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...

Remote Sensing Soil Moisture Analysis by Unmanned Aerial Vehicles Digital Imaging

NASA Astrophysics Data System (ADS)

Yeh, C. Y.; Lin, H. R.; Chen, Y. L.; Huang, S. Y.; Wen, J. C.

2017-12-01

In recent years, remote sensing analysis has been able to apply to the research of climate change, environment monitoring, geology, hydro-meteorological, and so on. However, the traditional methods for analyzing wide ranges of surface soil moisture of spatial distribution surveys may require plenty resources besides the high cost. In the past, remote sensing analysis performed soil moisture estimates through shortwave, thermal infrared ray, or infrared satellite, which requires lots of resources, labor, and money. Therefore, the digital image color was used to establish the multiple linear regression model. Finally, we can find out the relationship between surface soil color and soil moisture. In this study, we use the Unmanned Aerial Vehicle (UAV) to take an aerial photo of the fallow farmland. Simultaneously, we take the surface soil sample from 0-5 cm of the surface. The soil will be baking by 110° C and 24 hr. And the software ImageJ 1.48 is applied for the analysis of the digital images and the hue analysis into Red, Green, and Blue (R, G, B) hue values. The correlation analysis is the result from the data obtained from the image hue and the surface soil moisture at each sampling point. After image and soil moisture analysis, we use the R, G, B and soil moisture to establish the multiple regression to estimate the spatial distributions of surface soil moisture. In the result, we compare the real soil moisture and the estimated soil moisture. The coefficient of determination (R2) can achieve 0.5-0.7. The uncertainties in the field test, such as the sun illumination, the sun exposure angle, even the shadow, will affect the result; therefore, R2 can achieve 0.5-0.7 reflects good effect for the in-suit test by using the digital image to estimate the soil moisture. Based on the outcomes of the research, using digital images from UAV to estimate the surface soil moisture is acceptable. However, further investigations need to be collected more than ten days (four times a day) data to verify the relation between the image hue and the soil moisture for reliable moisture estimated model. And it is better to use the digital single lens reflex camera to prevent the deformation of the image and to have a better auto exposure. Keywords: soil, moisture, remote sensing

Improved Prediction of Quasi-Global Vegetation Conditions Using Remotely-Sensed Surface Soil Moisture

NASA Technical Reports Server (NTRS)

Bolten, John; Crow, Wade

2012-01-01

The added value of satellite-based surface soil moisture retrievals for agricultural drought monitoring is assessed by calculating the lagged rank correlation between remotely-sensed vegetation indices (VI) and soil moisture estimates obtained both before and after the assimilation of surface soil moisture retrievals derived from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) into a soil water balance model. Higher soil moisture/VI lag correlations imply an enhanced ability to predict future vegetation conditions using estimates of current soil moisture. Results demonstrate that the assimilation of AMSR-E surface soil moisture retrievals substantially improve the performance of a global drought monitoring system - particularly in sparsely-instrumented areas of the world where high-quality rainfall observations are unavailable.

[Detecting the moisture content of forest surface soil based on the microwave remote sensing technology.

PubMed

Li, Ming Ze; Gao, Yuan Ke; Di, Xue Ying; Fan, Wen Yi

2016-03-01

The moisture content of forest surface soil is an important parameter in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the moisture content of forest surface soil. With the aid of TDR-300 soil moisture content measuring instrument, the moisture contents of forest surface soils of 120 sample plots at Tahe Forestry Bureau of Daxing'anling region in Heilongjiang Province were measured. Taking the moisture content of forest surface soil as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting moisture content of forest surface soils were developed. The spatial distribution of moisture content of forest surface soil on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and the BP-neural network model, respectively. It indicated that the BP-neural network model had a better performance than the multilinear regression model in quantitative estimation of the moisture content of forest surface soil. The spatial distribution of forest surface soil moisture content in the study area was then obtained by using the BP neural network model simulation with the Quad-pol SAR data.

Hydrological Storage Length Scales Represented by Remote Sensing Estimates of Soil Moisture and Precipitation

NASA Astrophysics Data System (ADS)

Akbar, Ruzbeh; Short Gianotti, Daniel; McColl, Kaighin A.; Haghighi, Erfan; Salvucci, Guido D.; Entekhabi, Dara

2018-03-01

The soil water content profile is often well correlated with the soil moisture state near the surface. They share mutual information such that analysis of surface-only soil moisture is, at times and in conjunction with precipitation information, reflective of deeper soil fluxes and dynamics. This study examines the characteristic length scale, or effective depth Δz, of a simple active hydrological control volume. The volume is described only by precipitation inputs and soil water dynamics evident in surface-only soil moisture observations. To proceed, first an observation-based technique is presented to estimate the soil moisture loss function based on analysis of soil moisture dry-downs and its successive negative increments. Then, the length scale Δz is obtained via an optimization process wherein the root-mean-squared (RMS) differences between surface soil moisture observations and its predictions based on water balance are minimized. The process is entirely observation-driven. The surface soil moisture estimates are obtained from the NASA Soil Moisture Active Passive (SMAP) mission and precipitation from the gauge-corrected Climate Prediction Center daily global precipitation product. The length scale Δz exhibits a clear east-west gradient across the contiguous United States (CONUS), such that large Δz depths (>200 mm) are estimated in wetter regions with larger mean precipitation. The median Δz across CONUS is 135 mm. The spatial variance of Δz is predominantly explained and influenced by precipitation characteristics. Soil properties, especially texture in the form of sand fraction, as well as the mean soil moisture state have a lesser influence on the length scale.

Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Liu, Qing; Bindlish, Rajat; Cosh, Michael H.; Crow, Wade T.; deJeu, Richard; DeLannoy, Gabrielle J. M.; Huffman, George J.; Jackson, Thomas J.

2011-01-01

The contributions of precipitation and soil moisture observations to the skill of soil moisture estimates from a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). Soil moisture skill is measured against in situ observations in the continental United States at 44 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at four CalVal watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill (in terms of the anomaly time series correlation coefficient R) of AMSR-E retrievals is R=0.39 versus SCAN and R=0.53 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R=0.42 and R=0.46, respectively, versus SCAN measurements, and MERRA surface moisture skill is R=0.56 versus CalVal measurements. Adding information from either precipitation observations or soil moisture retrievals increases surface soil moisture skill levels by IDDeltaR=0.06-0.08, and root zone soil moisture skill levels by DeltaR=0.05-0.07. Adding information from both sources increases surface soil moisture skill levels by DeltaR=0.13, and root zone soil moisture skill by DeltaR=0.11, demonstrating that precipitation corrections and assimilation of satellite soil moisture retrievals contribute similar and largely independent amounts of information.

Soil Moisture or Groundwater?

NASA Astrophysics Data System (ADS)

Swenson, S. C.; Lawrence, D. M.

2017-12-01

Partitioning the vertically integrated water storage variations estimated from GRACE satellite data into the components of which it is comprised requires independent information. Land surface models, which simulate the transfer and storage of moisture and energy at the land surface, are often used to estimate water storage variability of snow, surface water, and soil moisture. To obtain an estimate of changes in groundwater, the estimates of these storage components are removed from GRACE data. Biases in the modeled water storage components are therefore present in the residual groundwater estimate. In this study, we examine how soil moisture variability, estimated using the Community Land Model (CLM), depends on the vertical structure of the model. We then explore the implications of this uncertainty in the context of estimating groundwater variations using GRACE data.

Representing the effects of alpine grassland vegetation cover on the simulation of soil thermal dynamics by ecosystem models applied to the Qinghai-Tibetan Plateau

USGS Publications Warehouse

Yi, S.; Li, N.; Xiang, B.; Wang, X.; Ye, B.; McGuire, A.D.

2013-01-01

Soil surface temperature is a critical boundary condition for the simulation of soil temperature by environmental models. It is influenced by atmospheric and soil conditions and by vegetation cover. In sophisticated land surface models, it is simulated iteratively by solving surface energy budget equations. In ecosystem, permafrost, and hydrology models, the consideration of soil surface temperature is generally simple. In this study, we developed a methodology for representing the effects of vegetation cover and atmospheric factors on the estimation of soil surface temperature for alpine grassland ecosystems on the Qinghai-Tibetan Plateau. Our approach integrated measurements from meteorological stations with simulations from a sophisticated land surface model to develop an equation set for estimating soil surface temperature. After implementing this equation set into an ecosystem model and evaluating the performance of the ecosystem model in simulating soil temperature at different depths in the soil profile, we applied the model to simulate interactions among vegetation cover, freeze-thaw cycles, and soil erosion to demonstrate potential applications made possible through the implementation of the methodology developed in this study. Results showed that (1) to properly estimate daily soil surface temperature, algorithms should use air temperature, downward solar radiation, and vegetation cover as independent variables; (2) the equation set developed in this study performed better than soil surface temperature algorithms used in other models; and (3) the ecosystem model performed well in simulating soil temperature throughout the soil profile using the equation set developed in this study. Our application of the model indicates that the representation in ecosystem models of the effects of vegetation cover on the simulation of soil thermal dynamics has the potential to substantially improve our understanding of the vulnerability of alpine grassland ecosystems to changes in climate and grazing regimes.

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.

Impacts of different types of measurements on estimating unsaturated flow parameters

NASA Astrophysics Data System (ADS)

Shi, Liangsheng; Song, Xuehang; Tong, Juxiu; Zhu, Yan; Zhang, Qiuru

2015-05-01

This paper assesses the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.

Impacts of Different Types of Measurements on Estimating Unsaturatedflow Parameters

NASA Astrophysics Data System (ADS)

Shi, L.

2015-12-01

This study evaluates the value of different types of measurements for estimating soil hydraulic parameters. A numerical method based on ensemble Kalman filter (EnKF) is presented to solely or jointly assimilate point-scale soil water head data, point-scale soil water content data, surface soil water content data and groundwater level data. This study investigates the performance of EnKF under different types of data, the potential worth contained in these data, and the factors that may affect estimation accuracy. Results show that for all types of data, smaller measurements errors lead to faster convergence to the true values. Higher accuracy measurements are required to improve the parameter estimation if a large number of unknown parameters need to be identified simultaneously. The data worth implied by the surface soil water content data and groundwater level data is prone to corruption by a deviated initial guess. Surface soil moisture data are capable of identifying soil hydraulic parameters for the top layers, but exert less or no influence on deeper layers especially when estimating multiple parameters simultaneously. Groundwater level is one type of valuable information to infer the soil hydraulic parameters. However, based on the approach used in this study, the estimates from groundwater level data may suffer severe degradation if a large number of parameters must be identified. Combined use of two or more types of data is helpful to improve the parameter estimation.

Estimation of Soil Moisture with L-band Multi-polarization Radar

NASA Technical Reports Server (NTRS)

Shi, J.; Chen, K. S.; Kim, Chung-Li Y.; Van Zyl, J. J.; Njoku, E.; Sun, G.; O'Neill, P.; Jackson, T.; Entekhabi, D.

2004-01-01

Through analyses of the model simulated data-base, we developed a technique to estimate surface soil moisture under HYDROS radar sensor (L-band multi-polarizations and 40deg incidence) configuration. This technique includes two steps. First, it decomposes the total backscattering signals into two components - the surface scattering components (the bare surface backscattering signals attenuated by the overlaying vegetation layer) and the sum of the direct volume scattering components and surface-volume interaction components at different polarizations. From the model simulated data-base, our decomposition technique works quit well in estimation of the surface scattering components with RMSEs of 0.12,0.25, and 0.55 dB for VV, HH, and VH polarizations, respectively. Then, we use the decomposed surface backscattering signals to estimate the soil moisture and the combined surface roughness and vegetation attenuation correction factors with all three polarizations.

A statistical method for estimating rates of soil development and ages of geologic deposits: A design for soil-chronosequence studies

USGS Publications Warehouse

Switzer, P.; Harden, J.W.; Mark, R.K.

