2 retrievals from the Microwave Imaging Radiometer using Aperture Synthesis (MIRAS) instrument are assimilated into the Noah LSM within LIS via an Ensemble Kalman Filter. The retrievals have a target volumetric accuracy of 4% at a resolution of 35-50 km. Parallel runs with and without SMOS assimilation are performed with precipitation forcing from intentionally degraded observations, and then validated against a model run using the best available precipitation data, as well as against selected station observations. The goal is to demonstrate how SMOS data assimilation can improve modeled soil states in the absence of dense rain gauge and radar networks.

Remote Sensing of Soil Moisture: A Comparison of Optical and Thermal Methods

NASA Astrophysics Data System (ADS)

Foroughi, H.; Naseri, A. A.; Boroomandnasab, S.; Sadeghi, M.; Jones, S. B.; Tuller, M.; Babaeian, E.

2017-12-01

Recent technological advances in satellite and airborne remote sensing have provided new means for large-scale soil moisture monitoring. Traditional methods for soil moisture retrieval require thermal and optical RS observations. In this study we compared the traditional trapezoid model parameterized based on the land surface temperature - normalized difference vegetation index (LST-NDVI) space with the recently developed optical trapezoid model OPTRAM parameterized based on the shortwave infrared transformed reflectance (STR)-NDVI space for an extensive sugarcane field located in Southwestern Iran. Twelve Landsat-8 satellite images were acquired during the sugarcane growth season (April to October 2016). Reference in situ soil moisture data were obtained at 22 locations at different depths via core sampling and oven-drying. The obtained results indicate that the thermal/optical and optical prediction methods are comparable, both with volumetric moisture content estimation errors of about 0.04 cm3 cm-3. However, the OPTRAM model is more efficient because it does not require thermal data and can be universally parameterized for a specific location, because unlike the LST-soil moisture relationship, the reflectance-soil moisture relationship does not significantly vary with environmental variables (e.g., air temperature, wind speed, etc.).

Why the predictions for monsoon rainfall fail?

NASA Astrophysics Data System (ADS)

Lee, J.

2016-12-01

To be in line with the Global Land/Atmosphere System Study (GLASS) of the Global Energy and Water Cycle Experiment (GEWEX) international research scheme, this study discusses classical arguments about the feedback mechanisms between land surface and precipitation to improve the predictions of African monsoon rainfall. In order to clarify the impact of antecedent soil moisture on subsequent rainfall evolution, several data sets will be presented. First, in-situ soil moisture field measurements acquired by the AMMA field campaign will be shown together with rain gauge data. This data set will validate various model and satellite data sets such as NOAH land surface model, TRMM rainfall, CMORPH rainfall and HadGEM climate models, SMOS soil moisture. To relate soil moisture with precipitation, two approaches are employed: one approach makes a direct comparison between the spatial distributions of soil moisture as an absolute value and rainfall, while the other measures a temporal evolution of the consecutive dry days (i.e. a relative change within the same soil moisture data set over time) and rainfall occurrences. Consecutive dry days shows consistent results of a negative feedback between soil moisture and rainfall across various data sets, contrary to the direct comparison of soil moisture state. This negative mechanism needs attention, as most climate models usually focus on a positive feedback only. The approach of consecutive dry days takes into account the systematic errors in satellite observations, reminding us that it may cause the misinterpretation to directly compare model with satellite data, due to their difference in data retrievals. This finding is significant, as the climate indices employed by the Intergovernmental Panel on Climate Change (IPCC) modelling archive are based on the atmospheric variable rathr than land.

Weekly gridded Aquarius L-band radiometer/scatterometer observations and salinity retrievals over the polar regions - Part 2: Initial product analysis

NASA Astrophysics Data System (ADS)

Brucker, L.; Dinnat, E. P.; Koenig, L. S.

2014-05-01

Following the development and availability of Aquarius weekly polar-gridded products, this study presents the spatial and temporal radiometer and scatterometer observations at L band (frequency ~1.4 GHz) over the cryosphere including the Greenland and Antarctic ice sheets, sea ice in both hemispheres, and over sub-Arctic land for monitoring the soil freeze/thaw state. We provide multiple examples of scientific applications for the L-band data over the cryosphere. For example, we show that over the Greenland Ice Sheet, the unusual 2012 melt event lead to an L-band brightness temperature (TB) sustained decrease of ~5 K at horizontal polarization. Over the Antarctic ice sheet, normalized radar cross section (NRCS) observations recorded during ascending and descending orbits are significantly different, highlighting the anisotropy of the ice cover. Over sub-Arctic land, both passive and active observations show distinct values depending on the soil physical state (freeze/thaw). Aquarius sea surface salinity (SSS) retrievals in the polar waters are also presented. SSS variations could serve as an indicator of fresh water input to the ocean from the cryosphere, however the presence of sea ice often contaminates the SSS retrievals, hindering the analysis. The weekly grided Aquarius L-band products used are distributed by the US Snow and Ice Data Center at http://nsidc.org/data/aquarius/index.html , and show potential for cryospheric studies.

Surface reflectance factor retrieval from Thematic Mapper data

NASA Technical Reports Server (NTRS)

Holm, Ronald G.; Jackson, Ray D.; Yuan, Benfan; Moran, M. Susan; Slater, Philip N.

1989-01-01

Based on the absolute radiometric calibration of the TM and the use of a radiative transfer program for atmospheric correction, ground reflectances were retrieved for several fields of crops and bare soil in TM bands 1-4 for six TM scenes acquired over a 12-month period. These reflectances were compared to those measured using ground-based and low-altitude, aircraft-mounted radiometers. When, for four TM acquisitions, the comparison was made between areas that had been carefully selected for their high uniformity, the reflectance factors agreed to + or - 0.01 over the reflectance range 0.02-0.55. When the comparison was made for two of the above acquisitions and two others on different dates, for larger areas not carefully selected to be of uniform reflectance, the reflectance factors agreed to + or - 0.02 (1 sigma RMS), over same reflectance range.

Data assimilation to extract soil moisture information from SMAP observations

USDA-ARS?s Scientific Manuscript database

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network(NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United Sta...

Global Drought Monitoring and Forecasting based on Satellite Data and Land Surface Modeling

NASA Astrophysics Data System (ADS)

Sheffield, J.; Lobell, D. B.; Wood, E. F.

2010-12-01

Monitoring drought globally is challenging because of the lack of dense in-situ hydrologic data in many regions. In particular, soil moisture measurements are absent in many regions and in real time. This is especially problematic for developing regions such as Africa where water information is arguably most needed, but virtually non-existent on the ground. With the emergence of remote sensing estimates of all components of the water cycle there is now the potential to monitor the full terrestrial water cycle from space to give global coverage and provide the basis for drought monitoring. These estimates include microwave-infrared merged precipitation retrievals, evapotranspiration based on satellite radiation, temperature and vegetation data, gravity recovery measurements of changes in water storage, microwave based retrievals of soil moisture and altimetry based estimates of lake levels and river flows. However, many challenges remain in using these data, especially due to biases in individual satellite retrieved components, their incomplete sampling in time and space, and their failure to provide budget closure in concert. A potential way forward is to use modeling to provide a framework to merge these disparate sources of information to give physically consistent and spatially and temporally continuous estimates of the water cycle and drought. Here we present results from our experimental global water cycle monitor and its African drought monitor counterpart (http://hydrology.princeton.edu/monitor). The system relies heavily on satellite data to drive the Variable Infiltration Capacity (VIC) land surface model to provide near real-time estimates of precipitation, evapotranspiraiton, soil moisture, snow pack and streamflow. Drought is defined in terms of anomalies of soil moisture and other hydrologic variables relative to a long-term (1950-2000) climatology. We present some examples of recent droughts and how they are identified by the system, including objective quantification and tracking of their spatial-temporal characteristics. Further we present strategies for merging various sources of information, including bias correction of satellite precipitation and assimilation of remotely sensed soil moisture, which can augment the monitoring in regions where satellite precipitation is most uncertain. Ongoing work is adding a drought forecast component based on a successful implementation over the U.S. and agricultural productivity estimates based on output from crop yield models. The forecast component uses seasonal global climate forecasts from the NCEP Climate Forecast System (CFS). These are merged with observed climatology in a Bayesian framework to produce ensemble atmospheric forcings that better capture the uncertainties. At the same time, the system bias corrects and downscales the monthly CFS data. We show some initial seasonal (up to 6-month lead) hydrologic forecast results for the African system. Agricultural monitoring is based on the precipitation, temperature and soil moisture from the system to force statistical and process based crop yield models. We demonstrate the feasibility of monitoring major crop types across the world and show a strategy for providing predictions of yields within our drought forecast mode.

Retrieving topsoil moisture using RADARSAT-2 data, a novel approach applied at the east of the Netherlands

NASA Astrophysics Data System (ADS)

Eweys, Omar Ali; Elwan, Abeer A.; Borham, Taha I.

2017-12-01

This manuscript proposes an approach for estimating soil moisture content over corn fields using C-band SAR data acquired by RADARSAT-2 satellite. An image based approach is employed to remove the vegetation contribution to the satellite signals. In particular, the absolute difference between like and cross polarized signals (ADLC) is employed for segmenting the canopy growth cycle into tiny stages. Each stage is represented by a Cumulative Distribution Function (CDF) of the like polarized signals. For periods of bare soils and vegetation cover, CDFs are compared and the vegetation contribution is quantified. The portion which represent the soil contributions (σHHsoil°) to the satellite signals; are employed for inversely running Oh model and the water cloud model for estimating soil moisture, canopy water content and canopy height respectively. The proposed approach shows satisfactory performance where high correlation of determination (R2) is detected between the field observations and the corresponding retrieved soil moisture, canopy water content and canopy height (R2 = 0.64, 0.97 and 0.98 respectively). Soil moisture retrieval is associated with root mean square error (RMSE) of 0.03 m3 m-3 while estimating canopy water content and canopy height have RMSE of 0.38 kg m-2 and 0.166 m respectively.

An enhanced VIIRS aerosol optical thickness (AOT) retrieval algorithm over land using a global surface reflectance ratio database

NASA Astrophysics Data System (ADS)

Zhang, Hai; Kondragunta, Shobha; Laszlo, Istvan; Liu, Hongqing; Remer, Lorraine A.; Huang, Jingfeng; Superczynski, Stephen; Ciren, Pubu

2016-09-01

The Visible/Infrared Imager Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership (S-NPP) satellite has been retrieving aerosol optical thickness (AOT), operationally and globally, over ocean and land since shortly after S-NPP launch in 2011. However, the current operational VIIRS AOT retrieval algorithm over land has two limitations in its assumptions for land surfaces: (1) it only retrieves AOT over the dark surfaces and (2) it assumes that the global surface reflectance ratios between VIIRS bands are constants. In this work, we develop a surface reflectance ratio database over land with a spatial resolution 0.1° × 0.1° using 2 years of VIIRS top of atmosphere reflectances. We enhance the current operational VIIRS AOT retrieval algorithm by applying the surface reflectance ratio database in the algorithm. The enhanced algorithm is able to retrieve AOT over both dark and bright surfaces. Over bright surfaces, the VIIRS AOT retrievals from the enhanced algorithm have a correlation of 0.79, mean bias of -0.008, and standard deviation (STD) of error of 0.139 when compared against the ground-based observations at the global AERONET (Aerosol Robotic Network) sites. Over dark surfaces, the VIIRS AOT retrievals using the surface reflectance ratio database improve the root-mean-square error from 0.150 to 0.123. The use of the surface reflectance ratio database also increases the data coverage of more than 20% over dark surfaces. The AOT retrievals over bright surfaces are comparable to MODIS Deep Blue AOT retrievals.

Comparing SMAP to Macro-scale and Hyper-resolution Land Surface Models over Continental U. S.

NASA Astrophysics Data System (ADS)

Pan, Ming; Cai, Xitian; Chaney, Nathaniel; Wood, Eric

2016-04-01

SMAP sensors collect moisture information in top soil at the spatial resolution of ~40 km (radiometer) and ~1 to 3 km (radar, before its failure in July 2015). Such information is extremely valuable for understanding various terrestrial hydrologic processes and their implications on human life. At the same time, soil moisture is a joint consequence of numerous physical processes (precipitation, temperature, radiation, topography, crop/vegetation dynamics, soil properties, etc.) that happen at a wide range of scales from tens of kilometers down to tens of meters. Therefore, a full and thorough analysis/exploration of SMAP data products calls for investigations at multiple spatial scales - from regional, to catchment, and to field scales. Here we first compare the SMAP retrievals to the Variable Infiltration Capacity (VIC) macro-scale land surface model simulations over the continental U. S. region at 3 km resolution. The forcing inputs to the model are merged/downscaled from a suite of best available data products including the NLDAS-2 forcing, Stage IV and Stage II precipitation, GOES Surface and Insolation Products, and fine elevation data. The near real time VIC simulation is intended to provide a source of large scale comparisons at the active sensor resolution. Beyond the VIC model scale, we perform comparisons at 30 m resolution against the recently developed HydroBloks hyper-resolution land surface model over several densely gauged USDA experimental watersheds. Comparisons are also made against in-situ point-scale observations from various SMAP Cal/Val and field campaign sites.

Estimating the spatial distribution of soil moisture based on Bayesian maximum entropy method with auxiliary data from remote sensing

NASA Astrophysics Data System (ADS)

Gao, Shengguo; Zhu, Zhongli; Liu, Shaomin; Jin, Rui; Yang, Guangchao; Tan, Lei

2014-10-01

Soil moisture (SM) plays a fundamental role in the land-atmosphere exchange process. Spatial estimation based on multi in situ (network) data is a critical way to understand the spatial structure and variation of land surface soil moisture. Theoretically, integrating densely sampled auxiliary data spatially correlated with soil moisture into the procedure of spatial estimation can improve its accuracy. In this study, we present a novel approach to estimate the spatial pattern of soil moisture by using the BME method based on wireless sensor network data and auxiliary information from ASTER (Terra) land surface temperature measurements. For comparison, three traditional geostatistic methods were also applied: ordinary kriging (OK), which used the wireless sensor network data only, regression kriging (RK) and ordinary co-kriging (Co-OK) which both integrated the ASTER land surface temperature as a covariate. In Co-OK, LST was linearly contained in the estimator, in RK, estimator is expressed as the sum of the regression estimate and the kriged estimate of the spatially correlated residual, but in BME, the ASTER land surface temperature was first retrieved as soil moisture based on the linear regression, then, the t-distributed prediction interval (PI) of soil moisture was estimated and used as soft data in probability form. The results indicate that all three methods provide reasonable estimations. Co-OK, RK and BME can provide a more accurate spatial estimation by integrating the auxiliary information Compared to OK. RK and BME shows more obvious improvement compared to Co-OK, and even BME can perform slightly better than RK. The inherent issue of spatial estimation (overestimation in the range of low values and underestimation in the range of high values) can also be further improved in both RK and BME. We can conclude that integrating auxiliary data into spatial estimation can indeed improve the accuracy, BME and RK take better advantage of the auxiliary information compared to Co-OK, and BME outperforms RK by integrating the auxiliary data in a probability form.

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.

L-band Soil Moisture Mapping using Small UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.; Gasiewski, A. J.; Stachura, M.; Elston, J.; Venkitasubramony, A.

2016-12-01

1. IntroductionSoil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, and impacts water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 promises to provide global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions as low as 5 km for some products. However, there exists a need for measurements of soil moisture on smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters (i.e., the height of the platform). Compared with various other proposed methods of validation based on either in-situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed ( km scale) coverage at very high spatial resolution ( 15 m) suitable for scaling scale studies, and at comparatively low operator cost. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site on September 8th and 9th, 2015 and Yuma Colorado Irrigation Research Foundation (IRF) site from June to August, 2016. These tests were flown at 25-50 m altitude to obtain differing spatial resolutions. The scientific intercomparisons of LDCR retrieved soil moisture and in-situ measurements will be presented. 2. References[1] McIntyre, E.M., A.J. Gasiewski, and D. Manda D, "Near Real-Time Passive C-Band Microwave Soil Moisture Retrieval During CLASIC 2007," Proc. IGARSS, 2008. [2] Robock, A., S. Steele-Dunne, J. Basara, W. Crow, and M. Moghaddam M, "In Situ Network and Scaling," SMAP Algorithm and Cal/Val Workshop, 2009. [3] Walker, A., "Airborne Microwave Radiometer Measurements During CanEx-SM10," Second SMAP Cal/Val Workshop, 2011.

Meta-barcoding of 'dirt' DNA from soil reflects vertebrate biodiversity.

PubMed

Andersen, Kenneth; Bird, Karen Lise; Rasmussen, Morten; Haile, James; Breuning-Madsen, Henrik; Kjaer, Kurt H; Orlando, Ludovic; Gilbert, M Thomas P; Willerslev, Eske

2012-04-01

DNA molecules originating from animals and plants can be retrieved directly from sediments and have been used for reconstructing both contemporary and past ecosystems. However, the extent to which such 'dirt' DNA reflects taxonomic richness and structural diversity remains contentious. Here, we couple second generation high-throughput sequencing with 16S mitochondrial DNA (mtDNA) meta-barcoding, to explore the accuracy and sensitivity of 'dirt' DNA as an indicator of vertebrate diversity, from soil sampled at safari parks, zoological gardens and farms with known species compositions. PCR amplification was successful in the full pH range of the investigated soils (6.2 ± 0.2 to 8.3 ± 0.2), but inhibition was detected in extracts from soil of high organic content. DNA movement (leaching) through strata was evident in some sporadic cases and is influenced by soil texture and structure. We find that DNA from the soil surface reflects overall taxonomic richness and relative biomass of individual species. However, one species that was recently introduced was not detected. Furthermore, animal behaviour was shown to influence DNA deposition rates. The approach potentially provides a quick methodological alternative to classical ecological surveys of biodiversity, and most reliable results are obtained with spatial sample replicates, while relative amounts of soil processed per site is of less importance. © 2011 Blackwell Publishing Ltd.

Synergistically combining Optical and Thermal radiative transfer modelswithin the EO-LDAS data assimilation framework to estimate land surfaceand component temperatures from MODIS and Sentinel-3

NASA Astrophysics Data System (ADS)

Timmermans, J.; Gomez-Dans, J. L.; Verhoef, W.; Tol, C. V. D.; Lewis, P.

2017-12-01

Evapotranspiration (ET) cannot be directly measured from space. Instead it relies on modelling approaches that use several land surface parameters (LSP), LAI and LST, in conjunction with meteorological parameters. Such a modelling approach presents two caveats: the validity of the model, and the consistency between the different input parameters. Often this second step is not considered, ignoring that without good inputs no decent output can provided. When LSP- dynamics contradict each other, the output of the model cannot be representative of reality. At present however, the LSPs used in large scale ET estimations originate from different single-sensor retrieval-approaches and even from different satellite sensors. In response, the Earth Observation Land Data Assimilation System (EOLDAS) was developed. EOLDAS uses a multi-sensor approach to couple different satellite observations/types to radiative transfer models (RTM), consistently. It is therefore capable of synergistically estimating a variety of LSPs. Considering that ET is most sensitive to the temperatures of the land surface (components), the goal of this research is to expand EOLDAS to the thermal domain. This research not only focuses on estimating LST, but also on retrieving (soil/vegetation, Sunlit/shaded) component temperatures, to facilitate dual/quad-source ET models. To achieve this, The Soil Canopy Observations of Photosynthesis and Energy (SCOPE) model was integrated into EOLDAS. SCOPE couples key-parameters to key-processes, such as photosynthesis, ET and optical/thermal RT. In this research SCOPE was also coupled to MODTRAN RTM, in order to estimate BOA component temperatures directly from TOA observations. This paper presents the main modelling steps of integrating these complex models into an operational platform. In addition it highlights the actual retrieval using different satellite observations, such as MODIS and Sentinel-3, and meteorological variables from the ERA-Interim.

Polarimetric Radar Observations of Forest State for Determination of Ecosystem Processes

NASA Technical Reports Server (NTRS)

Ulaby, Fawwaz T.; Dobson, M. Craig; Sharik, T.

1996-01-01

The objectives of this research are to test the hypotheses that ecologically significant forest state parameters may be estimated from SAR data. These include estimation of above ground biomass, plant water status, and near surface soil moisture under certain forest conditions. Test hypotheses in the northern hardwoods forest community, refine them if necessary, and establish techniques for retrieving this information from orbital SARs such as SIR-C/X-SAR. This report summarizes (1) recent progress, (2) significant results and (3) research plans concerning SIR-C/X-SAR research.

SMOS soil moisture validation with U.S. in situ newworks

USDA-ARS?s Scientific Manuscript database

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors using a variety of retrieval methods. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. Since it is a new sensor u...

Potential of bias correction for downscaling passive microwave and soil moisture data

USDA-ARS?s Scientific Manuscript database

Passive microwave satellites such as SMOS (Soil Moisture and Ocean Salinity) or SMAP (Soil Moisture Active Passive) observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...

Estimating error cross-correlations in soil moisture data sets using extended collocation analysis

USDA-ARS?s Scientific Manuscript database

Consistent global soil moisture records are essential for studying the role of hydrologic processes within the larger earth system. Various studies have shown the benefit of assimilating satellite-based soil moisture data into water balance models or merging multi-source soil moisture retrievals int...

Towards decadal soil salinity mapping using Landsat time series data

NASA Astrophysics Data System (ADS)

Fan, Xingwang; Weng, Yongling; Tao, Jinmei

2016-10-01

Salinization is one of the major soil problems around the world. However, decadal variation in soil salinization has not yet been extensively reported. This study exploited thirty years (1985-2015) of Landsat sensor data, including Landsat-4/5 TM (Thematic Mapper), Landsat-7 ETM+ (Enhanced Thematic Mapper Plus) and Landsat-8 OLI (Operational Land Imager), for monitoring soil salinity of the Yellow River Delta, China. The data were initially corrected for atmospheric effects, and then matched the spectral bands of EO-1 (Earth Observing One) ALI (Advanced Land Imager). Subsequently, soil salinity maps were derived with a previously developed PLSR (Partial Least Square Regression) model. On intra-annual scale, the retrievals showed that soil salinity increased in February, stabilized in March, and decreased in April. On inter-annual scale, soil salinity decreased within 1985-2000 (-0.74 g kg-1/10a, p < 0.001), and increased within 2000-2015 (0.79 g kg-1/10a, p < 0.001). Our study presents a new perspective for use of multiple Landsat data in soil salinity retrieval, and further the understanding of soil salinization development over the Yellow River Delta.

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.

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.

Greywater-induced soil hydrophobicity.

PubMed

Maimon, Adi; Gross, Amit; Arye, Gilboa

2017-10-01

Greywater (GW) reuse for irrigation is a common method of reducing domestic consumption of fresh water. Most of the scientific research and legislation efforts have focused on GW's health risks, while less attention has been given to its environmental outcomes. One of the environmental risks of GW irrigation is its possible effect on soil hydraulic properties. This research examined the ability of GW to induce soil hydrophobicity, as well as its degree and persistence. Fresh water (control) and three model GW solutions representing raw, treated and highly treated GW were used to wet fine-grained sand. Every treatment was subjected to five cycles of wetting, incubation (at 5 °C or 30 °C) and drying (60 °C). After each cycle, capillary rise was measured and the contact angle (CA) was calculated. Samples were also tested by the Wilhelmy plate method to retrieve advancing and receding CA and reservoir surface tension. Water repellence of the sand, as implied from the CA, increased with increasing GW concentration and was highest in the sand coated with the model raw GW and incubated at 5 °C. However, none of the treatments resulted in what is considered to be "water-repellent soil". Furthermore, when raw GW-coated sand was immersed in water, its surface tension was significantly reduced relative to the other treatments, implying a release of surface-active compounds from the sand into the water. It was postulated that untreated GW may induce sub-critical water repellence in sand. However, this effect is sensitive to biodegradation and washing processes and is therefore temporary. Copyright © 2017 Elsevier Ltd. All rights reserved.

Surface roughness estimation by inversion of the Hapke photometric model on optical data simulated using a ray tracing code

NASA Astrophysics Data System (ADS)

Champion, J.; Ristorcelli, T.; Ferrari, C. C.; Briottet, X.; Jacquemoud, S.

2013-12-01

Surface roughness is a key physical parameter that governs various processes (incident radiation distribution, temperature, erosion,...) on Earth and other Solar System objects. Its impact on the scattering function of incident electromagnetic waves is difficult to model. In the 80's, Hapke provided an approximate analytic solution for the bidirectional reflectance distribution function (BRDF) of a particulate medium and, later on, included the effect of surface roughness as a correction factor for the BRDF of a smooth surface. This analytical radiative transfer model is widely used in solar system science whereas its ability to remotely determine surface roughness is still a question at issue. The validation of the Hapke model has been only occasionally undertaken due to the lack of radiometric data associated with field measurement of surface roughness. We propose to validate it on Earth, on several volcanic terrains for which very high resolution digital elevation models are available at small scale. We simulate the BRDF of these DEMs thanks to a ray-tracing code and fit them with the Hapke model to retrieve surface roughness. The mean slope angle of the facets, which quantifies surface roughness, can be fairly well retrieved when most conditions are met, i.e. a random-like surface and little multiple scattering between the facets. A directional sensitivity analysis of the Hapke model confirms that both surface intrinsic optical properties (facet's reflectance or single scattering albedo) and roughness are the most influential variables on ground BRDFs. Their interactions in some directions explain why their separation may be difficult, unless some constraints are introduced in the inversion process. Simulation of soil surface BRDF at different illumination and viewing angles

A partial least square regression method to quantitatively retrieve soil salinity using hyper-spectral reflectance data

NASA Astrophysics Data System (ADS)

Qu, Yonghua; Jiao, Siong; Lin, Xudong

2008-10-01

Hetao Irrigation District located in Inner Mongolia, is one of the three largest irrigated area in China. In the irrigational agriculture region, for the reasons that many efforts have been put on irrigation rather than on drainage, as a result much sedimentary salt that usually is solved in water has been deposited in surface soil. So there has arisen a problem in such irrigation district that soil salinity has become a chief fact which causes land degrading. Remote sensing technology is an efficiency way to map the salinity in regional scale. In the principle of remote sensing, soil spectrum is one of the most important indications which can be used to reflect the status of soil salinity. In the past decades, many efforts have been made to reveal the spectrum characteristics of the salinized soil, such as the traditional statistic regression method. But it also has been found that when the hyper-spectral reflectance data are considered, the traditional regression method can't be treat the large dimension data, because the hyper-spectral data usually have too higher spectral band number. In this paper, a partial least squares regression (PLSR) model was established based on the statistical analysis on the soil salinity and the reflectance of hyper-spectral. Dataset were collect through the field soil samples were collected in the region of Hetao irrigation from the end of July to the beginning of August. The independent validation using data which are not included in the calibration model reveals that the proposed model can predicate the main soil components such as the content of total ions(S%), PH with higher determination coefficients(R2) of 0.728 and 0.715 respectively. And the rate of prediction to deviation(RPD) of the above predicted value are larger than 1.6, which indicates that the calibrated PLSR model can be used as a tool to retrieve soil salinity with accurate results. When the PLSR model's regression coefficients were aggregated according to the wavelength of visual (blue, green, red) and near infrared bands of LandSat Thematic Mapper(TM) sensor, some significant response values were observed, which indicates that the proposed method in this paper can be used to analysis the remotely sensed data from the space-boarded platform.

The impact of conventional surface data upon VAS regression retrievals in the lower troposphere

NASA Technical Reports Server (NTRS)

Lee, T. H.; Chesters, D.; Mostek, A.

1983-01-01

Surface temperature and dewpoint reports are added to the infrared radiances from the VISSR Atmospheric Sounder (VAS) in order to improve the retrieval of temperature and moisture profiles in the lower troposphere. The conventional (airways) surface data are combined with the twelve VAS channels as additional predictors in a ridge regression retrieval scheme, with the aim of using all available data to make high resolution space-time interpolations of the radiosonde network. For one day of VAS observations, retrievals using only VAS radiances are compared with retrievals using VAS radiances plus surface data. Temperature retrieval accuracy evaluated at coincident radiosonde sites shows a significant impact within the boundary layer. Dewpoint retrieval accuracy shows a broader improvement within the lowest tropospheric layers. The most dramatic impact of surface data is observed in the improved relative spatial and temporal continuity of low-level fields retrieved over the Midwestern United States.

Using Satellite Data and Land Surface Models to Monitor and Forecast Drought Conditions in Africa and Middle East

NASA Astrophysics Data System (ADS)

Arsenault, K. R.; Shukla, S.; Getirana, A.; Peters-Lidard, C. D.; Kumar, S.; McNally, A.; Zaitchik, B. F.; Badr, H. S.; Funk, C. C.; Koster, R. D.; Narapusetty, B.; Jung, H. C.; Roningen, J. M.

2017-12-01

Drought and water scarcity are among the important issues facing several regions within Africa and the Middle East. In addition, these regions typically have sparse ground-based data networks, where sometimes remotely sensed observations may be the only data available. Long-term satellite records can help with determining historic and current drought conditions. In recent years, several new satellites have come on-line that monitor different hydrological variables, including soil moisture and terrestrial water storage. Though these recent data records may be considered too short for the use in identifying major droughts, they do provide additional information that can better characterize where water deficits may occur. We utilize recent satellite data records of Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage (TWS) and the European Space Agency's Advanced Scatterometer (ASCAT) soil moisture retrievals. Combining these records with land surface models (LSMs), NASA's Catchment and the Noah Multi-Physics (MP), is aimed at improving the land model states and initialization for seasonal drought forecasts. The LSMs' total runoff is routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics, which can provide an additional means of validation against in situ streamflow data. The NASA Land Information System (LIS) software framework drives the LSMs and HyMAP and also supports the capability to assimilate these satellite retrievals, such as soil moisture and TWS. The LSMs are driven for 30+ years with NASA's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS/UCSB Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) rainfall dataset. The seasonal water deficit forecasts are generated using downscaled and bias-corrected versions of NASA's Goddard Earth Observing System Model (GEOS-5), and NOAA's Climate Forecast System (CFSv2) forecasts. These combined satellite and model records and forecasts are intended for use in different decision support tools, like the Famine Early Warning Systems Network (FEWS NET) and the Middle East-North Africa (MENA) Regional Drought Management System, for aiding and forecasting in water and food insecure regions.

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.

Hyperspectral Data Processing and Mapping of Soil Parameters: Preliminary Data from Tuscany (Italy)

NASA Astrophysics Data System (ADS)

Garfagnoli, F.; Moretti, S.; Catani, F.; Innocenti, L.; Chiarantini, L.

2010-12-01

Hyperspectral imaging has become a very powerful remote sensing tool for its capability of performing chemical and physical analysis of the observed areas. The objective of this study is to retrieve and characterize clay mineral content of the cultivated layer of soils, from both airborne hyperspectral and field spectrometry surveys in the 400-2500 nm spectral range. Correlation analysis is used to examine the possibility to predict the selected property using high-resolution reflectance spectra and images. The study area is located in the Mugello basin, about 30 km north of Firenze (Tuscany, Italy). Agriculturally suitable terrains are assigned mainly to annual crops, marginally to olive groves, vineyards and orchards. Soils mostly belong to Regosols and Cambisols orders. About 80 topsoil samples scattered all over the area were collected simultaneously with the flight of SIM.GA hyperspectral camera from Selex Galileo. The quantitative determination of clay minerals content in soil samples was performed by means of XRD and Rietveld refinement. An ASD FieldSpec spectroradiometer was used to obtain reflectance spectra from dried, crushed and sieved samples under controlled laboratory conditions. Different chemometric techniques (multiple linear regression, vertex component analysis, partial least squares regression and band depth analysis) were preliminarily tested to correlate mineralogical records with reflectance data. A one component partial least squares regression model yielded a preliminary R2 value of 0.65. A similar result was achieved by plotting the absorption peak depth at 2210 versus total clay mineral content (band-depth analysis). A complete hyperspectral geocoded reflectance dataset was collected using SIM.GA hyperspectral image sensor from Selex-Galileo, mounted on board of the University of Firenze ultra light aircraft. The approximate pixel resolution was 0.6 m (VNIR) and 1.2 m (SWIR). Airborne SIM.GA row data were firstly transformed into at-sensor radiance values, where calibration coefficients and parameters from laboratory measurements are applied to non-georeferred VNIR/SWIR DN values. Then, geocoded products are retrieved for each flight line by using a procedure developed in IDL Language and PARGE (PARametric Geocoding) software. When all compensation parameters are applied to hyperspectral data or to the final thematic map, orthorectified, georeferred and coregistered VNIR to SWIR images or maps are available for GIS application and 3D view. Airborne imagery has to be corrected for the influence of the atmosphere, solar illumination, sensor viewing geometry and terrain geometry information, for the retrieval of inherent surface reflectance properties. Then, different geophysical parameters can be investigated and retrieved by means of inversion algorithms. The experimental fitting of laboratory data on mineral content is used for airborne data inversion, whose results are in agreement with laboratory records, demonstrating the possibility to use this methodology for digital mapping of soil properties.