1988-01-01

A statistical method for estimating rates of soil development in a given region based on calibration from a series of dated soils is used to estimate ages of soils in the same region that are not dated directly. The method is designed specifically to account for sampling procedures and uncertainties that are inherent in soil studies. Soil variation and measurement error, uncertainties in calibration dates and their relation to the age of the soil, and the limited number of dated soils are all considered. Maximum likelihood (ML) is employed to estimate a parametric linear calibration curve, relating soil development to time or age on suitably transformed scales. Soil variation on a geomorphic surface of a certain age is characterized by replicate sampling of soils on each surface; such variation is assumed to have a Gaussian distribution. The age of a geomorphic surface is described by older and younger bounds. This technique allows age uncertainty to be characterized by either a Gaussian distribution or by a triangular distribution using minimum, best-estimate, and maximum ages. The calibration curve is taken to be linear after suitable (in certain cases logarithmic) transformations, if required, of the soil parameter and age variables. Soil variability, measurement error, and departures from linearity are described in a combined fashion using Gaussian distributions with variances particular to each sampled geomorphic surface and the number of sample replicates. Uncertainty in age of a geomorphic surface used for calibration is described using three parameters by one of two methods. In the first method, upper and lower ages are specified together with a coverage probability; this specification is converted to a Gaussian distribution with the appropriate mean and variance. In the second method, "absolute" older and younger ages are specified together with a most probable age; this specification is converted to an asymmetric triangular distribution with mode at the most probable age. The statistical variability of the ML-estimated calibration curve is assessed by a Monte Carlo method in which simulated data sets repeatedly are drawn from the distributional specification; calibration parameters are reestimated for each such simulation in order to assess their statistical variability. Several examples are used for illustration. The age of undated soils in a related setting may be estimated from the soil data using the fitted calibration curve. A second simulation to assess age estimate variability is described and applied to the examples. ?? 1988 International Association for Mathematical Geology.

Erosivity, surface runoff, and soil erosion estimation using GIS-coupled runoff-erosion model in the Mamuaba catchment, Brazil.

PubMed

Marques da Silva, Richarde; Guimarães Santos, Celso Augusto; Carneiro de Lima Silva, Valeriano; Pereira e Silva, Leonardo

2013-11-01

This study evaluates erosivity, surface runoff generation, and soil erosion rates for Mamuaba catchment, sub-catchment of Gramame River basin (Brazil) by using the ArcView Soil and Water Assessment Tool (AvSWAT) model. Calibration and validation of the model was performed on monthly basis, and it could simulate surface runoff and soil erosion to a good level of accuracy. Daily rainfall data between 1969 and 1989 from six rain gauges were used, and the monthly rainfall erosivity of each station was computed for all the studied years. In order to evaluate the calibration and validation of the model, monthly runoff data between January 1978 and April 1982 from one runoff gauge were used as well. The estimated soil loss rates were also realistic when compared to what can be observed in the field and to results from previous studies around of catchment. The long-term average soil loss was estimated at 9.4 t ha(-1) year(-1); most of the area of the catchment (60%) was predicted to suffer from a low- to moderate-erosion risk (<6 t ha(-1) year(-1)) and, in 20% of the catchment, the soil erosion was estimated to exceed > 12 t ha(-1) year(-1). Expectedly, estimated soil loss was significantly correlated with measured rainfall and simulated surface runoff. Based on the estimated soil loss rates, the catchment was divided into four priority categories (low, moderate, high and very high) for conservation intervention. The study demonstrates that the AvSWAT model provides a useful tool for soil erosion assessment from catchments and facilitates the planning for a sustainable land management in northeastern Brazil.

Representing the effects of alpine grassland vegetation cover on the simulation of soil thermal dynamics by ecosystem models applied to the Qinghai-Tibetan Plateau

NASA Astrophysics Data System (ADS)

Yi, S.; Li, N.; Xiang, B.; Wang, X.; Ye, B.; McGuire, A. D.

2013-07-01

surface temperature is a critical boundary condition for the simulation of soil temperature by environmental models. It is influenced by atmospheric and soil conditions and by vegetation cover. In sophisticated land surface models, it is simulated iteratively by solving surface energy budget equations. In ecosystem, permafrost, and hydrology models, the consideration of soil surface temperature is generally simple. In this study, we developed a methodology for representing the effects of vegetation cover and atmospheric factors on the estimation of soil surface temperature for alpine grassland ecosystems on the Qinghai-Tibetan Plateau. Our approach integrated measurements from meteorological stations with simulations from a sophisticated land surface model to develop an equation set for estimating soil surface temperature. After implementing this equation set into an ecosystem model and evaluating the performance of the ecosystem model in simulating soil temperature at different depths in the soil profile, we applied the model to simulate interactions among vegetation cover, freeze-thaw cycles, and soil erosion to demonstrate potential applications made possible through the implementation of the methodology developed in this study. Results showed that (1) to properly estimate daily soil surface temperature, algorithms should use air temperature, downward solar radiation, and vegetation cover as independent variables; (2) the equation set developed in this study performed better than soil surface temperature algorithms used in other models; and (3) the ecosystem model performed well in simulating soil temperature throughout the soil profile using the equation set developed in this study. Our application of the model indicates that the representation in ecosystem models of the effects of vegetation cover on the simulation of soil thermal dynamics has the potential to substantially improve our understanding of the vulnerability of alpine grassland ecosystems to changes in climate and grazing regimes.

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.

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.

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.

Relation between L-band soil emittance and soil water content

NASA Technical Reports Server (NTRS)

Stroosnijder, L.; Lascano, R. J.; Van Bavel, C. H. M.; Newton, R. W.

1986-01-01

An experimental relation between soil emittance (E) at L-band and soil surface moisture content (M) is compared with a theoretical one. The latter depends on the soil dielectric constant, which is a function of both soil moisture content and of soil texture. It appears that a difference of 10 percent in the surface clay content causes a change in the estimate of M on the order of 0.02 cu m/cu m. This is based on calculations with a model that simulates the flow of water and energy, in combination with a radiative transfer model. It is concluded that an experimental determination of the E-M relation for each soil type is not required, and that a rough estimate of the soil texture will lead to a sufficiently accurate estimate of soil moisture from a general, theoretical relationship obtained by numerical simulation.

Potential Predictability of U.S. Summer Climate with "Perfect" Soil Moisture

NASA Technical Reports Server (NTRS)

Yang, Fanglin; Kumar, Arun; Lau, K.-M.

2004-01-01

The potential predictability of surface-air temperature and precipitation over the United States continent was assessed for a GCM forced by observed sea surface temperatures and an estimate of observed ground soil moisture contents. The latter was obtained by substituting the GCM simulated precipitation, which is used to drive the GCM's land-surface component, with observed pentad-mean precipitation at each time step of the model's integration. With this substitution, the simulated soil moisture correlates well with an independent estimate of observed soil moisture in all seasons over the entire US continent. Significant enhancements on the predictability of surface-air temperature and precipitation were found in boreal late spring and summer over the US continent. Anomalous pattern correlations of precipitation and surface-air temperature over the US continent in the June-July-August season averaged for the 1979-2000 period increased from 0.01 and 0.06 for the GCM simulations without precipitation substitution to 0.23 and 0.3 1, respectively, for the simulations with precipitation substitution. Results provide an estimate for the limits of potential predictability if soil moisture variability is to be perfectly predicted. However, this estimate may be model dependent, and needs to be substantiated by other modeling groups.

Evaluating Remotely-Sensed Surface Soil Moisture Estimates Using Triple Collocation

USDA-ARS?s Scientific Manuscript database

Recent work has demonstrated the potential of enhancing remotely-sensed surface soil moisture validation activities through the application of triple collocation techniques which compare time series of three mutually independent geophysical variable estimates in order to acquire the root-mean-square...

[Soil moisture estimation method based on both ground-based remote sensing data and air temperature in a summer maize ecosystem.

PubMed

Wang, Min Zheng; Zhou, Guang Sheng

2016-06-01

Soil moisture is an important component of the soil-vegetation-atmosphere continuum (SPAC). It is a key factor to determine the water status of terrestrial ecosystems, and is also the main source of water supply for crops. In order to estimate soil moisture at different soil depths at a station scale, based on the energy balance equation and the water deficit index (WDI), a soil moisture estimation model was established in terms of the remote sensing data (the normalized difference vegetation index and surface temperature) and air temperature. The soil moisture estimation model was validated based on the data from the drought process experiment of summer maize (Zea mays) responding to different irrigation treatments carried out during 2014 at Gucheng eco-agrometeorological experimental station of China Meteorological Administration. The results indicated that the soil moisture estimation model developed in this paper was able to evaluate soil relative humidity at different soil depths in the summer maize field, and the hypothesis was reasonable that evapotranspiration deficit ratio (i.e., WDI) linearly depended on soil relative humidity. It showed that the estimation accuracy of 0-10 cm surface soil moisture was the highest (R 2 =0.90). The RMAEs of the estimated and measured soil relative humidity in deeper soil layers (up to 50 cm) were less than 15% and the RMSEs were less than 20%. The research could provide reference for drought monitoring and irrigation management.

Estimating Thermal Inertia with a Maximum Entropy Boundary Condition

NASA Astrophysics Data System (ADS)

Nearing, G.; Moran, M. S.; Scott, R.; Ponce-Campos, G.

2012-04-01

Thermal inertia, P [Jm-2s-1/2K-1], is a physical property the land surface which determines resistance to temperature change under seasonal or diurnal heating. It is a function of volumetric heat capacity, c [Jm-3K-1], and thermal conductivity, k [Wm-1K-1] of the soil near the surface: P=√ck. Thermal inertia of soil varies with moisture content due the difference between thermal properties of water and air, and a number of studies have demonstrated that it is feasible to estimate soil moisture given thermal inertia (e.g. Lu et al, 2009, Murray and Verhoef, 2007). We take the common approach to estimating thermal inertia using measurements of surface temperature by modeling the Earth's surface as a 1-dimensional homogeneous diffusive half-space. In this case, surface temperature is a function of the ground heat flux (G) boundary condition and thermal inertia and a daily value of P was estimated by matching measured and modeled diurnal surface temperature fluctuations. The difficulty is in measuring G; we demonstrate that the new maximum entropy production (MEP) method for partitioning net radiation into surface energy fluxes (Wang and Bras, 2011) provides a suitable boundary condition for estimating P. Adding the diffusion representation of heat transfer in the soil reduces the number of free parameters in the MEP model from two to one, and we provided a sensitivity analysis which suggests that, for the purpose of estimating P, it is preferable to parameterize the coupled MEP-diffusion model by the ratio of thermal inertia of the soil to the effective thermal inertia of convective heat transfer to the atmosphere. We used this technique to estimate thermal inertia at two semiarid, non-vegetated locations in the Walnut Gulch Experimental Watershed in southeast AZ, USA and compared these estimates to estimates of P made using the Xue and Cracknell (1995) solution for a linearized ground heat flux boundary condition, and we found that the MEP-diffusion model produced superior thermal inertia estimates. The MEP-diffusion estimates also agreed well with P estimates made using a boundary condition measured with buried flux plates. We further demonstrated the new method using diurnal surface temperature fluctuations estimated from day/night MODIS image pairs and, excluding instances where the soil was extremely dry, found a strong relationship between estimated thermal inertia and measured 5 cm soil moisture. Lu, S., Ju, Z.Q., Ren, T.S. & Horton, R. (2009). A general approach to estimate soil water content from thermal inertia. Agricultural and Forest Meteorology, 149, 1693-1698. Murray, T. & Verhoef, A. (2007). Moving towards a more mechanistic approach in the determination of soil heat flux from remote measurements - I. A universal approach to calculate thermal inertia. Agricultural and Forest Meteorology, 147, 80-87. Wang, J.F. & Bras, R.L. (2011). A model of evapotranspiration based on the theory of maximum entropy production. Water Resources Research, 47. Xue, Y. & Cracknell, A.P. (1995). Advanced thermal inertia modeling. International Journal of Remote Sensing, 16, 431-446.

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.

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.

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.

Using Remote Sensing Data to Evaluate Surface Soil Properties in Alabama Ultisols

NASA Technical Reports Server (NTRS)

Sullivan, Dana G.; Shaw, Joey N.; Rickman, Doug; Mask, Paul L.; Luvall, Jeff

2005-01-01

Evaluation of surface soil properties via remote sensing could facilitate soil survey mapping, erosion prediction and allocation of agrochemicals for precision management. The objective of this study was to evaluate the relationship between soil spectral signature and surface soil properties in conventionally managed row crop systems. High-resolution RS data were acquired over bare fields in the Coastal Plain, Appalachian Plateau, and Ridge and Valley provinces of Alabama using the Airborne Terrestrial Applications Sensor multispectral scanner. Soils ranged from sandy Kandiudults to fine textured Rhodudults. Surface soil samples (0-1 cm) were collected from 163 sampling points for soil organic carbon, particle size distribution, and citrate dithionite extractable iron content. Surface roughness, soil water content, and crusting were also measured during sampling. Two methods of analysis were evaluated: 1) multiple linear regression using common spectral band ratios, and 2) partial least squares regression. Our data show that thermal infrared spectra are highly, linearly related to soil organic carbon, sand and clay content. Soil organic carbon content was the most difficult to quantify in these highly weathered systems, where soil organic carbon was generally less than 1.2%. Estimates of sand and clay content were best using partial least squares regression at the Valley site, explaining 42-59% of the variability. In the Coastal Plain, sandy surfaces prone to crusting limited estimates of sand and clay content via partial least squares and regression with common band ratios. Estimates of iron oxide content were a function of mineralogy and best accomplished using specific band ratios, with regression explaining 36-65% of the variability at the Valley and Coastal Plain sites, respectively.