Mitigation of biases in SMOS Level 2 soil moisture retrieval algorithm

NASA Astrophysics Data System (ADS)

Mahmoodi, Ali; Richaume, Philippe; Kerr, Yann

2017-04-01

The Soil Moisture and Ocean Salinity (SMOS) mission of the European Space Agency (ESA) relies on the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer models to retrieve soil moisture (SM). These models require, as input, parameters which characterize the target like soil water content and temperature. The Soil Water Volume at Level 1 (SWVL1) from the European Centre for Medium-Range Weather Forecast (ECMWF) is used in the SMOS Level 2 SM algorithms as both an initial guess for SM in the iterative retrieval process and to compute fixed contributions from the so called "default" fractions. In case of mixed fractions of nominal (low vegetation land) and forest, retrieval is performed over one fraction while the contribution of the other is assumed to be fixed and known based on ECMWF data. Studies have shown that ECMWF SWVL1 is biased when compared to SMOS SM and represents values at a deeper layer of soil ( 7 cm) than that represented by SMOS ( 2 to 5 cm). This study uses a well know bias reduction technique based on matching of the Cumulative Distribution Functions (CDF) of the two distributions to help reduce the biases. Early results using a linear matching method provide very encouraging results. A complication with respect to performing CDF matching is that SMOS SM values are not available where they are needed, i.e. over the default fractions. In order to remedy this, we treat mixed fractions as homogeneous targets to retrieve SM over the whole target. The obtained values are then used to derive the CDF matching coefficients. A set of CDF coefficients derived using average and standard deviation of soil moisture values for 2014 has been used in reprocessing SMOS data for 2014 and 2015, as well as over selected sites (with in-situ data) over a longer period. The 2014 was selected due to its lower Radio Frequency Interference (RFI) contamination in comparison with other years. The application of CDF coefficients has lead to a wetter SM for many pixels (both in 2014 and 2015), where pixels are close to forested areas. It has also led to improvements in the frequency of successful retrievals for these pixels. These results are in agreement with our current state of knowledge that SMOS is dryer than expected near forests, and hence are encouraging and in support of future incorporation of CDF matching in the operational processor. We also discuss the performances of the CDF matched SM values in comparison with the operational ones over a number of sites where in-situ data is available, like Soil Climate Analysis Network (SCAN) in North America.

Size Matters: Assessing Optimum Soil Sample Size for Fungal and Bacterial Community Structure Analyses Using High Throughput Sequencing of rRNA Gene Amplicons

PubMed Central

Penton, C. Ryan; Gupta, Vadakattu V. S. R.; Yu, Julian; Tiedje, James M.

2016-01-01

We examined the effect of different soil sample sizes obtained from an agricultural field, under a single cropping system uniform in soil properties and aboveground crop responses, on bacterial and fungal community structure and microbial diversity indices. DNA extracted from soil sample sizes of 0.25, 1, 5, and 10 g using MoBIO kits and from 10 and 100 g sizes using a bead-beating method (SARDI) were used as templates for high-throughput sequencing of 16S and 28S rRNA gene amplicons for bacteria and fungi, respectively, on the Illumina MiSeq and Roche 454 platforms. Sample size significantly affected overall bacterial and fungal community structure, replicate dispersion and the number of operational taxonomic units (OTUs) retrieved. Richness, evenness and diversity were also significantly affected. The largest diversity estimates were always associated with the 10 g MoBIO extractions with a corresponding reduction in replicate dispersion. For the fungal data, smaller MoBIO extractions identified more unclassified Eukaryota incertae sedis and unclassified glomeromycota while the SARDI method retrieved more abundant OTUs containing unclassified Pleosporales and the fungal genera Alternaria and Cercophora. Overall, these findings indicate that a 10 g soil DNA extraction is most suitable for both soil bacterial and fungal communities for retrieving optimal diversity while still capturing rarer taxa in concert with decreasing replicate variation. PMID:27313569

4.4 Development of a 30-Year Soil Moisture Climatology for Situational Awareness and Public Health Applications

NASA Technical Reports Server (NTRS)

Case, Jonathan L.; Zavodsky, Bradley T.; White, Kristopher D.; Bell, Jesse E.

2015-01-01

This paper provided a brief background on the work being done at NASA SPoRT and the CDC to create a soil moisture climatology over the CONUS at high spatial resolution, and to provide a valuable source of soil moisture information to the CDC for monitoring conditions that could favor the development of Valley Fever. The soil moisture climatology has multi-faceted applications for both the NOAA/NWS situational awareness in the areas of drought and flooding, and for the Public Health community. SPoRT plans to increase its interaction with the drought monitoring and Public Health communities by enhancing this testbed soil moisture anomaly product. This soil moisture climatology run will also serve as a foundation for upgrading the real-time (currently southeastern CONUS) SPoRT-LIS to a full CONUS domain based on LIS version 7 and incorporating real-time GVF data from the Suomi-NPP Visible Infrared Imaging Radiometer Suite (Vargas et al. 2013) into LIS-Noah. The upgraded SPoRT-LIS run will serve as a testbed proof-of-concept of a higher-resolution NLDAS-2 modeling member. The climatology run will be extended to near real-time using the NLDAS-2 meteorological forcing from 2011 to present. The fixed 1981-2010 climatology shall provide the soil moisture "normals" for the production of real-time soil moisture anomalies. SPoRT also envisions a web-mapping type of service in which an end-user could put in a request for either an historical or real-time soil moisture anomaly graph for a specified county (as exemplified by Figure 2) and/or for local and regional maps of soil moisture proxy percentiles. Finally, SPoRT seeks to assimilate satellite soil moisture data from the current Soil Moisture Ocean Salinity (SMOS; Blankenship et al. 2014) and the recently-launched NASA Soil Moisture Active Passive (SMAP; Entekhabi et al. 2010) missions, using the EnKF capability within LIS. The 9-km combined active radar and passive microwave retrieval product from SMAP (Das et al. 2011) has the potential to provide valuable information about the near-surface soil moisture state for improving land surface modeling output.

Long-Term Evaluation of the AMSR-E Soil Moisture Product Over the Walnut Gulch Watershed, AZ

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Jackson, T. J.; Lakshmi, V.; Cosh, M. H.; Drusch, M.

2005-12-01

The Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) was launched aboard NASA's Aqua satellite on May 4th, 2002. Quantitative estimates of soil moisture using the AMSR-E provided data have required routine radiometric data calibration and validation using comparisons of satellite observations, extended targets and field campaigns. The currently applied NASA EOS Aqua ASMR-E soil moisture algorithm is based on a change detection approach using polarization ratios (PR) of the calibrated AMSR-E channel brightness temperatures. To date, the accuracy of the soil moisture algorithm has been investigated on short time scales during field campaigns such as the Soil Moisture Experiments in 2004 (SMEX04). Results have indicated self-consistency and calibration stability of the observed brightness temperatures; however the performance of the moisture retrieval algorithm has been poor. The primary objective of this study is to evaluate the quality of the current version of the AMSR-E soil moisture product for a three year period over the Walnut Gulch Experimental Watershed (150 km2) near Tombstone, AZ; the northern study area of SMEX04. This watershed is equipped with hourly and daily recording of precipitation, soil moisture and temperature via a network of raingages and a USDA-NRCS Soil Climate Analysis Network (SCAN) site. Surface wetting and drying are easily distinguished in this area due to the moderately-vegetated terrain and seasonally intense precipitation events. Validation of AMSR-E derived soil moisture is performed from June 2002 to June 2005 using watershed averages of precipitation, and soil moisture and temperature data from the SCAN site supported by a surface soil moisture network. Long-term assessment of soil moisture algorithm performance is investigated by comparing temporal variations of moisture estimates with seasonal changes and precipitation events. Further comparisons are made with a standard soil dataset from the European Centre for Medium-Range Weather Forecasts. The results of this research will contribute to a better characterization of the low biases and discrepancies currently observed in the AMSR-E soil moisture product.

Soil Moisture Extremes Observed by METOP ASCAT: Was 2012 an Exceptional Year?

NASA Astrophysics Data System (ADS)

Wagner, Wolfgang; Paulik, Christoph; Hahn, Sebastian; Melzer, Thomas; Parinussa, Robert; de Jeu, Richard; Dorigo, Wouter; Chung, Daniel; Enenkel, Markus

2013-04-01

In summer 2012 the international press reported widely about the severe drought that had befallen large parts of the United States. Yet, the US drought was only one of several major droughts that occurred in 2012: Southeastern Europe, Central Asia, Brazil, India, Southern Australia and several other regions suffered from similarly dry soil conditions. This raises the question whether 2012 was an exceptionally dry year? In this presentation we will address this question by analyzing global soil moisture patterns as observed by the Advanced Scatterometer (ASCAT) flown on board of the METOP-A satellite. We firstly compare the 2012 ASCAT soil moisture data to all available ASCAT measurements acquired by the instrument since the launch of METOP-A in November 2006. Secondly, we compare the 2012 data to a long-term soil moisture data set derived by merging the ASCAT soil moisture data with other active and passive microwave soil moisture retrievals as described by Liu et al. (2012) and Wagner et al. (2012) (see also http://www.esa-soilmoisture-cci.org/). A first trend analysis of the latter long-term soil moisture data set carried out by Dorigo et al. (2012) has revealed that over the period 1988-2010 significant trends were observed over 27 % of the area covered by the data set, of which 73 % were negative (soil drying) and only 27 % were positive (soil wetting). In this presentation we will show how the inclusion of the years 2011 and 2012 affects the areal extent and strengths of these significant trends. REFERENCES Dorigo, W., R. de Jeu, D. Chung, R. Parinussa, Y. Liu, W. Wagner, D. Fernández-Prieto (2012) Evaluating global trends (1988-2010) in harmonized multi-satellite surface soil moisture, Geophysical Research Letters, 39, L18405, 1-7. Liu, Y.Y., W.A. Dorigo, R.M. Parinussa, R.A.M. de Jeu, W. Wagner, M.F. McCabe, J.P. Evans, A.I.J.M. van Dijk (2012) Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297. 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.

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring

PubMed Central

Motte, Erwan; Zribi, Mehrez; Fanise, Pascal; Egido, Alejandro; Darrozes, José; Al-Yaari, Amen; Baghdadi, Nicolas; Baup, Frédéric; Dayau, Sylvia; Fieuzal, Remy; Frison, Pierre-Louis; Guyon, Dominique; Wigneron, Jean-Pierre

2016-01-01

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than −15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than −30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics. PMID:27213393

GLORI: A GNSS-R Dual Polarization Airborne Instrument for Land Surface Monitoring.

PubMed

Motte, Erwan; Zribi, Mehrez; Fanise, Pascal; Egido, Alejandro; Darrozes, José; Al-Yaari, Amen; Baghdadi, Nicolas; Baup, Frédéric; Dayau, Sylvia; Fieuzal, Remy; Frison, Pierre-Louis; Guyon, Dominique; Wigneron, Jean-Pierre

2016-05-20

Global Navigation Satellite System-Reflectometry (GNSS-R) has emerged as a remote sensing tool, which is complementary to traditional monostatic radars, for the retrieval of geophysical parameters related to surface properties. In the present paper, we describe a new polarimetric GNSS-R system, referred to as the GLObal navigation satellite system Reflectometry Instrument (GLORI), dedicated to the study of land surfaces (soil moisture, vegetation water content, forest biomass) and inland water bodies. This system was installed as a permanent payload on a French ATR42 research aircraft, from which simultaneous measurements can be carried out using other instruments, when required. Following initial laboratory qualifications, two airborne campaigns involving nine flights were performed in 2014 and 2015 in the Southwest of France, over various types of land cover, including agricultural fields and forests. Some of these flights were made concurrently with in situ ground truth campaigns. Various preliminary applications for the characterisation of agricultural and forest areas are presented. Initial analysis of the data shows that the performance of the GLORI instrument is well within specifications, with a cross-polarization isolation better than -15 dB at all elevations above 45°, a relative polarimetric calibration accuracy better than 0.5 dB, and an apparent reflectivity sensitivity better than -30 dB, thus demonstrating its strong potential for the retrieval of land surface characteristics.

Rainfall estimation by inverting SMOS soil moisture estimates: a comparison of different methods over Australia

USDA-ARS?s Scientific Manuscript database

Remote sensing of soil moisture has reached a level of maturity and accuracy for which the retrieved products can be used to improve hydrological and meteorological applications. In this study, the soil moisture product from the European Space Agency’s Soil Moisture and Ocean Salinity (SMOS) is used...

Utilization of microwave energy for decontamination of oil polluted soils.

PubMed

Iordache, Daniela; Niculae, Dumitru; Francisc, Ioan Hathazi

2010-01-01

Soil oil (petroleum) product pollution represents a great environmental threat as it may contaminate the neighboring soils and surface and underground water. Liquid fuel contamination may occur anywhere during oil (petroleum) product transportation, storing, handling and utilization. The polluted soil recovery represents a complex process due to the wide range of physical, chemical and biological properties of soils which should be analyzed in connection with the study of the contaminated soil behavior under the microwave field action. The soil, like any other non-metallic material, can be heated through microwave energy absorption due to the dielectric losses, expressed by its dielectric complex constant. Oil polluted soil behaves differently in a microwave field depending on the nature, structure and amount of the polluting fuel. Decontamination is performed through volatilization and retrieval of organic contaminant volatile components. After decontamination only a soil fixed residue remains, which cannot penetrate the underground anymore. In carrying out the soil recovery process by means of this technology we should also consider the soil characteristics such as: the soil type, temperature, moisture.The first part of the paper presents the theoretical aspects relating to the behavior of the polluted soil samples in the microwave field, as well as their relating experimental data. The experimental data resulting from the analysis of soils with a different level of pollution point out that the degree of pollutant recovery is high, contributing to changing the initial classification of soils from the point of view of pollution. The paper graphically presents the levels of microwave generated and absorbed power in soil samples, soil temperature during experimentations, specific processing parameters in a microwave field. It also presents the constructive solution of the microwave equipment designed for the contaminated soil in situ treatment.

Modelling and validation land-atmospheric heat fluxes by using classical surface parameters over the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Ma, W.; Ma, Y.; Hu, Z.; Zhong, L.

2017-12-01

In this study, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in situ long-term CAMP/Tibet observations. Firstly our field observation sites will be introduced based on ITPCAS (Institute of Tibetan Plateau Research, Chinese Academy of Sciences). Then, a land-atmosphere model was initialized by ingesting AMSR-E products, and the results were compared with the default model configuration and with in situ long-term CAMP/Tibet observations. The differences between the AMSR-E initialized model runs with the default model configuration and in situ data showed an apparent inconsistency in the model-simulated land surface heat fluxes. The results showed that the soil moisture was sensitive to the specific model configuration. To evaluate and verify the model stability, a long-term modeling study with AMSR-E soil moisture data ingestion was performed. Based on test simulations, AMSR-E data were assimilated into an atmospheric model for July and August 2007. The results showed that the land surface fluxes agreed well with both the in situ data and the results of the default model configuration. Therefore, the simulation can be used to retrieve land surface heat fluxes from an atmospheric model over the Tibetan Plateau.

Microwave brightness temperature and thermal inertia - towards synergistic method of high-resolution soil moisture retrieval

NASA Astrophysics Data System (ADS)

Lukowski, Mateusz; Usowicz, Boguslaw; Sagan, Joanna; Szlazak, Radoslaw; Gluba, Lukasz; Rojek, Edyta

2017-04-01

Soil moisture is an important parameter in many environmental studies, as it influences the exchange of water and energy at the interface between the land surface and the atmosphere. Accurate assessment of the soil moisture spatial and temporal variations is crucial for numerous studies; starting from a small scale of single field, then catchment, mesoscale basin, ocean conglomeration, finally ending at the global water cycle. Despite numerous advantages, such as fine accuracy (undisturbed by clouds or daytime conditions) and good temporal resolution, passive microwave remote sensing of soil moisture, e.g. SMOS and SMAP, are not applicable to a small scale - simply because of too coarse spatial resolution. On the contrary, thermal infrared-based methods of soil moisture retrieval have a good spatial resolution, but are often disturbed by clouds and vegetation interferences or night effects. The methods that base on point measurements, collected in situ by monitoring stations or during field campaigns, are sometimes called "ground truth" and may serve as a reference for remote sensing, of course after some up-scaling and approximation procedures that are, unfortunately, potential source of error. Presented research concern attempt to synergistic approach that join two remote sensing methods: passive microwave and thermal infrared, supported by in situ measurements. Microwave brightness temperature of soil was measured by ELBARA, the radiometer at 1.4 GHz frequency, installed at 6 meters high tower at Bubnow test site in Poland. Thermal inertia around the tower was modelled using the statistical-physical model whose inputs were: soil physical properties, its water content, albedo and surface temperatures measured by an infrared pyrometer, directed at the same footprint as ELBARA. The results coming from this method were compared to in situ data obtained during several field campaigns and by the stationary agrometeorological stations. The approach seems to be reasonable, as both variables, brightness temperature and thermal inertia, strongly depend on soil moisture. Despite the fact that the presented research focused on modelling in the small size, 4 ha test site, the method is promising for larger scales as well, due to similarities between ELBARA and SMOS and between pyrometer and satellite imaging spectrometers (Landsat, Sentinel etc.). The approach will merge advantages: high accuracy of passive microwave sensing with a good spatial resolution of thermal infrared methods. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States). 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp.

Integration of SMAP and SMOS L-Band Observations

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas J.; Chan, Steven; Colliander, Andreas; Kerr, Yaan

2017-01-01

Soil Moisture Active Passive (SMAP) mission and the ESA Soil Moisture and Ocean Salinity (SMOS) missions provide brightness temperature and soil moisture estimates every 2-3 days. SMAP brightness temperature observations were compared with SMOS observations at 40 Degrees incidence angle. The brightness temperatures from the two missions are not consistent and have a bias of about 2.7K over land with respect to each other. SMAP and SMOS missions use different retrieval algorithms and ancillary datasets which result in further inconsistencies between the soil moisture products. The reprocessed constant-angle SMOS brightness temperatures were used in the SMAP soil moisture retrieval algorithm to develop a consistent multi-satellite product. The integrated product will have an increased global revisit frequency (1 day) and period of record that would be unattainable by either one of the satellites alone. Results from the development and validation of the integrated product will be presented.

A novel approach to validate satellite soil moisture retrievals using precipitation data

NASA Astrophysics Data System (ADS)

Karthikeyan, L.; Kumar, D. Nagesh

2016-10-01

A novel approach is proposed that attempts to validate passive microwave soil moisture retrievals using precipitation data (applied over India). It is based on the concept that the expectation of precipitation conditioned on soil moisture follows a sigmoidal convex-concave-shaped curve, the characteristic of which was recently shown to be represented by mutual information estimated between soil moisture and precipitation. On this basis, with an emphasis over distribution-free nonparametric computations, a new measure called Copula-Kernel Density Estimator based Mutual Information (CKDEMI) is introduced. The validation approach is generic in nature and utilizes CKDEMI in tandem with a couple of proposed bootstrap strategies, to check accuracy of any two soil moisture products (here Advanced Microwave Scanning Radiometer-EOS sensor's Vrije Universiteit Amsterdam-NASA (VUAN) and University of Montana (MONT) products) using precipitation (India Meteorological Department) data. The proposed technique yields a "best choice soil moisture product" map which contains locations where any one of the two/none of the two/both the products have produced accurate retrievals. The results indicated that in general, VUA-NASA product has performed well over University of Montana's product for India. The best choice soil moisture map is then integrated with land use land cover and elevation information using a novel probability density function-based procedure to gain insight on conditions under which each of the products has performed well. Finally, the impact of using a different precipitation (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources) data set over the best choice soil moisture product map is also analyzed. The proposed methodology assists researchers and practitioners in selecting the appropriate soil moisture product for various assimilation strategies at both basin and continental scales.

Physical Retrieval of Surface Emissivity Spectrum from Hyperspectral Infrared Radiances

NASA Technical Reports Server (NTRS)

Li, Jun; Weisz, Elisabeth; Zhou, Daniel K.

2007-01-01

Retrieval of temperature, moisture profiles and surface skin temperature from hyperspectral infrared (IR) radiances requires spectral information about the surface emissivity. Using constant or inaccurate surface emissivities typically results in large retrieval errors, particularly over semi-arid or arid areas where the variation in emissivity spectrum is large both spectrally and spatially. In this study, a physically based algorithm has been developed to retrieve a hyperspectral IR emissivity spectrum simultaneously with the temperature and moisture profiles, as well as the surface skin temperature. To make the solution stable and efficient, the hyperspectral emissivity spectrum is represented by eigenvectors, derived from the laboratory measured hyperspectral emissivity database, in the retrieval process. Experience with AIRS (Atmospheric InfraRed Sounder) radiances shows that a simultaneous retrieval of the emissivity spectrum and the sounding improves the surface skin temperature as well as temperature and moisture profiles, particularly in the near surface layer.

Long-term ELBARA-II Assistance to SMOS Land Product and Algorithm Validation at the Valencia Anchor Station (MELBEX Experiment 2010-2013)

NASA Astrophysics Data System (ADS)

Lopez-Baeza, Ernesto; Wigneron, Jean-Pierre; Schwank, Mike; Miernecki, Maciej; Kerr, Yann; Casal, Tania; Delwart, Steven; Fernandez-Moran, Roberto; Mecklenburg, Susanne; Coll Pajaron, M. Amparo; Salgado Hernanz, Paula

The main activity of the Valencia Anchor Station (VAS) is currently now to support the validation of SMOS (Soil Moisture and Ocean Salinity) Level 2 and 3 land products (soil moisture, SM, and vegetation optical depth, TAU). With this aim, the European Space Agency (ESA) has provided the Climatology from Satellites Group of the University of Valencia with an ELBARA-II microwave radiometer under a loan agreement since September 2009. During this time, brightness temperatures (TB) have continuously been acquired, except during normal maintenance or minor repair interruptions. ELBARA-II is an L-band dual-polarization radiometer with two channels (1400-1418 MHz, 1409-1427 MHz). It is continuously measuring over a vineyard field (El Renegado, Caudete de las Fuentes, Valencia) from a 15 m platform with a constant protocol for calibration and angular scanning measurements with the aim to assisting the validation of SMOS land products and the calibration of the L-MEB (L-Band Emission of the Biosphere) -basis for the SMOS Level 2 Land Processor- over the VAS validation site. One of the advantages of using the VAS site is the possibility of studying two different environmental conditions along the year. While the vine cycle extends mainly between April and October, during the rest of the year the area remains under bare soil conditions, adequate for the calibration of the soil model. The measurement protocol currently running has shown to be robust during the whole operation time and will be extended in time as much as possible to continue providing a long-term data set of ELBARA-II TB measurements and retrieved SM and TAU. This data set is also showing to be useful in support of SMOS scientific activities: the VAS area and, specifically the ELBARA-II site, offer good conditions to control the long-term evolution of SMOS Level 2 and Level 3 land products and interpret eventual anomalies that may obscure sensor hidden biases. In addition, SM and TAU that are currently retrieved from the ELBARA-II TB data by inversion of the L-MEB model, can also be compared to the Level 2 and Level 3 SMOS products. L-band ELBARA-II measurements provide area-integrated estimations of SM and TAU that are much more representative of the soil and vegetation conditions at field scale than ground measurements (from capacitive probes for SM and destructive measurements for TAU). For instance, Miernecki et al., (2012) and Wigneron et al. (2012) showed that very good correlations could be obtained from TB data and SM retrievals obtained from both SMOS and ELBARA-II over the 2010-2011 time period. The analysis of the quality of these correlations over a long time period can be very useful to evaluate the SMOS measurements and retrieved products (Level 2 and 3). The present work that extends the analysis over almost 4 years now (2010-2013) emphasizes the need to (i) maintain the long-time record of ELBARA-II measurements (ii) enhance as much as possible the control over other parameters, especially, soil roughness (SR), vegetation water content (VWC) and surface temperature, to interpret the retrieved results obtained from both SMOS and ELBARA-II instruments.

Surface Emissivity Retrieved with Satellite Ultraspectral IR Measurements for Monitoring Global Change

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu; Smith, William L.; Schluessel, Peter

2009-01-01

Surface and atmospheric thermodynamic parameters retrieved with advanced ultraspectral remote sensors aboard Earth observing satellites are critical to general atmospheric and Earth science research, climate monitoring, and weather prediction. Ultraspectral resolution infrared radiance obtained from nadir observations provide atmospheric, surface, and cloud information. Presented here is the global surface IR emissivity retrieved from Infrared Atmospheric Sounding Interferometer (IASI) measurements under "clear-sky" conditions. Fast radiative transfer models, applied to the cloud-free (or clouded) atmosphere, are used for atmospheric profile and surface parameter (or cloud parameter) retrieval. The inversion scheme, dealing with cloudy as well as cloud-free radiances observed with ultraspectral infrared sounders, has been developed to simultaneously retrieve atmospheric thermodynamic and surface (or cloud microphysical) parameters. Rapidly produced surface emissivity is initially evaluated through quality control checks on the retrievals of other impacted atmospheric and surface parameters. Surface emissivity and surface skin temperature from the current and future operational satellites can and will reveal critical information on the Earth s ecosystem and land surface type properties, which can be utilized as part of long-term monitoring for the Earth s environment and global climate change.

The advanced qualtiy control techniques planned for the Internation Soil Moisture Network

NASA Astrophysics Data System (ADS)

Xaver, A.; Gruber, A.; Hegiova, A.; Sanchis-Dufau, A. D.; Dorigo, W. A.

2012-04-01

In situ soil moisture observations are essential to evaluate and calibrate modeled and remotely sensed soil moisture products. Although a number of meteorological networks and field campaigns measuring soil moisture exist on a global and long-term scale, their observations are not easily accessible and lack standardization of both technique and protocol. Thus, handling and especially comparing these datasets with satellite products or land surface models is a demanding issue. To overcome these limitations the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu/) has been initiated to act as a centralized data hosting facility. One advantage of the ISMN is that users are able to access the harmonized datasets easily through a web portal. Another advantage is the fully automated processing chain including the data harmonization in terms of units and sampling interval, but even more important is the advanced quality control system each measurement has to run through. The quality of in situ soil moisture measurements is crucial for the validation of satellite- and model-based soil moisture retrievals; therefore a sophisticated quality control system was developed. After a check for plausibility and geophysical limits a quality flag is added to each measurement. An enhanced flagging mechanism was recently defined using a spectrum based approach to detect spurious spikes, jumps and plateaus. The International Soil Moisture Network has already evolved to one of the most important distribution platforms for in situ soil moisture observations and is still growing. Currently, data from 27 networks in total covering more than 800 stations in Europe, North America, Australia, Asia and Africa is hosted by the ISMN. Available datasets also include historical datasets as well as near real-time measurements. The improved quality control system will provide important information for satellite-based as well as land surface model-based validation studies.

Pesticides in Surface and Ground Water of the San Joaquin-Tulare Basins, California: Analysis of Available Data, 1966 Through 1992

USGS Publications Warehouse

Domagalski, Joseph L.

1997-01-01

Available pesticide data (1966-92) for surface and ground water were analyzed for the San Joaquin-Tulare Basins, California, one of 60 large hydrologic systems being studied as part of the National Water-Quality Assessment Program of the U.S. Geological Survey. Most of the pesticide data were for the San Joaquin Valley, one of the most intensively farmed and irrigated areas of the United States. Data were obtained from the Storage and Retrieval data base of the U.S. Environmental Protection Agency, the water-quality data base of the U.S. Geological Survey, and from data files of State agencies. Pesticides detected in surface water include organochlorine pesticides, organophosphate pesticides, carbamate pesticides, and triazine herbicides. Pesticides detected in ground water include triazine and other organonitrogen herbicides and soil fumi gants. Surface-water data indicate seasonal patterns for the detection of organophosphate and carbamate pesticides, which are attributed to their use on almond orchards and alfafa fields. Organochlorine pesticides were detected primarily in river-bed sediments. Concentrations detected in bed sediments of the San Joaquin River near Vernalis are among the highest of any major river system in the United States. Patterns and timing of pesticide use indicate that pesticides might be present in surface-water systems during most months of a year. The most commonly detected pesticide in ground water is the soil fumigant, dibromochloropropane. Dibromochloropropane, used primarily on vineyards and orchards, was detected in ground water near the city of Fresno. Triazine and other organonitrogen herbicides were detected near vineyards and orchards in the same general locations as the detections of dibromochloropropane. Pesticides were detected in ground water of the east side of the valley floor, where the soils are sandy or coarsegrained, and water-soluble pesticides with long environmental half-lives were used. In contrast, fewer pesticides were detected in ground water of the west side of the valley, where soils generally are finer grained.

Spatially enhanced passive microwave derived soil moisture: capabilities and opportunities

USDA-ARS?s Scientific Manuscript database

Low frequency passive microwave remote sensing is a proven technique for soil moisture retrieval, but its coarse resolution restricts the range of applications. Downscaling, otherwise known as disaggregation, has been proposed as the solution to spatially enhance these coarse resolution soil moistur...

Validation of SMAP soil moisture for the SMAPVEX15 field campaign using a hyper-resolution model

USDA-ARS?s Scientific Manuscript database

Accurate global mapping of soil moisture is the goal of the Soil Moisture Active Passive (SMAP) mission, which is expected to improve the estimation of water, energy, and carbon exchanges between the land and the atmosphere. Like other satellite products, the SMAP soil moisture retrievals need to be...

Performance assessment of femoral knee components made from cobalt-chromium alloy and oxidized zirconium.

PubMed

Brandt, J-M; Guenther, L; O'Brien, S; Vecherya, A; Turgeon, T R; Bohm, E R

2013-12-01

The surface characteristics of the femoral component affect polyethylene wear in modular total knee replacements. In the present retrieval study, the surface characteristics of cobalt-chromium (CoCr) alloy and oxidized zirconium (OxZr) femoral components were assessed and compared. Twenty-six retrieved CoCr alloy femoral components were matched with twenty-six retrieved OxZr femoral components for implantation period, body-mass index, patient gender, implant type, and polyethylene insert thickness. The surface damage on the retrieved femoral components was evaluated using a semi-quantitative assessment method, scanning electron microscopy, and contact profilometry. The retrieved CoCr alloy femoral components showed less posterior surface gouging than OxZr femoral components; however, at a higher magnification, the grooving damage features on the retrieved CoCr alloy femoral components confirmed an abrasive wear mechanism. The surface roughness values Rp, Rpm, and Rpk for the retrieved CoCr alloy femoral components were found to be significantly higher than those of the retrieved OxZr femoral components (p≤0.031). The surface roughness values were higher on the medial condyles than on the lateral condyles of the retrieved CoCr alloy femoral components; such a difference was not observed on the retrieved OxZr femoral components. The surface roughness of CoCr alloy femoral components increased while the surface roughness of the OxZr femoral components remained unchanged after in vivo service. Therefore, the OxZr femoral components' resistance to abrasive wear may enable lower polyethylene wear and ensure long-term durability in vivo. Level IV. Crown Copyright © 2013. Published by Elsevier B.V. All rights reserved.

Precipitation Estimation Using L-Band and C-Band Soil Moisture Retrievals

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Brocca, Luca; Crow, Wade T.; Burgin, Mariko S.; De Lannoy, Gabrielle J. M.

2016-01-01

An established methodology for estimating precipitation amounts from satellite-based soil moisture retrievals is applied to L-band products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) satellite missions and to a C-band product from the Advanced Scatterometer (ASCAT) mission. The precipitation estimates so obtained are evaluated against in situ (gauge-based) precipitation observations from across the globe. The precipitation estimation skill achieved using the L-band SMAP and SMOS data sets is higher than that obtained with the C-band product, as might be expected given that L-band is sensitive to a thicker layer of soil and thereby provides more information on the response of soil moisture to precipitation. The square of the correlation coefficient between the SMAP-based precipitation estimates and the observations (for aggregations to approximately100 km and 5 days) is on average about 0.6 in areas of high rain gauge density. Satellite missions specifically designed to monitor soil moisture thus do provide significant information on precipitation variability, information that could contribute to efforts in global precipitation estimation.

A Passive Microwave L-Band Boreal Forest Freeze/Thaw and Vegetation Phenology Study

NASA Astrophysics Data System (ADS)

Roy, A.; Sonnentag, O.; Pappas, C.; Mavrovic, A.; Royer, A.; Berg, A. A.; Rowlandson, T. L.; Lemay, J.; Helgason, W.; Barr, A.; Black, T. A.; Derksen, C.; Toose, P.