Martian surface materials

NASA Technical Reports Server (NTRS)

Moore, H. J.

1991-01-01

A semiquantitative appreciation for the physical properties of the Mars surface materials and their global variations can be gained from the Viking Lander and remote sensing observations. Analyses of Lander data yields estimates of the mechanical properties of the soil-like surface materials and best guess estimates can be made for the remote sensing signatures of the soil-like materials at the landing sites. Results show that significant thickness of powderlike surface materials with physical properties similar to drift material are present on Mars and probably pervasive in the Tharsis region. It also appears likely that soil-like materials similar to crusty to cloddy material are typical for Mars, and that soil-like material similar to blocky material are common on Mars.

Estimating Long Term Surface Soil Moisture in the GCIP Area From Satellite Microwave Observations

NASA Technical Reports Server (NTRS)

Owe, Manfred; deJeu, Vrije; VandeGriend, Adriaan A.

2000-01-01

Soil moisture is an important component of the water and energy balances of the Earth's surface. Furthermore, it has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are representative only of the immediate site which was sampled. Simple averaging of point values to obtain spatial means often leads to substantial errors. Since remotely sensed observations are already a spatially averaged or areally integrated value, they are ideally suited for measuring land surface parameters, and as such, are a logical input to regional or larger scale land process models. A nine-year database of surface soil moisture is being developed for the Central United States from satellite microwave observations. This region forms much of the GCIP study area, and contains most of the Mississippi, Rio Grande, and Red River drainages. Daytime and nighttime microwave brightness temperatures were observed at a frequency of 6.6 GHz, by the Scanning Multichannel Microwave Radiometer (SMMR), onboard the Nimbus 7 satellite. The life of the SMMR instrument spanned from Nov. 1978 to Aug. 1987. At 6.6 GHz, the instrument provided a spatial resolution of approximately 150 km, and an orbital frequency over any pixel-sized area of about 2 daytime and 2 nighttime passes per week. Ground measurements of surface soil moisture from various locations throughout the study area are used to calibrate the microwave observations. Because ground measurements are usually only single point values, and since the time of satellite coverage does not always coincide with the ground measurements, the soil moisture data were used to calibrate a regional water balance for the top 1, 5, and 10 cm surface layers in order to interpolate daily surface moisture values. Such a climate-based approach is often more appropriate for estimating large-area spatially averaged soil moisture because meteorological data are generally more spatially representative than isolated point measurements of soil moisture. Vegetation radiative transfer characteristics, such as the canopy transmissivity, were estimated from vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and the 37 GHz Microwave Polarization Difference Index (MPDI). Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. Real time estimates should improve weather and climate modelling efforts, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.

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.

Surface Runoff Estimation Using SMOS Observations, Rain-gauge Measurements and Satellite Precipitation Estimations. Comparison with Model Predictions

NASA Astrophysics Data System (ADS)

Garcia Leal, Julio A.; Lopez-Baeza, Ernesto; Khodayar, Samiro; Estrela, Teodoro; Fidalgo, Arancha; Gabaldo, Onofre; Kuligowski, Robert; Herrera, Eddy

Surface runoff is defined as the amount of water that originates from precipitation, does not infiltrates due to soil saturation and therefore circulates over the surface. A good estimation of runoff is useful for the design of draining systems, structures for flood control and soil utilisation. For runoff estimation there exist different methods such as (i) rational method, (ii) isochrone method, (iii) triangular hydrograph, (iv) non-dimensional SCS hydrograph, (v) Temez hydrograph, (vi) kinematic wave model, represented by the dynamics and kinematics equations for a uniforme precipitation regime, and (vii) SCS-CN (Soil Conservation Service Curve Number) model. This work presents a way of estimating precipitation runoff through the SCS-CN model, using SMOS (Soil Moisture and Ocean Salinity) mission soil moisture observations and rain-gauge measurements, as well as satellite precipitation estimations. The area of application is the Jucar River Basin Authority area where one of the objectives is to develop the SCS-CN model in a spatial way. The results were compared to simulations performed with the 7-km COSMO-CLM (COnsortium for Small-scale MOdelling, COSMO model in CLimate Mode) model. The use of SMOS soil moisture as input to the COSMO-CLM model will certainly improve model simulations.

Application of Multitemporal Remotely Sensed Soil Moisture for the Estimation of Soil Physical Properties

NASA Technical Reports Server (NTRS)

Mattikalli, N. M.; Engman, E. T.; Jackson, T. J.; Ahuja, L. R.

1997-01-01

This paper demonstrates the use of multitemporal soil moisture derived from microwave remote sensing to estimate soil physical properties. The passive microwave ESTAR instrument was employed during June 10-18, 1992, to obtain brightness temperature (TB) and surface soil moisture data in the Little Washita watershed, Oklahoma. Analyses of spatial and temporal variations of TB and soil moisture during the dry-down period revealed a direct relationship between changes in T and soil moisture and soil physical (viz. texture) and hydraulic (viz. saturated hydraulic conductivity, K(sat)) properties. Statistically significant regression relationships were developed for the ratio of percent sand to percent clay (RSC) and K(sat), in terms of change components of TB and surface soil moisture. Validation of results using field measured values and soil texture map indicated that both RSC and K(sat) can be estimated with reasonable accuracy. These findings have potential applications of microwave remote sensing to obtain quick estimates of the spatial distributions of K(sat), over large areas for input parameterization of hydrologic models.

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.

A method of determining surface runoff by

Treesearch

Donald E. Whelan; Lemuel E. Miller; John B. Cavallero

1952-01-01

To determine the effects of watershed management on flood runoff, one must make a reliable estimate of how much the surface runoff can be reduced by a land-use program. Since surface runoff is the difference between precipitation and the amount of water that soaks into the soil, such an estimate must be based on the infiltration capacity of the soil.

Nutrient Estimation Using Subsurface Sensing Methods

USDA-ARS?s Scientific Manuscript database

This report investigates the use of precision management techniques for measuring soil conductivity on feedlot surfaces to estimate nutrient value for crop production. An electromagnetic induction soil conductivity meter was used to collect apparent soil electrical conductivity (ECa) from feedlot p...

Coupling diffusion and maximum entropy models to estimate thermal inertia

USDA-ARS?s Scientific Manuscript database

Thermal inertia is a physical property of soil at the land surface related to water content. We have developed a method for estimating soil thermal inertia using two daily measurements of surface temperature, to capture the diurnal range, and diurnal time series of net radiation and specific humidi...

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

An Evaluation of Total Solar Reflectance and Spectral Band Ratioing Techniques for Estimating Soil Water Content

NASA Technical Reports Server (NTRS)

Reginato, R. J.; Vedder, J. F.; Idso, S. B.; Jackson, R. D.; Blanchard, M. B.; Goettelman, R.

1977-01-01

For several days in March of 1975, reflected solar radiation measurements were obtained from smooth and rough surfaces of wet, drying, and continually dry Avondale loam at Phoenix, Arizona, with pyranometers located 50 cm above the ground surface and a multispectral scanner flown at a 300-m height. The simple summation of the different band radiances measured by the multispectral scanner proved equally as good as the pyranometer data for estimating surface soil water content if the multispectral scanner data were standardized with respect to the intensity of incoming solar radiation or the reflected radiance from a reference surface, such as the continually dry soil. Without this means of standardization, multispectral scanner data are most useful in a spectral band ratioing context. Our results indicated that, for the bands used, no significant information on soil water content could be obtained by band ratioing. Thus the variability in soil water content should insignificantly affect soil-type discrimination based on identification of type-specific spectral signatures. Therefore remote sensing, conducted in the 0.4- to 1.0-micron wavelength region of the solar spectrum, would seem to be much More suited to identifying crop and soil types than to estimating of soil water content.

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.

Synergistic method for boreal soil moisture and soil freeze retrievals using active and passive microwave instruments

NASA Astrophysics Data System (ADS)

Smolander, Tuomo; Lemmetyinen, Juha; Rautiainen, Kimmo; Schwank, Mike; Pulliainen, Jouni

2017-04-01

Soil moisture and soil freezing are important for diverse hydrological, biogeochemical, and climatological applications. They affect surface energy balance, surface and subsurface water flow, and exchange rates of carbon with the atmosphere. Soil freezing controls important biogeochemical processes, like photosynthetic activity of plants and microbial activity within soils. Permafrost covers approximately 24% of the land surface in the Northern Hemisphere and seasonal freezing occurs on approximately 51% of the area. The retrieval method presented is based on an inversion technique and applies a semiempirical backscattering model that describes the dependence of radar backscattering of forest as a function of stem volume, soil permittivity, the extinction coefficient of forest canopy, surface roughness, incidence angle, and radar frequency. It gives an estimate of soil permittivity using active microwave measurements. Applying a Bayesian assimilation scheme, it is also possible to use other soil permittivity retrievals to regulate this estimate to combine for example low resolution passive observations with high resolution active observations for a synergistic retrieval. This way the higher variance in the active retrieval can be constricted with the passive retrieval when at the same time the spatial resolution of the product is improved compared to the passive-only retrieval. The retrieved soil permittivity estimate can be used to detect soil freeze/thaw state by considering the soil to be frozen when the estimate is below a threshold value. The permittivity retrieval can also be used to estimate the relative moisture of the soil. The method was tested using SAR (Synthetic Aperture Radar) measurements from ENVISAT ASAR instrument for the years 2010-2012 and from Sentinel-1 satellite for the years 2015-2016 in Sodankylä area in Northern Finland. The synergistic method was tested combining the SAR measurements with a SMOS (Soil Moisture Ocean Salinity) radiometer based retrieval. The results were validated using in situ measurements from automatic soil state observation stations in Sodankylä calibration and validation (CAL-VAL) site, which is a reference site for several EO (Earth Observation) data products.

The L-band PBMR measurements of surface soil moisture in FIFE. [First International satellite land surface climatology project Field Experiment

NASA Technical Reports Server (NTRS)

Wang, James R.; Shiue, James C.; Schmugge, Thomas J.; Engman, Edwin T.

1990-01-01

The NASA Langley Research Center's L-band pushbroom microwave radiometer (PBMR) aboard the NASA C-130 aircraft was used to map surface soil moisture at and around the Konza Prairie Natural Research Area in Kansas during the four intensive field campaigns of FIFE in May-October 1987. There was a total of 11 measurements was made when soils were known to be saturated. This measurement was used for the calibration of the vegetation effect on the microwave absorption. Based on this calibration, the data from other measurements on other days were inverted to generate the soil moisture maps. Good agreement was found when the estimated soil moisture values were compared to those independently measured on the ground at a number of widely separated locations. There was a slight bias between the estimated and measured values, the estimated soil moisture on the average being lower by about 1.8 percent. This small bias, however, was accounted for by the difference in time of the radiometric measurements and the soil moisture ground sampling.

Comparison of evaporative fluxes from porous surfaces resolved by remotely sensed and in-situ temperature and soil moisture data

NASA Astrophysics Data System (ADS)

Wallen, B.; Trautz, A.; Smits, K. M.

2014-12-01

The estimation of evaporation has important implications in modeling climate at the regional and global scale, the hydrological cycle and estimating environmental stress on agricultural systems. In field and laboratory studies, remote sensing and in-situ techniques are used to collect thermal and soil moisture data of the soil surface and subsurface which is then used to estimate evaporative fluxes, oftentimes using the sensible heat balance method. Nonetheless, few studies exist that compare the methods due to limited data availability and the complexity of many of the techniques, making it difficult to understand flux estimates. This work compares different methods used to quantify evaporative flux based on remotely sensed and in-situ temperature and soil moisture data. A series of four laboratory experiments were performed under ambient and elevated air temperature conditions with homogeneous and heterogeneous soil configurations in a small two-dimensional soil tank interfaced with a small wind tunnel apparatus. The soil tank and wind tunnel were outfitted with a suite of sensors that measured soil temperature (surface and subsurface), air temperature, soil moisture, and tank weight. Air and soil temperature measurements were obtained using infrared thermography, heat pulse sensors and thermistors. Spatial and temporal thermal data were numerically inverted to obtain the evaporative flux. These values were then compared with rates of mass loss from direct weighing of the samples. Results demonstrate the applicability of different methods under different surface boundary conditions; no one method was deemed most applicable under every condition. Infrared thermography combined with the sensible heat balance method was best able to determine evaporative fluxes under stage 1 conditions while distributed temperature sensing combined with the sensible heat balance method best determined stage 2 evaporation. The approaches that appear most promising for determining the surface energy balance incorporates soil moisture rate of change over time and atmospheric conditions immediately above the soil surface. An understanding of the fidelity regarding predicted evaporation rates based upon stages of evaporation enables a more deliberate selection of the suite of sensors required for data collection.