2016-12-01

The boreal forest is the second largest land biome in the world and thus plays a major role in the global and regional climate systems. The extent, timing and duration of seasonal freeze/thaw (F/T) state influences vegetation developmental stages (phenology) and, consequently, constitute an important control on how boreal forest ecosystems exchange carbon, water and energy with the atmosphere. The effective retrieval of seasonal F/T state from L-Band radiometry was demonstrated using satellite mission. However, disentangling the seasonally differing contributions from forest overstory and understory vegetation, and the soil surface to the satellite signal remains challenging. Here we present initial results from a radiometer field campaign to improve our understanding of the L-Band derived boreal forest F/T signal and vegetation phenology. Two L-Band surface-based radiometers (SBR) are installed on a micrometeorological tower at the Southern Old Black Spruce site in central Saskatchewan over the 2016-2017 F/T season. One radiometer unit is installed on the flux tower so it views forest including all overstory and understory vegetation and the moss-covered ground surface. A second radiometer unit is installed within the boreal forest overstory, viewing the understory and the ground surface. The objectives of our study are (i) to disentangle the L-Band F/T signal contribution of boreal forest overstory from the understory and ground surface, (ii) to link the L-Band F/T signal to related boreal forest structural and functional characteristics, and (iii) to investigate the use of the L-Band signal to characterize boreal forest carbon, water and energy fluxes. The SBR observations above and within the forest canopy are used to retrieve the transmissivity (γ) and the scattering albedo (ω), two parameters that describe the emission of the forest canopy though the F/T season. These two forest parameters are compared with boreal forest structural and functional characteristics including eddy-covariance measurements of carbon dioxide, water and energy exchanges, sap flux density measurements of tree-level water dynamics, L-Band tree permittivity and temperature. The study will lead to improved monitoring of soil F/T and vegetation phenology at the boreal forest-scale from satellite L-Band observations.

A Prototype Physical Database for Passive Microwave Retrievals of Precipitation over the US Southern Great Plains

NASA Technical Reports Server (NTRS)

Ringerud, S.; Kummerow, C. D.; Peters-Lidard, C. D.

2015-01-01

An accurate understanding of the instantaneous, dynamic land surface emissivity is necessary for a physically based, multi-channel passive microwave precipitation retrieval scheme over land. In an effort to assess the feasibility of the physical approach for land surfaces, a semi-empirical emissivity model is applied for calculation of the surface component in a test area of the US Southern Great Plains. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM PR) are utilized along with coincident brightness temperatures (Tbs) from the TRMM Microwave Imager (TMI), and cloud-resolving model profiles. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled top of the atmosphere Tbs show correlations to observations of 0.9, biases of 1K or less, root-mean-square errors on the order of 5K, and improved agreement over the use of climatological emissivity values. The synthesis of these models and data sets leads to the creation of a simple prototype Tb database that includes both dynamic surface and atmospheric information physically consistent with the land surface model, emissivity model, and atmospheric information.

Size Matters: Assessing Optimum Soil Sample Size for Fungal and Bacterial Community Structure Analyses Using High Throughput Sequencing of rRNA Gene Amplicons

DOE PAGES

Penton, C. Ryan; Gupta, Vadakattu V. S. R.; Yu, Julian; ...

2016-06-02

We examined the effect of different soil sample sizes obtained from an agricultural field, under a single cropping system uniform in soil properties and aboveground crop responses, on bacterial and fungal community structure and microbial diversity indices. DNA extracted from soil sample sizes of 0.25, 1, 5, and 10 g using MoBIO kits and from 10 and 100 g sizes using a bead-beating method (SARDI) were used as templates for high-throughput sequencing of 16S and 28S rRNA gene amplicons for bacteria and fungi, respectively, on the Illumina MiSeq and Roche 454 platforms. Sample size significantly affected overall bacterial and fungalmore » community structure, replicate dispersion and the number of operational taxonomic units (OTUs) retrieved. Richness, evenness and diversity were also significantly affected. The largest diversity estimates were always associated with the 10 g MoBIO extractions with a corresponding reduction in replicate dispersion. For the fungal data, smaller MoBIO extractions identified more unclassified Eukaryota incertae sedis and unclassified glomeromycota while the SARDI method retrieved more abundant OTUs containing unclassified Pleosporales and the fungal genera Alternaria and Cercophora. Overall, these findings indicate that a 10 g soil DNA extraction is most suitable for both soil bacterial and fungal communities for retrieving optimal diversity while still capturing rarer taxa in concert with decreasing replicate variation.« less

Size Matters: Assessing Optimum Soil Sample Size for Fungal and Bacterial Community Structure Analyses Using High Throughput Sequencing of rRNA Gene Amplicons

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

Penton, C. Ryan; Gupta, Vadakattu V. S. R.; Yu, Julian

We examined the effect of different soil sample sizes obtained from an agricultural field, under a single cropping system uniform in soil properties and aboveground crop responses, on bacterial and fungal community structure and microbial diversity indices. DNA extracted from soil sample sizes of 0.25, 1, 5, and 10 g using MoBIO kits and from 10 and 100 g sizes using a bead-beating method (SARDI) were used as templates for high-throughput sequencing of 16S and 28S rRNA gene amplicons for bacteria and fungi, respectively, on the Illumina MiSeq and Roche 454 platforms. Sample size significantly affected overall bacterial and fungalmore » community structure, replicate dispersion and the number of operational taxonomic units (OTUs) retrieved. Richness, evenness and diversity were also significantly affected. The largest diversity estimates were always associated with the 10 g MoBIO extractions with a corresponding reduction in replicate dispersion. For the fungal data, smaller MoBIO extractions identified more unclassified Eukaryota incertae sedis and unclassified glomeromycota while the SARDI method retrieved more abundant OTUs containing unclassified Pleosporales and the fungal genera Alternaria and Cercophora. Overall, these findings indicate that a 10 g soil DNA extraction is most suitable for both soil bacterial and fungal communities for retrieving optimal diversity while still capturing rarer taxa in concert with decreasing replicate variation.« less

Aerosol and Surface Parameter Retrievals for a Multi-Angle, Multiband Spectrometer

NASA Technical Reports Server (NTRS)

Broderick, Daniel

2012-01-01

This software retrieves the surface and atmosphere parameters of multi-angle, multiband spectra. The synthetic spectra are generated by applying the modified Rahman-Pinty-Verstraete Bidirectional Reflectance Distribution Function (BRDF) model, and a single-scattering dominated atmosphere model to surface reflectance data from Multiangle Imaging SpectroRadiometer (MISR). The aerosol physical model uses a single scattering approximation using Rayleigh scattering molecules, and Henyey-Greenstein aerosols. The surface and atmosphere parameters of the models are retrieved using the Lavenberg-Marquardt algorithm. The software can retrieve the surface and atmosphere parameters with two different scales. The surface parameters are retrieved pixel-by-pixel while the atmosphere parameters are retrieved for a group of pixels where the same atmosphere model parameters are applied. This two-scale approach allows one to select the natural scale of the atmosphere properties relative to surface properties. The software also takes advantage of an intelligent initial condition given by the solution of the neighbor pixels.

Evaluation of a Soil Moisture Data Assimilation System Over the Conterminous United States

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Crow, W. T.; Zhan, X.; Reynolds, C. A.; Jackson, T. J.

2008-12-01

A data assimilation system has been designed to integrate surface soil moisture estimates from the EOS Advanced Microwave Scanning Radiometer (AMSR-E) with an online soil moisture model used by the USDA Foreign Agriculture Service for global crop estimation. USDA's International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) ingests global soil moisture within a Crop Assessment Data Retrieval and Evaluation (CADRE) Decision Support System (DSS) to provide nowcasts of crop conditions and agricultural-drought. This information is primarily used to derive mid-season crop yield estimates for the improvement of foreign market access for U.S. agricultural products. The CADRE is forced by daily meteorological observations (precipitation and temperature) provided by the Air Force Weather Agency (AFWA) and World Meteorological Organization (WMO). The integration of AMSR-E observations into the two-layer soil moisture model employed by IPAD can potentially enhance the reliability of the CADRE soil moisture estimates due to AMSR-E's improved repeat time and greater spatial coverage. Assimilation of the AMSR-E soil moisture estimates is accomplished using a 1-D Ensemble Kalman filter (EnKF) at daily time steps. A diagnostic calibration of the filter is performed using innovation statistics by accurately weighting the filter observation and modeling errors for three ranges of vegetation biomass density estimated using historical data from the Advanced Very High Resolution Radiometer (AVHRR). Assessment of the AMSR-E assimilation has been completed for a five year duration over the conterminous United States. To evaluate the ability of the filter to compensate for incorrect precipitation forcing into the model, a data denial approach is employed by comparing soil moisture results obtained from separate model simulations forced with precipitation products of varying uncertainty. An analysis of surface and root-zone anomalies is presented for each model simulation over the conterminous United States, as well as statistical assessments for each simulation over various land cover types.

L-MEB Model Calibration Over the Valencia Anchor Station Area

NASA Astrophysics Data System (ADS)

Lopez-Baeza, E.

2009-04-01

In the framework of ESA's SMOS (Soil Moisture and Ocean Salinity) Mission, several studies are being carried out over different types of land surfaces to study their microwave L-band emission (1.4 GHz). These studies are being integrated in the SMOS emission model (L-MEB, L-band Microwave Emission of the Biosphere, Wigneron et al. 2007), which is the core of the SMOS algorithm for the retrieval of land surface parameters from SMOS data. To contribute to Cal/Val activities at the Valencia Anchor Station (VAS) area (Caudete de las Fuentes, Valencia, Spain), one of the primary validation areas for SMOS land data and products (ESA SMOS Cal/Val AO, Project ID 3252, Lopez-Baeza et al., 2005), a number of experiments have been carried out to study the vegetation influence over the L-band emission proceeding from the soil surface. In the VAS area, a reduced number of homogeneous units have been defined according to the type and use of the soil, mainly, shrubs, vineyards, orchards (almond-and olive-trees) and Mediterranean pine forests. In order to implement the SMOS algorithm over this reference area, it is necessary to characterize and calibrate the L-MEB model for the different cover types. This work is significantly contributing to the definition of the VAS site as a validation area for SMOS land products of the size of a SMOS pixel (SMOS reference pixel). Shrubs and vineyards are the two most significant vegetation types which cover a large percentage of the area and for which very little information at L-band is available in the literature. These two types of vegetation covers have been studied in two separate dedicated experiments under the common name of MELBEX (Mediterranean Ecosystem L-Band characterisation EXperiment). The first one (MELBEX-I) took place over a shrub area characterised by a significant proportion of bare soil with superficial stones. The second one (MELBEX-II) was carried out from March to December 2007 over a large vineyard area. During the time period of both experiments, there was a large range of vegetation and soil moisture conditions related to significant rainfall events, different temperature and vegetation biomass conditions, etc. The experiments consisted on the continuous acquisition of L-band polarimetric and multi angular measurements (from 20 to 60 degrees of elevation) over both types of vegetation using the EMIRAD L-band radiometer from the Electromagnetic System Group (EMI) of the Technical University of Denmark. In addition, measurements of infrared temperature, gravimetric and volumetric soil moisture, soil temperature profile, soil roughness and Leaf Area Index (LAI) were also monitored with an adequate frequency. This paper presents the main results from both experiments. In particular, the main L-MEB parameters that characterize these two significant Mediterranean Ecosystem species have been computed and evaluated for soil moisture retrieval purposes in the framework of the imminent SMOS over land measurements. References Lopez-Baeza, E. et al. (2207): Validation of SMOS Products over Mediterranean Ecosystem Vegetation at the Valencia Anchor Station Reference Area. ESA SMOS Cal/Val Announcement of Opportunities (ID 3252). P.I.: E. Lopez-Baeza Wigneron J-P, Y. Kerr, P. Waldteufel, K. Saleh, M.-J. Escorihuela, P. Richaume, P. Ferrazzoli, P. de Rosnay, R. Gurney, J.-C. Calvet, J.P. Grant, M. Guglielmetti, B. Hornbuckle, C. Mätzler, T. Pellarin, M. Schwank (2007): L-band Microwave Emission of the Biosphere (L-MEB) Model: Description and calibration against experimental data sets over crop fields, Remote Sens. Env., 107, p. 639-655, 2007

Spectrophotometric properties of materials observed by Pancam on the Mars Exploration Rovers: 3. Sols 500-1525

NASA Astrophysics Data System (ADS)

Johnson, Jeffrey R.; Grundy, William M.; Lemmon, Mark T.; Bell, James F.; Deen, R. G.

2015-03-01

The Panoramic Camera (Pancam) on the Mars Exploration Rovers Spirit and Opportunity acquired visible/near-infrared (432-1009 nm) multispectral observations of soils and rocks under varying viewing and illumination geometries. Data retrieved from these images were modeled using radiative transfer theory to study the microphysical and surface scattering nature of materials at both sites. Nearly 57,000 individual measurements from 1900 images were collected of rock and soil units identified by their color and morphologic properties over a wide range of phase angles (0-150°). Images were acquired between Sols 500 and 1525 in the Columbia Hills and regions around Home Plate in Gusev Crater and in the plains and craters between Erebus and Victoria Craters in Meridiani Planum. Corrections for diffuse skylight incorporated sky models based on observations of atmospheric opacity throughout the mission. Disparity maps created from Pancam stereo images allowed estimates of local facet orientations. For Spirit, soils at lower elevations near Home Plate were modeled with lower single scattering albedo (w) values than those on the summit of Husband Hill, but otherwise soils exhibited similar scattering properties to previous Gusev soils. Dark ripple sands at the El Dorado dunes were among the most forward-scattering materials modeled. Silica-rich soils and nodules near Home Plate were analyzed for the first time, and exhibited increased forward scattering behavior with increasing wavelength, consistent with microporosity inferred from previous high resolution images and thermal infrared spectroscopy. For Opportunity, the opposition effect width parameter for sandstone outcrop rocks was modeled for the first time, and demonstrated average values consistent with surfaces of intermediate porosity and/or grain size distribution between those modeled for spherule-rich soils and darker, clast-poor soils. Soils outside a wind streak emanating from the northern rim of Victoria Crater exhibited w values ∼16% higher than soils inside the streak. Overall, w values and scattering properties for outcrop rocks, spherule-rich soils, and rover tracks were similar to previous Meridiani Planum analyses, emphasizing the homogeneity of these materials across nearly 12 km of rover odometry.

L-band HIgh Spatial Resolution Soil Moisture Mapping using SMALL UnManned Aerial Systems

NASA Astrophysics Data System (ADS)

Dai, E.; Venkitasubramony, A.; Gasiewski, A. J.; Stachura, M.; Elston, J. S.; Walter, B.; Lankford, D.; Corey, C.

2017-12-01

Soil moisture is of fundamental importance to many hydrological, biological and biogeochemical processes, plays an important role in the development and evolution of convective weather and precipitation, water resource management, agriculture, and flood runoff prediction. The launch of NASA's Soil Moisture Active/Passive (SMAP) mission in 2015 provided new passive global measurements of soil moisture and surface freeze/thaw state at fixed crossing times and spatial resolutions of 36 km. However, there exists a need for measurements of soil moisture on much smaller spatial scales and arbitrary diurnal times for SMAP validation, precision agriculture and evaporation and transpiration studies of boundary layer heat transport. The Lobe Differencing Correlation Radiometer (LDCR) provides a means of mapping soil moisture on spatial scales as small as several meters. Compared with other methods of validation based on either in-situ measurements [1,2] or existing airborne sensors suitable for manned aircraft deployment [3], the integrated design of the LDCR on a lightweight small UAS (sUAS) is capable of providing sub-watershed ( km scale) coverage at very high spatial resolution ( 15 m) suitable for scaling studies, and at comparatively low operator cost. To demonstrate the LDCR several flights had been performed during field experiments at the Canton Oklahoma Soilscape site and Yuma Colorado Irrigation Research Foundation (IRF) site in 2015 and 2016, respectively, using LDCR Revision A and Tempest sUAS. The scientific intercomparisons of LDCR retrieved soil moisture and in-situ measurements will be presented. LDCR Revision B has been built and integrated into SuperSwift sUAS and additional field experiments will be performed at IRF in 2017. In Revision B the IF signal is sampled at 80 MS/s to enable digital correlation and RFI mitigation capabilities, in addition to analog correlation. [1] McIntyre, E.M., A.J. Gasiewski, and D. Manda D, "Near Real-Time Passive C-Band Microwave Soil Moisture Retrieval During CLASIC 2007," Proc. IGARSS, 2008. [2] Robock, A., S. Steele-Dunne, J. Basara, W. Crow, and M. Moghaddam M, "In Situ Network and Scaling," SMAP Algorithm and Cal/Val Workshop, 2009. [3] Walker, A., "Airborne Microwave Radiometer Measurements During CanEx-SM10," Second SMAP Cal/Val Workshop, 2011.

XCO2 retrieval error over deserts near critical surface albedo

NASA Astrophysics Data System (ADS)

Zhang, Qiong; Shia, Run-Lie; Sander, Stanley P.; Yung, Yuk L.

2016-02-01

Large retrieval errors in column-weighted CO2 mixing ratio (XCO2) over deserts are evident in the Orbiting Carbon Observatory 2 version 7 L2 products. We argue that these errors are caused by the surface albedo being close to a critical surface albedo (αc). Over a surface with albedo close to αc, increasing the aerosol optical depth (AOD) does not change the continuum radiance. The spectral signature caused by changing the AOD is identical to that caused by changing the absorbing gas column. The degeneracy in the retrievals of AOD and XCO2 results in a loss of degrees of freedom and information content. We employ a two-stream-exact single scattering radiative transfer model to study the physical mechanism of XCO2 retrieval error over a surface with albedo close to αc. Based on retrieval tests over surfaces with different albedos, we conclude that over a surface with albedo close to αc, the XCO2 retrieval suffers from a significant loss of accuracy. We recommend a bias correction approach that has significantly improved the XCO2 retrieval from the California Laboratory for Atmospheric Remote Sensing data in the presence of aerosol loading.

Using Remotely Sensed Soil Moisture to Estimate Fire Risk in Tropical Peatlands

NASA Astrophysics Data System (ADS)

Dadap, N.; Cobb, A.; Hoyt, A.; Harvey, C. F.; Konings, A. G.

2017-12-01

Tropical peatlands in Equatorial Asia have become more vulnerable to fire due to deforestation and peatland drainage over the last 30 years. In these regions, water table depth has been shown to play an important role in mediating fire risk as it serves as a proxy for peat moisture content. However, water table depth observations are sparse and expensive. Soil moisture could provide a more direct indicator of fire risk than water table depth. In this study, we use new soil moisture retrievals from the Soil Moisture Active Passive (SMAP) satellite to demonstrate that - contrary to popular wisdom - remotely sensed soil moisture observations are possible over most Southeast Asian peatlands. Soil moisture estimation in this region was previously thought to be impossible over tropical peatlands because of dense vegetation cover. We show that vegetation density is sufficiently low across most Equatorial Asian peatlands to allow soil moisture estimation, and hypothesize that deforestation and other anthropogenic changes in land cover have combined to reduce overall vegetation density sufficient to allow soil moisture estimation. We further combine burned area estimates from the Global Fire Emissions Database and SMAP soil moisture retrievals to show that soil moisture provides a strong signal for fire risk in peatlands, with fires occurring at a much greater rate over drier soils. We will also develop an explicit fire risk model incorporating soil moisture with additional climatic, land cover, and anthropogenic predictor variables.

Retrieval of background surface reflectance with BRD components from pre-running BRDF

NASA Astrophysics Data System (ADS)

Choi, Sungwon; Lee, Kyeong-Sang; Jin, Donghyun; Lee, Darae; Han, Kyung-Soo

2016-10-01

Many countries try to launch satellite to observe the Earth surface. As important of surface remote sensing is increased, the reflectance of surface is a core parameter of the ground climate. But observing the reflectance of surface by satellite have weakness such as temporal resolution and being affected by view or solar angles. The bidirectional effects of the surface reflectance may make many noises to the time series. These noises can lead to make errors when determining surface reflectance. To correct bidirectional error of surface reflectance, using correction model for normalized the sensor data is necessary. A Bidirectional Reflectance Distribution Function (BRDF) is making accuracy higher method to correct scattering (Isotropic scattering, Geometric scattering, Volumetric scattering). To correct bidirectional error of surface reflectance, BRDF was used in this study. To correct bidirectional error of surface reflectance, we apply Bidirectional Reflectance Distribution Function (BRDF) to retrieve surface reflectance. And we apply 2 steps for retrieving Background Surface Reflectance (BSR). The first step is retrieving Bidirectional Reflectance Distribution (BRD) coefficients. Before retrieving BSR, we did pre-running BRDF to retrieve BRD coefficients to correct scatterings (Isotropic scattering, Geometric scattering, Volumetric scattering). In pre-running BRDF, we apply BRDF with observed surface reflectance of SPOT/VEGETATION (VGT-S1) and angular data to get BRD coefficients for calculating scattering. After that, we apply BRDF again in the opposite direction with BRD coefficients and angular data to retrieve BSR as a second step. As a result, BSR has very similar reflectance to one of VGT-S1. And reflectance in BSR is shown adequate. The highest reflectance of BSR is not over 0.4μm in blue channel, 0.45μm in red channel, 0.55μm in NIR channel. And for validation we compare reflectance of clear sky pixel from SPOT/VGT status map data. As a result of comparing BSR with VGT-S1, bias is from 0.0116 to 0.0158 and RMSE is from 0.0459 to 0.0545. They are very reasonable results, so we confirm that BSR is similar to VGT-S1. And weakness of this study is missing pixel in BSR which are observed less time to retrieve BRD components. If missing pixels are filled, BSR is better to retrieve surface products with more accuracy. And we think that after filling the missing pixel and being more accurate, it can be useful data to retrieve surface product which made by surface reflectance like cloud masking and retrieving aerosol.

GOME-2 Tropospheric Ozone Profile Retrievals from Joint UV/Visible Measurement

NASA Astrophysics Data System (ADS)

Liu, X.; Zoogman, P.; Chance, K.; Cai, Z.; Nowlan, C. R.; Huang, G.; Gonzalez Abad, G.

2016-12-01

It has been shown from sensitivity studies that adding visible measurements in the Chappuis ozone band to UV measurements in the Hartley/Huggins ozone bands can significantly enhance retrieval sensitivity to lower tropospheric ozone from backscattered solar radiances due to deeper photon penetration in the visible to the surface than in the ultraviolet. The first NASA EVI (Earth Venture Instrument) TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument is being developed to measure backscattered solar radiation in two channels ( 290-490 and 540-740 nm) and make atmospheric pollution measurements over North America from the Geostationary orbit. However, this retrieval enhancement has yet to be demonstrated from existing measurements due to the weak ozone absorption in the visible and strong interferences from surface reflectance and aerosols and the requirement of accurate radiometric calibration across different spectral channels. We present GOME-2 retrievals from joint UV/visible measurements using the SAO ozone profile retrieval algorithm, to directly explore the retrieval improvement in lower tropospheric ozone from additional visible measurements. To reduce the retrieval interference from surface reflectance, we add characterization of surface spectral reflectance in the visible based on combining EOFs (Empirical Orthogonal Functions) derived from ASTER and other surface reflectance spectra with MODIS BRDF climatology into the ozone profile algorithm. The impacts of various types of aerosols and surface BRDF on the retrievals will be investigated. In addition, we will also perform empirical radiometric calibration of the GOME-2 data based on radiative transfer simulations. We will evaluate the retrieval improvement of joint UV/visible retrieval over the UV retrieval based on fitting quality and validation against ozonesonde observations.

SEOM's Sentinel-3/OLCI' project CAWA: advanced GRASP aerosol retrieval

NASA Astrophysics Data System (ADS)

Dubovik, Oleg; litvinov, Pavel; Huang, Xin; Aspetsberger, Michael; Fuertes, David; Brockmann, Carsten; Fischer, Jürgen; Bojkov, Bojan

2016-04-01

The CAWA "Advanced Clouds, Aerosols and WAter vapour products for Sentinel-3/OLCI" ESA-SEOM project aims on the development of advanced atmospheric retrieval algorithms for the Sentinel-3/OLCI mission, and is prepared using Envisat/MERIS and Aqua/MODIS datasets. This presentation discusses mainly CAWA aerosol product developments and results. CAWA aerosol retrieval uses recently developed GRASP algorithm (Generalized Retrieval of Aerosol and Surface Properties) algorithm described by Dubovik et al. (2014). GRASP derives extended set of atmospheric parameters using multi-pixel concept - a simultaneous fitting of a large group of pixels under additional a priori constraints limiting the time variability of surface properties and spatial variability of aerosol properties. Over land GRASP simultaneously retrieves properties of both aerosol and underlying surface even over bright surfaces. GRAPS doesn't use traditional look-up-tables and performs retrieval as search in continuous space of solution. All radiative transfer calculations are performed as part of the retrieval. The results of comprehensive sensitivity tests, as well as results obtained from real Envisat/MERIS data will be presented. The tests analyze various aspects of aerosol and surface reflectance retrieval accuracy. In addition, the possibilities of retrieval improvement by means of implementing synergetic inversion of a combination of OLCI data with observations by SLSTR are explored. Both the results of numerical tests, as well as the results of processing several years of Envisat/MERIS data illustrate demonstrate reliable retrieval of AOD (Aerosol Optical Depth) and surface BRDF. Observed retrieval issues and advancements will be discussed. For example, for some situations we illustrate possibilities of retrieving aerosol absorption - property that hardly accessible from satellite observations with no multi-angular and polarimetric capabilities.

Directional reflectance factors for monitoring spatial changes in soil surface structure and soil organic matter erosion in agricultural systems

NASA Astrophysics Data System (ADS)

Croft, H.; Anderson, K.

2012-04-01

Soils can experience rapid structural degradation in response to land cover changes, resulting in reduced soil productivity, increased erodibility and a loss of soil organic matter (SOM). The breakdown of soil aggregates through slaking and raindrop impact is linked to organic matter turnover, with subsequently eroded material often displaying proportionally more SOM. A reduction in aggregate stability is reflected in a decline in soil surface roughness (SSR), indicating that a soil structural change can be used to highlight soil vulnerability to SOM loss through mineralisation or erosion. Accurate, spatially-continuous measurements of SSR are therefore needed at a variety of spatial and temporal scales to understand the spatial nature of SOM erosion and deposition. Remotely-sensed data can provide a cost-effective means of monitoring changes in soil surface condition over broad spatial extents. Previous work has demonstrated the ability of directional reflectance factors to monitor soil crusting within a controlled laboratory experiment, due to changes in the levels of self-shadowing effects by soil aggregates. However, further research is needed to test this approach in situ, where other soil variables may affect measured reflectance factors and to investigate the use of directional reflectance factors for monitoring soil erosion processes. This experiment assesses the potential of using directional reflectance factors to monitor changes in SSR, aggregate stability and soil organic carbon (SOC) content for two agricultural conditions. Five soil plots representing tilled and seedbed soils were subjected to different durations of natural rainfall, producing a range of different levels of SSR. Directional reflectance factors were measured concomitantly with sampling for soil structural and biochemical tests at each soil plot. Soil samples were taken to measure aggregate stability (wet sieving), SOC (loss on ignition) and soil moisture (gravimetric method). SSM values varied from 8.70 to 20.05% and SOC from 1.33 to 1.05%, across all soil plots. Each plot was characterised using a close-range laser scanning device with a 2 mm sampling interval. The point laser data were geostatistically analysed to provide a spatially-distributed measure of SSR, giving sill variance values from 3.15 to 22.99. Reflectance factors from the soil states were measured using a ground-based hyperspectral spectroradiometer (400-2500 nm) attached to an A-frame device. This method allowed measurement at a range of viewing zenith angles from extreme forwardscatter (-60°) to extreme backscatter (+60°) at a 10° sampling resolution in the solar principal plane. Reflectance measurements were compared to geostatistically-derived indicators of SSR from the laser profile data. Forward-scattered reflectance factors exhibited a very strong relationship to SSR (R2 = 0.84 at -60°; p< 0.05), demonstrating the operational potential of directional reflectance for providing SSR measurements, despite conflicting variation in SSM. SSM also presented an interesting directional signal (R2 = 0.99 at +20°; p< 0.01). Furthermore, the results showed an important link between SRR decline as measured using directional reflectance, with a decline in aggregate stability and SOC content. These findings provide an empirical and theoretical basis for the future retrieval of spatially-continuous assessments of soil surface structure and carbon turnover within a landscape context.

Near-surface temperature and salinity stratification as observed with dual-sensor Lagrangian drifters deployed during SPURS-2 field campaign

NASA Astrophysics Data System (ADS)

Volkov, Denis; Dong, Shenfu; Goni, Gustavo; Lumpkin, Rick; Foltz, Greg

2017-04-01

Despite the importance of sea surface salinity (SSS) as an indicator of the hydrological cycle, many details of air-sea interaction responsible for freshwater fluxes and processes determining the near-surface salinity stratification and its variability are still poorly understood. This is primarily due to the lack of dedicated observations. The advent of satellites capable of monitoring SSS, such as the Soil Moisture and Ocean Salinity (SMOS), Aquarius, and Soil Moisture Active-Passive (SMAP) missions, has greatly advanced our knowledge of SSS distribution and variability. However, the spatial resolution of satellite retrievals is too coarse to study the upper-ocean salinity changes due to patchy and transient rain events. Furthermore, the satellites measure salinity within the upper 1 cm skin layer, which can significantly differ from in situ SSS measured at 5 m depth by most Argo floats. Differences between the Aquarius and Argo SSS can be as large as ±0.5 psu. In order to study the near-surface salinity structure in great detail and to link the satellite observations of SSS with all the oceanic and atmospheric processes that control its variability, the National Aeronautics and Space Administration has initiated two field campaigns within the framework of Salinity Processes in the Upper-Ocean Regional Study (SPURS) project (http://spurs.jpl.nasa.gov/). The first campaign, SPURS-1, took place in the evaporation-dominated subtropical North Atlantic Ocean in 2012-2013. The second campaign, SPURS-2, focused on a 3×3° domain in the Inter-Tropical Convergence Zone (ITCZ) in the eastern equatorial Pacific (123.5-126.5°W and 8.5-11.5°N), where the near-surface salinity is strongly dominated by precipitation. The first SPURS-2 cruise took place in Aug-Sep 2016 on board the R/V Roger Revelle, during which a complex multi-instrument oceanographic survey was conducted. As part of this field campaign, we deployed 6 dual-sensor Lagrangian drifters, specifically designed to measure temperature and salinity near the surface ( 20 cm) and at 5 m depth. The main objectives of this deployment were (i) to validate the satellite SSS retrievals and to investigate the causes for the satellite-Argo SSS bias in the precipitation-dominated SPURS-2 region, and (ii) to explore salinity stratification in the upper 5 m and processes that determine it, in particular in relation to rain events. Throughout the experiment, we have observed systematic differences of 0.01-0.02 psu between the near-surface and 5 m salinity. Rain and low wind events have caused salinity differences of up to 2 psu. Strong evaporation on sunny and low wind days has caused the surface to be saltier than the 5-m depth layer by up to 0.4 psu. The mixing time scale between the surface and 5-m depth has been less than a day. Overall, the drifter observations have shown that the bias between Argo and satellite retrievals in the precipitation-dominated region can be largely due to the surface-subsurface salinity differences.

Vesta surface thermal properties map

USGS Publications Warehouse

Capria, Maria Teresa; Tosi, F.; De Santis, Maria Cristina; Capaccioni, F.; Ammannito, E.; Frigeri, A.; Zambon, F; Fonte, S.; Palomba, E.; Turrini, D.; Titus, T.N.; Schroder, S.E.; Toplis, M.J.; Liu, J.Y.; Combe, J.-P.; Raymond, C.A.; Russell, C.T.

2014-01-01

The first ever regional thermal properties map of Vesta has been derived from the temperatures retrieved by infrared data by the mission Dawn. The low average value of thermal inertia, 30 ± 10 J m−2 s−0.5 K−1, indicates a surface covered by a fine regolith. A range of thermal inertia values suggesting terrains with different physical properties has been determined. The lower thermal inertia of the regions north of the equator suggests that they are covered by an older, more processed surface. A few specific areas have higher than average thermal inertia values, indicative of a more compact material. The highest thermal inertia value has been determined on the Marcia crater, known for its pitted terrain and the presence of hydroxyl in the ejecta. Our results suggest that this type of terrain can be the result of soil compaction following the degassing of a local subsurface reservoir of volatiles.

The Influence of Antenna Pattern on Faraday Rotation in Remote Sensing at L-band

NASA Technical Reports Server (NTRS)

LeVine, David M.; Jacob, S. Daniel

2007-01-01

Faraday rotation is a change in the polarization vector of electromagnetic radiation that occurs as the waves propagate from the Earth surface through the ionosphere to a spaceborne sensor. This change can cause errors in monitoring parameters at the surface such as soil moisture and sea surface salinity and it is an important consideration for radiometers on future missions in space such as NASA's Aquarius mission and ESA's SMOS mission. Two prominent strategies for compensating for Faraday rotation are using a sum of the signal at two polarizations and using the correlation between the signals at the two polarizations. These strategies work for an idealized antenna. This paper evaluates the strategies in the context of realistic antennas such as will be built for the Aquarius radiometer. Realistic antennas will make small differences that need to be included in planning for retrieval algorithms in future missions.

Estimation of land surface heat fluxes based on visible infrared imaging radiometer suite data: case study in northern China

NASA Astrophysics Data System (ADS)

Li, Xiaojun; Xin, Xiaozhou; Peng, Zhiqing; Zhang, Hailong; Li, Li; Shao, Shanshan; Liu, Qinhuo

2017-10-01

Evapotranspiration (ET) plays an important role in surface-atmosphere interactions and can be monitored using remote sensing data. The visible infrared imaging radiometer suite (VIIRS) sensor is a generation of optical satellite sensors that provide daily global coverage at 375- to 750-m spatial resolutions with 22 spectral channels (0.412 to 12.05 μm) and capable of monitoring ET from regional to global scales. However, few studies have focused on methods of acquiring ET from VIIRS images. The objective of this study is to introduce an algorithm that uses the VIIRS data and meteorological variables to estimate the energy budgets of land surfaces, including the net radiation, soil heat flux, sensible heat flux, and latent heat fluxes. A single-source model that based on surface energy balance equation is used to obtain surface heat fluxes within the Zhangye oasis in China. The results were validated using observations collected during the HiWATER (Heihe Watershed Allied Telemetry Experimental Research) project. To facilitate comparison, we also use moderate resolution imaging spectrometer (MODIS) data to retrieve the regional surface heat fluxes. The validation results show that it is feasible to estimate the turbulent heat flux based on the VIIRS sensor and that these data have certain advantages (i.e., the mean bias error of sensible heat flux is 15.23 W m-2) compared with MODIS data (i.e., the mean bias error of sensible heat flux is -29.36 W m-2). Error analysis indicates that, in our model, the accuracies of the estimated sensible heat fluxes rely on the errors in the retrieved surface temperatures and the canopy heights.

AMSR2 Soil Moisture Product Validation

NASA Technical Reports Server (NTRS)

Bindlish, R.; Jackson, T.; Cosh, M.; Koike, T.; Fuiji, X.; de Jeu, R.; Chan, S.; Asanuma, J.; Berg, A.; Bosch, D.;

Estimation of Key Parameters of the Coupled Energy and Water Model by Assimilating Land Surface Data

NASA Astrophysics Data System (ADS)

Abdolghafoorian, A.; Farhadi, L.

2017-12-01

Accurate estimation of land surface heat and moisture fluxes, as well as root zone soil moisture, is crucial in various hydrological, meteorological, and agricultural applications. Field measurements of these fluxes are costly and cannot be readily scaled to large areas relevant to weather and climate studies. Therefore, there is a need for techniques to make quantitative estimates of heat and moisture fluxes using land surface state observations that are widely available from remote sensing across a range of scale. In this work, we applies the variational data assimilation approach to estimate land surface fluxes and soil moisture profile from the implicit information contained Land Surface Temperature (LST) and Soil Moisture (SM) (hereafter the VDA model). The VDA model is focused on the estimation of three key parameters: 1- neutral bulk heat transfer coefficient (CHN), 2- evaporative fraction from soil and canopy (EF), and 3- saturated hydraulic conductivity (Ksat). CHN and EF regulate the partitioning of available energy between sensible and latent heat fluxes. Ksat is one of the main parameters used in determining infiltration, runoff, groundwater recharge, and in simulating hydrological processes. In this study, a system of coupled parsimonious energy and water model will constrain the estimation of three unknown parameters in the VDA model. The profile of SM (LST) at multiple depths is estimated using moisture diffusion (heat diffusion) equation. In this study, the uncertainties of retrieved unknown parameters and fluxes are estimated from the inverse of Hesian matrix of cost function which is computed using the Lagrangian methodology. Analysis of uncertainty provides valuable information about the accuracy of estimated parameters and their correlation and guide the formulation of a well-posed estimation problem. The results of proposed algorithm are validated with a series of experiments using a synthetic data set generated by the simultaneous heat and water (SHAW) model. In addition, the feasibility of extending this algorithm to use remote sensing observations that have low temporal resolution is examined by assimilating the limited number of land surface moisture and temperature observations.

Measuring soil and tree temperatures during prescribed fires with thermocouple probes

Treesearch

Stephen S. Sackett; Sally M. Haase

1992-01-01

Soil and cambium temperatures must be known to ascertain certain effects of prescribed fires on trees. Thermocouple-based systems were devised for measuring soil and cambium temperatures during prescribed fires. The systems, which incorporate both commercially available and custom components, perform three basic functions: data collection, data retrieval, and data...

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.

Evaluation of the AMSR-E Data Calibration Over Land

NASA Technical Reports Server (NTRS)

Njoku, E.; Chan, T.; Crosson, W.; Limaye, A.

2004-01-01

Land observations by the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), particularly of soil and vegetation moisture changes, have numerous applications in hydrology, ecology and climate. Quantitative retrieval of soil and vegetation parameters relies on accurate calibration of the brightness temperature measurements. Analyses of the spectral and polarization characteristics of early versions of the AMSR-E data revealed significant calibration biases over land at 6.9 GHz. The biases were estimated and removed in the current archived version of the data Radiofrequency interference (RFI) observed at 6.9 GHz is more difficult to quanti@ however. A calibration analysis of AMSR-E data over land is presented in this paper for a complete annual cycle from June 2002 through September 2003. The analysis indicates the general high quality of the data for land applications (except for RFI), and illustrates seasonal trends of the data for different land surface types and regions.

A soil moisture index derived from thermal infrared sensor on-board geostationary satellites over Europe, Africa and Australia

NASA Astrophysics Data System (ADS)

Ghilain, Nicolas; Trigo, Isabel; Arboleda, Alirio; Barrios, Jose-Miguel; Batelaan, Okke; Gellens-Meulenberghs, Françoise

2017-04-01

Soil moisture plays a central role in the water cycle. In particular, it is a major component which variability controls the evapotranspiration process. Over the past years, there has been a large commitment of the remote sensing research community to develop satellites and retrieval algorithm for soil moisture monitoring over continents. Most of those rely on the observation in the microwave lengths, making use either of passive, active or both methods combined. However, the available derived products are given at a relatively low spatial resolution for applications at the kilometer scale over entire continents, and with a revisit time that may not be adequate for all applications, as for example agriculture. Thermal infrared observations from a combination of geostationary satellites offer a global view of continents every hour (or even at higher frequency) at a few kilometers resolution, which makes them attractive as another, and potentially complementary, source of information of surface soil moisture. In this study, the Copernicus LST and the LSA-SAF LST are used to derive soil moisture over entire continents (Europe, Africa, Australia). The derived soil moisture is validated against in-situ observations and compared to other available products from remote sensing (SMOS, ASCAT) and from numerical weather prediction (ECMWF). We will present the result of this validation, and will show how it could be used in continental scale evapotranspiration monitoring.

SoilSCAPE in-Situ Observations of Soil Moisture for SMAP Validation: Pushing the Envelopes of Spatial Coverage and Energy Efficiency in Sparse Wireless Sensor Networks (Invited)

NASA Astrophysics Data System (ADS)

Moghaddam, M.; Silva, A.; Clewley, D.; Akbar, R.; Entekhabi, D.

2013-12-01

Soil Moisture Sensing Controller and oPtimal Estimator (SoilSCAPE) is a wireless in-situ sensor network technology, developed under the support of NASA ESTO/AIST program, for multi-scale validation of soil moisture retrievals from the Soil Moisture Active and Passive (SMAP) mission. The SMAP sensor suite is expected to produce soil moisture retrievals at 3 km scale from the radar instrument, at 36 km from the radiometer, and at 10 km from the combination of the two sensors. To validate the retrieved soil moisture maps at any of these scales, it is necessary to perform in-situ observations at multiple scales (ten, hundreds, and thousands of meters), representative of the true spatial variability of soil moisture fields. The most recent SoilSCAPE network, deployed in the California central valley, has been designed, built, and deployed to accomplish this goal, and is expected to become a core validation site for SMAP. The network consists of up to 150 sensor nodes, each comprised of 3-4 soil moisture sensors at various depths, deployed over a spatial extent of 36 km by 36 km. The network contains multiple sub-networks, each having up to 30 nodes, whose location is selected in part based on maximizing the land cover diversity within the 36 km cell. The network has achieved unprecedented energy efficiency, longevity, and spatial coverage using custom-designed hardware and software protocols. The network architecture utilizes a nested strategy, where a number of end devices (EDs) communicate to a local coordinator (LC) using our recently developed hardware with ultra-efficient circuitry and best-effort-timeslot allocation communication protocol. The LCs in turn communicates with the base station (BS) via text messages and a new compression scheme. The hardware and software technologies required to implement this latest deployment of the SoilSCAPE network will be presented in this paper, and several data sets resulting from the measurements will be shown. The data are available publicly in near-real-time from the project web site, and are also available and searchable via an extensive set of metadata fields through the ORNL-DAAC.

Estimating surface fluxes over middle and upper streams of the Heihe River Basin with ASTER imagery

NASA Astrophysics Data System (ADS)

Ma, W.; Ma, Y.; Hu, Z.; Su, B.; Wang, J.; Ishikawa, H.

2009-06-01

Surface fluxes are important boundary conditions for climatological modeling and the Asian monsoon system. Recent availability of high-resolution, multi-band imagery from the ASTER (Advanced Space-borne Thermal Emission and Reflection Radiometer) sensor has enabled us to estimate surface fluxes to bridge the gap between local scale flux measurements using micrometeorological instruments and regional scale land-atmosphere exchanges of water and heat fluxes that are fundamental for the understanding of the water cycle in the Asian monsoon system. A Surface Energy Balance System (SEBS) method based on ASTER data and field observations has been proposed and tested for deriving net radiation flux (Rn), soil heat flux (G0), sensible heat flux (H) and latent heat flux (λ E) over heterogeneous land surface in this paper. As a case study, the methodology was applied to the experimental area of the WATER (Watershed Allied Telemetry Experimental Research), located at the mid-to-upstream sections of the Heihe River, northwest China. The ASTER data of 3 May and 4 June in 2008 was used in this paper for the case of mid-to-upstream sections of the Heihe River Basin. To validate the proposed methodology, the ground-measured land surface heat fluxes (net radiation flux (Rn), soil heat flux (G0), sensible heat flux (H) and latent heat flux (λ E)) were compared to the ASTER derived values. The results show that the derived surface variables and land surface heat fluxes in different months over the study area are in good accordance with the land surface status. It is therefore concluded that the proposed methodology is successful for the retrieval of land surface heat fluxes using the ASTER data and filed observation over the study area.

Use of In Situ Cloud Condensation Nuclei, Extinction, and Aerosol Size Distribution Measurements to Test a Method for Retrieving Cloud Condensation Nuclei Profiles From Surface Measurements

NASA Technical Reports Server (NTRS)

Ghan, Stephen J.; Rissman, Tracey A.; Ellman, Robert; Ferrare, Richard A.; Turner, David; Flynn, Connor; Wang, Jian; Ogren, John; Hudson, James; Jonsson, Haflidi H.;

A Satellite-Based Imaging Instrumentation Concept for Hyperspectral Thermal Remote Sensing.

PubMed

Udelhoven, Thomas; Schlerf, Martin; Segl, Karl; Mallick, Kaniska; Bossung, Christian; Retzlaff, Rebecca; Rock, Gilles; Fischer, Peter; Müller, Andreas; Storch, Tobias; Eisele, Andreas; Weise, Dennis; Hupfer, Werner; Knigge, Thiemo

2017-07-01

This paper describes the concept of the hyperspectral Earth-observing thermal infrared (TIR) satellite mission HiTeSEM (High-resolution Temperature and Spectral Emissivity Mapping). The scientific goal is to measure specific key variables from the biosphere, hydrosphere, pedosphere, and geosphere related to two global problems of significant societal relevance: food security and human health. The key variables comprise land and sea surface radiation temperature and emissivity, surface moisture, thermal inertia, evapotranspiration, soil minerals and grain size components, soil organic carbon, plant physiological variables, and heat fluxes. The retrieval of this information requires a TIR imaging system with adequate spatial and spectral resolutions and with day-night following observation capability. Another challenge is the monitoring of temporally high dynamic features like energy fluxes, which require adequate revisit time. The suggested solution is a sensor pointing concept to allow high revisit times for selected target regions (1-5 days at off-nadir). At the same time, global observations in the nadir direction are guaranteed with a lower temporal repeat cycle (>1 month). To account for the demand of a high spatial resolution for complex targets, it is suggested to combine in one optic (1) a hyperspectral TIR system with ~75 bands at 7.2-12.5 µm (instrument NEDT 0.05 K-0.1 K) and a ground sampling distance (GSD) of 60 m, and (2) a panchromatic high-resolution TIR-imager with two channels (8.0-10.25 µm and 10.25-12.5 µm) and a GSD of 20 m. The identified science case requires a good correlation of the instrument orbit with Sentinel-2 (maximum delay of 1-3 days) to combine data from the visible and near infrared (VNIR), the shortwave infrared (SWIR) and TIR spectral regions and to refine parameter retrieval.

Modelling of the L-band brightness temperatures measured with ELBARA III radiometer on Bubnow wetland

NASA Astrophysics Data System (ADS)

Gluba, Lukasz; Sagan, Joanna; Lukowski, Mateusz; Szlazak, Radoslaw; Usowicz, Boguslaw

2017-04-01

Microwave radiometry has become the main tool for investigating soil moisture (SM) with remote sensing methods. ESA - SMOS (Soil Moisture and Ocean Salinity) satellite operating at L-band provides global distribution of soil moisture. An integral part of SMOS mission are calibration and validation activities involving measurements with ELBARA III which is an L-band microwave passive radiometer. It is done in order to improve soil moisture retrievals - make them more time-effective and accurate. The instrument is located at Bubnow test-site, on the border of cultivated field, fallow, meadow and natural wetland being a part of Polesie National Park (Poland). We obtain both temporal and spatial dependences of brightness temperatures for varied types of land covers with the ELBARA III directed at different azimuths. Soil moisture is retrieved from brightness temperature using L-band Microwave Emission of the Biosphere (L-MEB) model, the same as currently used radiative transfer model for SMOS. Parametrization of L-MEB, as well as input values are still under debate. We discuss the results of SM retrievals basing on data obtained during first year of the radiometer's operation. We analyze temporal dependences of retrieved SM for one-parameter (SM), two-parameter (SM, τ - optical depth) and three-parameter (SM, τ, Hr - roughness parameter) retrievals, as well as spatial dependences for specific dates. Special case of Simplified Roughness Parametrization, combining the roughness parameter and optical depth, is considered. L-MEB processing is supported by the continuous measurements of soil moisture and temperature obtained from nearby agrometeorological station, as well as studies on the soil granulometric composition of the Bubnow test-site area. Furthermore, for better estimation of optical depth, the satellite-derived Normalized Difference Vegetation Index (NDVI) was employed, supported by measured in situ vegetation parameters (such as Leaf Area Index and Vegetation Water Content) obtained during the field campaigns. The values of NDVI around ELBARA III radiometer are provided by Sentinel-2 satellite with approximately 10 m spatial resolution and average 10 days of time interval within studied period of time. The work was partially funded under two ESA projects: 1) "ELBARA_PD (Penetration Depth)" No. 4000107897/13/NL/KML, funded by the Government of Poland through an ESA-PECS contract (Plan for European Cooperating States). 2) "Technical Support for the fabrication and deployment of the radiometer ELBARA-III in Bubnow, Poland" No. 4000113360/15/NL/FF/gp.

Experiment of Rain Retrieval over Land Using Surface Emissivity Map Derived from TRMM TMI and JRA25

NASA Astrophysics Data System (ADS)

Furuzawa, Fumie; Masunaga, Hirohiko; Nakamura, Kenji

2010-05-01

We are developing a data-set of global land surface emissivity calculated from TRMM TMI brightness temperature (TB) and atmospheric profile data of Japanese 25-year Reanalysis Project (JRA-25) for the region identified as no-rain by TRMM PR, assuming zero cloud liquid water beyond 0-C level. For the evaluation, some characteristics of global monthly emissivity maps, for example, dependency of emissivity on each TMI frequency or each local time or seasonal/annual variation are checked. Moreover, these data are classified based on JRA25 land type or soilwetness and compared. Histogram of polarization difference of emissivity is similar to that of TB and mostly reflects the variability of land type or soil wetness, while histogram of vertical emissivity show a small difference. Next, by interpolating this instantaneous dataset with Gaussian function weighting, we derive an emissivity over neighboring rainy region and assess the interpolated emissivity by running radiative transfer model using PR rain profile and comparing with observed TB. Preliminary rain retrieval from the emissivities for some frequencies and TBs is evaluated based on PR rain profile and TMI rain rate. Moreover, another method is tested to estimate surface temperature from two emissivities, based on their statistical relation for each land type. We will show the results for vertical and horizontal emissivities of each frequency.

On the Balancing of the SMOS Ocean Salinity Retrieval Cost Function

NASA Astrophysics Data System (ADS)

Sabia, R.; Camps, A.; Portabella, M.; Talone, M.; Ballabrera, J.; Gourrion, J.; Gabarró, C.; Aretxabaleta, A. L.; Font, J.

2009-04-01

The Soil Moisture and Ocean Salinity (SMOS) mission will be launched in mid 2009 to provide synoptic sea surface salinity (SSS) measurements with good temporal resolution [1]. To obtain a proper estimation of the SSS fields derived from the multi-angular brightness temperatures (TB) measured by the Microwave Interferometric Radiometer by Aperture Synthesis (MIRAS) sensor, a comprehensive inversion procedure has been defined [2]. Nevertheless, several salinity retrieval issues remain critical, namely: 1) Scene-dependent bias in the simulated TBs, 2) L-band forward geophysical model function definition, 3) Auxiliary data uncertainties, 4) Constraints in the cost function (inversion), especially in salinity term, and 5) Adequate spatio-temporal averaging. These issues will have to be properly addressed in order to meet the proposed accuracy requirement of the mission: a demanding 0.1 psu (practical salinity units) after averaging in a 30-day and 2°x2° spatio-temporal boxes. The salinity retrieval cost function minimizes the difference between the multi-angular measured SMOS TBs (yet simulated, so far) and the modeled TBs, weighted by the corresponding radiometric noise of the measurements. Furthermore, due to the fact that the minimization problem is both non-linear and ill-posed, background reference terms are needed to nudge the solution and ensuring convergence at the same time [3]. Constraining terms in SSS, sea surface temperature (SST) and wind speed are considered with their respective uncertainties. Moreover, whether SSS constraints have to be included or not as part of the retrieval procedure is still a matter of debate. On one hand, neglecting background reference information on SSS might prevent from retrieving salinity with the prescribed accuracy or at least within reasonable error. Conversely, including constraints in SSS, relying for instance on the climatology, may force the retrieved value to be too close to the reference prior values, thus producing spurious retrievals. In [4] it has been studied the impact of the different auxiliary salinity uncertainties in the accuracy of the retrieval. It has been shown that using physically-consistent salinity field uncertainties of the order of less than 0.5 psu (either as the standard deviation of the considered SSS field or as the standard deviation of the misfit between the original and the auxiliary SSS field) the SSS term turns out to be too constraining. A half-way solution could be envisaged by using empirical weights (regularization factors) which could smooth the overall influence of the SSS term still using the auxiliary fields with their corresponding physically-sounded uncertainties. This operation should be performed for the SST and wind speed term as well. The need for a comprehensive balancing of the different terms included in the cost function is also stressed by recent studies [5], which point out that the even the observational term (TBs) will need to be properly weighted by an effective ratio, taking into account the specific correlation patterns existing in the MIRAS measurements. Simulated data using the SMOS End-to-end Processor Simulator (SEPS), in its full-mode, including the measured antenna patterns for each antenna and all the instrument errors, are used in this study. The salinity retrieval process and the SSS maps (for each satellite overpass) are performed with UPC SMOS-Level 2 Processor Simulator (SMOS-L2PS). The relative weight for each of the terms included in the cost function (observational and background terms) is assessed in different cost function configurations. Regularization factors are introduced to ensure that SMOS information content is fully exploited. Preliminary results on the cost function balancing will be shown at the conference. References [1] Font, J., G. Lagerloef, D. Le Vine, A. Camps, and O.Z. Zanife, The Determination of Surface Salinity with the European SMOS Space Mission, IEEE Trans. Geosci. Remote Sens., 42 (10), 2196-2205, 2004. [2] Zine, S., J. Boutin, J. Font, N. Reul, P. Waldteufel, C. Gabarró, J. Tenerelli, F. Petitcolin, J.L. Vergely, M. Talone, and S. Delwart, Overview of the SMOS Sea Surface Salinity Prototype Processor, IEEE Trans. Geosc. Remote Sens, 46 (3), 621-645, 2008. [3] Gabarró, C., M. Portabella, M. Talone and J. Font, Analysis of the SMOS Ocean Salinity Inversion Algorithm, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Barcelona, Spain, 971-974, 2007. [4] Sabia, R, Sea Surface Salinity Retrieval Error Budget within the ESA Soil Moisture and Ocean Salinity Mission, Ph.D. Dissertation, Barcelona, Spain, October 2008. [5] Talone, M., A. Camps, C. Gabarró, R. Sabia, J. Gourrion, M. Vall•llossera, B. Mourre, and J. Font, Contributions to the Improvement of the SMOS Level 2 Retrieval Algorithm: Optimization of the Cost Function, Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Boston, Massachusetts USA, 2008.

SMOS: a satellite mission to measure ocean surface salinity

NASA Astrophysics Data System (ADS)

Font, Jordi; Kerr, Yann H.; Srokosz, Meric A.; Etcheto, Jacqueline; Lagerloef, Gary S.; Camps, Adriano; Waldteufel, Philippe

2001-01-01

The ESA's SMOS (Soil Moisture and Ocean Salinity) Earth Explorer Opportunity Mission will be launched by 2005. Its baseline payload is a microwave L-band (21 cm, 1.4 GHz) 2D interferometric radiometer, Y shaped, with three arms 4.5 m long. This frequency allows the measurement of brightness temperature (Tb) under the best conditions to retrieve soil moisture and sea surface salinity (SSS). Unlike other oceanographic variables, until now it has not been possible to measure salinity from space. However, large ocean areas lack significant salinity measurements. The 2D interferometer will measure Tb at large and different incidence angles, for two polarizations. It is possible to obtain SSS from L-band passive microwave measurements if the other factors influencing Tb (SST, surface roughness, foam, sun glint, rain, ionospheric effects and galactic/cosmic background radiation) can be accounted for. Since the radiometric sensitivity is low, SSS cannot be recovered to the required accuracy from a single measurement as the error is about 1-2 psu. If the errors contributing to the uncertainty in Tb are random, averaging the independent data and views along the track, and considering a 200 km square, allow the error to be reduced to 0.1-0.2 pus, assuming all ancillary errors are budgeted.

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.

Helium Ion Microscopy: A Promising Tool for Probing Biota-Mineral Interfaces

NASA Astrophysics Data System (ADS)

Lybrand, R.; Zaharescu, D. G.; Gallery, R. E.

2017-12-01

The study of biogeochemical interfaces in soil requires powerful technologies that can enhance our ability to characterize mineral surfaces and interacting organisms at micro- to nanoscale resolutions. We aim to demonstrate potential applications of Helium Ion Microscopy in the earth and ecological sciences using, as an example, samples from a field experiment. We assessed samples deployed for one year along climatic and topographic gradients in two Critical Zone Observatories (CZOs): a desert to mixed conifer forest gradient (Catalina CZO) and a humid hardwood forest (Calhoun CZO). Sterile ground rock (basalt, quartz, and granite; 53-250 µm) was sealed into nylon mesh bags and buried in the surface soils of both CZOs. We employed helium ion and scanning electron microscopies to compare retrieved ground rock samples with sterile unreacted mineral controls in conjunction with the Environmental Molecular Sciences Laboratory at Pacific Northwest National Laboratory, USA. Our work showed early colonization of mesh bag materials by fungal and bacterial organisms from the field systems and identified morphological changes in mineral grains following exposure to the soil environment. Biological specimens observed on grain surfaces exhibited contrasting features depending on mineral type and ecosystem location, including fungal hyphae that varied in length, diameter, and surface morphologies. We also present imagery that provides evidence for incipient stages of mineral transformation at the fungal-mineral interface. Our findings demonstrate that helium ion microscopy can be successfully used to characterize grain features and biological agents of weathering in experimental field samples, representing a promising avenue for research in the biogeosciences. Future directions of this work will couple high resolution imaging with measures of aqueous and solid geochemistry, fungal morphological characterization, and microbial profiling to better understand mineral transformation along gradients of climate and topography.

Retrieval of reflections from ambient noise using illumination diagnosis

NASA Astrophysics Data System (ADS)

Vidal, C. Almagro; Draganov, D.; van der Neut, J.; Drijkoningen, G.; Wapenaar, K.

2014-09-01

Seismic interferometry (SI) enables the retrieval of virtual sources at the location of receivers. In the case of passive SI, no active sources are used for the retrieval of the reflection response of the subsurface, but ambient-noise recordings only. The resulting retrieved response is determined by the illumination characteristics of the recorded ambient noise. Characteristics like geometrical distribution and signature of the noise sources, together with the complexity of the medium and the length of the noise records, determine the quality of the retrieved virtual-shot events. To retrieve body wave reflections, one needs to correlate body-wave noise. A source of such noise might be regional seismicity. In regions with notable human presence, the dominant noise sources are generally located at or close to the surface. In the latter case, the noise will be dominated by surface waves and consequently also the retrieved virtual common-source panels will contain dominant retrieved surface waves, drowning out possible retrieved reflections. In order to retrieve reflection events, suppression of the surface waves becomes the most important pre-processing goal. Because of the reasons mentioned above, we propose a fast method to evaluate the illumination characteristics of ambient noise using the correlation results from ambient-noise records. The method is based on the analysis of the so-called source function of the retrieved virtual-shot panel, and evaluates the apparent slowness of arrivals in the correlation results that pass through the position of the virtual source and at zero time. The results of the diagnosis are used to suppress the retrieval of surface waves and therefore to improve the quality of the retrieved reflection response. We explain the approach using modelled data from transient and continuous noise sources and an example from a passive field data set recorded at Annerveen, Northern Netherlands.

Patterns Of Moisture Storage During Canadian Prairie Drought

NASA Astrophysics Data System (ADS)

Agboma, C. O.; Snelgrove, K. R.

2008-12-01

Comprehensive studies of soil moisture storage patterns during drought episodes and normal years on the Canadian Prairie are rare. These studies have become increasingly imperative and desirable for an understanding and quantification of the influences of the land surface moisture on atmospheric processes. These influences or "memory" of the soil moisture may play an important role under conditions of extreme climate such as drought and flood. The recollection of a wet or dry anomaly by the soil moisture memory is a fundamental component of any regional land-atmosphere interactions, which possess significant implications for seasonal forecasting. The 13,000km2 Upper Assiniboine River Basin in Central Saskatchewan with its outlet at Kamsack is the domain of this study; via deploying a land surface model variously known as the Variable Infiltration Capacity/Xinanjiang/ARNO model driven offline both in the water and energy balance modes, it was possible to capture the dynamics and seasonal response of the soil moisture storage up to a depth of about 1-metre. Meteorological inputs required to drive the model were retrieved respectively from Environment Canada and the North American Regional Reanalysis (NARR) dataset at daily and sub-daily time steps correspondingly. The North American Land Data Assimilation System (NLDAS) served as the repository from which the soil and vegetation parameters were obtained. The patterns in seasonal and inter-annual soil moisture storage as well as changes in the total water storage anomaly averaged over the entire basin were captured during a period of 11 years commencing 1994. The role of the observed patterns in the regional land-atmosphere interactions is being assessed to ascertain the relevance of the inherent memory in soil moisture as one of the slow drivers of the Canadian Prairie regional climate system with the key objective of attaining a better understanding of drought evolution, continuation and eventual cessation over this region.

Performance Metrics for Soil Moisture Retrievals and Applications Requirements

USDA-ARS?s Scientific Manuscript database

Quadratic performance metrics such as root-mean-square error (RMSE) and time series correlation are often used to assess the accuracy of geophysical retrievals and true fields. These metrics are generally related; nevertheless each has advantages and disadvantages. In this study we explore the relat...

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;

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

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

VIRTIS on Venus Express: retrieval of real surface emissivity on global scales

NASA Astrophysics Data System (ADS)

Arnold, Gabriele E.; Kappel, David; Haus, Rainer; Telléz Pedroza, Laura; Piccioni, Giuseppe; Drossart, Pierre

2015-09-01

The extraction of surface emissivity data provides the data base for surface composition analyses and enables to evaluate Venus' geology. The Visible and InfraRed Thermal Imaging Spectrometer (VIRTIS) aboard ESA's Venus Express mission measured, inter alia, the nightside thermal emission of Venus in the near infrared atmospheric windows between 1.0 and 1.2 μm. These data can be used to determine information about surface properties on global scales. This requires a sophisticated approach to understand and consider the effects and interferences of different atmospheric and surface parameters influencing the retrieved values. In the present work, results of a new technique for retrieval of the 1.0 - 1.2 μm - surface emissivity are summarized. It includes a Multi-Window Retrieval Technique, a Multi-Spectrum Retrieval technique (MSR), and a detailed reliability analysis. The MWT bases on a detailed radiative transfer model making simultaneous use of information from different atmospheric windows of an individual spectrum. MSR regularizes the retrieval by incorporating available a priori mean values, standard deviations as well as spatial-temporal correlations of parameters to be retrieved. The capability of this method is shown for a selected surface target area. Implications for geologic investigations are discussed. Based on these results, the work draws conclusions for future Venus surface composition analyses on global scales using spectral remote sensing techniques. In that context, requirements for observational scenarios and instrumental performances are investigated, and recommendations are derived to optimize spectral measurements for Venus' surface studies.

The Use of Commercial Remote Sensing Systems in Predicting Helicopter Brownout Conditions

DTIC Science & Technology

2009-09-01

REFERENCES Anthoni, J. F. (2000). Soil Erosion and Conservation – Part 2. Retrieved 15 August 2009, from http://www.seafriends.org.nz/ enviro / soil ... soils susceptible to helicopter brownout. Helicopter brownout occurs when downwash disturbs the dust and sand beneath the aircraft during takeoff...destruction, as well as personnel injury or death. The likelihood of helicopter brownout is related to soil moisture content, particle size distribution, and

SMOS near-real-time soil moisture product: processor overview and first validation results

NASA Astrophysics Data System (ADS)

Rodríguez-Fernández, Nemesio J.; Muñoz Sabater, Joaquin; Richaume, Philippe; de Rosnay, Patricia; Kerr, Yann H.; Albergel, Clement; Drusch, Matthias; Mecklenburg, Susanne

2017-10-01

Measurements of the surface soil moisture (SM) content are important for a wide range of applications. Among them, operational hydrology and numerical weather prediction, for instance, need SM information in near-real-time (NRT), typically not later than 3 h after sensing. The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite is the first mission specifically designed to measure SM from space. The ESA Level 2 SM retrieval algorithm is based on a detailed geophysical modelling and cannot provide SM in NRT. This paper presents the new ESA SMOS NRT SM product. It uses a neural network (NN) to provide SM in NRT. The NN inputs are SMOS brightness temperatures for horizontal and vertical polarizations and incidence angles from 30 to 45°. In addition, the NN uses surface soil temperature from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS). The NN was trained on SMOS Level 2 (L2) SM. The swath of the NRT SM retrieval is somewhat narrower (˜ 915 km) than that of the L2 SM dataset (˜ 1150 km), which implies a slightly lower revisit time. The new SMOS NRT SM product was compared to the SMOS Level 2 SM product. The NRT SM data show a standard deviation of the difference with respect to the L2 data of < 0.05 m3 m-3 in most of the Earth and a Pearson correlation coefficient higher than 0.7 in large regions of the globe. The NRT SM dataset does not show a global bias with respect to the L2 dataset but can show local biases of up to 0.05 m3 m-3 in absolute value. The two SMOS SM products were evaluated against in situ measurements of SM from more than 120 sites of the SCAN (Soil Climate Analysis Network) and the USCRN (US Climate Reference Network) networks in North America. The NRT dataset obtains similar but slightly better results than the L2 data. In summary, the NN SMOS NRT SM product exhibits performances similar to those of the Level 2 SM product but it has the advantage of being available in less than 3.5 h after sensing, complying with NRT requirements. The new product is processed at ECMWF and it is distributed by ESA and via the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) multicast service (EUMETCast).