Prediction of Root Zone Soil Moisture using Remote Sensing Products and In-Situ Observation under Climate Change Scenario

NASA Astrophysics Data System (ADS)

Singh, G.; Panda, R. K.; Mohanty, B.

2015-12-01

Prediction of root zone soil moisture status at field level is vital for developing efficient agricultural water management schemes. In this study, root zone soil moisture was estimated across the Rana watershed in Eastern India, by assimilation of near-surface soil moisture estimate from SMOS satellite into a physically-based Soil-Water-Atmosphere-Plant (SWAP) model. An ensemble Kalman filter (EnKF) technique coupled with SWAP model was used for assimilating the satellite soil moisture observation at different spatial scales. The universal triangle concept and artificial intelligence techniques were applied to disaggregate the SMOS satellite monitored near-surface soil moisture at a 40 km resolution to finer scale (1 km resolution), using higher spatial resolution of MODIS derived vegetation indices (NDVI) and land surface temperature (Ts). The disaggregated surface soil moisture were compared to ground-based measurements in diverse landscape using portable impedance probe and gravimetric samples. Simulated root zone soil moisture were compared with continuous soil moisture profile measurements at three monitoring stations. In addition, the impact of projected climate change on root zone soil moisture were also evaluated. The climate change projections of rainfall were analyzed for the Rana watershed from statistically downscaled Global Circulation Models (GCMs). The long-term root zone soil moisture dynamics were estimated by including a rainfall generator of likely scenarios. The predicted long term root zone soil moisture status at finer scale can help in developing efficient agricultural water management schemes to increase crop production, which lead to enhance the water use efficiency.

Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation

NASA Astrophysics Data System (ADS)

Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Boyles, Ryan

2016-12-01

Surface soil moisture is a critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purposes are sensors that are installed at depths of approximately 5 cm. There are however, sensor technologies and network designs that do not provide an estimate at this depth. If soil moisture estimates at deeper depths could be extrapolated to the near surface, in situ networks providing estimates at other depths would see their values enhanced. Soil moisture sensors from the U.S. Climate Reference Network (USCRN) were used to generate models of 5 cm soil moisture, with 10 cm soil moisture measurements and antecedent precipitation as inputs, via machine learning techniques. Validation was conducted with the available, in situ, 5 cm resources. It was shown that a 5 cm estimate, which was extrapolated from a 10 cm sensor and antecedent local precipitation, produced a root-mean-squared-error (RMSE) of 0.0215 m3/m3. Next, these machine-learning-generated 5 cm estimates were also compared to AMSR-E estimates at these locations. These results were then compared with the performance of the actual in situ readings against the AMSR-E data. The machine learning estimates at 5 cm produced an RMSE of approximately 0.03 m3/m3 when an optimized gain and offset were applied. This is necessary considering the performance of AMSR-E in locations characterized by high vegetation water contents, which are present across North Carolina. Lastly, the application of this extrapolation technique is applied to the ECONet in North Carolina, which provides a 10 cm depth measurement as its shallowest soil moisture estimate. A raw RMSE of 0.028 m3/m3 was achieved, and with a linear gain and offset applied at each ECONet site, an RMSE of 0.013 m3/m3 was possible.

Carbon to organic matter ratios for soils in Rocky Mountain coniferous forests

Treesearch

Theresa B. Jain; Russell T. Graham; David L. Adams

1997-01-01

Vegetation type, soils, climate, and conversion ratios influence estimates of terrestrial C. Our objectives were to (i) determine carbon to organic matter (C/OM) ratios for brown cubical rotten wood, litter, surface humus, soil wood, and mineral soils; (ii) evaluate the validity of using 0.58 and 0.50 ratios for estimating C in mineral and organic soil components,...

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.

Estimation of effective hydrologic properties of soils from observations of vegetation density. M.S. Thesis; [water balance of watersheds in Clinton, Maine and Santa Paula, California

NASA Technical Reports Server (NTRS)

Tellers, T. E.

1980-01-01

An existing one-dimensional model of the annual water balance is reviewed. Slight improvements are made in the method of calculating the bare soil component of evaporation, and in the way surface retention is handled. A natural selection hypothesis, which specifies the equilibrium vegetation density for a given, water limited, climate-soil system, is verified through comparisons with observed data and is employed in the annual water balance of watersheds in Clinton, Ma., and Santa Paula, Ca., to estimate effective areal average soil properties. Comparison of CDF's of annual basin yield derived using these soil properties with observed CDF's provides excellent verification of the soil-selection procedure. This method of parameterization of the land surface should be useful with present global circulation models, enabling them to account for both the non-linearity in the relationship between soil moisture flux and soil moisture concentration, and the variability of soil properties from place to place over the Earth's surface.

Effects of vegetation types on soil moisture estimation from the normalized land surface temperature versus vegetation index space

NASA Astrophysics Data System (ADS)

Zhang, Dianjun; Zhou, Guoqing

2015-12-01

Soil moisture (SM) is a key variable that has been widely used in many environmental studies. Land surface temperature versus vegetation index (LST-VI) space becomes a common way to estimate SM in optical remote sensing applications. Normalized LST-VI space is established by the normalized LST and VI to obtain the comparable SM in Zhang et al. (Validation of a practical normalized soil moisture model with in situ measurements in humid and semiarid regions [J]. International Journal of Remote Sensing, DOI: 10.1080/01431161.2015.1055610). The boundary conditions in the study were set to limit the point A (the driest bare soil) and B (the wettest bare soil) for surface energy closure. However, no limitation was installed for point D (the full vegetation cover). In this paper, many vegetation types are simulated by the land surface model - Noah LSM 3.2 to analyze the effects on soil moisture estimation, such as crop, grass and mixed forest. The locations of point D are changed with vegetation types. The normalized LST of point D for forest is much lower than crop and grass. The location of point D is basically unchanged for crop and grass.

Calibration and validation of the COSMOS rover for surface soil moisture

USDA-ARS?s Scientific Manuscript database

The mobile COsmic-ray Soil Moisture Observing System (COSMOS) rover may be useful for validating satellite-based estimates of near surface soil moisture, but the accuracy with which the rover can measure 0-5 cm soil moisture has not been previously determined. Our objectives were to calibrate and va...

Remote sensing of soil moisture content over bare fields at 1.4 GHz frequency

NASA Technical Reports Server (NTRS)

Wang, J. R.; Choudhury, B. J.

1980-01-01

A simple method of estimating moisture content (W) of a bare soil from the observed brightness temperature (T sub B) at 1.4 GHz is discussed. The method is based on a radiative transfer model calculation, which has been successfully used in the past to account for many observational results, with some modifications to take into account the effect of surface roughness. Besides the measured T sub B's, the three additional inputs required by the method are the effective soil thermodynamic temperature, the precise relation between W and the smooth field brightness temperature T sub B and a parameter specifying the surface roughness characteristics. The soil effective temperature can be readily measured and the procedures of estimating surface roughness parameter and obtaining the relation between W and smooth field brightness temperature are discussed in detail. Dual polarized radiometric measurements at an off-nadir incident angle are sufficient to estimate both surface roughness parameter and W, provided that the relation between W and smooth field brightness temperature at the same angle is known. The method of W estimate is demonstrated with two sets of experimental data, one from a controlled field experiment by a mobile tower and the other, from aircraft overflight. The results from both data sets are encouraging when the estimated W's are compared with the acquired ground truth of W's in the top 2 cm layer. An offset between the estimated and the measured W's exists in the results of the analyses, but that can be accounted for by the presently poor knowledge of the relationship between W and smooth field brightness temperature for various types of soils. An approach to quantify this relationship for different soils and thus improve the method of W estimate is suggested.

Assimilation of Remotely Sensed Soil Moisture Profiles into a Crop Modeling Framework for Reliable Yield Estimations

NASA Astrophysics Data System (ADS)

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

2017-12-01

Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the growing seasons from 2015-2017. Soil moisture profiles compared favorably to in situ data and simulated crop yields compared well with observed yields.

Using IKONOS Imagery to Estimate Surface Soil Property Variability in Two Alabama Physiographies

NASA Technical Reports Server (NTRS)

Sullivan, Dana; Shaw, Joey; Rickman, Doug

2005-01-01

Knowledge of surface soil properties is used to assess past erosion and predict erodibility, determine nutrient requirements, and assess surface texture for soil survey applications. This study was designed to evaluate high resolution IKONOS multispectral data as a soil- mapping tool. Imagery was acquired over conventionally tilled fields in the Coastal Plain and Tennessee Valley physiographic regions of Alabama. Acquisitions were designed to assess the impact of surface crusting, roughness and tillage on our ability to depict soil property variability. Soils consisted mostly of fine-loamy, kaolinitic, thermic Plinthic Kandiudults at the Coastal Plain site and fine, kaolinitic, thermic Rhodic Paleudults at the Tennessee Valley site. Soils were sampled in 0.20 ha grids to a depth of 15 cm and analyzed for % sand (0.05 - 2 mm), silt (0.002 -0.05 mm), clay (less than 0.002 mm), citrate dithionite extractable iron (Fe(sub d)) and soil organic carbon (SOC). Four methods of evaluating variability in soil attributes were evaluated: 1) kriging of soil attributes, 2) co-kriging with soil attributes and reflectance data, 3) multivariate regression based on the relationship between reflectance and soil properties, and 4) fuzzy c-means clustering of reflectance data. Results indicate that co-kriging with remotely sensed data improved field scale estimates of surface SOC and clay content compared to kriging and regression methods. Fuzzy c-means worked best using RS data acquired over freshly tilled fields, reducing soil property variability within soil zones compared to field scale soil property variability.

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 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.

Estimating soil water content from ground penetrating radar coarse root reflections

NASA Astrophysics Data System (ADS)

Liu, X.; Cui, X.; Chen, J.; Li, W.; Cao, X.

2016-12-01

Soil water content (SWC) is an indispensable variable for understanding the organization of natural ecosystems and biodiversity. Especially in semiarid and arid regions, soil moisture is the plants primary source of water and largely determine their strategies for growth and survival, such as root depth, distribution and competition between them. Ground penetrating radar (GPR), a kind of noninvasive geophysical technique, has been regarded as an accurate tool for measuring soil water content at intermediate scale in past decades. For soil water content estimation with surface GPR, fixed antenna offset reflection method has been considered to have potential to obtain average soil water content between land surface and reflectors, and provide high resolution and few measurement time. In this study, 900MHz surface GPR antenna was used to estimate SWC with fixed offset reflection method; plant coarse roots (with diameters greater than 5 mm) were regarded as reflectors; a kind of advanced GPR data interpretation method, HADA (hyperbola automatic detection algorithm), was introduced to automatically obtain average velocity by recognizing coarse root hyperbolic reflection signals on GPR radargrams during estimating SWC. In addition, a formula was deduced to determine interval average SWC between two roots at different depths as well. We examined the performance of proposed method on a dataset simulated under different scenarios. Results showed that HADA could provide a reasonable average velocity to estimate SWC without knowledge of root depth and interval average SWC also be determined. When the proposed method was applied to estimation of SWC on a real-field measurement dataset, a very small soil water content vertical variation gradient about 0.006 with depth was captured as well. Therefore, the proposed method could be used to estimate average soil water content from ground penetrating radar coarse root reflections and obtain interval average SWC between two roots at different depths. It is very promising for measuring root-zone-soil-moisture and mapping soil moisture distribution around a shrub or even in field plot scale.

Estimation of soil hydraulic properties with microwave techniques

NASA Technical Reports Server (NTRS)

Oneill, P. E.; Gurney, R. J.; Camillo, P. J.

1985-01-01

Useful quantitative information about soil properties may be obtained by calibrating energy and moisture balance models with remotely sensed data. A soil physics model solves heat and moisture flux equations in the soil profile and is driven by the surface energy balance. Model generated surface temperature and soil moisture and temperature profiles are then used in a microwave emission model to predict the soil brightness temperature. The model hydraulic parameters are varied until the predicted temperatures agree with the remotely sensed values. This method is used to estimate values for saturated hydraulic conductivity, saturated matrix potential, and a soil texture parameter. The conductivity agreed well with a value measured with an infiltration ring and the other parameters agreed with values in the literature.

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.

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.