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

NASA Astrophysics Data System (ADS)

Rawat, Kishan Singh; Sehgal, Vinay Kumar; Pradhan, Sanatan; Ray, Shibendu S.

2018-03-01

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σ o_{RH}), differences of circular vertical and horizontal σ o (σ o_{RV} {-} σ o_{RH}) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height ({RMS}_{height}). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σ o. Near surface SM measurements were related to σ o_{RH}, σ o_{RV} {-} σ o_{RH} derived using 5.35 GHz (C-band) image of RISAT-1 and {RMS}_{height}. The roughness component derived in terms of {RMS}_{height} showed a good positive correlation with σ o_{RV} {-} σ o_{RH} (R2 = 0.65). By considering all the major influencing factors (σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}), an SEM was developed where SM (volumetric) predicted values depend on σ o_{RH}, σ o_{RV} {-} σ o_{RH}, and {RMS}_{height}. This SEM showed R2 of 0.87 and adjusted R2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement ({SM}_{Observed}) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash-Sutcliffe efficiency (NSE) = 0.91 ({≈ } 1), index of agreement (d) = 1, coefficient of determination (R2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences ({S}d2) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σ o. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

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.

Retriever, a multiprotein complex for retromer-independent endosomal cargo recycling

PubMed Central

McNally, Kerrie E.; Faulkner, Rebecca; Steinberg, Florian; Gallon, Matthew; Ghai, Rajesh; Pim, David; Langton, Paul; Pearson, Neil; Danson, Chris M.; Nägele, Heike; Morris, Lindsey M; Singla, Arnika; Overlee, Brittany L; Heesom, Kate J.; Sessions, Richard; Banks, Lawrence; Collins, Brett M; Berger, Imre; Billadeau, Daniel D.; Burstein, Ezra; Cullen, Peter J.

2018-01-01

Following endocytosis and entry into the endosomal network, integral membrane proteins undergo sorting for lysosomal degradation or are alternatively retrieved and recycled back to the cell surface. Here we describe the discovery of an ancient and conserved multi-protein complex which orchestrates cargo retrieval and recycling and importantly, is biochemically and functionally distinct to the established retromer pathway. Composed of a heterotrimer of DSCR3, C16orf62 and VPS29, and bearing striking similarity with retromer, we have called this complex ‘retriever’. We establish that retriever associates with the cargo adaptor sorting nexin 17 (SNX17) and couples to the CCC and WASH complexes to prevent lysosomal degradation and promote cell surface recycling of α5β1-integrin. Through quantitative proteomic analysis we identify over 120 cell surface proteins, including numerous integrins, signalling receptors and solute transporters, which require SNX17-retriever to maintain their surface levels. Our identification of retriever establishes a major new endosomal retrieval and recycling pathway. PMID:28892079

40 CFR 792.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 31 2010-07-01 2010-07-01 true Storage and retrieval of records and data. 792.190 Section 792.190 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... mutagenicity tests, specimens of soil, water, and plants, and wet specimens of blood, urine, feces, and...

40 CFR 160.190 - Storage and retrieval of records and data.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Storage and retrieval of records and data. 160.190 Section 160.190 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY (CONTINUED... soil, water, and plants, and wet specimens of blood, urine, feces, and biological fluids, do not need...

Using data of remote sensing to retrieve surface's evapotranspiration in northwest of China

NASA Astrophysics Data System (ADS)

Yu, Xinwen; Zheng, Youfei; Guo, Jianmao; He, Jinhai

2005-08-01

Northwestern China is a semi-arid or arid area in China. Ningnan (or South Ningxia) district is located south of Ningxia Province belong to Northwestern part of China. Climate in this region is more dry and lack of precipitation. Because global climate have been changing, temperature has been increasing and rainfall has been decreasing in South Ningxia. The ecology has been deteriorating, such as vegetation cover destroying, water losing and soil erosion. Therefore, the people who live in South Ningxia have been poor. Recently, Chinese government put into effect on strategy of "great development of Chinese northwest", aiming to improve environmental and ecological conditions and rise people's living standard. South Ningxia district was defined as area of emigration where the measurements of returning land for farming to forestry were taken into account . How to evaluate the plans and measurements is very important to continue to improving local environmental and ecological conditions further. The basic index of evaluation is soil water profit and loss statement while evapotranspiration (ET) is an important component in statement income and outcome of soil water. It is a very complicated problem to estimate evapotranspiration (ET) over large area of natural surface. In this paper, the natural surface was classified as 5 categories based on information from remote sensing, each categories being dealt with special way. Using data of remote sensing and weather stations, the result of regional evapotranspiration over Ningnan(South Ningxia) was given out, and verified and discussing are also made out. The work helps to assess whether or not improve environmental and ecological conditions.

Monitoring cover crops using radar remote sensing in southern Ontario, Canada

NASA Astrophysics Data System (ADS)

Shang, J.; Huang, X.; Liu, J.; Wang, J.

2016-12-01

Information on agricultural land surface conditions is important for developing best land management practices to maintain the overall health of the fields. The climate condition supports one harvest per year for the majority of the field crops in Canada, with a relative short growing season between May and September. During the non-growing-season months (October to the following April), many fields are traditionally left bare. In more recent year, there has been an increased interest in planting cover crops. Benefits of cover crops include boosting soil organic matters, preventing soil from erosion, retaining soil moisture, and reducing surface runoff hence protecting water quality. Optical remote sensing technology has been exploited for monitoring cover crops. However limitations inherent to optical sensors such as cloud interference and signal saturation (when leaf area index is above 2.5) impeded its operational application. Radar remote sensing on the other hand is not hindered by unfavorable weather conditions, and the signal continues to be sensitive to crop growth beyond the saturation point of optical sensors. It offers a viable means for capturing timely information on field surface conditions (with or without crop cover) or crop development status. This research investigated the potential of using multi-temporal RADARSAT-2 C-band synthetic aperture radar (SAR) data collected in 2015 over multiple fields of winter wheat, corn and soybean crops in southern Ontario, Canada, to retrieve information on the presence of cover crops and their growth status. Encouraging results have been obtained. This presentation will report the methodology developed and the results obtained.

Improved surface-wave retrieval from ambient seismic noise by multi-dimensional deconvolution

NASA Astrophysics Data System (ADS)

Wapenaar, Kees; Ruigrok, Elmer; van der Neut, Joost; Draganov, Deyan

2011-01-01

The methodology of surface-wave retrieval from ambient seismic noise by crosscorrelation relies on the assumption that the noise field is equipartitioned. Deviations from equipartitioning degrade the accuracy of the retrieved surface-wave Green's function. A point-spread function, derived from the same ambient noise field, quantifies the smearing in space and time of the virtual source of the Green's function. By multidimensionally deconvolving the retrieved Green's function by the point-spread function, the virtual source becomes better focussed in space and time and hence the accuracy of the retrieved surface-wave Green's function may improve significantly. We illustrate this at the hand of a numerical example and discuss the advantages and limitations of this new methodology.

Australian Soil Moisture Field Experiments in Support of Soil Moisture Satellite Observations

NASA Technical Reports Server (NTRS)

Kim, Edward; Walker, Jeff; Rudiger, Christopher; Panciera, Rocco

2010-01-01

Large-scale field campaigns provide the critical fink between our understanding retrieval algorithms developed at the point scale, and algorithms suitable for satellite applications at vastly larger pixel scales. Retrievals of land parameters must deal with the substantial sub-pixel heterogeneity that is present in most regions. This is particularly the case for soil moisture remote sensing, because of the long microwave wavelengths (L-band) that are optimal. Yet, airborne L-band imagers have generally been large, heavy, and required heavy-lift aircraft resources that are expensive and difficult to schedule. Indeed, US soil moisture campaigns, have been constrained by these factors, and European campaigns have used non-imagers due to instrument and aircraft size constraints. Despite these factors, these campaigns established that large-scale soil moisture remote sensing was possible, laying the groundwork for satellite missions. Starting in 2005, a series of airborne field campaigns have been conducted in Australia: to improve our understanding of soil moisture remote sensing at large scales over heterogeneous areas. These field data have been used to test and refine retrieval algorithms for soil moisture satellite missions, and most recently with the launch of the European Space Agency's Soil Moisture Ocean Salinity (SMOS) mission, to provide validation measurements over a multi-pixel area. The campaigns to date have included a preparatory campaign in 2005, two National Airborne Field Experiments (NAFE), (2005 and 2006), two campaigns to the Simpson Desert (2008 and 2009), and one Australian Airborne Cal/val Experiment for SMOS (AACES), just concluded in the austral spring of 2010. The primary airborne sensor for each campaign has been the Polarimetric L-band Microwave Radiometer (PLMR), a 6-beam pushbroom imager that is small enough to be compatible with light aircraft, greatly facilitating the execution of the series of campaigns, and a key to their success. An L-band imaging radar is being added to the complement to provide simultaneous active-passive L-band observations, for algorithm development activities in support of NASA's upcoming Soil Moisture Active Passive (.S"M) mission. This paper will describe the campaigns, their objectives, their datasets, and some of the unique advantages of working with small/light sensors and aircraft. We will also review the main scientific findings, including improvements to the SMOS retrieval algorithm enabled by NAFE observations and the evaluation of the Simpson Desert as a calibration target for L-band satellite missions. Plans for upcoming campaigns will also be discussed.

SMOS+RAINFALL: Evaluating the ability of different methodologies to improve rainfall estimations using soil moisture data from SMOS

NASA Astrophysics Data System (ADS)

Pellarin, Thierry; Brocca, Luca; Crow, Wade; Kerr, Yann; Massari, Christian; Román-Cascón, Carlos; Fernández, Diego

2017-04-01

Recent studies have demonstrated the usefulness of soil moisture retrieved from satellite for improving rainfall estimations of satellite based precipitation products (SBPP). The real-time version of these products are known to be biased from the real precipitation observed at the ground. Therefore, the information contained in soil moisture can be used to correct the inaccuracy and uncertainty of these products, since the value and behavior of this soil variable preserve the information of a rain event even for several days. In this work, we take advantage of the soil moisture data from the Soil Moisture and Ocean Salinity (SMOS) satellite, which provides information with a quite appropriate temporal and spatial resolution for correcting rainfall events. Specifically, we test and compare the ability of three different methodologies for this aim: 1) SM2RAIN, which directly relate changes in soil moisture to rainfall quantities; 2) The LMAA methodology, which is based on the assimilation of soil moisture in two models of different complexity (see EGU2017-5324 in this same session); 3) The SMART method, based on the assimilation of soil moisture in a simple hydrological model with a different assimilation/modelling technique. The results are tested for 6 years over 10 sites around the world with different features (land surface, rainfall climatology, orography complexity, etc.). These preliminary and promising results are shown here for the first time to the scientific community, as also the observed limitations of the different methodologies. Specific remarks on the technical configurations, filtering/smoothing of SMOS soil moisture or re-scaling techniques are also provided from the results of different sensitivity experiments.

Bayesian Hierarchical Modeling for Big Data Fusion in Soil Hydrology

NASA Astrophysics Data System (ADS)

Mohanty, B.; Kathuria, D.; Katzfuss, M.

2016-12-01

Soil moisture datasets from remote sensing (RS) platforms (such as SMOS and SMAP) and reanalysis products from land surface models are typically available on a coarse spatial granularity of several square km. Ground based sensors on the other hand provide observations on a finer spatial scale (meter scale or less) but are sparsely available. Soil moisture is affected by high variability due to complex interactions between geologic, topographic, vegetation and atmospheric variables. Hydrologic processes usually occur at a scale of 1 km or less and therefore spatially ubiquitous and temporally periodic soil moisture products at this scale are required to aid local decision makers in agriculture, weather prediction and reservoir operations. Past literature has largely focused on downscaling RS soil moisture for a small extent of a field or a watershed and hence the applicability of such products has been limited. The present study employs a spatial Bayesian Hierarchical Model (BHM) to derive soil moisture products at a spatial scale of 1 km for the state of Oklahoma by fusing point scale Mesonet data and coarse scale RS data for soil moisture and its auxiliary covariates such as precipitation, topography, soil texture and vegetation. It is seen that the BHM model handles change of support problems easily while performing accurate uncertainty quantification arising from measurement errors and imperfect retrieval algorithms. The computational challenge arising due to the large number of measurements is tackled by utilizing basis function approaches and likelihood approximations. The BHM model can be considered as a complex Bayesian extension of traditional geostatistical prediction methods (such as Kriging) for large datasets in the presence of uncertainties.

Development and Validation of The SMAP Enhanced Passive Soil Moisture Product

NASA Technical Reports Server (NTRS)

Chan, S.; Bindlish, R.; O'Neill, P.; Jackson, T.; Chaubell, J.; Piepmeier, J.; Dunbar, S.; Colliander, A.; Chen, F.; Entekhabi, D.;

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

DOE PAGES

Bai, Yun; Zhang, Jiahua; Zhang, Sha; ...

2017-01-04

Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, fourmore » older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2 = 0.60 ( RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 ( RMSE 5 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE 5 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.« less

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

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

Bai, Yun; Zhang, Jiahua; Zhang, Sha

Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, fourmore » older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2 = 0.60 ( RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 ( RMSE 5 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE 5 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.« less

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution.

PubMed

Gao, Qi; Zribi, Mehrez; Escorihuela, Maria Jose; Baghdadi, Nicolas

2017-08-26

The recent deployment of ESA's Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015-November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m³/m³ and 0.059 m³/m³ for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded.

Using precipitation, vertical root distribution, and satellite-retrieved vegetation information to parameterize water stress in a Penman-Monteith approach to evapotranspiration modeling under Mediterranean climate

NASA Astrophysics Data System (ADS)

Bai, Yun; Zhang, Jiahua; Zhang, Sha; Koju, Upama Ashish; Yao, Fengmei; Igbawua, Tertsea

2017-03-01

Recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 (RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 (RMSE = 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 (RMSE = 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at 100 m Resolution

PubMed Central

Gao, Qi; Zribi, Mehrez

2017-01-01

The recent deployment of ESA’s Sentinel operational satellites has established a new paradigm for remote sensing applications. In this context, Sentinel-1 radar images have made it possible to retrieve surface soil moisture with a high spatial and temporal resolution. This paper presents two methodologies for the retrieval of soil moisture from remotely-sensed SAR images, with a spatial resolution of 100 m. These algorithms are based on the interpretation of Sentinel-1 data recorded in the VV polarization, which is combined with Sentinel-2 optical data for the analysis of vegetation effects over a site in Urgell (Catalunya, Spain). The first algorithm has already been applied to observations in West Africa by Zribi et al., 2008, using low spatial resolution ERS scatterometer data, and is based on change detection approach. In the present study, this approach is applied to Sentinel-1 data and optimizes the inversion process by taking advantage of the high repeat frequency of the Sentinel observations. The second algorithm relies on a new method, based on the difference between backscattered Sentinel-1 radar signals observed on two consecutive days, expressed as a function of NDVI optical index. Both methods are applied to almost 1.5 years of satellite data (July 2015–November 2016), and are validated using field data acquired at a study site. This leads to an RMS error in volumetric moisture of approximately 0.087 m3/m3 and 0.059 m3/m3 for the first and second methods, respectively. No site calibrations are needed with these techniques, and they can be applied to any vegetation-covered area for which time series of SAR data have been recorded. PMID:28846601

The auto-tuned land data assimilation system (ATLAS)

USDA-ARS?s Scientific Manuscript database

Land data assimilation systems are tasked with the merging remotely-sensed soil moisture retrievals with information derived from a soil water balance model driven (principally) by observed rainfall. The performance of such systems is frequently degraded by the imprecise specification of parameters ...

Development of a remote sensing algorithm to retrieve atmospheric aerosol properties using multiwavelength and multipixel information

NASA Astrophysics Data System (ADS)

Hashimoto, Makiko; Nakajima, Teruyuki

2017-06-01

We developed a satellite remote sensing algorithm to retrieve the aerosol optical properties using satellite-received radiances for multiple wavelengths and pixels. Our algorithm utilizes spatial inhomogeneity of surface reflectance to retrieve aerosol properties, and the main target is urban aerosols. This algorithm can simultaneously retrieve aerosol optical thicknesses (AOT) for fine- and coarse-mode aerosols, soot volume fraction in fine-mode aerosols (SF), and surface reflectance over heterogeneous surfaces such as urban areas that are difficult to obtain by conventional pixel-by-pixel methods. We applied this algorithm to radiances measured by the Greenhouse Gases Observing Satellite/Thermal and Near Infrared Sensor for Carbon Observations-Cloud and Aerosol Image (GOSAT/TANSO-CAI) at four wavelengths and were able to retrieve the aerosol parameters in several urban regions and other surface types. A comparison of the retrieved AOTs with those from the Aerosol Robotic Network (AERONET) indicated retrieval accuracy within ±0.077 on average. It was also found that the column-averaged SF and the aerosol single scattering albedo (SSA) underwent seasonal changes as consistent with the ground surface measurements of SSA and black carbon at Beijing, China.

Investigating Seed Longevity of Big Sagebrush (Artemisia tridentata)

USGS Publications Warehouse

Wijayratne, Upekala C.; Pyke, David A.

2009-01-01

The Intermountain West is dominated by big sagebrush communities (Artemisia tridentata subspecies) that provide habitat and forage for wildlife, prevent erosion, and are economically important to recreation and livestock industries. The two most prominent subspecies of big sagebrush in this region are Wyoming big sagebrush (A. t. ssp. wyomingensis) and mountain big sagebrush (A. t. ssp. vaseyana). Increased understanding of seed bank dynamics will assist with sustainable management and persistence of sagebrush communities. For example, mountain big sagebrush may be subjected to shorter fire return intervals and prescribed fire is a tool used often to rejuvenate stands and reduce tree (Juniperus sp. or Pinus sp.) encroachment into these communities. A persistent seed bank for mountain big sagebrush would be advantageous under these circumstances. Laboratory germination trials indicate that seed dormancy in big sagebrush may be habitat-specific, with collections from colder sites being more dormant. Our objective was to investigate seed longevity of both subspecies by evaluating viability of seeds in the field with a seed retrieval experiment and sampling for seeds in situ. We chose six study sites for each subspecies. These sites were dispersed across eastern Oregon, southern Idaho, northwestern Utah, and eastern Nevada. Ninety-six polyester mesh bags, each containing 100 seeds of a subspecies, were placed at each site during November 2006. Seed bags were placed in three locations: (1) at the soil surface above litter, (2) on the soil surface beneath litter, and (3) 3 cm below the soil surface to determine whether dormancy is affected by continued darkness or environmental conditions. Subsets of seeds were examined in April and November in both 2007 and 2008 to determine seed viability dynamics. Seed bank samples were taken at each site, separated into litter and soil fractions, and assessed for number of germinable seeds in a greenhouse. Community composition data from each site, as well as several environmental variables, were used to evaluate seed viability within the context of habitat variation. Initial viability of seeds used in the seed retrieval experiment was 81 and 92 percent for mountain and Wyoming big sagebrush, respectively. After remaining in the field for 24 months, buried Wyoming big sagebrush seeds retained 28-58 percent viability,11-23 percent of seeds under litter remained viable, and no seeds remained viable on the surface (estimates are 95-percent confidence intervals). The odds of remaining viable did not change from 12 to 24 months. However, after 24 months the odds of seeds beneath litter being viable decreased to 75 percent of the odds of viability at 12 months. Similar to Wyoming big sagebrush, buried seeds of mountain big sagebrush were 31-68 percent viable, seeds under litter retained 10-22 percent of their viability, and no surface seeds were viable after 24 months. Both subspecies of big sagebrush had some portion of seed that remained viable for more than one growing season provided they were buried or under litter. Although seeds beneath litter may remain viable in intact communities, seeds are susceptible to incineration during fires. Nine months after seed dispersal, seed bank estimates for Wyoming big sagebrush ranged from 19 to 49 viable seeds/m2 in litter samples and 19-57 viable seeds/m2 in soil samples (95-percent confidence interval). For mountain big sagebrush, estimates were 27-75 viable seeds/m2 in litter samples and 54-139 viable seeds/m2 in soil (95-percent confidence interval). The number of viable seeds present in the seed bank 9 months after seed dispersal was not significantly different from the number present immediately after seed dispersal. Seed viability was highest in mountain big sagebrush sites for seeds on the surface and beneath litter, but decreased after one season. Buried seeds of both subspecies were in equal abundances and may be insulated from the effect

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.

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;

Retrieval of Spatio-temporal Evaporation by Integrating Landsat OLI Optical and Thermal Data

NASA Astrophysics Data System (ADS)

Wandera, L. N.; Tol, C. V. D.; Mallick, K.; Bayat, B.; Verbeiren, B.; van Griensven, A.; Verhoef, W.; Suliga, J.; Barrios, J. M.; Chormański, J.; Kleniewska, M.

2017-12-01

Soil-Vegetation-Atmosphere (SVAT) Transfer Models are capable of providing continuous predictions of evapotranspiration (ET). However, providing these models with reliable spatio-temporal information of vegetation and soil properties remains challenging. Thus, combining optical and thermal satellite information might assists to overcome this challenge when using SVAT models. In this study, using a radiative transfer model of solar and sky radiation (RTMo), we simulate Landsat 8 reflectance bands (2-7). We then apply a numerical optimization approach to invert the model and retrieve the corresponding canopy attributes leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm), leaf brown material (Cs), Leaf Area Index (LAI) and the leaf angle distribution function in the canopy at overpass time. The retrievals are then directly used as inputs into our SVAT model of choice, Soil Canopy Observations of Photochemistry and Energy Fluxes (SCOPE). Using a model for transfer of thermal radiation emitted by vegetation and soil (RTMt), we proceed to simulate Landsat radiance for the corresponding reflectance data using a lookup table (LUT). These variables were then used to develop a crop factor (Kc) map. A reference ET was generated and applied to the Kc map to obtain actual ET. We proceeded to interpolate the ET between the image acquisition dates to have a complete time series. The retrieval maps for the specific variables captured seasonal variability patterns for the respective variables. The generated KC map showed similar trend with the LAI maps. There was an underestimation of actual ET when the simulation was not constrained to the thermal information. The interpolation of ET between acquisition image dates reflected the seasonal trends. Key Word: SVAT, optical, thermal, remote sensing, evapotranspiration

Spatial variability of soil moisture retrieved by SMOS satellite

NASA Astrophysics Data System (ADS)

Lukowski, Mateusz; Marczewski, Wojciech; Usowicz, Boguslaw; Rojek, Edyta; Slominski, Jan; Lipiec, Jerzy

2015-04-01

Standard statistical methods assume that the analysed variables are independent. Since the majority of the processes observed in the nature are continuous in space and time, this assumption introduces a significant limitation for understanding the examined phenomena. In classical approach, valuable information about the locations of examined observations is completely lost. However, there is a branch of statistics, called geostatistics, which is the study of random variables, but taking into account the space where they occur. A common example of so-called "regionalized variable" is soil moisture. Using in situ methods it is difficult to estimate soil moisture distribution because it is often significantly diversified. Thanks to the geostatistical methods, by employing semivariance analysis, it is possible to get the information about the nature of spatial dependences and their lengths. Since the Soil Moisture and Ocean Salinity mission launch in 2009, the estimation of soil moisture spatial distribution for regional up to continental scale started to be much easier. In this study, the SMOS L2 data for Central and Eastern Europe were examined. The statistical and geostatistical features of moisture distributions of this area were studied for selected natural soil phenomena for 2010-2014 including: freezing, thawing, rainfalls (wetting), drying and drought. Those soil water "states" were recognized employing ground data from the agro-meteorological network of ground-based stations SWEX and SMUDP2 data from SMOS. After pixel regularization, without any upscaling, the geostatistical methods were applied directly on Discrete Global Grid (15-km resolution) in ISEA 4H9 projection, on which SMOS observations are reported. Analysis of spatial distribution of SMOS soil moisture, carried out for each data set, in most cases did not show significant trends. It was therefore assumed that each of the examined distributions of soil moisture in the adopted scale satisfies ergodicity and quasi-stationarity assumptions, required for geostatistical analysis. The semivariograms examinations revealed that spatial dependences occurring in the surface soil moisture distributions for the selected area were more or less 200 km. The exception was the driest of the studied days, when the spatial correlations of soil moisture were not disturbed for a long time by any rainfall. Spatial correlation length on that day was about 400 km. Because of zonal character of frost, the spatial dependences in the examined surface soil moisture distributions during freezing/thawing found to be disturbed. Probably, the amount of water remains the same, but it is not detected by SMOS, hence analysing dielectric constant instead of soil moisture would be more appropriate. Some spatial relations of soil moisture and freezing distribution with existing maps of soil granulometric fractions and soil specific surface area for Poland have also been found. The work was partially funded under the ELBARA_PD (Penetration Depth) project No. 4000107897/13/NL/KML. ELBARA_PD project is funded by the Government of Poland through an ESA (European Space Agency) Contract under the PECS (Plan for European Cooperating States).

Analyzing the impact of sensor characteristics on retrieval methods of solar-induced fluorescence

NASA Astrophysics Data System (ADS)

Ding, Wenjuan; Zhao, Feng; Yang, Lizi

2017-02-01

In this study, we evaluated the influence of retrieval algorithms and sensor characteristics, such as spectral resolution (SR) and signal to noise ratio (SNR), on the retrieval accuracy of fluorescence signal (Fs). Here Fs was retrieved by four commonly used retrieval methods, namely the original Fraunhofer Line Discriminator method (FLD), the 3 bands FLD (3FLD), the improved FLD (iFLD) and the spectral fitting method (SFM). Fs was retrieved in the oxygen A band centered at around 761nm (O2-A). We analyzed the impact of sensor characteristics on four retrieval methods based on simulated data which were generated by the model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), and obtained consistent conclusions when compared with experimental data. Results presented in this study indicate that both retrieval algorithms and sensor characteristics affect the retrieval accuracy of Fs. When applied to the actual measurement, we should choose the instrument with higher performance and adopt appropriate retrieval method according to measuring instruments and conditions.

Are Planetary Regolith Particles Back Scattering? Response to a Paper by M. Mishchenko

NASA Technical Reports Server (NTRS)

Hapke, Bruce

1996-01-01

In a recent paper Mishchenko asserts that soil particles are strongly forward scattering, whereas particles on the surfaces of objects in the solar system have been inferred to be back scattering. Mishchenko suggests that this apparent discrepancy is an artifact caused by using an approximate light scattering model to analyse the data, and that planetary regolith particles are actually strong forward scatterers. The purpose of the present paper is to point out the errors in Mishchenko's paper and to show from both theoretical arguments and experimental data that inhomogencous composite particles which are large compared to the wavelength of visible light, such as rock fragments and agglutinates, can be strongly back scattering and are the fundamental scatterers in media composed of them. Such particles appear to be abundant in planetary regoliths and can account for the back scattering character of the surfaces of many bodies in the solar system. If the range of phase angles covered by a data set is insufficient, serious errors in retrieving the particle scattering properties can result whether an exact or approximate scattering model is used. However, if the data set includes both large and small phase angles, approximate regolith scattering models can correctly retrieve the sign of the particle scattering asymmetry.

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

2013-01-01

Uncertainties in the retrievals of microwaveland-surface emissivities are quantified over two types of land surfaces: desert and tropical rainforest. Retrievals from satellite-based microwave imagers, including the Special Sensor Microwave Imager, the Tropical Rainfall Measuring Mission Microwave Imager, and the Advanced Microwave Scanning Radiometer for Earth Observing System, are 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 inthe retrievals. Generally, these retrievals tend to agree better at lower frequencies than at higher ones, with systematic differences ranging 1%-4% (3-12 K) over desert and 1%-7% (3-20 K) over rainforest. The random errors within each retrieval dataset are in the range of 0.5%-2% (2-6 K). In particular, at 85.5/89.0 GHz, there are very large differences between the different retrieval datasets, and within each retrieval dataset itself. Further investigation reveals that these differences are most likely caused by rain/cloud contamination, which can lead to random errors up to 10-17 K under the most severe conditions.

Special cases of AIRS v4.0.x retrievals: missing forecast surface pressure and regression-only retrieval

NASA Technical Reports Server (NTRS)

Hearty, Thomas; Manning, Evan

2005-01-01

This memo examines the differences that can be expected when performing two special cases of retrievals with the v.4.0.x PGE: (1) retrivals without the surface pressure from the NOAA Global Forecast System (GFS) and (2) regression only retrievals. An understanding of these differences is important for users who may want to give up some accuracy in the retrieval in exchange for a rapid solution.

A MODIS-based vegetation index climatology

USDA-ARS?s Scientific Manuscript database

Our motivation here is to provide information for the NASA Soil Moisture Active Passive (SMAP) satellite soil moisture retrieval algorithms (launch in 2014). Vegetation attenuates the signal and the algorithms must correct for this effect. One approach is to use data that describes the canopy water ...

Wildfire Risk Mapping over the State of Mississippi: Land Surface Modeling Approach

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

Cooke, William H.; Mostovoy, Georgy; Anantharaj, Valentine G

2012-01-01

Three fire risk indexes based on soil moisture estimates were applied to simulate wildfire probability over the southern part of Mississippi using the logistic regression approach. The fire indexes were retrieved from: (1) accumulated difference between daily precipitation and potential evapotranspiration (P-E); (2) top 10 cm soil moisture content simulated by the Mosaic land surface model; and (3) the Keetch-Byram drought index (KBDI). The P-E, KBDI, and soil moisture based indexes were estimated from gridded atmospheric and Mosaic-simulated soil moisture data available from the North American Land Data Assimilation System (NLDAS-2). Normalized deviations of these indexes from the 31-year meanmore » (1980-2010) were fitted into the logistic regression model describing probability of wildfires occurrence as a function of the fire index. It was assumed that such normalization provides more robust and adequate description of temporal dynamics of soil moisture anomalies than the original (not normalized) set of indexes. The logistic model parameters were evaluated for 0.25 x0.25 latitude/longitude cells and for probability representing at least one fire event occurred during 5 consecutive days. A 23-year (1986-2008) forest fires record was used. Two periods were selected and examined (January mid June and mid September December). The application of the logistic model provides an overall good agreement between empirical/observed and model-fitted fire probabilities over the study area during both seasons. The fire risk indexes based on the top 10 cm soil moisture and KBDI have the largest impact on the wildfire odds (increasing it by almost 2 times in response to each unit change of the corresponding fire risk index during January mid June period and by nearly 1.5 times during mid September-December) observed over 0.25 x0.25 cells located along the state of Mississippi Coast line. This result suggests a rather strong control of fire risk indexes on fire occurrence probability over this region.« less

Assimilation of neural network soil moisture in land surface models

NASA Astrophysics Data System (ADS)

Rodriguez-Fernandez, Nemesio; de Rosnay, Patricia; Albergel, Clement; Aires, Filipe; Prigent, Catherine; Kerr, Yann; Richaume, Philippe; Muñoz-Sabater, Joaquin; Drusch, Matthias

2017-04-01

In this study a set of land surface data assimilation (DA) experiments making use of satellite derived soil moisture (SM) are presented. These experiments have two objectives: (1) to test the information content of satellite remote sensing of soil moisture for numerical weather prediction (NWP) models, and (2) to test a simplified assimilation of these data through the use of a Neural Network (NN) retrieval. Advanced Scatterometer (ASCAT) and Soil Moisture and Ocean Salinity (SMOS) data were used. The SMOS soil moisture dataset was obtained specifically for this project training a NN using SMOS brightness temperatures as input and using as reference for the training European Centre for Medium-Range Weather Forecasts (ECMWF) H-TESSEL SM fields. In this way, the SMOS NN SM dataset has a similar climatology to that of the model and it does not present a global bias with respect to the model. The DA experiments are computed using a surface-only Land Data Assimilation System (so-LDAS) based on the HTESSEL land surface model. This system is very computationally efficient and allows to perform long surface assimilation experiments (one whole year, 2012). SMOS NN SM DA experiments are compared to ASCAT SM DA experiments. In both cases, experiments with and without 2 m air temperature and relative humidity DA are discussed using different observation errors for the ASCAT and SMOS datasets. Seasonal, geographical and soil-depth-related differences between the results of those experiments are presented and discussed. The different SM analysed fields are evaluated against a large number of in situ measurements of SM. On average, the SM analysis gives in general similar results to the model open loop with no assimilation even if significant differences can be seen for specific sites with in situ measurements. The sensitivity to observation errors to the SM dataset slightly differs depending on the networks of in situ measurements, however it is relatively low for the tests conducted here. Finally, the effect of the soil moisture analysis on the NWP is evaluated comparing experiments for different configurations of the system, with and without (Open Loop) soil moisture data assimilation. ssimilation of ASCAT soil moisture improves the forecast in the tropics and adds information with respect to the near surface conventional observations. In contrast, SMOS degrades the forecast in the Tropics in July-September. In the Southern hemisphere ASCAT degrades the forecast in July-September both alone and using 2m air temperature and relative humidity. On the other hand, experiments using SMOS (even without screen level variables) improve the forecast for all the seasons, in particular, in July-December. In the northern hemisphere both with ASCAT and SMOS, the experiments using 2m air temperature and relative humidity improve the forecast in April-September. SMOS alone has a significant positive effect in July-September for experiments with low observation error. Maps of the forecast skill with respect to the open loop experiment show that SMOS improves the forecast in North America and to a lesser extent in northern Asia for up to 72 hours.

Impact of forested fallows on fertility and mercury content in soils of the Tapajós River region, Brazilian Amazon.

PubMed

Patry, Cynthia; Davidson, Robert; Lucotte, Marc; Béliveau, Annie

2013-08-01

Recent research on slash-and-burn agriculture conducted in the Amazonian basin has suggested that soils must be left under forested fallows for at least 10 to 15 years to regain fertility levels comparable to non-disturbed forests in order to allow for short cycle crop cultivation. However, small scale farmers tend nowadays to re-burn secondary forests as soon as after 3 to 5 years, thus could contribute to further reduce soil fertility and could enhance the transfer of mercury (Hg) naturally present in soils of the region towards water courses. The present research project sets out to characterize the impact of forested fallows of differing age and land-use history on soils properties (fertility and Hg contents) in the region of the Tapajós River, an active pioneer front of the Brazilian Amazon. To do this, soil samples in forested fallows of variable age and in control primary forests were retrieved. In general, soil fertility of grouped forested fallows of different ages was similar to that of the primary forests. But when discriminating soils according to their texture, forested fallows on coarse grained soils still had much higher NH4/NO3 ratios, NH4 and Ca contents than primary forests, this even 15 years after burning. The impact of repeated burnings was also assessed. Fallows on coarse grained soils showed an impoverishment for all variables related to fertility when the number of burnings was 5 or more. For fallows on fine grained soils that underwent 5 or more burnings, NO3 contents were low although a cation enrichment was observed. Total soil Hg content was also sensitive to repeated burnings, showing similar losses for forested fallows established on both types of soil. However, Hg linked to coarse particles appeared to migrate back towards fine particles at the surface of coarse grained soils in fallows older than 7 years. Copyright © 2013 Elsevier B.V. All rights reserved.

Evaluation of the operational Aerosol Layer Height retrieval algorithm for Sentinel-5 Precursor: application to O2 A band observations from GOME-2A

NASA Astrophysics Data System (ADS)

Sanders, A. F. J.; de Haan, J. F.; Sneep, M.; Apituley, A.; Stammes, P.; Vieitez, M. O.; Tilstra, L. G.; Tuinder, O. N. E.; Koning, C. E.; Veefkind, J. P.

2015-06-01

An algorithm setup for the operational Aerosol Layer Height product for TROPOMI on the Sentinel-5 Precursor mission is described and discussed, applied to GOME-2A data, and evaluated with lidar measurements. The algorithm makes a spectral fit of reflectance at the O2 A band in the near-infrared and the fit window runs from 758 to 770 nm. The aerosol profile is parameterized by a scattering layer with constant aerosol volume extinction coefficient and aerosol single scattering albedo and with a fixed pressure thickness. The algorithm's target parameter is the height of this layer. In this paper, we apply the algorithm to observations from GOME-2A in a number of systematic and extensive case studies and we compare retrieved aerosol layer heights with lidar measurements. Aerosol scenes cover various aerosol types, both elevated and boundary layer aerosols, and land and sea surfaces. The aerosol optical thicknesses for these scenes are relatively moderate. Retrieval experiments with GOME-2A spectra are used to investigate various sensitivities, in which particular attention is given to the role of the surface albedo. From retrieval simulations with the single-layer model, we learn that the surface albedo should be a fit parameter when retrieving aerosol layer height from the O2 A band. Current uncertainties in surface albedo climatologies cause biases and non-convergences when the surface albedo is fixed in the retrieval. Biases disappear and convergence improves when the surface albedo is fitted, while precision of retrieved aerosol layer pressure is still largely within requirement levels. Moreover, we show that fitting the surface albedo helps to ameliorate biases in retrieved aerosol layer height when the assumed aerosol model is inaccurate. Subsequent retrievals with GOME-2A spectra confirm that convergence is better when the surface albedo is retrieved simultaneously with aerosol parameters. However, retrieved aerosol layer pressures are systematically low (i.e., layer high in the atmosphere) to the extent that retrieved values are not realistically representing actual extinction profiles anymore. When the surface albedo is fixed in retrievals with GOME-2A spectra, convergence deteriorates as expected, but retrieved aerosol layer pressures become much higher (i.e., layer lower in atmosphere). The comparison with lidar measurements indicates that retrieved aerosol layer heights are indeed representative of the underlying profile in that case. Finally, subsequent retrieval simulations with two-layer aerosol profiles show that a model error in the assumed profile (two layers in the simulation but only one in the retrieval) is partly absorbed by the surface albedo when this parameter is fitted. This is expected in view of the correlations between errors in fit parameters and the effect is relatively small for elevated layers (less than 100 hPa). In case one of the scattering layers is near the surface (boundary layer aerosols), the effect becomes surprisingly large such that the retrieved height of the single layer is above the two-layer profile. Furthermore, we find that the retrieval solution, once retrieval converges, hardly depends on the starting values for the fit. Sensitivity experiments with GOME-2A spectra also show that aerosol layer height is indeed relatively robust against inaccuracies in the assumed aerosol model, even when the surface albedo is not fitted. We show spectral fit residuals, which can be used for further investigations. Fit residuals may be partly explained by spectroscopic uncertainties, which is suggested by an experiment showing the improvement of convergence when the absorption cross section is scaled in agreement with Butz et al. (2012) and Crisp et al. (2012) and a temperature offset to the a priori ECMWF temperature profile is fitted. Retrieved temperature offsets are always negative and quite large (ranging between -4 and -8 K), which is not expected if temperature offsets absorb remaining inaccuracies in meteorological data. Other sensitivity experiments investigate fitting of stray light and fluorescence emissions. We find negative radiance offsets and negative fluorescence emissions, also for non-vegetated areas, but from the results it is not clear whether fitting these parameters improves the retrieval. Based on the present results, the operational baseline for the Aerosol Layer Height product currently will not fit the surface albedo. The product will be particularly suited for elevated, optically thick aerosol layers. In addition to its scientific value in climate research, anticipated applications of the product for TROPOMI are providing aerosol height information for aviation safety and improving interpretation of the Absorbing Aerosol Index.

Evaluation of the operational Aerosol Layer Height retrieval algorithm for Sentinel-5 Precursor: application to O2 A band observations from GOME-2A

NASA Astrophysics Data System (ADS)

Sanders, A. F. J.; de Haan, J. F.; Sneep, M.; Apituley, A.; Stammes, P.; Vieitez, M. O.; Tilstra, L. G.; Tuinder, O. N. E.; Koning, C. E.; Veefkind, J. P.

2015-11-01

An algorithm setup for the operational Aerosol Layer Height product for TROPOMI on the Sentinel-5 Precursor mission is described and discussed, applied to GOME-2A data, and evaluated with lidar measurements. The algorithm makes a spectral fit of reflectance at the O2 A band in the near-infrared and the fit window runs from 758 to 770 nm. The aerosol profile is parameterised by a scattering layer with constant aerosol volume extinction coefficient and aerosol single scattering albedo and with a fixed pressure thickness. The algorithm's target parameter is the height of this layer. In this paper, we apply the algorithm to observations from GOME-2A in a number of systematic and extensive case studies, and we compare retrieved aerosol layer heights with lidar measurements. Aerosol scenes cover various aerosol types, both elevated and boundary layer aerosols, and land and sea surfaces. The aerosol optical thicknesses for these scenes are relatively moderate. Retrieval experiments with GOME-2A spectra are used to investigate various sensitivities, in which particular attention is given to the role of the surface albedo. From retrieval simulations with the single-layer model, we learn that the surface albedo should be a fit parameter when retrieving aerosol layer height from the O2 A band. Current uncertainties in surface albedo climatologies cause biases and non-convergences when the surface albedo is fixed in the retrieval. Biases disappear and convergence improves when the surface albedo is fitted, while precision of retrieved aerosol layer pressure is still largely within requirement levels. Moreover, we show that fitting the surface albedo helps to ameliorate biases in retrieved aerosol layer height when the assumed aerosol model is inaccurate. Subsequent retrievals with GOME-2A spectra confirm that convergence is better when the surface albedo is retrieved simultaneously with aerosol parameters. However, retrieved aerosol layer pressures are systematically low (i.e., layer high in the atmosphere) to the extent that retrieved values no longer realistically represent actual extinction profiles. When the surface albedo is fixed in retrievals with GOME-2A spectra, convergence deteriorates as expected, but retrieved aerosol layer pressures become much higher (i.e., layer lower in atmosphere). The comparison with lidar measurements indicates that retrieved aerosol layer heights are indeed representative of the underlying profile in that case. Finally, subsequent retrieval simulations with two-layer aerosol profiles show that a model error in the assumed profile (two layers in the simulation but only one in the retrieval) is partly absorbed by the surface albedo when this parameter is fitted. This is expected in view of the correlations between errors in fit parameters and the effect is relatively small for elevated layers (less than 100 hPa). If one of the scattering layers is near the surface (boundary layer aerosols), the effect becomes surprisingly large, in such a way that the retrieved height of the single layer is above the two-layer profile. Furthermore, we find that the retrieval solution, once retrieval converges, hardly depends on the starting values for the fit. Sensitivity experiments with GOME-2A spectra also show that aerosol layer height is indeed relatively robust against inaccuracies in the assumed aerosol model, even when the surface albedo is not fitted. We show spectral fit residuals, which can be used for further investigations. Fit residuals may be partly explained by spectroscopic uncertainties, which is suggested by an experiment showing the improvement of convergence when the absorption cross section is scaled in agreement with Butz et al. (2013) and Crisp et al. (2012), and a temperature offset to the a priori ECMWF temperature profile is fitted. Retrieved temperature offsets are always negative and quite large (ranging between -4 and -8 K), which is not expected if temperature offsets absorb remaining inaccuracies in meteorological data. Other sensitivity experiments investigate fitting of stray light and fluorescence emissions. We find negative radiance offsets and negative fluorescence emissions, also for non-vegetated areas, but from the results it is not clear whether fitting these parameters improves the retrieval. Based on the present results, the operational baseline for the Aerosol Layer Height product currently will not fit the surface albedo. The product will be particularly suited for elevated, optically thick aerosol layers. In addition to its scientific value in climate research, anticipated applications of the product for TROPOMI are providing aerosol height information for aviation safety and improving interpretation of the Absorbing Aerosol Index.

Microwave-derived soil moisture over Mediterranean land uses: from ground-based radiometry to SMOS first observations

NASA Astrophysics Data System (ADS)

Saleh, Kauzar; Antolín, Carmen; Juglea, Silvia; Kerr, Yann; Millán-Scheiding, Cristina; Novello, Nathalie; Pardé, Mickael; Wigneron, Jean-Pierre; Zribi, Mehrez; López-Baeza, Ernesto

2010-05-01

This communication will present the main results of a series of airborne and ground-based experiments conducted at the Valencia Anchor Station (VAS) site for the implementation of the SMOS emission model L-MEB (L-band Microwave Emission model of the Biosphere, Wigneron et al., 2007), and will evaluate the performance of L-MEB against SMOS measurements. The L-MEB model has been implemented in the context of the SMOS mission and through numerous radiometry experiments over different land uses. Within L-MEB, each land use is characterised by model parameterisations that are used to describe the radiative transfer at L-band. They describe, for instance, the attenuation properties of different canopies, or the effect of soil roughness on the surface emission. In recent years, the Valencia Anchor Station site (VAS) has hosted various radiometry experiments. These were performed at different scales, from the plot scale to the regional scale (up to 50 km), using ground-based and airborne-based radiometry. The main results are discussed in this communication, and some preliminary comparisons with SMOS measurements are presented. 1) Ground-based experiments. MELBEX-I was a ground-radiometry experiment run in 2005 using the L-band radiometer EMIRAD over a plot of shrub land. We will present results from this experiment (Cano et al., 2009), that highlighted a constant (and small) contribution of Mediterranean shrub land to the overall emission, and investigated the role of exposed rocks in the surface emission. MELBEX-II was a ground-radiometry experiment run in 2007 using the EMIRAD L-band radiometer over a plot of vineyards throughout the whole vegetation cycle. Vineyards are the main land use at the VAS site, therefore parameterisations for vineyards are key for the validation of SMOS data at VAS. This communication will discuss, in particular, estimates of microwave surface roughness throughout the crop year, and changes in the canopy microwave properties throughout the plant growing cycle. 2) Airborne-based experiments. 2.1) ESA's SMOS Rehearsal 2008. For this campaign an area of 100 km2 of vineyards in winter-like conditions was flown on four days using the EMIRAD radiometer. Soil moisture could be retrieved with good accuracy but only after surface roughness was determined. In fact, the campaign highlighted that close to specular modelling of the surface reflectivity using 0-6 cm measurements of soil moisture underestimated the surface emission. This was observed also in other airborne data sets (Saleh et al. 2009). 2.2) CNES CAROLS campaigns. In 2009, the L-band CAROLS radiometer was flown on three occasions over an area of 1500 km2 covering vineyards, shrub land and Mediterranean pine forest. Main results of CAROLS 2009 will be presented in this communication, and the emphasis will be on comparing local to regional scale results given that CAROLS flights were performed at ~4000 m above the surface. For soil moisture, SVAT-derived, field soil moisture, and surface-type derived soil moisture will be used as ground truth. 3) SMOS data Finally, the results of the above mentioned experiments concerning L-MEB parameterisations will be the basis for comparisons between simulated brightness temperatures (TB) and measured TBs from SMOS at the VAS site. These exercises will be conducted in order to have an assessment of the L-MEB performance in a highly studied and monitored area, and to help pinpointing future areas of investigation in microwave radiometry. References Cano A., Saleh K., Wigneron J.P., Antolín C., Balling J., Kerr Y.H., Kruszewski A., Millán-Scheiding C., Søbjaerg S.S., Skou N., López-Baeza E. (2009), The SMOS Medierranean Ecosystem L-band experiment (MELBEX-I) over natural shrubs, Remote Sensing of Environment, in press. Saleh K., Kerr Y.H., Richaume P., Escorihuela, M.J., Panciera R., Delwart S., Walker J., Boulet G., Maisongrande P., Wursteisen P., Wigneron, J.P. (2009), Soil moisture retrievals at L-band using a two-step inversion approach (COSMOS/NAFE'05), Remote Sensing of Environment, vol. 113, 6, 1304-1312. Wigneron, J.-P., Kerr, Y., Waldteufel, P., Saleh, K., Escorihuela, M.-J., Richaume, P., Ferrazzoli, P., Grant, J. P., Hornbuckle, B., de Rosnay, P., Calvet, J.-C., Pellarin, T., Gurney, R., Mätzler, C. (2007), L-band Microwave Emission of the Biosphere (L-MEB) Model: description and calibration against experimental data sets over crop fields, Remote Sensing of Environment, vol (107), 639-655.

Science Data System Contribution to Calibrating and Validating SMAP Data Products

NASA Astrophysics Data System (ADS)

Cuddy, D.

2015-12-01

NASA's Soil Moisture Active Passive (SMAP) mission retrieves global surface soil moisture and freeze/thaw state using measurements acquired by a radiometer and a synthetic aperture radar that fly on an Earth orbiting satellite. The SMAP observatory launched from Vandenberg Air Force Base on January 31, 2015 into a near-polar, sun-synchronous orbit. This paper describes the contribution of the SMAP Science Data System (SDS) to the calibration and on-going validation of the radar backscatter and radiometer brightness temperatures. The Science Data System designed, implemented and operated the software that generates data products that contain various geophysical parameters including soil moisture and freeze/thaw states, daily maps of these geophysical parameters, as well as modeled analyses of global soil moisture and carbon flux in Boreal regions. The SDS is a fully automated system that processes the incoming raw data from the instruments, incorporates spacecraft and instrument engineering data, and uses both dynamic and static ancillary products provided by the scientific community. The standard data products appear in Hierarchical Data Format-5 (HDF5) format. These products contain metadata that conform to the ISO 19115 standard. The Alaska Satellite Facility (ASF) hosts and distributes SMAP radar data products. The National Snow and Ice Data Center (NSIDC) hosts and distributes all of the other SMAP data products.

Statistical-physical model of the hydraulic conductivity

NASA Astrophysics Data System (ADS)

Usowicz, B.; Marczewski, W.; Usowicz, J. B.; Lukowski, M. I.

2012-04-01

The water content in unsaturated subsurface soil layer is determined by processes of exchanging mass and energy between media of soil and atmosphere, and particular members of layered media. Generally they are non-homogeneous on different scales, considering soil porosity, soil texture including presence of vegetation elements in the root zone, and canopy above the surface, and varying biomass density of plants above the surface in clusters. That heterogeneity determines statistically effective values of particular physical properties. This work considers mainly those properties which determine the hydraulic conductivity of soil. This property is necessary for characterizing physically water transfer in the root zone and access of nutrient matter for plants, but it also the water capacity on the field scale. The temporal variability of forcing conditions and evolutionarily changing vegetation causes substantial effects of impact on the water capacity in large scales, bringing the evolution of water conditions in the entire area, spanning a possible temporal state in the range between floods and droughts. The dynamic of this evolution of water conditions is highly determined by vegetation but is hardly predictable in evaluations. Hydrological models require feeding with input data determining hydraulic properties of the porous soil which are proposed in this paper by means of the statistical-physical model of the water hydraulic conductivity. The statistical-physical model was determined for soils being typical in Euroregion Bug, Eastern Poland. The model is calibrated on the base of direct measurements in the field scales, and enables determining typical characteristics of water retention by the retention curves bounding the hydraulic conductivity to the state of water saturation of the soil. The values of the hydraulic conductivity in two reference states are used for calibrating the model. One is close to full saturation, and another is for low water content far from saturation, in a particular case of the soil type. Effects of calibrating a soil depends on assumed ranges of soil properties engaged to recognizing the soil type. Among those properties, the key role is for the bulk density, the porosity and its dependence on the specific area of the soil. The aim of this work is to provide such variables of auxiliary data to SMOS, which would bring a relation of the soil moisture to the water capacity, under retrieving SM from SMOS L1C data. * 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.

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.

Passive L-Band H Polarized Microwave Emission During the Corn Growth Cycle

NASA Astrophysics Data System (ADS)

Joseph, A. T.; van der Velde, R.; O'Neill, P. E.; Kim, E. J.; Lang, R. H.; Gish, T. J.

2012-12-01

Hourly L-band (1.4 GHz) horizontally (H) polarized brightness temperatures (TB's) measured during five episodes (more than two days of continuous measurements) of the 2002 corn growth cycle are analyzed. These TB measurements were acquired as a part of a combined active/passive microwave field campaign, and were obtained at five incidence and three azimuth angles relative to the row direction. In support of this microwave data collection, intensive ground sampling took place once a week. Moreover, the interpretation of the hourly TB's could also rely on the data obtained using the various automated instruments installed in the same field. In this paper, the soil moisture and temperature measured at fixed time intervals have been employed as input for the tau-omega model to reproduce the hourly TB. Through the calibration of the vegetation and surface roughness parameterizations, the impact of the vegetation morphological changes on the microwave emission and the dependence of the soil surface roughness parameter, hr, on soil moisture are investigated. This analysis demonstrates that the b parameter, appearing in the representation of the canopy opacity, has an angular dependence that varies throughout the growing period and also that the parameter hr increases as the soil dries in a portion of the dry-down cycle. The angular dependence of the b parameter imposes the largest uncertainty on TB simulations near senescence as the response of b to the incidence is also affected by the crop row orientation. On the other hand, the incorporation of a soil moisture dependent hr parameterization was responsible for the largest error reduction of TB simulations in the early growth cycle. A.T. Joseph, R. Van der Velde, P.E. O'Neill, R.H. Lang, and T. Gish, "Soil moisture retrieval during a corn growth cycle using L-band (1.6 GHz) radar observations", IEEE Transactions on Geoscience and Remote Sensing, vol. 46, DOI:10.1109/TGRS.2008.917214, Aug. 2008. M.C. Dobson, F.T. Ulaby, M.T. Hallikainen and M.A. El-Rayes, "Microwave dielectic behavior of wet soil - Part II: Dielectric mixing models", IEEE Transactions on Geoscience and Remote Sensing, vol. GE-23, pp. 35- 46, Jan., 1985. A.K. Fung, Z. Li and K.S. Chen, "Backscattering from a randomly rough dielectric surface", IEEE Transactions on Geoscience Remote Sensing, vol. 30, pp. 356-369, Mar., 1992.

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.

The NetLander mission: a geophysical network on the surface of Mars

NASA Astrophysics Data System (ADS)

Ferri, F.; Counil, J. L.; Marsal, O.; Rocard, F.; Bonneville, R.; NetLander Team

2001-12-01

The NetLander mission aims at deploying on the surface of Mars a network of four identical landers which will perform simultaneous measurements in order to study the internal structure of Mars, its subsurface, surface, atmosphere and ionosphere. Seismic measurements will evidence the main transitions (lithosphere-mantle-core) as well as mantle discontinuities and crustal structure. The geodetic measurements will allow to determine the state of the core, liquid or not, and to retrieve the density of the core and mantle. The magnetic experiment will retrieve the conductivity profile down to several hundred of kilometers depth, gathering information on temperature gradient and phase transitions. The search for ground water, liquid or solid, will be performed locally by three experiments: seismometers, magnetometers and a ground penetrating radar. Local geology and surface mineralogy will be investigated through a multispectral stereo panoramic camera. A dedicated package will study the thermal properties of the soil at the landing sites. The NetLander will investigate the atmospheric vertical structure at the entry sites, complementing the existing three profiles. The network's ability to measure spatial and seasonal variations of pressure and the near-surface relative humidity will provide an unprecedented opportunity to characterize the H2O cycle. The meteorological package will also provide data relevant to the initiation and evolution of dust processes. Ionospheric investigations, coming along mainly with radio science, radar and electromagnetic sounding, will allow studying ionization processes and monitoring both the large-scale and small-scale plasma variations. The NetLander is a CNES led European mission to be launched in 2007. The nine instruments forming the payload will be provided by space agencies and research laboratories from more than ten European countries and USA.

Potential of Sentinel Satellites for Schistosomiasis Monitoring

NASA Astrophysics Data System (ADS)

Li, C.-R.; Tang, L.-L.; Niu, H.-B.; Zhou, X.-N.; Liu, Z.-Y.; Ma, L.-L.; Zhou, Y.-S.

2012-04-01

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis is the unique intermediate host of Schistosoma, and hence monitoring and controlling of the number of oncomelania is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to oncomelania breeding and reproduction, such as temperature, moisture, vegetation, soil, and rainfall, and can also provide the efficient information to determine the location, area, and spread tendency of oncomelania. Many studies show that the correlation coefficient between oncomelania densities and remote sensing environmental factors depends largely on suitable and high quality remote sensing data used in retrieve environmental factors. Research achievements on retrieving environmental factors (which are related to the living, multiplying and transmission of oncomelania) by multi-source remote data are shown firstly, including: (a) Vegetation information (e.g., Modified Soil-Adjusted Vegetation Index, Normalized Difference Moisture Index, Fractional Vegetation Cover) extracted from optical remote sensing data, such as Landsat TM, HJ-1A/HSI image; (b) Surface temperature retrieval from Thermal Infrared (TIR) and passive-microwave remote sensing data; (c) Water region, soil moisture, forest height retrieval from synthetic aperture radar data, such as Envisat SAR, DLR's ESAR image. Base on which, the requirements of environmental factor accuracy for schistosomiasis monitoring will be analyzed and summarized. Our work on applying remote sensing technique to schistosomiasis monitoring is then presented. The fuzzy information theory is employed to analyze the sensitivity and feasibility relation between oncomelania densities and environmental factors. Then a mechanism model of predicting oncomelania distribution and densities is developed. The new model is validated with field data of Dongting Lake and the dynamic monitoring of schistosomiasis breeding in Dongting Lake region is presented. Finally, emphasis are placed on analyzing the potential of Sentinel satellites for schistosomiasis monitoring. The requirements of optical high resolution data on spectral resolution, spatial resolution, radiometric resolution/accuracy, as well as the requirements of synthetic aperture radar data on operation frequency, spatial resolution, polarization, radiometric accuracy, repeat cycle are presented and then compared with the parameters of Sentinel satellites. The parameters of Sentinel satellites are also compared with those of available remote satellites, such as Envisat, Landsat, whose data are being used for schistosomiasis monitoring. The application potential of Sentinel satellites for the schistosomiasis monitoring will be concluded in the end, which will benefit for the mission operation, model development, etc.

A Multi-Channel Method for Retrieving Surface Temperature for High-Emissivity Surfaces from Hyperspectral Thermal Infrared Images

PubMed Central

Zhong, Xinke; Labed, Jelila; Zhou, Guoqing; Shao, Kun; Li, Zhao-Liang

2015-01-01

The surface temperature (ST) of high-emissivity surfaces is an important parameter in climate systems. The empirical methods for retrieving ST for high-emissivity surfaces from hyperspectral thermal infrared (HypTIR) images require spectrally continuous channel data. This paper aims to develop a multi-channel method for retrieving ST for high-emissivity surfaces from space-borne HypTIR data. With an assumption of land surface emissivity (LSE) of 1, ST is proposed as a function of 10 brightness temperatures measured at the top of atmosphere by a radiometer having a spectral interval of 800–1200 cm−1 and a spectral sampling frequency of 0.25 cm−1. We have analyzed the sensitivity of the proposed method to spectral sampling frequency and instrumental noise, and evaluated the proposed method using satellite data. The results indicated that the parameters in the developed function are dependent on the spectral sampling frequency and that ST of high-emissivity surfaces can be accurately retrieved by the proposed method if appropriate values are used for each spectral sampling frequency. The results also showed that the accuracy of the retrieved ST is of the order of magnitude of the instrumental noise and that the root mean square error (RMSE) of the ST retrieved from satellite data is 0.43 K in comparison with the AVHRR SST product. PMID:26061199

Development of predictive mapping techniques for soil survey and salinity mapping

NASA Astrophysics Data System (ADS)

Elnaggar, Abdelhamid A.

Conventional soil maps represent a valuable source of information about soil characteristics, however they are subjective, very expensive, and time-consuming to prepare. Also, they do not include explicit information about the conceptual mental model used in developing them nor information about their accuracy, in addition to the error associated with them. Decision tree analysis (DTA) was successfully used in retrieving the expert knowledge embedded in old soil survey data. This knowledge was efficiently used in developing predictive soil maps for the study areas in Benton and Malheur Counties, Oregon and accessing their consistency. A retrieved soil-landscape model from a reference area in Harney County was extrapolated to develop a preliminary soil map for the neighboring unmapped part of Malheur County. The developed map had a low prediction accuracy and only a few soil map units (SMUs) were predicted with significant accuracy, mostly those shallow SMUs that have either a lithic contact with the bedrock or developed on a duripan. On the other hand, the developed soil map based on field data was predicted with very high accuracy (overall was about 97%). Salt-affected areas of the Malheur County study area are indicated by their high spectral reflectance and they are easily discriminated from the remote sensing data. However, remote sensing data fails to distinguish between the different classes of soil salinity. Using the DTA method, five classes of soil salinity were successfully predicted with an overall accuracy of about 99%. Moreover, the calculated area of salt-affected soil was overestimated when mapped using remote sensing data compared to that predicted by using DTA. Hence, DTA could be a very helpful approach in developing soil survey and soil salinity maps in more objective, effective, less-expensive and quicker ways based on field data.

Retrieval and Mapping of Heavy Metal Concentration in Soil Using Time Series Landsat 8 Imagery

NASA Astrophysics Data System (ADS)

Fang, Y.; Xu, L.; Peng, J.; Wang, H.; Wong, A.; Clausi, D. A.

2018-04-01

Heavy metal pollution is a critical global environmental problem which has always been a concern. Traditional approach to obtain heavy metal concentration relying on field sampling and lab testing is expensive and time consuming. Although many related studies use spectrometers data to build relational model between heavy metal concentration and spectra information, and then use the model to perform prediction using the hyperspectral imagery, this manner can hardly quickly and accurately map soil metal concentration of an area due to the discrepancies between spectrometers data and remote sensing imagery. Taking the advantage of easy accessibility of Landsat 8 data, this study utilizes Landsat 8 imagery to retrieve soil Cu concentration and mapping its distribution in the study area. To enlarge the spectral information for more accurate retrieval and mapping, 11 single date Landsat 8 imagery from 2013-2017 are selected to form a time series imagery. Three regression methods, partial least square regression (PLSR), artificial neural network (ANN) and support vector regression (SVR) are used to model construction. By comparing these models unbiasedly, the best model are selected to mapping Cu concentration distribution. The produced distribution map shows a good spatial autocorrelation and consistency with the mining area locations.

Total Electron Content Retrieved From L-Band Radiometers and Potential Improvements to the IGS Model

NASA Astrophysics Data System (ADS)

Soldo, Yan; Hong, Liang; El-Nimri, Salem; Le Vine, David M.

2018-04-01

In recent years, several L-band microwave instruments have been launched into Earth's orbit to measure soil moisture and ocean salinity (e.g., Soil Moisture and Ocean Salinity [SMOS], Aquarius, and Soil Moisture Active/Passive [SMAP]). As the microwave signal travels through the ionosphere, the polarization vector rotates (Faraday rotation) and it is possible to estimate the total electron content (TEC) along the path by measuring this change. A comparison is presented of the TEC retrieved from Aquarius and SMAP over the ocean with the values provided by the IGS (International Global Navigation Satellite System Service (GNSS)). The TEC retrieved from Aquarius and SMAP measurements show good agreement with each other and, on a global scale, are in agreement with the TEC provided by the IGS. However, there are cases in which the TEC from the two satellite sensors are in good agreement with each other but differ significantly from the IGS TEC. The comparison suggests that the L-band instruments are a reliable source of TEC over the ocean and could be a valuable supplementary source of TEC values that could be assimilated in the IGS models, especially over the ocean, where GNSS ground stations are sparse.

Surface-induced brightness temperature variations and their effects on detecting thin cirrus clouds using IR emission channels in the 8-12 microns region

NASA Technical Reports Server (NTRS)

Gao, Bo-Cai; Wiscombe, W. J.

1994-01-01

A method for detecting cirrus clouds in terms of brightness temperature differences between narrowbands at 8, 11, and 12 microns has been proposed by Ackerman et al. In this method, the variation of emissivity with wavelength for different surface targets was not taken into consideration. Based on state-of-the-art laboratory measurements of reflectance spectra of terrestrial materials by Salisbury and D'Aria, it is found that the brightness temperature differences between the 8- and 11-microns bands for soils, rocks, and minerals, and dry vegetation can vary between approximately -8 and +8 K due solely to surface emissivity variations. The large brightness temperature differences are sufficient to cause false detection of cirrus clouds from remote sensing data acquired over certain surface targets using the 8-11-12-microns method directly. It is suggested that the 8-11-12-microns method should be improved to include the surface emissivity effects. In addition, it is recommended that in the future the variation of surface emissivity with wavelength should be taken into account in algorithms for retrieving surface temperatures and low-level atmospheric temperature and water vapor profiles.

Constraining physics of very hot super-Earths with the James Webb Telescope. The case of CoRot-7b

NASA Astrophysics Data System (ADS)

Samuel, B.; Leconte, J.; Rouan, D.; Forget, F.; Léger, A.; Schneider, J.