Inversely Estimating the Vertical Profile of the Soil CO2 Production Rate in a Deciduous Broadleaf Forest Using a Particle Filtering Method

PubMed Central

Sakurai, Gen; Yonemura, Seiichiro; Kishimoto-Mo, Ayaka W.; Murayama, Shohei; Ohtsuka, Toshiyuki; Yokozawa, Masayuki

2015-01-01

Carbon dioxide (CO2) efflux from the soil surface, which is a major source of CO2 from terrestrial ecosystems, represents the total CO2 production at all soil depths. Although many studies have estimated the vertical profile of the CO2 production rate, one of the difficulties in estimating the vertical profile is measuring diffusion coefficients of CO2 at all soil depths in a nondestructive manner. In this study, we estimated the temporal variation in the vertical profile of the CO2 production rate using a data assimilation method, the particle filtering method, in which the diffusion coefficients of CO2 were simultaneously estimated. The CO2 concentrations at several soil depths and CO2 efflux from the soil surface (only during the snow-free period) were measured at two points in a broadleaf forest in Japan, and the data were assimilated into a simple model including a diffusion equation. We found that there were large variations in the pattern of the vertical profile of the CO2 production rate between experiment sites: the peak CO2 production rate was at soil depths around 10 cm during the snow-free period at one site, but the peak was at the soil surface at the other site. Using this method to estimate the CO2 production rate during snow-cover periods allowed us to estimate CO2 efflux during that period as well. We estimated that the CO2 efflux during the snow-cover period (about half the year) accounted for around 13% of the annual CO2 efflux at this site. Although the method proposed in this study does not ensure the validity of the estimated diffusion coefficients and CO2 production rates, the method enables us to more closely approach the “actual” values by decreasing the variance of the posterior distribution of the values. PMID:25793387

Surface albedo from bidirectional reflectance

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Irons, J. R.; Daughtry, C. S. T.

1991-01-01

The validity of integrating over discrete wavelength bands is examined to estimate total shortwave bidirectional reflectance of vegetated and bare soil surfaces. Methods for estimating albedo from multiple angle, discrete wavelength band radiometer measurements are studied. These methods include a numerical integration technique and the integration of an empirically derived equation for bidirectional reflectance. It is concluded that shortwave albedos estimated through both techniques agree favorably with the independent pyranometer measurements. Absolute rms errors are found to be 0.5 percent or less for both grass sod and bare soil surfaces.

Field-scale moisture estimates using COSMOS sensors: a validation study with temporary networks and leaf-area-indices

USDA-ARS?s Scientific Manuscript database

The Cosmic-ray Soil Moisture Observing System (COSMOS) is a new and innovative method for estimating surface and near surface soil moisture at large (~700 m) scales. This system accounts for liquid water within its measurement volume. Many of the sites used in the early validation of the system had...

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.

Towards Improved High-Resolution Land Surface Hydrologic Reanalysis Using a Physically-Based Hydrologic Model and Data Assimilation

NASA Astrophysics Data System (ADS)

Shi, Y.; Davis, K. J.; Zhang, F.; Duffy, C.; Yu, X.

2014-12-01

A coupled physically based land surface hydrologic model, Flux-PIHM, has been developed by incorporating a land surface scheme into the Penn State Integrated Hydrologic Model (PIHM). The land surface scheme is adapted from the Noah land surface model. Flux-PIHM has been implemented and manually calibrated at the Shale Hills watershed (0.08 km2) in central Pennsylvania. Model predictions of discharge, point soil moisture, point water table depth, sensible and latent heat fluxes, and soil temperature show good agreement with observations. When calibrated only using discharge, and soil moisture and water table depth at one point, Flux-PIHM is able to resolve the observed 101 m scale soil moisture pattern at the Shale Hills watershed when an appropriate map of soil hydraulic properties is provided. A Flux-PIHM data assimilation system has been developed by incorporating EnKF for model parameter and state estimation. Both synthetic and real data assimilation experiments have been performed at the Shale Hills watershed. Synthetic experiment results show that the data assimilation system is able to simultaneously provide accurate estimates of multiple parameters. In the real data experiment, the EnKF estimated parameters and manually calibrated parameters yield similar model performances, but the EnKF method significantly decreases the time and labor required for calibration. The data requirements for accurate Flux-PIHM parameter estimation via data assimilation using synthetic observations have been tested. Results show that by assimilating only in situ outlet discharge, soil water content at one point, and the land surface temperature averaged over the whole watershed, the data assimilation system can provide an accurate representation of watershed hydrology. Observations of these key variables are available with national and even global spatial coverage (e.g., MODIS surface temperature, SMAP soil moisture, and the USGS gauging stations). National atmospheric reanalysis products, soil databases and land cover databases (e.g., NLDAS-2, SSURGO, NLCD) can provide high resolution forcing and input data. Therefore the Flux-PIHM data assimilation system could be readily expanded to other watersheds to provide regional scale land surface and hydrologic reanalysis with high spatial temporal resolution.

100 years of Pb deposition and transport in soils in Champaign, Illinois, U.S.A

USGS Publications Warehouse

Zhang, Y.

2003-01-01

In Illinois, atmospheric deposition is one major source of heavy metal inputs to agricultural land. The atmospheric Pb deposition and transport record in agricultural soils in Champaign, Illinois, was established by studying surface and subsurface soil samples collected during the past 100 years from the Morrow Plots on the campus of the University of Illinois at Urbana-Champaign. The Pb content in the soil samples was measured and the Ph deposition fluxes were calculated. The Pb content in surface soils increased sharply in the first half of the 20th century, and stayed invariant since. The maximum Pb flux from the atmosphere was estimated to be 27 (??14) ??g cm-2 yr-1 around 1940. The major pollution source for this increase probably was residential coal burning. It was estimated that in 50 yr, more than 50% of the Pb input had been lost from the surface soils.

10Be inventories in Alpine soils and their potentiality for dating land surfaces

NASA Astrophysics Data System (ADS)

Egli, Markus; Brandová, Dagmar; Böhlert, Ralph; Favilli, Filippo; Kubik, Peter W.

2010-05-01

To exploit natural archives and geomorphic objects it is necessary to date them first. Landscape evolution of Alpine areas is often strongly related to the activities of glaciers in the Pleistocene and Holocene. At sites where no organic matter for radiocarbon dating exists and where suitable boulders for surface exposure dating (using in situ produced cosmogenic nuclides) are absent, dating of soils could give information about the timing of landscape evolution. We explored the applicability of soil dating using the inventory of meteoric Be-10 in Alpine soils. For this purpose, a set of 6 soil profiles in the Swiss and Italian Alps was investigated. The surface at these sites had already been dated (using the radiocarbon technique or surface exposure dating using in situ produced Be-10). Consequently, a direct comparison of the ages of the soils using meteoric Be-10 and other dating techniques was made possible. The estimation of Be-10 deposition rates is subject to severe limitations and strongly influences the obtained results. We tested three scenarios using a) the meteoric Be-10 deposition rates as a function of the annual precipitation rate, b) a constant Be-10 input for the Central Alps and c) as b) but assuming a pre-exposure of the parent material. The obtained ages that are based on the Be-10 inventory in soils and on scenario a) for the Be-10 input agreed reasonably well with the expected age (obtained from surface exposure or radiocarbon dating). The ages obtained from soils using scenario b) produced mostly ages that were too old whereas the approach using scenario c) seemed to yield better results than scenario b). Erosion calculations can, in theory, be performed using the Be-10 inventory and Be-10 deposition rates. An erosion estimation was possible using scenario a) and c), but not using b). The estimated erosion rates are in a reasonable range. The dating of soils using Be-10 has several potential error sources. Analytical errors as well as errors from other parameters such as bulk soil density and soil skeleton content have to be taken into account. The error range was from 8 up to 21%. Furthermore, uncertainties in estimating Be-10 deposition rates substantially influence the calculated ages. Relative age estimates and, under optimal conditions, a numerical dating can be carried out. Age determination of Alpine soils using Be-10 gives another possibility to date surfaces when other methods fail or are not possible at all. It is, however, not straightforward, quite laborious and may consequently have some distinct limitations.

Surface Soil Moisture Retrieval Using SSM/I and Its Comparison with ESTAR: A Case Study Over a Grassland Region

NASA Technical Reports Server (NTRS)

Jackson, T.; Hsu, A. Y.; ONeill, P. E.

1999-01-01

This study extends a previous investigation on estimating surface soil moisture using the Special Sensor Microwave/Imager (SSM/I) over a grassland region. Although SSM/I is not optimal for soil moisture retrieval, it can under some conditions provide information. Rigorous analyses over land have been difficult due to the lack of good validation data sets. A scientific objective of the Southern Great Plains 1997 (SGP97) Hydrology Experiment was to investigate whether the retrieval algorithms for surface soil moisture developed at higher spatial resolution using truck-and aircraft-based passive microwave sensors can be extended to the coarser resolutions expected from satellite platform. With the data collected for the SGP97, the objective of this study is to compare the surface soil moisture estimated from the SSM/I data with those retrieved from the L-band Electronically Scanned Thinned Array Radiometer (ESTAR) data, the core sensor for the experiment, using the same retrieval algorithm. The results indicated that an error of estimate of 7.81% could be achieved with SSM/I data as contrasted to 2.82% with ESTAR data over three intensive sampling areas of different vegetation regimes. It confirms the results of previous study that SSM/I data can be used to retrieve surface soil moisture information at a regional scale under certain conditions.

Estimates of Soil Moisture Using the Land Information System for Land Surface Water Storage: Case Study for the Western States Water Mission

NASA Astrophysics Data System (ADS)

Liu, P. W.; Famiglietti, J. S.; Levoe, S.; Reager, J. T., II; David, C. H.; Kumar, S.; Li, B.; Peters-Lidard, C. D.

2017-12-01

Soil moisture is one of the critical factors in terrestrial hydrology. Accurate soil moisture information improves estimation of terrestrial water storage and fluxes, that is essential for water resource management including sustainable groundwater pumping and agricultural irrigation practices. It is particularly important during dry periods when water stress is high. The Western States Water Mission (WSWM), a multiyear mission project of NASA's Jet Propulsion Laboratory, is operated to understand and estimate quantities of the water availability in the western United States by integrating observations and measurements from in-situ and remote sensing sensors, and hydrological models. WSWM data products have been used to assess and explore the adverse impacts of the California drought (2011-2016) and provide decision-makers information for water use planning. Although the observations are often more accurate, simulations using land surface models can provide water availability estimates at desired spatio-temporal scales. The Land Information System (LIS), developed by NASA's Goddard Space Flight Center, integrates developed land surface models and data processing and management tools, that enables to utilize the measurements and observations from various platforms as forcings in the high performance computing environment to forecast the hydrologic conditions. The goal of this study is to implement the LIS in the western United States for estimates of soil moisture. We will implement the NOAH-MP model at the 12km North America Land Data Assimilation System grid and compare to other land surface models included in the LIS. Findings will provide insight into the differences between model estimates and model physics. Outputs from a multi-model ensemble from LIS can also be used to enhance estimated reliability and provide quantification of uncertainty. We will compare the LIS-based soil moisture estimates to the SMAP enhanced 9 km soil moisture product to understand the mechanistic differences between the model and observation. These outcomes will contribute to the WSWM for providing robust products.

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.

Evaluation of a cosmic-ray neutron sensor network for improved land surface model prediction

NASA Astrophysics Data System (ADS)

Baatz, Roland; Hendricks Franssen, Harrie-Jan; Han, Xujun; Hoar, Tim; Reemt Bogena, Heye; Vereecken, Harry

2017-05-01

In situ soil moisture sensors provide highly accurate but very local soil moisture measurements, while remotely sensed soil moisture is strongly affected by vegetation and surface roughness. In contrast, cosmic-ray neutron sensors (CRNSs) allow highly accurate soil moisture estimation on the field scale which could be valuable to improve land surface model predictions. In this study, the potential of a network of CRNSs installed in the 2354 km2 Rur catchment (Germany) for estimating soil hydraulic parameters and improving soil moisture states was tested. Data measured by the CRNSs were assimilated with the local ensemble transform Kalman filter in the Community Land Model version 4.5. Data of four, eight and nine CRNSs were assimilated for the years 2011 and 2012 (with and without soil hydraulic parameter estimation), followed by a verification year 2013 without data assimilation. This was done using (i) a regional high-resolution soil map, (ii) the FAO soil map and (iii) an erroneous, biased soil map as input information for the simulations. For the regional soil map, soil moisture characterization was only improved in the assimilation period but not in the verification period. For the FAO soil map and the biased soil map, soil moisture predictions improved strongly to a root mean square error of 0.03 cm3 cm-3 for the assimilation period and 0.05 cm3 cm-3 for the evaluation period. Improvements were limited by the measurement error of CRNSs (0.03 cm3 cm-3). The positive results obtained with data assimilation of nine CRNSs were confirmed by the jackknife experiments with four and eight CRNSs used for assimilation. The results demonstrate that assimilated data of a CRNS network can improve the characterization of soil moisture content on the catchment scale by updating spatially distributed soil hydraulic parameters of a land surface model.