2014-03-01

Context. Transit detection from space using ultra-precise photometry led to the first detection of super-Earths with solid surfaces: CoRot-7b and Kepler-10b. Because they lie only a few stellar radii from their host stars, these two rocky planets are expected to be extremely hot. Aims: Assuming that these planets are in a synchronous rotation state and receive strong stellar winds and fluxes, previous studies have suggested that they must be atmosphere-free and that a lava ocean is present on their hot dayside. In this article, we use several dedicated thermal models of the irradiated planet to study how observations with NIRSPEC on the James Webb Space Telescope (JWST) could further confirm and constrain, or reject the atmosphere-free lava ocean planet model for very hot super-Earths. Methods: Using CoRoT-7b as a working case, we explore the consequences on the phase-curve of a non tidal-locked rotation, with the presence/absence of an atmosphere, and for different values of the surface albedo. We then simulate future observations of the reflected light and thermal emission from CoRoT-7b with NIRSPEC-JWST and look for detectable signatures, such as time lag, of those peculiarities. We also study the possibility to retrieve the latitudinal surface temperature distribution from the observed SED. Results: We demonstrate that we should be able to constrain several parameters after observations of two orbits (42 h) thanks to the broad range of wavelengths accessible with JWST: i) the Bond albedo is retrieved to within ±0.03 in most cases. ii) The lag effect allows us to retrieve the rotation period within 3 h of a non phase-locked planet, whose rotation would be half the orbital period; for longer period, the accuracy is reduced. iii) Any spin period shorter than a limit in the range 30-800 h, depending on the thickness of the thermal layer in the soil, would be detected. iv) The presence of a thick gray atmosphere with a pressure of one bar, and a specific opacity higher than 10-5 m-2 kg-1 is detectable. v) With spectra up to 4.5 μm, the latitudinal temperature profile can be retrieved to within 30 K with a risk of a totally wrong solution in 5% of the cases. This last result is obtained for a signal-to-noise ratio around 5 per resel, which should be reached on Corot-7 after a total exposure time of ~70 h with NIRSPEC and only three hours on a V = 8 star. Conclusions: We conclude that it should thus be possible to distinguish the reference situation of a lava ocean with phase-locking and no atmosphere from other cases. In addition, obtaining the surface temperature map and the albedo brings important constraints on the nature or the physical state of the soil of hot super-Earths.

Measurements, interpretation and climate change effects evaluation for pyroclastic bare soil evaporation

NASA Astrophysics Data System (ADS)

Rianna, G.; Pagano, L.; Mercogliano, P.; Montesarchio, M.

2012-12-01

A physical model has been designed to achieve the following goals: to mark out the main features of the soil-atmosphere interaction; to quantify the water and energy fluxes through the soil surface during several years; to monitor the trends of the main variables regulating the hydraulic and thermal conditions. It is constituted by a soil volume (about 1mc) exposed to weather forcing; it is instrumented at four depths by sensors for measuring suction, water content and temperature. Therefore, a station allows knowing the meteo variables (rainfall, wind velocity and direction, air temperature, air pressure and relative humidity) and the two directly measurable components of the energy balance at the soil surface (net radiation and soil heat flux). Under the soil specimen, three shear beam load cells measure the soil weight and, hence, because the soil particles weight can be assumed as constant, the sample water storage. As first attempt, the soil surface is kept bare to avoid the complications led by overlapping processes induced by vegetation (interception, transpiration). Since May 2010, the soil involved in testing is pyroclastic material (silty sand) representative of air fall deposits covering a large part of Campania (South Italy) and erupted in the last 10,000 years by different volcanic centres (Phlegrean fields, Vesuvius). Because of their genesis, these soils show peculiar features: high porosity, low weight of soil unit volume, high water retention capacity; they cause an unusual hydraulic behaviour, halfway between coarse and fine soils in terms of saturated hydraulic permeability and mean slope of soil-water characteristic curve. In turn, these elements induce, among other things, that the currently adopted predictive approaches to estimate, for example, infiltration and evaporation processes are not directly suitable for these soils as the available parameters, even for grain sizes comparable to those of pyroclastic soils, fail to reproduce the observed trends. The measurements of water weight changes obtained during the dry days of the monitoring time span (2010-2012) furnish an adequately accurate estimate of daily evaporation values and so they can be used to calibrate the parameters of an approach for estimating evaporation method; in this work, the selected model is FAO-56 Dual Crop Coefficient Method for the case of bare soil (Allen et al., 2005) Firstly,the performance of method adopting the set of parameters recommended for soils with the same grain size is tested ; checking poor capability of this set parameters to reproduce the observed values, is retrieved the set for which is obtained best fitting between monitored and estimated values. The dataset covering the monitoring period and the approach so developed can be then used for a preliminary estimate of the effects of incoming climatic changes on evaporation processes in a specific Mediterranean context. Measured air temperatures and precipitation values are modified according their predicted climate anomalies for examined area (Giorgi and Lionello, 2008) in the XXI century; the effect of these variations on potential and actual evaporation through FAO approach are then investigated.

Benchmarking the performance of a land data assimilation system for agricultural drought monitoring

USDA-ARS?s Scientific Manuscript database

The application of land data assimilation systems to operational agricultural drought monitoring requires the development of (at least) three separate system sub-components: 1) a retrieval model to invert satellite-derived observations into soil moisture estimates, 2) a prognostic soil water balance...

Computer aided modeling of soil mix designs to predict characteristics and properties of stabilized road bases.

DOT National Transportation Integrated Search

2009-07-01

"Considerable data exists for soils that were tested and documented, both for native properties and : properties with pozzolan stabilization. While the data exists there was no database for the Nebraska : Department of Roads to retrieve this data for...

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).

Large-scale assessment of soil erosion in Africa: satellites help to jointly account for dynamic rainfall and vegetation cover

NASA Astrophysics Data System (ADS)

Vrieling, Anton; Hoedjes, Joost C. B.; van der Velde, Marijn

2015-04-01

Efforts to map and monitor soil erosion need to account for the erratic nature of the soil erosion process. Soil erosion by water occurs on sloped terrain when erosive rainfall and consequent surface runoff impact soils that are not well-protected by vegetation or other soil protective measures. Both rainfall erosivity and vegetation cover are highly variable through space and time. Due to data paucity and the relative ease of spatially overlaying geographical data layers into existing models like USLE (Universal Soil Loss Equation), many studies and mapping efforts merely use average annual values for erosivity and vegetation cover as input. We first show that rainfall erosivity can be estimated from satellite precipitation data. We obtained average annual erosivity estimates from 15 yr of 3-hourly TRMM Multi-satellite Precipitation Analysis (TMPA) data (1998-2012) using intensity-erosivity relationships. Our estimates showed a positive correlation (r = 0.84) with long-term annual erosivity values of 37 stations obtained from literature. Using these TMPA erosivity retrievals, we demonstrate the large interannual variability, with maximum annual erosivity often exceeding two to three times the mean value, especially in semi-arid areas. We then calculate erosivity at a 10-daily time-step and combine this with vegetation cover development for selected locations in Africa using NDVI - normalized difference vegetation index - time series from SPOT VEGETATION. Although we do not integrate the data at this point, the joint analysis of both variables stresses the need for joint accounting for erosivity and vegetation cover for large-scale erosion assessment and monitoring.

Extending the Deep Blue aerosol record from SeaWiFS and MODIS to NPP-VIIRS

NASA Technical Reports Server (NTRS)

Sayer, Andrew M.; Hsu, Nai-Yung Christina; Bettenhausen, Corey; Lee, Jaehwa

2015-01-01

Deep Blue expands AOD coverage to deserts and other bright surfaces. Using multiple similar satellite sensors enables us to obtain a long data record. The Deep Blue family consists of three separate aerosol optical depth (AOD) retrieval algorithms: 1. Bright Land: Surface reflectance database, BRDF correction. AOD retrieved separately at each of 412, 470/490, (650) nm. SSA retrieved for heavy dust events. 2. Dark Land: Spectral/directional surface reflectance relationship. AOD retrieved separately at 470/490 and 650 nm. 3. Water: Surface BRDF including glint, foam, underlight. Multispectral inversion (Not present in MODISdataset) All report the AOD at 550 nm, and Ångström exponent (AE).

Empirical Soil Moisture Estimation with Spaceborne L-band Polarimetric Radars: Aquarius, SMAP, and PALSAR-2

NASA Astrophysics Data System (ADS)

Burgin, M. S.; van Zyl, J. J.

2017-12-01

Traditionally, substantial ancillary data is needed to parametrize complex electromagnetic models to estimate soil moisture from polarimetric radar data. The Soil Moisture Active Passive (SMAP) baseline radar soil moisture retrieval algorithm uses a data cube approach, where a cube of radar backscatter values is calculated using sophisticated models. In this work, we utilize the empirical approach by Kim and van Zyl (2009) which is an optional SMAP radar soil moisture retrieval algorithm; it expresses radar backscatter of a vegetated scene as a linear function of soil moisture, hence eliminating the need for ancillary data. We use 2.5 years of L-band Aquarius radar and radiometer derived soil moisture data to determine two coefficients of a linear model function on a global scale. These coefficients are used to estimate soil moisture with 2.5 months of L-band SMAP and L-band PALSAR-2 data. The estimated soil moisture is compared with the SMAP Level 2 radiometer-only soil moisture product; the global unbiased RMSE of the SMAP derived soil moisture corresponds to 0.06-0.07 cm3/cm3. In this study, we leverage the three diverse L-band radar data sets to investigate the impact of pixel size and pixel heterogeneity on soil moisture estimation performance. Pixel sizes range from 100 km for Aquarius, over 3, 9, 36 km for SMAP, to 10m for PALSAR-2. Furthermore, we observe seasonal variation in the radar sensitivity to soil moisture which allows the identification and quantification of seasonally changing vegetation. Utilizing this information, we further improve the estimation performance. The research described in this paper is supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Copyright 2017. All rights reserved.

Using satellite data on meteorological and vegetation characteristics and soil surface humidity in the Land Surface Model for the vast territory of agricultural destination

NASA Astrophysics Data System (ADS)

Muzylev, Eugene; Startseva, Zoya; Uspensky, Alexander; Vasilenko, Eugene; Volkova, Elena; Kukharsky, Alexander

2017-04-01

The model of water and heat exchange between vegetation covered territory and atmosphere (LSM, Land Surface Model) for vegetation season has been developed to calculate soil water content, evapotranspiration, infiltration of water into the soil, vertical latent and sensible heat fluxes and other water and heat balances components as well as soil surface and vegetation cover temperatures and depth distributions of moisture and temperature. The LSM is suited for utilizing satellite-derived estimates of precipitation, land surface temperature and vegetation characteristics and soil surface humidity for each pixel. Vegetation and meteorological characteristics being the model parameters and input variables, correspondingly, have been estimated by ground observations and thematic processing measurement data of scanning radiometers AVHRR/NOAA, SEVIRI/Meteosat-9, -10 (MSG-2, -3) and MSU-MR/Meteor-M № 2. Values of soil surface humidity has been calculated from remote sensing data of scatterometers ASCAT/MetOp-A, -B. The case study has been carried out for the territory of part of the agricultural Central Black Earth Region of European Russia with area of 227300 km2 located in the forest-steppe zone for years 2012-2015 vegetation seasons. The main objectives of the study have been: - to built estimates of precipitation, land surface temperatures (LST) and vegetation characteristics from MSU-MR measurement data using the refined technologies (including algorithms and programs) of thematic processing satellite information matured on AVHRR and SEVIRI data. All technologies have been adapted to the area of interest; - to investigate the possibility of utilizing satellite-derived estimates of values above in the LSM including verification of obtained estimates and development of procedure of their inputting into the model. From the AVHRR data there have been built the estimates of precipitation, three types of LST: land skin temperature Tsg, air temperature at a level of vegetation cover (taken for vegetation temperature) Ta and efficient radiation temperature Ts.eff, as well as land surface emissivity E, normalized difference vegetation index NDVI, vegetation cover fraction B, and leaf area index LAI. The SEVIRI-based retrievals have included precipitation, LST Tls and Ta, E at daylight and nighttime, LAI (daily), and B. From the MSU-MR data there have been retrieved values of all the same characteristics as from the AVHRR data. The MSU-MR-based daily and monthly sums of precipitation have been calculated using the developed earlier and modified Multi Threshold Method (MTM) intended for the cloud detection and identification of its types around the clock as well as allocation of precipitation zones and determination of instantaneous maximum rainfall intensities for each pixel at that the transition from assessing rainfall intensity to estimating their daily values is a key element of the MTM. Measurement data from 3 IR MSU-MR channels (3.8, 11 i 12 μm) as well as their differences have been used in the MTM as predictors. Controlling the correctness of the MSU-MR-derived rainfall estimates has been carried out when comparing with analogous AVHRR- and SEVIRI-based retrievals and with precipitation amounts measured at the agricultural meteorological station of the study region. Probability of rainfall zones determination from the MSU-MR data, to match against the actual ones, has been 75-85% as well as for the AVHRR and SEVIRI data. The time behaviors of satellite-derived and ground-measured daily and monthly precipitation sums for vegetation season and yeaŗ correspondingly, have been in good agreement with each other although the first ones have been smoother than the latter. Discrepancies have existed for a number of local maxima for which satellite-derived precipitation estimates have been less than ground-measured values. It may be due to the different spatial scales of areal satellite-derived and point ground-based estimates. Some spatial displacement of the satellite-determined rainfall maxima and minima regarding to ground-based data can be explained by the discrepancy between the cloud location on satellite images and in reality at high angles of the satellite sightings and considerable altitudes of the cloud tops. Reliability of MSU-MR-derived rainfall estimates at each time step obtained using the MTM has been verified by comparing their values determined from the MSU-MR, AVHRR and SEVIRI measurements and distributed over the study area with similar estimates obtained by interpolation of ground observation data. The MSU-MR-derived estimates of temperatures Tsg, Ts.eff, and Ta have been obtained using computational algorithm developed on the base of the MTM and matured on AVHRR and SEVIRI data for the region under investigation. Since the apparatus MSU-MR is similar to radiometer AVHRR, the developed methods of satellite estimating Tsg, Ts.eff, and Ta from AVHRR data could be easily transferred to the MSU-MR data. Comparison of the ground-measured and MSU-MR-, AVHRR- and SEVIRI-derived LSTs has shown that the differences between all the estimates for the vast majority of observation terms have not exceed the RMSE of these quantities built from the AVHRR data. The similar conclusion has been also made from the results of building the time behavior of the MSU-MR-derived value of LAI for vegetation season. Satellite-based estimates of precipitation, LST, LAI and B have been utilized in the model with the help of specially developed procedures of replacing these values determined from observations at agricultural meteorological stations by their satellite-derived values taking into account spatial heterogeneity of their fields. Adequacy of such replacement has been confirmed by the results of comparing modeled and ground-measured values of soil moisture content W and evapotranspiration Ev. Discrepancies between the modeled and ground-measured values of W and Ev have been in the range of 10-15 and 20-25 %, correspondingly. It may be considered as acceptable result. Resulted products of the model calculations using satellite data have been spatial fields of W, Ev, vertical sensible and latent heat fluxes and other water and heat regime characteristics for the region of interest over the year 2012-2015 vegetation seasons. Thus, there has been shown the possibility of utilizing MSU-MR/Meteor-M №2 data jointly with those of other satellites in the LSM to calculate characteristics of water and heat regimes for the area under consideration. Besides the first trial estimations of the soil surface moisture from ASCAT scatterometers data for the study region have been obtained for the years 2014-2015 vegetation seasons, their comparison has been performed with the results of modeling for several agricultural meteorological stations of the region that has been carried out utilizing ground-based and satellite data, specific requirements for the obtained information have been formulated. To date, estimates of surface moisture built from ASCAT data can be used for the selection of the model soil parameter values and the initial soil moisture conditions for the vegetation season.

Integrating NASA Satellite Data Into USDA World Agricultural Outlook Board Decision Making Environment To Improve Agricultural Estimates

NASA Technical Reports Server (NTRS)

Teng, William; Shannon, Harlan; deJeu, Richard; Kempler, Steve

2012-01-01

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. The goal of the current project is to improve WAOB estimates by integrating NASA satellite precipitation and soil moisture observations into WAOB's decision making environment. Precipitation (Level 3 gridded) is from the TRMM Multi-satellite Precipitation Analysis (TMPA). Soil moisture (Level 2 swath and Level 3 gridded) is generated by the Land Parameter Retrieval Model (LPRM) and operationally produced by the NASA Goddard Earth Sciences Data and Information Services Center (GBS DISC). A root zone soil moisture (RZSM) product is also generated, via assimilation of the Level 3 LPRM data by a land surface model (part of a related project). Data services to be available for these products include GeoTIFF, GDS (GrADS Data Server), WMS (Web Map Service), WCS (Web Coverage Service), and NASA Giovanni. Project benchmarking is based on retrospective analyses of WAOB analog year comparisons. The latter are between a given year and historical years with similar weather patterns and estimated crop yields. An analog index (AI) was developed to introduce a more rigorous, statistical approach for identifying analog years. Results thus far show that crop yield estimates derived from TMPA precipitation data are closer to measured yields than are estimates derived from surface-based precipitation measurements. Work is continuing to include LPRM surface soil moisture data and model-assimilated RZSM.

Inference of precipitation through thermal infrared measurements of soil moisture

NASA Technical Reports Server (NTRS)

Wetzel, P. J.; Atlas, D.

1981-01-01

The physics of microwave radiative transfer is well understood so that causal models can be assembled which relate the observed brightness temperatures to assumed distributions of hydrometeors (both liquid and ice), non-precipitating clouds, water vapor oxygen, and surface conditions. Present models assume a Marshall Palmer size distribution of liquid hydrometers from the surface to the freezing level (near the 0 C isotherm) and a variable thickness of frozen hydrometeors above that with various reasonable distribution of the other relevant constituents. The validity of such models is discussed. All uncertainties in the rain rate retrieval algorithms can be expressed in terms of specific model uncertainties which can be addressed through appropriate measurements. Those factors which must be known to achieve umambiguous results can be identified so that rainfall measuring algorithms can be developed and improved. The emissivity of the underlying surface significantly affects the contrast that may be measured between areas covered by rain and those which are dry. Sensing strategies for measuring rain over the ocean and rain over land are reviewed.

NASA CYGNSS Satellite Measurements and Applications

NASA Astrophysics Data System (ADS)

Murray, J. J.; Ruf, C. S.; Baker, N. L.; Green, D. S.; Stough, T.

2017-12-01

NASA launched the CYGNSS mission 15 December 2016 which comprises a constellation of eight satellites flying in a low inclination (tropical) Earth orbit. Each satellite measures up to four independent GPS signals scattered by the ocean, to obtain surface roughness, near surface wind speed, and air-sea latent heat flux. Utilizing such a large number of satellites, these measurements which are uniquely able to penetrate clouds and heavy precipitation, allows CYGNSS to frequently sample tropical cyclone intensification and of the diurnal cycle of winds. Additionally, data retrievals over land have proven effective to map surface water and soil moisture. Engineering commissioning of the constellation was successfully completed in March 2017 and the mission is now conducting science measurements. An overview of the CYGNSS system, mission and measurement concept will be presented, together with highlights of early on-orbit performance. Scientific results obtained during the 2017 hurricane season and featured at the NASA CYGNSS Applications Workshop in Monterey, CA 31 October - 2 November 2, 2017 will also be presented.

Surface Reflectance of Mars Observed by CRISM-MRO: 1. Multi-angle Approach for Retrieval of Surface Reflectance from CRISM Observations (mars-reco)

NASA Technical Reports Server (NTRS)

Ceamanos, Xavier; Doute, S.; Fernando, J.; Pinet, P.; Lyapustin, A.

2013-01-01

This article addresses the correction for aerosol effects in near-simultaneous multiangle observations acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter. In the targeted mode, CRISM senses the surface of Mars using 11 viewing angles, which allow it to provide unique information on the scattering properties of surface materials. In order to retrieve these data, however, appropriate strategies must be used to compensate the signal sensed by CRISM for aerosol contribution. This correction is particularly challenging as the photometric curve of these suspended particles is often correlated with the also anisotropic photometric curve of materials at the surface. This article puts forward an innovative radiative transfer based method named Multi-angle Approach for Retrieval of Surface Reflectance from CRISM Observations (MARS-ReCO). The proposed method retrieves photometric curves of surface materials in reflectance units after removing aerosol contribution. MARS-ReCO represents a substantial improvement regarding previous techniques as it takes into consideration the anisotropy of the surface, thus providing more realistic surface products. Furthermore, MARS-ReCO is fast and provides error bars on the retrieved surface reflectance. The validity and accuracy of MARS-ReCO is explored in a sensitivity analysis based on realistic synthetic data. According to experiments, MARS-ReCO provides accurate results (up to 10 reflectance error) under favorable acquisition conditions. In the companion article, photometric properties of Martian materials are retrieved using MARS-ReCO and validated using in situ measurements acquired during the Mars Exploration Rovers mission.

Attitude angle effects on Nimbus-7 Scanning Multichannel Microwave Radiometer radiances and geophysical parameter retrievals

NASA Technical Reports Server (NTRS)

Macmillan, Daniel S.; Han, Daesoo

1989-01-01

The attitude of the Nimbus-7 spacecraft has varied significantly over its lifetime. A summary of the orbital and long-term behavior of the attitude angles and the effects of attitude variations on Scanning Multichannel Microwave Radiometer (SMMR) brightness temperatures is presented. One of the principal effects of these variations is to change the incident angle at which the SMMR views the Earth's surface. The brightness temperatures depend upon the incident angle sensitivities of both the ocean surface emissivity and the atmospheric path length. Ocean surface emissivity is quite sensitive to incident angle variation near the SMMR incident angle, which is about 50 degrees. This sensitivity was estimated theoretically for a smooth ocean surface and no atmosphere. A 1-degree increase in the angle of incidence produces a 2.9 C increase in the retrieved sea surface temperature and a 5.7 m/sec decrease in retrieved sea surface wind speed. An incident angle correction is applied to the SMMR radiances before using them in the geophysical parameter retrieval algorithms. The corrected retrieval data is compared with data obtained without applying the correction.

SMAP radiometer-based soil moisture products status and validation

USDA-ARS?s Scientific Manuscript database

The NASA Soil Moisture Active Passive (SMAP) mission has been providing L-band brightness temperature measurements of the globe since 2015. These are used with retrieval algorithms to generate global products every 2-3 days. SMAP has recently implemented several new products to enhance both the spat...

Active–passive soil moisture retrievals during the SMAP validation experiment 2012

USDA-ARS?s Scientific Manuscript database

The goal of this study is to assess the performance of the active–passive algorithm for the NASA Soil Moisture Active Passive mission (SMAP) using airborne and ground observations from a field campaign. The SMAP active–passive algorithm disaggregates the coarse-resolution radiometer brightness tempe...

Error sources in passive and active microwave satellite soil moisture over Australia

USDA-ARS?s Scientific Manuscript database

Development of a long-term climate record of soil moisture (SM) involves combining historic and present satellite-retrieved SM data sets. This in turn requires a consistent characterization and deep understanding of the systematic differences and errors in the individual data sets, which vary due to...

Intercomparison of MODIS Albedo Retrievals and In Situ Measurements Across the Global FLUXNET Network

NASA Technical Reports Server (NTRS)

Cescatti, Alessandro; Marcolla, Barbara; Vannan, Suresh K. Santhana; Pan, Jerry Yun; Roman, Miguel O.; Yang, Xiaoyuan; Ciais, Philippe; Cook, Robert B.; Law, Beverly E.; Matteucci, Girogio;

Validation and Scaling of Soil Moisture in a Semi-Arid Environment: SMAP Validation Experiment 2015 (SMAPVEX15)

NASA Technical Reports Server (NTRS)

Colliander, Andreas; Cosh, Michael H.; Misra, Sidharth; Jackson, Thomas J.; Crow, Wade T.; Chan, Steven; Bindlish, Rajat; Chae, Chun; Holifield Collins, Chandra; Yueh, Simon H.

2017-01-01

The NASA SMAP (Soil Moisture Active Passive) mission conducted the SMAP Validation Experiment 2015 (SMAPVEX15) in order to support the calibration and validation activities of SMAP soil moisture data products. The main goals of the experiment were to address issues regarding the spatial disaggregation methodologies for improvement of soil moisture products and validation of the in situ measurement upscaling techniques. To support these objectives high-resolution soil moisture maps were acquired with the airborne PALS (Passive Active L-band Sensor) instrument over an area in southeast Arizona that includes the Walnut Gulch Experimental Watershed (WGEW), and intensive ground sampling was carried out to augment the permanent in situ instrumentation. The objective of the paper was to establish the correspondence and relationship between the highly heterogeneous spatial distribution of soil moisture on the ground and the coarse resolution radiometer-based soil moisture retrievals of SMAP. The high-resolution mapping conducted with PALS provided the required connection between the in situ measurements and SMAP retrievals. The in situ measurements were used to validate the PALS soil moisture acquired at 1-km resolution. Based on the information from a dense network of rain gauges in the study area, the in situ soil moisture measurements did not capture all the precipitation events accurately. That is, the PALS and SMAP soil moisture estimates responded to precipitation events detected by rain gauges, which were in some cases not detected by the in situ soil moisture sensors. It was also concluded that the spatial distribution of the soil moisture resulted from the relatively small spatial extents of the typical convective storms in this region was not completely captured with the in situ stations. After removing those cases (approximately10 of the observations) the following metrics were obtained: RMSD (root mean square difference) of0.016m3m3 and correlation of 0.83. The PALS soil moisture was also compared to SMAP and in situ soil moisture at the 36-km scale, which is the SMAP grid size for the standard product. PALS and SMAP soil moistures were found to be very similar owing to the close match of the brightness temperature measurements and the use of a common soil moisture retrieval algorithm. Spatial heterogeneity, which was identified using the high-resolution PALS soil moisture and the intensive ground sampling, also contributed to differences between the soil moisture estimates. In general, discrepancies found between the L-band soil moisture estimates and the 5-cm depth in situ measurements require methodologies to mitigate the impact on their interpretations in soil moisture validation and algorithm development. Specifically, the metrics computed for the SMAP radiometer-based soil moisture product over WGEW will include errors resulting from rainfall, particularly during the monsoon season when the spatial distribution of soil moisture is especially heterogeneous.

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.

Ground based measurements on reflectance towards validating atmospheric correction algorithms on IRS-P6 AWiFS data

NASA Astrophysics Data System (ADS)

Rani Sharma, Anu; Kharol, Shailesh Kumar; Kvs, Badarinath; Roy, P. S.

In Earth observation, the atmosphere has a non-negligible influence on the visible and infrared radiation which is strong enough to modify the reflected electromagnetic signal and at-target reflectance. Scattering of solar irradiance by atmospheric molecules and aerosol generates path radiance, which increases the apparent surface reflectance over dark surfaces while absorption by aerosols and other molecules in the atmosphere causes loss of brightness to the scene, as recorded by the satellite sensor. In order to derive precise surface reflectance from satellite image data, it is indispensable to apply the atmospheric correction which serves to remove the effects of molecular and aerosol scattering. In the present study, we have implemented a fast atmospheric correction algorithm to IRS-P6 AWiFS satellite data which can effectively retrieve surface reflectance under different atmospheric and surface conditions. The algorithm is based on MODIS climatology products and simplified use of Second Simulation of Satellite Signal in Solar Spectrum (6S) radiative transfer code, which is used to generate look-up-tables (LUTs). The algorithm requires information on aerosol optical depth for correcting the satellite dataset. The proposed method is simple and easy to implement for estimating surface reflectance from the at sensor recorded signal, on a per pixel basis. The atmospheric correction algorithm has been tested for different IRS-P6 AWiFS False color composites (FCC) covering the ICRISAT Farm, Patancheru, Hyderabad, India under varying atmospheric conditions. Ground measurements of surface reflectance representing different land use/land cover, i.e., Red soil, Chick Pea crop, Groundnut crop and Pigeon Pea crop were conducted to validate the algorithm and found a very good match between surface reflectance and atmospherically corrected reflectance for all spectral bands. Further, we aggregated all datasets together and compared the retrieved AWiFS reflectance with aggregated ground measurements which showed a very good correlation of 0.96 in all four spectral bands (i.e. green, red, NIR and SWIR). In order to quantify the accuracy of the proposed method in the estimation of the surface reflectance, the root mean square error (RMSE) associated to the proposed method was evaluated. The analysis of the ground measured versus retrieved AWiFS reflectance yielded smaller RMSE values in case of all four spectral bands. EOS TERRA/AQUA MODIS derived AOD exhibited very good correlation of 0.92 and the data sets provides an effective means for carrying out atmospheric corrections in an operational way. Keywords: Atmospheric correction, 6S code, MODIS, Spectroradiometer, Sun-Photometer

Cloud characterization and clear-sky correction from Landsat-7

USGS Publications Warehouse

Cahalan, Robert F.; Oreopoulos, L.; Wen, G.; Marshak, S.; Tsay, S. -C.; DeFelice, Tom

2001-01-01

Landsat, with its wide swath and high resolution, fills an important mesoscale gap between atmospheric variations seen on a few kilometer scale by local surface instrumentation and the global view of coarser resolution satellites such as MODIS. In this important scale range, Landsat reveals radiative effects on the few hundred-meter scale of common photon mean-free-paths, typical of scattering in clouds at conservative (visible) wavelengths, and even shorter mean-free-paths of absorptive (near-infrared) wavelengths. Landsat also reveals shadowing effects caused by both cloud and vegetation that impact both cloudy and clear-sky radiances. As a result, Landsat has been useful in development of new cloud retrieval methods and new aerosol and surface retrievals that account for photon diffusion and shadowing effects. This paper discusses two new cloud retrieval methods: the nonlocal independent pixel approximation (NIPA) and the normalized difference nadir radiance method (NDNR). We illustrate the improvements in cloud property retrieval enabled by the new low gain settings of Landsat-7 and difficulties found at high gains. Then, we review the recently developed “path radiance” method of aerosol retrieval and clear-sky correction using data from the Department of Energy Atmospheric Radiation Measurement (ARM) site in Oklahoma. Nearby clouds change the solar radiation incident on the surface and atmosphere due to indirect illumination from cloud sides. As a result, if clouds are nearby, this extra side-illumination causes clear pixels to appear brighter, which can be mistaken for extra aerosol or higher surface albedo. Thus, cloud properties must be known in order to derive accurate aerosol and surface properties. A three-dimensional (3D) Monte Carlo (MC) radiative transfer simulation illustrates this point and suggests a method to subtract the cloud effect from aerosol and surface retrievals. The main conclusion is that cloud, aerosol, and surface retrievals are linked and must be treated as a combined system. Landsat provides the range of scales necessary to observe the 3D cloud radiative effects that influence joint surface-atmospheric retrievals.

Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results.

PubMed

Mei, Linlu; Rozanov, Vladimir; Vountas, Marco; Burrows, John P; Levy, Robert C; Lotz, Wolfhardt

2017-08-01

The MEdium Resolution Imaging Spectrometer (MERIS) instrument on board ESA Envisat made measurements from 2002 to 2012. Although MERIS was limited in spectral coverage, accurate Aerosol Optical Thickness (AOT) from MERIS data are retrieved by using appropriate additional information. We introduce a new AOT retrieval algorithm for MERIS over land surfaces, referred to as eXtensible Bremen AErosol Retrieval (XBAER). XBAER is similar to the "dark-target" (DT) retrieval algorithm used for Moderate-resolution Imaging Spectroradiometer (MODIS), in that it uses a lookup table (LUT) to match to satellite-observed reflectance and derive the AOT. Instead of a global parameterization of surface spectral reflectance, XBAER uses a set of spectral coefficients to prescribe surface properties. In this manner, XBAER is not limited to dark surfaces (vegetation) and retrieves AOT over bright surface (desert, semiarid, and urban areas). Preliminary validation of the MERIS-derived AOT and the ground-based Aerosol Robotic Network (AERONET) measurements yield good agreement, the resulting regression equation is y = (0.92 × ± 0.07) + (0.05 ± 0.01) and Pearson correlation coefficient of R = 0.78. Global monthly means of AOT have been compared from XBAER, MODIS and other satellite-derived datasets.

Reliable Method for Assessing Seed Germination, Dormancy, and Mortality under Field Conditions.

PubMed

Pace, Brian A; Alexander, Helen M; Emry, D Jason; Mercer, Kristin L

2016-11-06

We describe techniques for approximating seed bank dynamics over time using Helianthus annuus as an example study species. Strips of permeable polyester fabric and glue can be folded and glued to construct a strip of compartments that house seeds and identifying information, while allowing contact with soil leachate, water, microorganisms, and ambient temperature. Strips may be constructed with a wide range of compartment numbers and sizes and allow the researcher to house a variety of genotypes within a single species, different species, or seeds that have experienced different treatments. As opposed to individual seed packets, strips are more easily retrieved as a unit. While replicate packets can be included within a strip, different strips can act as blocks or can be retrieved at different times for observation of seed behavior over time. We used a high temperature glue gun to delineate compartments and sealed the strips once the seed and tags identifying block and removal times were inserted. The seed strips were then buried in the field at the desired depth, with the location marked for later removal. Burrowing animal predators were effectively excluded by use of a covering of metal mesh hardware cloth on the soil surface. After the selected time interval for burial, strips were dug up and seeds were assessed for germination, dormancy and mortality. While clearly dead seeds can often be distinguished from ungerminated living ones by eye, dormant seeds were conclusively identified using a standard Tetrazolium chloride colorimetric test for seed viability.