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%.

Soil water content and evaporation determined by thermal parameters obtained from ground-based and remote measurements

NASA Technical Reports Server (NTRS)

Reginato, R. J.; Idso, S. B.; Jackson, R. D.; Vedder, J. F.; Blanchard, M. B.; Goettelman, R.

1976-01-01

Soil water contents from both smooth and rough bare soil were estimated from remotely sensed surface soil and air temperatures. An inverse relationship between two thermal parameters and gravimetric soil water content was found for Avondale loam when its water content was between air-dry and field capacity. These parameters, daily maximum minus minimum surface soil temperature and daily maximum soil minus air temperature, appear to describe the relationship reasonably well. These two parameters also describe relative soil water evaporation (actual/potential). Surface soil temperatures showed good agreement among three measurement techniques: in situ thermocouples, a ground-based infrared radiation thermometer, and the thermal infrared band of an airborne multispectral scanner.

High resolution land surface geophysical parameters estimation from ALOS PALSAR data

USDA-ARS?s Scientific Manuscript database

High resolution land surface geophysical products, such as soil moisture, surface roughness and vegetation water content, are essential for a variety of applications ranging from water management to regional climate predictions. In India high resolution geophysical products, in particular soil moist...

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.

Validating HYLARSMET: a Hydrologically Consistent Land Surface Model for Soil Moisture and Evapotranspiration Modelling over Southern Africa using Remote Sensing and Meteorological Data

NASA Astrophysics Data System (ADS)

Sinclair, Scott; Pegram, Geoff; Mengitsu, Michael; Everson, Colin

2015-04-01

Timeous knowledge of the spatial distribution of soil moisture and evapotranspiration over a large region in fine detail has great value for coping with two weather extremes: flash floods and droughts, since the state of the wetness of the land surface has a major impact on runoff response. Also, the ability to monitor the wetness of the soil and the actual evapotranspiration over large regions, without having to laboriously take expensive samples, is a bonus for agricultural managers who need to predict crop yields. We present samples of the daily national Soil Moisture and Evapotranspiration estimates on a grid of 7300 locations centred in 12 km squares, then move on to the results of a validation study for soil moisture and evapotranspiration estimated using the PyTOPKAPI hydrological model in Land Surface Modelling mode, a system called HYLARSMET. The HYLARSMET estimates are compared with detailed evapotranspiration and soil moisture measurements made at the Baynesfield experimental farm in the KwaZulu-Natal province of South Africa, run by the University of KZN. The HYLARSMET evapotranspiration estimates compared very well with the measured estimates for the two chosen crop types, in spite of the fact that the HYLARSMET estimates were not designed to explicitly account for the crop types at each site. The same seasonality effects were evident in all 3 estimates, and there was a stronger ET relationship between HYLARSMET and the Soybean site (Pearson r = 0.81) than for Maize, (r = 0.59). The soil moisture relationship was stronger between the two in situ measured estimates (r = 0.98 at 0.5 m depth) than it was between HYLARSMET and the field estimates (r about 0.52 in both cases). Overall there was a reasonably good relationship between HYLARSMET and the in situ measurements of ET and SM at each site, indicating the value of the modelling procedure.

Using machine learning to produce near surface soil moisture estimates from deeper in situ records at U.S. Climate Reference Network (USCRN) locations: Analysis and applications to AMSR-E satellite validation

USDA-ARS?s Scientific Manuscript database

Surface soil moisture is critical parameter for understanding the energy flux at the land atmosphere boundary. Weather modeling, climate prediction, and remote sensing validation are some of the applications for surface soil moisture information. The most common in situ measurement for these purpo...

A new device to estimate abundance of moist-soil plant seeds

USGS Publications Warehouse

Penny, E.J.; Kaminski, R.M.; Reinecke, K.J.

2006-01-01

Methods to sample the abundance of moist-soil seeds efficiently and accurately are critical for evaluating management practices and determining food availability. We adapted a portable, gasoline-powered vacuum to estimate abundance of seeds on the surface of a moist-soil wetland in east-central Mississippi and evaluated the sampler by simulating conditions that researchers and managers may experience when sampling moist-soil areas for seeds. We measured the percent recovery of known masses of seeds by the vacuum sampler in relation to 4 experimentally controlled factors (i.e., seed-size class, sample mass, soil moisture class, and vacuum time) with 2-4 levels per factor. We also measured processing time of samples in the laboratory. Across all experimental factors, seed recovery averaged 88.4% and varied little (CV = 0.68%, n = 474). Overall, mean time to process a sample was 30.3 ? 2.5 min (SE, n = 417). Our estimate of seed recovery rate (88%) may be used to adjust estimates for incomplete seed recovery, or project-specific correction factors may be developed by investigators. Our device was effective for estimating surface abundance of moist-soil plant seeds after dehiscence and before habitats were flooded.

Event-based estimation of water budget components using the network of multi-sensor capacitance probes

USDA-ARS?s Scientific Manuscript database

A time-scale-free approach was developed for estimation of water fluxes at boundaries of monitoring soil profile using water content time series. The approach uses the soil water budget to compute soil water budget components, i.e. surface-water excess (Sw), infiltration less evapotranspiration (I-E...

Development of a predictive model to estimate the effect of soil solarization on survival of soilborne inoculum of Phytophthora ramorum and Phytophthora pini

Treesearch

Fumiaki Funahashi; Jennifer L. Parke

2017-01-01

Soil solarization has been shown to be an effective tool to manage Phytophthora spp. within surface soils, but estimating the minimum time required to complete local eradication under variable weather conditions remains unknown. A mathematical model could help predict the effectiveness of solarization at different sites and soil depths....

Coupled land surface-subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

NASA Astrophysics Data System (ADS)

Phuong Tran, Anh; Dafflon, Baptiste; Hubbard, Susan S.

2017-09-01

Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface-subsurface hydrological-thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon-climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological-thermal processes associated with annual freeze-thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets - including soil liquid water content, temperature and electrical resistivity tomography (ERT) data - to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological-thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface-subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice-liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological-thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological-thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface-subsurface, deterministic-stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological-thermal dynamics.

Statistical analysis of $sup 239-240$Pu and $sup 241$Am contamination of soil and vegetation on NAEG study sites

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

Gilbert, R.O.; Eberhardt, L.L.; Fowler, E.B.

Reported here are results of the statistical design and analysis work conducted during Calendar Year 1974 for the Nevada Applied Ecology Group (NAEG) at plutonium study sites on the Nevada Test Site (NTS) and the Tonopah Test Range (TTR). Estimates of $sup 239-240$Pu inventory in surface soil (0 to 5-cm depth) are given for each of the NAEG intensive study sites, together with activity maps based on FIDLER surveys showing the field areas to which these estimates apply. There is evidence of a preliminary nature to suggest that the plutonium present in surface soil may be covered by a thinmore » (less than 2.5 cm) layer of soil whose alpha activity is considerably less than that directly below. Computer-drawn $sup 239-240$Pu concentration contours and three-dimensional surfaces in soil and vegetation are given for Area 13 and GMX as a first attempt at estimating the geographical distribution of $sup 239-240$Pu at these sites. (CH)« less

Submicron structures provide preferential spots for carbon and nitrogen sequestration in soils

PubMed Central

Vogel, Cordula; Mueller, Carsten W.; Höschen, Carmen; Buegger, Franz; Heister, Katja; Schulz, Stefanie; Schloter, Michael; Kögel-Knabner, Ingrid

2014-01-01

The sequestration of carbon and nitrogen by clay-sized particles in soils is well established, and clay content or mineral surface area has been used to estimate the sequestration potential of soils. Here, via incubation of a sieved (<2 mm) topsoil with labelled litter, we find that only some of the clay-sized surfaces bind organic matter (OM). Surprisingly, <19% of the visible mineral areas show an OM attachment. OM is preferentially associated with organo-mineral clusters with rough surfaces. By combining nano-scale secondary ion mass spectrometry and isotopic tracing, we distinguish between new labelled and pre-existing OM and show that new OM is preferentially attached to already present organo-mineral clusters. These results, which provide evidence that only a limited proportion of the clay-sized surfaces contribute to OM sequestration, revolutionize our view of carbon sequestration in soils and the widely used carbon saturation estimates. PMID:24399306

Impact of Uncertainties in Meteorological Forcing Data and Land Surface Parameters on Global Estimates of Terrestrial Water Balance Components

NASA Astrophysics Data System (ADS)

Nasonova, O. N.; Gusev, Ye. M.; Kovalev, Ye. E.

2009-04-01

Global estimates of the components of terrestrial water balance depend on a technique of estimation and on the global observational data sets used for this purpose. Land surface modelling is an up-to-date and powerful tool for such estimates. However, the results of modelling are affected by the quality of both a model and input information (including meteorological forcing data and model parameters). The latter is based on available global data sets containing meteorological data, land-use information, and soil and vegetation characteristics. Now there are a lot of global data sets, which differ in spatial and temporal resolution, as well as in accuracy and reliability. Evidently, uncertainties in global data sets will influence the results of model simulations, but to which extent? The present work is an attempt to investigate this issue. The work is based on the land surface model SWAP (Soil Water - Atmosphere - Plants) and global 1-degree data sets on meteorological forcing data and the land surface parameters, provided within the framework of the Second Global Soil Wetness Project (GSWP-2). The 3-hourly near-surface meteorological data (for the period from 1 July 1982 to 31 December 1995) are based on reanalyses and gridded observational data used in the International Satellite Land-Surface Climatology Project (ISLSCP) Initiative II. Following the GSWP-2 strategy, we used a number of alternative global forcing data sets to perform different sensitivity experiments (with six alternative versions of precipitation, four versions of radiation, two pure reanalysis products and two fully hybridized products of meteorological data). To reveal the influence of model parameters on simulations, in addition to GSWP-2 parameter data sets, we produced two alternative global data sets with soil parameters on the basis of their relationships with the content of clay and sand in a soil. After this the sensitivity experiments with three different sets of parameters were performed. As a result, 16 variants of global annual estimates of water balance components were obtained. Application of alternative data sets on radiation, precipitation, and soil parameters allowed us to reveal the influence of uncertainties in input data on global estimates of water balance components.

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.

Evaluation of HYDRUS-1D for Estimating Evapotranspiration of Bell Pepper Regulated by Cloud-based Fertigation System in Greenhouse

NASA Astrophysics Data System (ADS)

Ito, Y.; Honda, R.; Takesako, H.; Ozawa, K.; Kita, E.; Kanno, M.; Noborio, K.

2017-12-01

A fertile surface layer, contaminated with radiocesium resulting from the accident of the Fukushima Daiichi Nuclear Power Plant in 2011, was removed and replaced by non-fertile soil in Fukushima farmlands. In a greenhouse, we used a commercially-available cloud-based fertigation system (CBFS) for regulating an application rate of liquid fertilizer to bell pepper grown in the non-fertile soil. Although the CBFS regulates the application rate based on a weekly trend of volumetric water content (Θw) remotely measured at the soil surface using a soil moisture sensor if all applied water being consumed by plants in a greenhouse is not known. Evapotranspiration of green pepper grown with the CBFS was estimated by HYDRUS-1D. Experiments in a greenhouse were conducted in Fukushima, Japan, from September 1st to October 31st in 2016. Bell pepper plants were transplanted in the begging of June in 2016. The Penman-Monteith equation was used to estimate evapotranspiration, representing transpiration since the soil surface was covered with plastic mulch. Time domain reflectometry (TDR) probes were horizontally installed to monitor changes in Θw at 5, 10, 20, and 30 cm deep from the soil surface. The van Genuchten-Mualem hydraulic model for water and heat flow in soil was used for HYDRUS-1D. A precipitation rate for the upper boundary condition was given as an irrigation rate. We assumed wind speed was always 0.6 m s-1 for the Penman-Monteith equation. The amount of evapotranspiration estimated with the Penman-Monteith equation agreed well with the amount of irrigated water measured. The evapotranspiration simulated with HYDRUS-1D agreed well with that estimated with the Penman-Monteith equation. However, Θw at all depth were underestimated with Hydrus-1D by approximately 0.05 m3 m-3 and differences of Θw between measured and estimated with HYDRYS-1D became larger at deeper the soil depths. This might be attributed to larger water flow occurred because of a free drainage used for the lower boundary condition. Although transpiration from plants should be measured directly to properly evaluate irrigation rate regulated by the CBFS, HYDRUS-1D was found to estimate evapotranspiration with enough accuracy. We will further evaluate the applicability of HYDRUS-1D to estimate evapotranspiration throughout a growing period.