Derived Land Surface Emissivity From Suomi NPP CrIS

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Larar, Allen M.; Liu, Xu

2012-01-01

Presented here is the land surface IR spectral emissivity retrieved from the Cross-track Infrared Sounder (CrIS) measurements. The CrIS is aboard the Suomi National Polar-orbiting Partnership (NPP) satellite launched on October 28, 2011. We describe the retrieval algorithm, demonstrate the surface emissivity retrieved with CrIS measurements, and inter-comparison with the Infrared Atmospheric Sounding Interferometer (IASI) emissivity. We also demonstrate that surface emissivity from satellite measurements can be used in assistance of monitoring global surface climate change, as a long-term measurement of IASI and CrIS will be provided by the series of EUMETSAT MetOp and US Joint Polar Satellite System (JPSS) satellites. Monthly mean surface properties are produced using last 5-year IASI measurements. A temporal variation indicates seasonal diversity and El Nino/La Nina effects not only shown on the water but also on the land. Surface spectral emissivity and skin temperature from current and future operational satellites can be utilized as a means of long-term monitoring of the Earth's environment. CrIS spectral emissivity are retrieved and compared with IASI. The difference is small and could be within expected retrieval error; however it is under investigation.

Azimuthal Signature of Coincidental Brightness Temperature and Normalized Radar Cross-Section Obtained Using Airborne PALS Instrument

NASA Technical Reports Server (NTRS)

Colliander, Andreas; Kim, Seungbum; Yueh, Simon; Cosh, Mike; Jackson, Tom; Njoku, Eni

2010-01-01

Coincidental airborne brightness temperature (TB) and normalized radar-cross section (NRCS) measurements were carried out with the PALS (Passive and Active L- and S-band) instrument in the SMAPVEX08 (SMAP Validation Experiment 2008) field campaign. This paper describes results obtained from a set of flights which measured a field in 45(sup o) steps over the azimuth angle. The field contained mature soy beans with distinct row structure. The measurement shows that both TB and NRCS experience modulation effects over the azimuth as expected based on the theory. The result is useful in development and validation of land surface parameter forward models and retrieval algorithms, such as the soil moisture algorithm for NASA's SMAP (Soil Moisture Active and Passive) mission. Although the footprint of the SMAP will not be sensitive to the small resolution scale effects as the one presented in this paper, it is nevertheless important to understand the effects at smaller scale.

Enhanced Deep Blue Aerosol Retrieval Algorithm: The Second Generation

NASA Technical Reports Server (NTRS)

Hsu, N. C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A. M.; Hansell, R.; Seftor, C. S.; Huang, J.; Tsay, S.-C.

2013-01-01

The aerosol products retrieved using the MODIS collection 5.1 Deep Blue algorithm have provided useful information about aerosol properties over bright-reflecting land surfaces, such as desert, semi-arid, and urban regions. However, many components of the C5.1 retrieval algorithm needed to be improved; for example, the use of a static surface database to estimate surface reflectances. This is particularly important over regions of mixed vegetated and non- vegetated surfaces, which may undergo strong seasonal changes in land cover. In order to address this issue, we develop a hybrid approach, which takes advantage of the combination of pre-calculated surface reflectance database and normalized difference vegetation index in determining the surface reflectance for aerosol retrievals. As a result, the spatial coverage of aerosol data generated by the enhanced Deep Blue algorithm has been extended from the arid and semi-arid regions to the entire land areas.

Retrieval with Infrared Atmospheric Sounding Interferometer and Validation during JAIVEx

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Liu, Xu; Larar, Allen M.; Smith, William L.; Taylor, Jonathan P.; Schluessel, Peter; Strow, L. Larrabee; Mango, Stephen A.

2008-01-01

A state-of-the-art IR-only retrieval algorithm has been developed with an all-season-global EOF Physical Regression and followed by 1-D Var. Physical Iterative Retrieval for IASI, AIRS, and NAST-I. The benefits of this retrieval are to produce atmospheric structure with a single FOV horizontal resolution (approx. 15 km for IASI and AIRS), accurate profiles above the cloud (at least) or down to the surface, surface parameters, and/or cloud microphysical parameters. Initial case study and validation indicates that surface, cloud, and atmospheric structure (include TBL) are well captured by IASI and AIRS measurements. Coincident dropsondes during the IASI and AIRS overpasses are used to validate atmospheric conditions, and accurate retrievals are obtained with an expected vertical resolution. JAIVEx has provided the data needed to validate the retrieval algorithm and its products which allows us to assess the instrument ability and/or performance. Retrievals with global coverage are under investigation for detailed retrieval assessment. It is greatly desired that these products be used for testing the impact on Atmospheric Data Assimilation and/or Numerical Weather Prediction.

Atmospheric, Cloud, and Surface Parameters Retrieved from Satellite Ultra-spectral Infrared Sounder Measurements

NASA Technical Reports Server (NTRS)

Zhou, Daniel K.; Liu, Xu; Larar, Allen M.; Smith, William L.; Yang, Ping; Schluessel, Peter; Strow, Larrabee

2007-01-01

An advanced retrieval algorithm with a fast radiative transfer model, including cloud effects, is used for atmospheric profile and cloud parameter retrieval. This physical inversion scheme has been developed, dealing with cloudy as well as cloud-free radiance observed with ultraspectral infrared sounders, to simultaneously retrieve surface, atmospheric thermodynamic, and cloud microphysical parameters. A fast radiative transfer model, which applies to the clouded atmosphere, is used for atmospheric profile and cloud parameter retrieval. A one-dimensional (1-d) variational multivariable inversion solution is used to improve an iterative background state defined by an eigenvector-regression-retrieval. The solution is iterated in order to account for non-linearity in the 1-d variational solution. This retrieval algorithm is applied to the MetOp satellite Infrared Atmospheric Sounding Interferometer (IASI) launched on October 19, 2006. IASI possesses an ultra-spectral resolution of 0.25 cm(exp -1) and a spectral coverage from 645 to 2760 cm(exp -1). Preliminary retrievals of atmospheric soundings, surface properties, and cloud optical/microphysical properties with the IASI measurements are obtained and presented.

MAPIR: An Airborne Polarmetric Imaging Radiometer in Support of Hydrologic Satellite Observations

NASA Technical Reports Server (NTRS)

Laymon, C.; Al-Hamdan, M.; Crosson, W.; Limaye, A.; McCracken, J.; Meyer, P.; Richeson, J.; Sims, W.; Srinivasan, K.; Varnevas, K.

2010-01-01

In this age of dwindling water resources and increasing demands, accurate estimation of water balance components at every scale is more critical to end users than ever before. Several near-term Earth science satellite missions are aimed at global hydrologic observations. The Marshall Airborne Polarimetric Imaging Radiometer (MAPIR) is a dual beam, dual angle polarimetric, scanning L band passive microwave radiometer system developed by the Observing Microwave Emissions for Geophysical Applications (OMEGA) team at MSFC to support algorithm development and validation efforts in support of these missions. MAPIR observes naturally-emitted radiation from the ground primarily for remote sensing of land surface brightness temperature from which we can retrieve soil moisture and possibly surface or water temperature and ocean salinity. MAPIR has achieved Technical Readiness Level 6 with flight heritage on two very different aircraft, the NASA P-3B, and a Piper Navajo.

[The progress in retrieving land surface temperature based on thermal infrared and microwave remote sensing technologies].

PubMed

Zhang, Jia-Hua; Li, Xin; Yao, Feng-Mei; Li, Xian-Hua

2009-08-01

Land surface temperature (LST) is an important parameter in the study on the exchange of substance and energy between land surface and air for the land surface physics process at regional and global scales. Many applications of satellites remotely sensed data must provide exact and quantificational LST, such as drought, high temperature, forest fire, earthquake, hydrology and the vegetation monitor, and the models of global circulation and regional climate also need LST as input parameter. Therefore, the retrieval of LST using remote sensing technology becomes one of the key tasks in quantificational remote sensing study. Normally, in the spectrum bands, the thermal infrared (TIR, 3-15 microm) and microwave bands (1 mm-1 m) are important for retrieval of the LST. In the present paper, firstly, several methods for estimating the LST on the basis of thermal infrared (TIR) remote sensing were synthetically reviewed, i. e., the LST measured with an ground-base infrared thermometer, the LST retrieval from mono-window algorithm (MWA), single-channel algorithm (SCA), split-window techniques (SWT) and multi-channels algorithm(MCA), single-channel & multi-angle algorithm and multi-channels algorithm & multi-angle algorithm, and retrieval method of land surface component temperature using thermal infrared remotely sensed satellite observation. Secondly, the study status of land surface emissivity (epsilon) was presented. Thirdly, in order to retrieve LST for all weather conditions, microwave remotely sensed data, instead of thermal infrared data, have been developed recently, and the LST retrieval method from passive microwave remotely sensed data was also introduced. Finally, the main merits and shortcomings of different kinds of LST retrieval methods were discussed, respectively.

Time Changes of the European Gravity Field from GRACE: A Comparison with Ground Measurements from Superconducting Gravimeters and with Hydrology Model Predictions

NASA Technical Reports Server (NTRS)

Hinderer, J.; Lemoine, Frank G.; Crossley, D.; Boy, J.-P.

2004-01-01

We investigate the time-variable gravity changes in Europe retrieved from the initial GRACE monthly solutions spanning a 18 month duration from April 2002 to October 2003. Gravity anomaly maps are retrieved in Central Europe from the monthly satellite solutions we compare the fields according to various truncation levels (typically between degree 10 and 20) of the initial fields (expressed in spherical harmonics to degree 120). For these different degrees, an empirical orthogonal function (EOF) decomposition of the time-variable gravity field leads us to its main spatial and temporal characteristics. We show that the dominant signal is found to be annual with an amplitude and a phase both in agreement with predictions in Europe modeled using snow and soil-moisture variations from recent hydrology models. We compare these GRACE gravity field changes to surface gravity observations from 6 superconducting gravimeters of the GGP (Global Geodynamics Project) European sub-network, with a special attention to loading corrections. Initial results suggest that all 3 data sets (GRACE, hydrology and GGP) are responding to annual changes in near-surface water in Europe of a few microGal (at length scales of approx.1000 km) that show a high value in winter and a summer minimum. We also point out that the GRACE gravity field evolution seems to indicate that there is a trend in gravity between summer 2002 and summer 2003 which can be related to the 2003 heatwave in Europe and its hydrological consequences (drought). Despite the limited time span of our analysis and the uncertainties in retrieving a regional solution from the network of gravimeters, the calibration and validation aspects of the GRACE data processing based on the annual hydrology cycle in Europe are in progress.

Analytical results and sample locations of reanalyzed NURE stream-sediment and soil samples for the Humboldt River basin mineral-environmental assessment, northern Nevada

USGS Publications Warehouse

Folger, H. W.

2000-01-01

The U.S. Geological Survey (USGS), in cooperation with the Bureau of Land Management (BLM), began a study in 1996 to describe to the geochemistry of the Humboldt River Basin. The principal sample media evaluated are stream-sediment and soil samples retrieved from the National Uranium Resource Evaluation (NURE) archives located in Denver, Colorado. Samples were retrieved from the Wells, McDermitt, Vya, Lovelock, Winnemucca, Elko, Ely, Millett, Reno, and Tonopah 1? x 2? quadrangles in northern Nevada. The data are appropriate for large-scale reconnaissance resource evaluations and landscape geochemical-geoenvironmental evaluations. The analytical results are presented in this report.

Retrieval of Compositional End-Members From Mars Exploration Rover Opportunity Observations in a Soil-Filled Fracture in Marathon Valley, Endeavour Crater Rim

NASA Astrophysics Data System (ADS)

Stein, N. T.; Arvidson, R. E.; O'Sullivan, J. A.; Catalano, J. G.; Guinness, E. A.; Politte, D. V.; Gellert, R.; VanBommel, S. J.

2018-01-01

The Opportunity rover investigated a gentle swale on the rim of Endeavour crater called Marathon Valley where a series of bright planar outcrops are cut into polygons by fractures. A wheel scuff performed on one of the soil-filled fracture zones revealed the presence of three end-members identified on the basis of Pancam multispectral imaging observations covering 0.4 to 1 μm: red and dark pebbles, and a bright soil clod. Multiple overlapping Alpha Particle X-ray Spectrometer (APXS) measurements were collected on three targets within the scuff zone. The field of view of each APXS measurement contained various proportions of the Pancam-based end-members. Application of a log maximum likelihood method for retrieving the composition of the end-members using the 10 APXS measurements shows that the dark pebble end-member is compositionally similar to average Mars soil, with slightly elevated S and Fe. In contrast, the red pebble end-member exhibits enrichments in Al and Si and is depleted in Fe and Mg relative to average Mars soil. The soil clod end-member is enriched in Mg, S, and Ni. Thermodynamic modeling of the soil clod end-member composition indicates a dominance of sulfate minerals. We hypothesize that acidic fluids in fractures leached and oxidized the basaltic host rock, forming the red pebbles, and then evaporated to leave behind sulfate-cemented soil.

Petroleum Contamination and Plant Identity Influence Soil and Root Microbial Communities While AMF Spores Retrieved from the Same Plants Possess Markedly Different Communities

PubMed Central

Iffis, Bachir; St-Arnaud, Marc; Hijri, Mohamed

2017-01-01

Phytoremediation is a promising in situ green technology based on the use of plants to cleanup soils from organic and inorganic pollutants. Microbes, particularly bacteria and fungi, that closely interact with plant roots play key roles in phytoremediation processes. In polluted soils, the root-associated microbes contribute to alleviation of plant stress, improve nutrient uptake and may either degrade or sequester a large range of soil pollutants. Therefore, improving the efficiency of phytoremediation requires a thorough knowledge of the microbial diversity living in the rhizosphere and in close association with plant roots in both the surface and the endosphere. This study aims to assess fungal ITS and bacterial 16S rRNA gene diversity using high-throughput sequencing in rhizospheric soils and roots of three plant species (Solidago canadensis, Populus balsamifera, and Lycopus europaeus) growing spontaneously in three petroleum hydrocarbon polluted sedimentation basins. Microbial community structures of rhizospheric soils and roots were compared with those of microbes associated with arbuscular mycorrhizal fungal (AMF) spores to determine the links between the root and rhizosphere communities and those associated with AMF. Our results showed a difference in OTU richness and community structure composition between soils and roots for both bacteria and fungi. We found that petroleum hydrocarbon pollutant (PHP) concentrations have a significant effect on fungal and bacterial community structures in both soils and roots, whereas plant species identity showed a significant effect only on the roots for bacteria and fungi. Our results also showed that the community composition of bacteria and fungi in soil and roots varied from those associated with AMF spores harvested from the same plants. This let us to speculate that in petroleum hydrocarbon contaminated soils, AMF may release chemical compounds by which they recruit beneficial microbes to tolerate or degrade the PHPs present in the soil. PMID:28848583

Petroleum Contamination and Plant Identity Influence Soil and Root Microbial Communities While AMF Spores Retrieved from the Same Plants Possess Markedly Different Communities.

PubMed

Iffis, Bachir; St-Arnaud, Marc; Hijri, Mohamed

2017-01-01

Phytoremediation is a promising in situ green technology based on the use of plants to cleanup soils from organic and inorganic pollutants. Microbes, particularly bacteria and fungi, that closely interact with plant roots play key roles in phytoremediation processes. In polluted soils, the root-associated microbes contribute to alleviation of plant stress, improve nutrient uptake and may either degrade or sequester a large range of soil pollutants. Therefore, improving the efficiency of phytoremediation requires a thorough knowledge of the microbial diversity living in the rhizosphere and in close association with plant roots in both the surface and the endosphere. This study aims to assess fungal ITS and bacterial 16S rRNA gene diversity using high-throughput sequencing in rhizospheric soils and roots of three plant species ( Solidago canadensis, Populus balsamifera , and Lycopus europaeus ) growing spontaneously in three petroleum hydrocarbon polluted sedimentation basins. Microbial community structures of rhizospheric soils and roots were compared with those of microbes associated with arbuscular mycorrhizal fungal (AMF) spores to determine the links between the root and rhizosphere communities and those associated with AMF. Our results showed a difference in OTU richness and community structure composition between soils and roots for both bacteria and fungi. We found that petroleum hydrocarbon pollutant (PHP) concentrations have a significant effect on fungal and bacterial community structures in both soils and roots, whereas plant species identity showed a significant effect only on the roots for bacteria and fungi. Our results also showed that the community composition of bacteria and fungi in soil and roots varied from those associated with AMF spores harvested from the same plants. This let us to speculate that in petroleum hydrocarbon contaminated soils, AMF may release chemical compounds by which they recruit beneficial microbes to tolerate or degrade the PHPs present in the soil.

A New Neural Network Approach Including First-Guess for Retrieval of Atmospheric Water Vapor, Cloud Liquid Water Path, Surface Temperature and Emissivities Over Land From Satellite Microwave Observations

NASA Technical Reports Server (NTRS)

Aires, F.; Prigent, C.; Rossow, W. B.; Rothstein, M.; Hansen, James E. (Technical Monitor)

2000-01-01

The analysis of microwave observations over land to determine atmospheric and surface parameters is still limited due to the complexity of the inverse problem. Neural network techniques have already proved successful as the basis of efficient retrieval methods for non-linear cases, however, first-guess estimates, which are used in variational methods to avoid problems of solution non-uniqueness or other forms of solution irregularity, have up to now not been used with neural network methods. In this study, a neural network approach is developed that uses a first-guess. Conceptual bridges are established between the neural network and variational methods. The new neural method retrieves the surface skin temperature, the integrated water vapor content, the cloud liquid water path and the microwave surface emissivities between 19 and 85 GHz over land from SSM/I observations. The retrieval, in parallel, of all these quantities improves the results for consistency reasons. A data base to train the neural network is calculated with a radiative transfer model and a a global collection of coincident surface and atmospheric parameters extracted from the National Center for Environmental Prediction reanalysis, from the International Satellite Cloud Climatology Project data and from microwave emissivity atlases previously calculated. The results of the neural network inversion are very encouraging. The r.m.s. error of the surface temperature retrieval over the globe is 1.3 K in clear sky conditions and 1.6 K in cloudy scenes. Water vapor is retrieved with a r.m.s. error of 3.8 kg/sq m in clear conditions and 4.9 kg/sq m in cloudy situations. The r.m.s. error in cloud liquid water path is 0.08 kg/sq m . The surface emissivities are retrieved with an accuracy of better than 0.008 in clear conditions and 0.010 in cloudy conditions. Microwave land surface temperature retrieval presents a very attractive complement to the infrared estimates in cloudy areas: time record of land surface temperature will be produced.

Advances in the Two Source Energy Balance (TSEB) model using very high resolution remote sensing data in vineyards

USDA-ARS?s Scientific Manuscript database

The thermal-based Two Source Energy Balance (TSEB) model partitions the water and energy fluxes from vegetation and soil components providing thus the ability for estimating soil evaporation (E) and canopy transpiration (T) separately. However, it is crucial for ET partitioning to retrieve reliable ...

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...

An improved triple collocation algorithm for decomposing autocorrelated and white soil moisture retrieval errors

USDA-ARS?s Scientific Manuscript database

If not properly account for, auto-correlated errors in observations can lead to inaccurate results in soil moisture data analysis and reanalysis. Here, we propose a more generalized form of the triple collocation algorithm (GTC) capable of decomposing the total error variance of remotely-sensed surf...

Continental-Scale Evaluation of Assimilated Soil Moisture Retrievals From the Advanced Microwave Scanning Radiometer

USDA-ARS?s Scientific Manuscript database

Soil moisture is a fundamental data source used in crop growth stage and crop stress models developed by the USDA Foreign Agriculture Service for global crop estimation. USDA’s International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA). Currently, the PECAD DSS utiliz...

Inferences of Strength of Soil Deposits along MER Rover Traverses

NASA Astrophysics Data System (ADS)

Richter, L.; Schmitz, N.; Weiss, S.; Mer/Athena Team

As the two MER Mars Exploration Rovers ,Spirit' and ,Opportunity' traverse terrains within Gusev crater and at Meridiani Planum, respectively, they leave behind wheel tracks that are routinely imaged by the different sets of cameras as part of the Athena instrument suite. Stereo observations of these tracks reveal wheel sinkage depths which are diagnostic of the strength of the soil-like deposits crossed by the vehicles, and observations of track morphology at different imaging scales - including that of the Microscopic Imager - allow estimations of soil grain size distributions. This presentation will discuss results of systematic analyses of MER-A and -B wheel track observations with regard to solutions for soil bearing strength and soil shear strength. Data are analyzed in the context of wheel-soil theory calibrated to the shape of the MER wheel and by consulting comparisons with terrestrial soils. Results are applicable to the top ˜20-30 cm of the soil deposits, the depth primarily affected by the stress distribution under the wheels. The large number of wheel track observations per distance travelled enables investigations of variations of soil physical properties as a function of spatial scale, type of surface feature encountered, and local topography. Exploiting relationships between soil strength and degree of soil consolidation known from lunar regolith and dry terrestrial soils allows one to relate inferred soil strengths to bulk density. This provides a means to ground-truth radar Fresnel reflection coefficients obtained for the landing sites from Earth-based observations. Moreover, bulk density is correlated with soil dielectric constant, a parameter of direct relevance also for Mars-orbiting radars. The obtained estimates for soil bulk density are also used to determine local thermal conductivity of near-surface materials, based on correlations between the two quantities, and to subsequently estimate thermal inertia. This represents an independent method to provide ground truth to thermal inertia determined from orbital thermal measurements of the MER landing sites (MGS TES, MODY THEMIS, MEX PFS & OMEGA), in addition to thermal inertia retrievals from the Athena Mini-TES instrument. Key results suggest different types of soils as judged from their strength, with most materials encountered being similar in consistency to terrestrial sandy loams. Relatively looser soils have been identified on the slopes of crater walls and in local 1 soil patches of smooth appearance, being interpreted as deposits of unconsolidated dust-like soils. Bulk densities for the different soils vary between ˜1100 and ˜1500 kgm-3 . Results of chemical measurements are currently being exploited to relate soil strength to inferred enrichments in salts possibly acting as cementing agents. Thermal inertias of the soil component obtained from the bulk density estimates range between ˜130 and ˜150 Jm-2 s-1/2 K-1 for the MER-A Gusev site and between ˜130 and ˜140 Jm-2 s-1/2 K-1 for the MER-B Meridiani site. 2

Retrieval of Atmospheric Water Vapor Profiles from the Special Sensor Microwave TEMPERATURE-2

NASA Astrophysics Data System (ADS)

Al-Khalaf, Abdulrahman Khal

1995-01-01

Radiometric measurements from the Special Sensor Microwave/Temperature-2 (SSM/T-2) instrument are used to retrieve atmospheric water vapor profiles over ocean, land, coast, and ice/snow backgrounds. These measurements are used to retrieve vertical distribution of integrated water vapor (IWV) and total integrated water vapor (TIWV) using a physical algorithm. The algorithm infers the presence of cloud at a given height from super-saturation of the retrieved humidity at that height then the algorithm estimate the cloud liquid water content. Retrievals of IWV over five different layers are validated against available ground truth such as global radiosondes and ECMWF analyses. Over ocean, the retrieved total integrated water vapor (TIWV) and IWV close to the surface compare quite well, with those from radiosonde observations and the European Center for Medium Range Weather Forecasts (ECMWF) analyses. However, comparisons to radiosonde results are better than (ECMWF) analyses. TIWV root mean square (RMS) difference was 5.95 mm and TWV RMS difference for the lowest layer (SFC-850 mb) was 2.8 mm for radiosonde comparisons. Water vapor retrieval over land is less accurate than over ocean due to the low contrast between the surface and the atmosphere near the surface; therefore, land retrievals are more reliable at layers above 700 mb. However, TIWV and IWV at all layers compare appropriately with ground truth. Over coastal areas the agreement between retrieved water vapor profiles and ground truth is quite good for both TIWV and IWV for the five layers. The natural variability and large variations in the surface emissivity over ice and snow fields leads toward poor results. Clouds degrade retrievals over land and coast, improve the retrievals a little over ocean, and improve dramatically over snow/ice. Examples of retrieved relative humidity profiles were shown to illustrate the algorithm performance for the actual profile retrieval. The overall features of the retrieved profiles compared well with those from radiosonde data and ECMWF analyses. However, due to the limited number of channels, the retrieved profiles generally do not reproduce the fine details when a rapid change in relative humidity versus height was observed.

Ice surface temperature retrieval from AVHRR, ATSR, and passive microwave satellite data: Algorithm development and application

NASA Technical Reports Server (NTRS)

Key, Jeff; Maslanik, James; Steffen, Konrad

1995-01-01

During the second phase project year we have made progress in the development and refinement of surface temperature retrieval algorithms and in product generation. More specifically, we have accomplished the following: (1) acquired a new advanced very high resolution radiometer (AVHRR) data set for the Beaufort Sea area spanning an entire year; (2) acquired additional along-track scanning radiometer(ATSR) data for the Arctic and Antarctic now totalling over eight months; (3) refined our AVHRR Arctic and Antarctic ice surface temperature (IST) retrieval algorithm, including work specific to Greenland; (4) developed ATSR retrieval algorithms for the Arctic and Antarctic, including work specific to Greenland; (5) developed cloud masking procedures for both AVHRR and ATSR; (6) generated a two-week bi-polar global area coverage (GAC) set of composite images from which IST is being estimated; (7) investigated the effects of clouds and the atmosphere on passive microwave 'surface' temperature retrieval algorithms; and (8) generated surface temperatures for the Beaufort Sea data set, both from AVHRR and special sensor microwave imager (SSM/I).

Occurrence and species distribution of pathogenic Mucorales in unselected soil samples from France.

PubMed

Mousavi, B; Costa, J M; Arné, P; Guillot, J; Chermette, R; Botterel, F; Dannaoui, E

2018-04-01

Mucormycosis is a life-threatening invasive fungal disease that affects a variety of patient groups. Although Mucorales are mostly opportunistic pathogens originating from soil or decaying vegetation, there are currently few data on prevalence of this group of fungi in the environment. The aim of the present study was to assess the prevalence and diversity of species of Mucorales from soil samples collected in France. Two grams of soil were homogenized in sterile saline and plated on Sabouraud dextrose agar and RPMI agar supplemented with itraconazole or voriconazole. Both media contained chloramphenicol and gentamicin. The plates were incubated at 35 ± 2 °C and checked daily for fungal growth for a maximum of 7 d. Mucorales were subcultured for purity. Each isolate was identified phenotypically and molecular identification was performed by ITS sequencing. A total of 170 soil samples were analyzed. Forty-one isolates of Mucorales were retrieved from 38 culture-positive samples. Among the recovered isolates, 27 Rhizopus arrhizus, 11 Mucor circinelloides, one Lichtheimia corymbifera, one Rhizopus microsporus and one Cunninghamella bertholletiae were found. Positive soil samples came from cultivated fields but also from other types of soil such as flower beds. Mucorales were retrieved from samples obtained in different geographical regions of France. Voriconazole-containing medium improved the recovery of Mucorales compared with other media. The present study showed that pathogenic Mucorales are frequently recovered from soil samples in France. Species diversity should be further analyzed on a larger number of soil samples from different geographic areas in France and in other countries.

On the angular variation of thermal infrared emissivity of inorganic soils

NASA Astrophysics Data System (ADS)

GarcíA-Santos, Vicente; Valor, Enric; Caselles, Vicente; ÁNgeles Burgos, M.; Coll, CéSar

2012-10-01

Land surface temperature (LST), a key parameter for many environmental studies, can be most readily estimated by using thermal infrared (TIR) sensors onboard satellites. Accurate LST are contingent upon simultaneously accurate estimates of land surface emissivity (ɛ), which depend on sensor viewing angle and the anisotropy of optical and structural properties of surfaces. In the case of inorganic bare soils (IBS), there are still few data that quantify emissivity angular effects. The present work deals with the angular variation of TIR emissivity for twelve IBS types, representative of nine of the twelve soil textures found on Earth according to United States Department of Agriculture classification. Emissivity was measured with a maximum error of ±0.01, in several spectral ranges within the atmospheric window 7.7-14.3 μm, at different zenithal (θ) and azimuthal (φ) angles. Results showed that ɛ of all IBS studied is almost azimuthally isotropic, and also zenithally up to θ = 40°, from which ɛ values decrease with the increase of θ. This decrease is most pronounced in sandy IBS which is rich in quartz reaching a maximum difference from nadir of +0.101 at θ = 70°. On the other hand, clayey IBS did not show a significant decrease of ɛ up to θ= 60°. A parameterization of the relative-to-nadir emissivity in terms ofθ and sand and clay percentage was established. Finally, the impact of ignoring ɛangular effects on the retrievals of LST, using split-window-type algorithms, and of outgoing longwave radiation, was analyzed. Results showed systematic errors ranging between ±0.4 K to ±1.3 K for atmospheres with water vapor values lower than 4 cm in the case of LST, and errors between 2%-8%, in the estimation of different terms of the surface energy balance.

The EUSTACE project: delivering global, daily information on surface air temperature

NASA Astrophysics Data System (ADS)

Rayner, Nick

2017-04-01

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, in the EUSTACE project (2015-June 2018, https://www.eustaceproject.eu) we are developing an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals is used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. As the data volumes involved are considerable, such work needs to include development of new "Big Data" analysis methods. We will present recent progress along this road in the EUSTACE project: 1. providing new, consistent, multi-component estimates of uncertainty in surface skin temperature retrievals from satellites; 2. identifying inhomogeneities in daily surface air temperature measurement series from weather stations and correcting for these over Europe; 3. estimating surface air temperature over all surfaces of Earth from surface skin temperature retrievals; 4. using new statistical techniques to provide information on higher spatial and temporal scales than currently available, making optimum use of information in data-rich eras. Information will also be given on how interested users can become involved.

The EUSTACE project: delivering global, daily information on surface air temperature

NASA Astrophysics Data System (ADS)

Ghent, D.; Rayner, N. A.

2016-12-01

Day-to-day variations in surface air temperature affect society in many ways; however, daily surface air temperature measurements are not available everywhere. A global daily analysis cannot be achieved with measurements made in situ alone, so incorporation of satellite retrievals is needed. To achieve this, in the EUSTACE project (2015-June 2018, https://www.eustaceproject.eu) we are developing an understanding of the relationships between traditional (land and marine) surface air temperature measurements and retrievals of surface skin temperature from satellite measurements, i.e. Land Surface Temperature, Ice Surface Temperature, Sea Surface Temperature and Lake Surface Water Temperature. Here we discuss the science needed to produce a fully-global daily analysis (or ensemble of analyses) of surface air temperature on the centennial scale, integrating different ground-based and satellite-borne data types. Information contained in the satellite retrievals is used to create globally-complete fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place. As the data volumes involved are considerable, such work needs to include development of new "Big Data" analysis methods. We will present recent progress along this road in the EUSTACE project, i.e.: • providing new, consistent, multi-component estimates of uncertainty in surface skin temperature retrievals from satellites; • identifying inhomogeneities in daily surface air temperature measurement series from weather stations and correcting for these over Europe; • estimating surface air temperature over all surfaces of Earth from surface skin temperature retrievals; • using new statistical techniques to provide information on higher spatial and temporal scales than currently available, making optimum use of information in data-rich eras. Information will also be given on how interested users can become involved.

Development of a Global Evaporative Stress Index Based on Thermal and Microwave LST towards Improved Monitoring of Agricultural Drought

NASA Astrophysics Data System (ADS)

Hain, C.; Anderson, M. C.; Otkin, J.; Holmes, T. R.; Gao, F.

2017-12-01

This presentation will describe the development of a global agricultural monitoring tool, with a focus on providing early warning of developing vegetation stress for agricultural decision-makers and stakeholders at relatively high spatial resolution (5-km). The tool is based on remotely sensed estimates of evapotranspiration, retrieved via energy balance principals using observations of land surface temperature. The Evaporative Stress Index (ESI) represents anomalies in the ratio of actual-to-potential ET generated with the ALEXI surface energy balance model. The LST inputs to ESI have been shown to provide early warning information about the development of vegetation stress with stress-elevated canopy temperatures observed well before a decrease in greenness is detected in remotely sensed vegetation indices. As a diagnostic indicator of actual ET, the ESI requires no information regarding antecedent precipitation or soil moisture storage capacity - the current available moisture to vegetation is deduced directly from the remotely sensed LST signal. This signal also inherently accounts for both precipitation and non-precipitation related inputs/sinks to the plant-available soil moisture pool (e.g., irrigation) which can modify crop response to rainfall anomalies. Independence from precipitation data is a benefit for global agricultural monitoring applications due to sparseness in existing ground-based precipitation networks, and time delays in public reporting. Several enhancements to the current ESI framework will be addressed as requested from project stakeholders: (a) integration of "all-sky" MW Ka-band LST retrievals to augment "clear-sky" thermal-only ESI in persistently cloudy regions; (b) operational production of ESI Rapid Change Indices which provide important early warning information related to onset of actual vegetation stress; and (c) assessment of ESI as a predictor of global yield anomalies; initial studies have shown the ability of intra-seasonal ESI to provide an early indication of at-harvest agricultural yield anomalies.