Thermal and Hydrologic Signatures of Soil Controls on Evaporation: A Combined Energy and Water Balance Approach with Implications for Remote Sensing of Evaporation

NASA Technical Reports Server (NTRS)

Salvucci, Guido D.

2000-01-01

The overall goal of this research is to examine the feasibility of applying a newly developed diagnostic model of soil water evaporation to large land areas using remotely sensed input parameters. The model estimates the rate of soil evaporation during periods when it is limited by the net transport resulting from competing effects of capillary rise and drainage. The critical soil hydraulic properties are implicitly estimated via the intensity and duration of the first stage (energy limited) evaporation, removing a major obstacle in the remote estimation of evaporation over large areas. This duration, or 'time to drying' (t(sub d)) is revealed through three signatures detectable in time series of remote sensing variables. The first is a break in soil albedo that occurs as a small vapor transmission zone develops near the surface. The second is a break in either surface to air temperature differences or in the diurnal surface temperature range, both of which indicate increased sensible heat flux (and/or storage) required to balance the decrease in latent heat flux. The third is a break in the temporal pattern of near surface soil moisture. Soil moisture tends to decrease rapidly during stage I drying (as water is removed from storage), and then become more or less constant during soil limited, or 'stage II' drying (as water is merely transmitted from deeper soil storage). The research tasks address: (1) improvements in model structure, including extensions to transpiration and aggregation over spatially variable soil and topographic landscape attributes; and (2) applications of the model using remotely sensed input parameters.

Thermal and Hydrologic Signatures of Soil Controls on Evaporation: A Combined Energy and Water Balance Approach with Implications for Remote Sensing of Evaporation

NASA Technical Reports Server (NTRS)

Salvucci, Guido D.

1997-01-01

The overall goal of this research is to examine the feasibility of applying a newly developed diagnostic model of soil water evaporation to large land areas using remotely sensed input parameters. The model estimates the rate of soil evaporation during periods when it is limited by the net transport resulting from competing effects of capillary rise and drainage. The critical soil hydraulic properties are implicitly estimated via the intensity and duration of the first stage (energy limited) evaporation, removing a major obstacle in the remote estimation of evaporation over large areas. This duration, or "time to drying" (t(sub d)), is revealed through three signatures detectable in time series of remote sensing variables. The first is a break in soil albedo that occurs as a small vapor transmission zone develops near the surface. The second is a break in either surface to air temperature differences or in the diurnal surface temperature range, both of which indicate increased sensible heat flux (and/or storage) required to balance the decrease in latent heat flux. The third is a break in the temporal pattern of near surface soil moisture. Soil moisture tends to decrease rapidly during stage 1 drying (as water is removed from storage), and then become more or less constant during soil limited, or "stage 2" drying (as water is merely transmitted from deeper soil storage). The research tasks address: (1) improvements in model structure, including extensions to transpiration and aggregation over spatially variable soil and topographic landscape attributes; and (2) applications of the model using remotely sensed input parameters.

Soil moisture and properties estimation by assimilating soil temperatures using particle batch smoother: A new perspective for DTS

NASA Astrophysics Data System (ADS)

Dong, J.; Steele-Dunne, S. C.; Ochsner, T. E.; Van De Giesen, N.

2015-12-01

Soil moisture, hydraulic and thermal properties are critical for understanding the soil surface energy balance and hydrological processes. Here, we will discuss the potential of using soil temperature observations from Distributed Temperature Sensing (DTS) to investigate the spatial variability of soil moisture and soil properties. With DTS soil temperature can be measured with high resolution (spatial <1m, and temporal < 1min) in cables up to kilometers in length. Soil temperature evolution is primarily controlled by the soil thermal properties, and the energy balance at the soil surface. Hence, soil moisture, which affects both soil thermal properties and the energy that participates the evaporation process, is strongly correlated to the soil temperatures. In addition, the dynamics of the soil moisture is determined by the soil hydraulic properties.Here we will demonstrate that soil moisture, hydraulic and thermal properties can be estimated by assimilating observed soil temperature at shallow depths using the Particle Batch Smoother (PBS). The PBS can be considered as an extension of the particle filter, which allows us to infer soil moisture and soil properties using the dynamics of soil temperature within a batch window. Both synthetic and real field data will be used to demonstrate the robustness of this approach. We will show that the proposed method is shown to be able to handle different sources of uncertainties, which may provide a new view of using DTS observations to estimate sub-meter resolution soil moisture and properties for remote sensing product validation.

Soil Moisture Content Estimation using GPR Reflection Travel Time

NASA Astrophysics Data System (ADS)

Lunt, I. A.; Hubbard, S. S.; Rubin, Y.

2003-12-01

Ground-penetrating radar (GPR) reflection travel time data were used to estimate changes in soil water content under a range of soil saturation conditions throughout the growing season at a California winery. Data were collected during four data acquisition campaigns over an 80 by 180 m area using 100 MHz surface GPR antennae. GPR reflections were associated with a thin, low permeability clay layer located between 0.8 to 1.3 m below the ground surface that was calibrated with borehole information and mapped across the study area. Field infiltration tests and neutron probe logs suggest that the thin clay layer inhibited vertical water flow, and was coincident with high volumetric water content (VWC) values. The GPR reflection two-way travel time and the depth of the reflector at borehole locations were used to calculate an average dielectric constant for soils above the reflector. A site-specific relationship between the dielectric constant and VWC was then used to estimate the depth-averaged VWC of the soils above the reflector. Compared to average VWC measurements from calibrated neutron probe logs over the same depth interval, the average VWC estimates obtained from GPR reflections had an RMS error of 2 percent. We also investigated the estimation of VWC using reflections associated with an advancing water front, and found that estimates of average VWC to the water front could be obtained with similar accuracy. These results suggested that the two-way travel time to a GPR reflection associated with a geological surface or wetting front can be used under natural conditions to obtain estimates of average water content when borehole control is available. The GPR reflection method therefore has potential for monitoring soil water content over large areas and under variable hydrological conditions.

Estimation of bare soil evaporation for different depths of water table in the wind-blown sand area of the Ordos Basin, China

NASA Astrophysics Data System (ADS)

Chen, Li; Wang, Wenke; Zhang, Zaiyong; Wang, Zhoufeng; Wang, Qiangmin; Zhao, Ming; Gong, Chengcheng

2018-04-01

Soil surface evaporation is a significant component of the hydrological cycle, occurring at the interface between the atmosphere and vadose zone, but it is affected by factors such as groundwater level, soil properties, solar radiation and others. In order to understand the soil evaporation characteristics in arid regions, a field experiment was conducted in the Ordos Basin, central China, and high accuracy sensors of soil moisture, moisture potential and temperature were installed in three field soil profiles with water-table depths (WTDs) of about 0.4, 1.4 and 2.2 m. Soil-surface-evaporation values were estimated by observed data combined with Darcy's law. Results showed that: (1) soil-surface-evaporation rate is linked to moisture content and it is also affected by air temperature. When there is sufficient moisture in the soil profile, soil evaporation increases with rising air temperature. For a WTD larger than the height of capillary rise, the soil evaporation is related to soil moisture content, and when air temperature is above 25 °C, the soil moisture content reduces quickly and the evaporation rate lowers; (2) phreatic water contributes to soil surface evaporation under conditions in which the WTD is within the capillary fringe. This indicates that phreatic water would not participate in soil evaporation for a WTD larger than the height of capillary rise. This finding developed further the understanding of phreatic evaporation, and this study provides valuable information on recognized soil evaporation processes in the arid environment.

Utilization of Satellite Data in Land Surface Hydrology: Sensitivity and Assimilation

NASA Technical Reports Server (NTRS)

Lakshmi, Venkataraman; Susskind, Joel

1999-01-01

This paper investigates the sensitivity of potential evapotranspiration to input meteorological variables, viz- surface air temperature and surface vapor pressure. The sensitivity studies have been carried out for a wide range of land surface variables such as wind speed, leaf area index and surface temperatures. Errors in the surface air temperature and surface vapor pressure result in errors of different signs in the computed potential evapotranspiration. This result has implications for use of estimated values from satellite data or analysis of surface air temperature and surface vapor pressure in large scale hydrological modeling. The comparison of cumulative potential evapotranspiration estimates using ground observations and satellite observations over Manhattan, Kansas for a period of several months shows very little difference between the two. The cumulative differences between the ground based and satellite based estimates of potential evapotranspiration amounted to less that 20mm over a 18 month period and a percentage difference of 15%. The use of satellite estimates of surface skin temperature in hydrological modeling to update the soil moisture using a physical adjustment concept is studied in detail including the extent of changes in soil moisture resulting from the assimilation of surface skin temperature. The soil moisture of the surface layer is adjusted by 0.9mm over a 10 day period as a result of a 3K difference between the predicted and the observed surface temperature. This is a considerable amount given the fact that the top layer can hold only 5mm of water.

Deforestation and cultivation mobilize mercury from topsoil.

PubMed

Gamby, Rebecca L; Hammerschmidt, Chad R; Costello, David M; Lamborg, Carl H; Runkle, James R

2015-11-01

Terrestrial biomass and soils are a primary global reservoir of mercury (Hg) derived from natural and anthropogenic sources; however, relatively little is known about the fate and stability of Hg in the surface soil reservoir and its susceptibility to change as a result of deforestation and cultivation. In southwest Ohio, we measured Hg concentrations in soils of deciduous old- and new-growth forests, as well as fallow grassland and agricultural soils that had once been forested to examine how, over decadal to century time scales, man-made deforestation and cultivation influence Hg mobility from temperate surface soils. Mercury concentrations in surficial soils were significantly greater in the old-growth than new-growth forest, and both forest soils had greater Hg concentrations than cultivated and fallow fields. Differences in Hg:lead ratios between old-growth forest and agricultural topsoils suggest that about half of the Hg lost from deforested and cultivated Ohio soils may have been volatilized and the other half eroded. The estimated mobilization potential of Hg as a result of deforestation was 4.1 mg m(-2), which was proportional to mobilization potentials measured at multiple locations in the Amazon relative to concentrations in forested surface soils. Based on this relationship and an estimate of the global average of Hg concentrations in forested soils, we approximate that about 550 M mol of Hg has been mobilized globally from soil as a result of deforestation during the past two centuries. This estimate is comparable to, if not greater than, the amount of anthropogenic Hg hypothesized by others to have been sequestered by the soil reservoir since Industrialization. Our results suggest that deforestation and soil cultivation are significant anthropogenic processes that exacerbate Hg mobilization from soil and its cycling in the environment. Copyright © 2015 Elsevier B.V. All rights reserved.

Inferring Land Surface Model Parameters for the Assimilation of Satellite-Based L-Band Brightness Temperature Observations into a Soil Moisture Analysis System

NASA Technical Reports Server (NTRS)

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

2012-01-01

The Soil Moisture and Ocean Salinity (SMOS) satellite mission provides global measurements of L-band brightness temperatures at horizontal and vertical polarization and a variety of incidence angles that are sensitive to moisture and temperature conditions in the top few centimeters of the soil. These L-band observations can therefore be assimilated into a land surface model to obtain surface and root zone soil moisture estimates. As part of the observation operator, such an assimilation system requires a radiative transfer model (RTM) that converts geophysical fields (including soil moisture and soil temperature) into modeled L-band brightness temperatures. At the global scale, the RTM parameters and the climatological soil moisture conditions are still poorly known. Using look-up tables from the literature to estimate the RTM parameters usually results in modeled L-band brightness temperatures that are strongly biased against the SMOS observations, with biases varying regionally and seasonally. Such biases must be addressed within the land data assimilation system. In this presentation, the estimation of the RTM parameters is discussed for the NASA GEOS-5 land data assimilation system, which is based on the ensemble Kalman filter (EnKF) and the Catchment land surface model. In the GEOS-5 land data assimilation system, soil moisture and brightness temperature biases are addressed in three stages. First, the global soil properties and soil hydraulic parameters that are used in the Catchment model were revised to minimize the bias in the modeled soil moisture, as verified against available in situ soil moisture measurements. Second, key parameters of the "tau-omega" RTM were calibrated prior to data assimilation using an objective function that minimizes the climatological differences between the modeled L-band brightness temperatures and the corresponding SMOS observations. Calibrated parameters include soil roughness parameters, vegetation structure parameters, and the single scattering albedo. After this climatological calibration, the modeling system can provide L-band brightness temperatures with a global mean absolute bias of less than 10K against SMOS observations, across multiple incidence angles and for horizontal and vertical polarization. Third, seasonal and regional variations in the residual biases are addressed by estimating the vegetation optical depth through state augmentation during the assimilation of the L-band brightness temperatures. This strategy, tested here with SMOS data, is part of the baseline approach for the Level 4 Surface and Root Zone Soil Moisture data product from the planned Soil Moisture Active Passive (SMAP) satellite mission.

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review

PubMed Central

Zhang, Dianjun; Zhou, Guoqing

2016-01-01

As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research. PMID:27548168

Estimation of Soil Moisture from Optical and Thermal Remote Sensing: A Review.

PubMed

Zhang, Dianjun; Zhou, Guoqing

2016-08-17

As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.

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.

HCMM energy budget data as a model input for assessing regions of high potential groundwater pollution. [South Dakota

NASA Technical Reports Server (NTRS)

Moore, D. G. (Principal Investigator); Heilman, J. L.

1980-01-01

The author has identified the following significant results. Significant relationships were found between surface soil temperatures estimated from HCMM radiometric temperatures and depth to ground water and near surface soil moisture.

Hydrologic downscaling of soil moisture using global data without site-specific calibration

USDA-ARS?s Scientific Manuscript database

Numerous applications require fine-resolution (10-30 m) soil moisture patterns, but most satellite remote sensing and land-surface models provide coarse-resolution (9-60 km) soil moisture estimates. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales soil moistu...

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.

Coupled Land Surface-Subsurface Hydrogeophysical Inverse Modeling to Estimate Soil Organic Carbon Content in an Arctic Tundra

NASA Astrophysics Data System (ADS)

Tran, A. P.; Dafflon, B.; Hubbard, S.

2017-12-01

Soil organic carbon (SOC) is crucial for predicting carbon climate feedbacks in the vulnerable organic-rich Arctic region. However, it is challenging to achieve this property due to the general limitations of conventional core sampling and analysis methods. In this study, we develop an inversion scheme that uses single or multiple datasets, including soil liquid water content, temperature and ERT data, to estimate the vertical profile of SOC content. Our approach relies on the fact that SOC content strongly influences soil hydrological-thermal parameters, and therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. The scheme includes several advantages. First, this is the first time SOC content is estimated by using a coupled hydrogeophysical inversion. Second, by using the Community Land Model, we can account for the land surface dynamics (evapotranspiration, snow accumulation and melting) and ice/liquid phase transition. Third, we combine a deterministic and an adaptive Markov chain Monte Carlo optimization algorithm to better estimate the posterior distributions of desired model parameters. Finally, the simulated subsurface variables are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using synthetic experiments. The results show that compared to inversion of single dataset, joint inversion of these datasets significantly reduces parameter uncertainty. The joint inversion approach is able to estimate SOC content within the shallow active layer with high reliability. Next, we apply the scheme to estimate OC content along an intensive ERT transect in Barrow, Alaska using multiple datasets acquired in the 2013-2015 period. The preliminary results show a good agreement between modeled and measured soil temperature, thaw layer thickness and electrical resistivity. The accuracy of estimated SOC content will be evaluated by comparison with measurements from soil samples along the transect. Our study presents a new surface-subsurface, deterministic-stochastic hydrogeophysical inversion approach, as well as the benefit of including multiple types of data to estimate SOC and associated hydrological-thermal dynamics.

Impact of the assimilation of satellite soil moisture and LST on the hydrological cycle

NASA Astrophysics Data System (ADS)

Laiolo, Paola; Gabellani, Simone; Delogu, Fabio; Silvestro, Francesco; Rudari, Roberto; Campo, Lorenzo; Boni, Giorgio

2014-05-01

The reliable estimation of hydrological variables (e.g. soil moisture, evapotranspiration, surface temperature) in space and time is of fundamental importance in operational hydrology to improve the forecast of the rainfall-runoff response of catchments and, consequently, flood predictions. Nowadays remote sensing can offer a chance to provide good space-time estimates of several hydrological variables and then improve hydrological model performances especially in environments with scarce ground based data. The aim of this work is to investigate the impacts on the performances of a distributed hydrological model (Continuum) of the assimilation of satellite-derived soil moisture products and Land Surface (LST). In this work three different soil moisture (SM) products, derived by ASCAT sensor, are used. These data are provided by the EUMETSAT's H-SAF (Satellite Application Facility on Support to Operational Hydrology and Water Management) program. The considered soil moisture products are: large scale surface soil moisture (SM OBS 1 - H07), small scale surface soil moisture (SM OBS 2 - H08) and profile index in the roots region (SM DAS 2 - H14). These data are compared with soil moisture estimated by Continuum model on the Orba catchment (800 km2), in the northern part of Italy, for the period July 2012-June 2013. Different assimilation experiments have been performed. The first experiment consists in the assimilation of the SM products by using a simple Nudging technique; the second one is the assimilation of only LST data, derived from MSG satellite, and the third is the assimilation of both SM products and LST. The benefits on the model predictions of discharge, LST and soil moisture dynamics were tested.

Measuring Soil Moisture in Skeletal Soils Using a COSMOS Rover

NASA Astrophysics Data System (ADS)

Medina, C.; Neely, H.; Desilets, D.; Mohanty, B.; Moore, G. W.

2017-12-01

The presence of coarse fragments directly influences the volumetric water content of the soil. Current surface soil moisture sensors often do not account for the presence of coarse fragments, and little research has been done to calibrate these sensors under such conditions. The cosmic-ray soil moisture observation system (COSMOS) rover is a passive, non-invasive surface soil moisture sensor with a footprint greater than 100 m. Despite its potential, the COSMOS rover has yet to be validated in skeletal soils. The goal of this study was to validate measurements of surface soil moisture as taken by a COSMOS rover on a Texas skeletal soil. Data was collected for two soils, a Marfla clay loam and Chinati-Boracho-Berrend association, in West Texas. Three levels of data were collected: 1) COSMOS surveys at three different soil moistures, 2) electrical conductivity surveys within those COSMOS surveys, and 3) ground-truth measurements. Surveys with the COSMOS rover covered an 8000-h area and were taken both after large rain events (>2") and a long dry period. Within the COSMOS surveys, the EM38-MK2 was used to estimate the spatial distribution of coarse fragments in the soil around two COSMOS points. Ground truth measurements included coarse fragment mass and volume, bulk density, and water content at 3 locations within each EM38 survey. Ground-truth measurements were weighted using EM38 data, and COSMOS measurements were validated by their distance from the samples. There was a decrease in water content as the percent volume of coarse fragment increased. COSMOS estimations responded to both changes in coarse fragment percent volume and the ground-truth volumetric water content. Further research will focus on creating digital soil maps using landform data and water content estimations from the COSMOS rover.

Assessment of model behavior and acceptable forcing data uncertainty in the context of land surface soil moisture estimation

NASA Astrophysics Data System (ADS)

Dumedah, Gift; Walker, Jeffrey P.

2017-03-01

The sources of uncertainty in land surface models are numerous and varied, from inaccuracies in forcing data to uncertainties in model structure and parameterizations. Majority of these uncertainties are strongly tied to the overall makeup of the model, but the input forcing data set is independent with its accuracy usually defined by the monitoring or the observation system. The impact of input forcing data on model estimation accuracy has been collectively acknowledged to be significant, yet its quantification and the level of uncertainty that is acceptable in the context of the land surface model to obtain a competitive estimation remain mostly unknown. A better understanding is needed about how models respond to input forcing data and what changes in these forcing variables can be accommodated without deteriorating optimal estimation of the model. As a result, this study determines the level of forcing data uncertainty that is acceptable in the Joint UK Land Environment Simulator (JULES) to competitively estimate soil moisture in the Yanco area in south eastern Australia. The study employs hydro genomic mapping to examine the temporal evolution of model decision variables from an archive of values obtained from soil moisture data assimilation. The data assimilation (DA) was undertaken using the advanced Evolutionary Data Assimilation. Our findings show that the input forcing data have significant impact on model output, 35% in root mean square error (RMSE) for 5cm depth of soil moisture and 15% in RMSE for 15cm depth of soil moisture. This specific quantification is crucial to illustrate the significance of input forcing data spread. The acceptable uncertainty determined based on dominant pathway has been validated and shown to be reliable for all forcing variables, so as to provide optimal soil moisture. These findings are crucial for DA in order to account for uncertainties that are meaningful from the model standpoint. Moreover, our results point to a proper treatment of input forcing data in general land surface and hydrological model estimation.

Aerodynamic method for obtaining the soil water retention curve

NASA Astrophysics Data System (ADS)

Alekseev, V. V.; Maksimov, I. I.

2013-07-01

A new method for the rapid plotting of the soil water retention curve (SWRC) has been proposed that considers the soil water as an environment limited by the soil solid phase on one side and by the soil air on the other side. Both contact surfaces have surface energies, which play the main role in water retention. The use of an idealized soil model with consideration for the nonequilibrium thermodynamic laws and the aerodynamic similarity principles allows us to estimate the volumetric specific surface areas of soils and, using the proposed pedotransfer function (PTF), to plot the SWRC. The volumetric specific surface area of the solid phase, the porosity, and the specific free surface energy at the water-air interface are used as the SWRC parameters. Devices for measuring the parameters are briefly described. The differences between the proposed PTF and the experimental data have been analyzed using the statistical processing of the data.

The SMAP Level 4 Surface and Root-zone Soil Moisture (L4_SM) Product

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Crow, Wade; Koster, Randal; Kimball, John

2010-01-01

The Soil Moisture Active and Passive (SMAP) mission is being developed by NASA for launch in 2013 as one of four first-tier missions recommended by the U.S. National Research Council Committee on Earth Science and Applications from Space in 2007. The primary science objectives of SMAP are to enhance understanding of land surface controls on the water, energy and carbon cycles, and to determine their linkages. Moreover, the high resolution soil moisture mapping provided by SMAP has practical applications in weather and seasonal climate prediction, agriculture, human health, drought and flood decision support. In this paper we describe the assimilation of SMAP observations for the generation of the planned SMAP Level 4 Surface and Root-zone Soil Moisture (L4_SM) product. The SMAP mission makes simultaneous active (radar) and passive (radiometer) measurements in the 1.26-1.43 GHz range (L-band) from a sun-synchronous low-earth orbit. Measurements will be obtained across a 1000 km wide swath using conical scanning at a constant incidence angle (40 deg). The radar resolution varies from 1-3 km over the outer 70% of the swath to about 30 km near the center of the swath. The radiometer resolution is 40 km across the entire swath. The radiometer measurements will allow high-accuracy but coarse resolution (40 km) measurements. The radar measurements will add significantly higher resolution information. The radar is however very sensitive to surface roughness and vegetation structure. The combination of the two measurements allows optimal blending of the advantages of each instrument. SMAP directly observes only surface soil moisture (in the top 5 cm of the soil column). Several of the key applications targeted by SMAP, however, require knowledge of root zone soil moisture (approximately top 1 m of the soil column), which is not directly measured by SMAP. The foremost objective of the SMAP L4_SM product is to fill this gap and provide estimates of root zone soil moisture that are informed by and consistent with SMAP observations. Such estimates are obtained by merging SMAP observations with estimates from a land surface model in a soil moisture data assimilation system. The land surface model component of the assimilation system is driven with observations-based surface meteorological forcing data, including precipitation, which is the most important driver for soil moisture. The model also encapsulates knowledge of key land surface processes, including the vertical transfer of soil moisture between the surface and root zone reservoirs. Finally, the model interpolates and extrapolates SMAP observations in time and in space. The L4_SM product thus provides a comprehensive and consistent picture of land surface hydrological conditions based on SMAP observations and complementary information from a variety of sources. The assimilation algorithm considers the respective uncertainties of each component and yields a product that is superior to satellite or model data alone. Error estimates for the L4_SM product are generated as a by-product of the data assimilation system.

Likelihood parameter estimation for calibrating a soil moisture using radar backscatter

USDA-ARS?s Scientific Manuscript database

Assimilating soil moisture information contained in synthetic aperture radar imagery into land surface model predictions can be done using a calibration, or parameter estimation, approach. The presence of speckle, however, necessitates aggregating backscatter measurements over large land areas in or...

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.

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.

Statistical design and analysis of environmental studies for plutonium and other transuranics at NAEG ''safety-shot'' sites

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

Gilbert, R.O.; Eberhardt, L.L.; Fowler, E.B.

This paper is centered around the use of stratified random sampling for estimating the total amount (inventory) of $sup 239-240$Pu and uranium in surface soil at ten ''safety-shot'' sites on the Nevada Test Site (NTS) and Tonopah Test Range (TTR) that are currently being studied by the Nevada Applied Ecology Group (NAEG). The use of stratified random sampling has resulted in estimates of inventory at these desert study sites that have smaller standard errors than would have been the case had simple random sampling (no stratification) been used. Estimates of inventory are given for $sup 235$U, $sup 238$U, and $supmore » 239-240$Pu in soil at A Site of Area 11 on the NTS. Other results presented include average concentrations of one or more of these isotopes in soil and vegetation and in soil profile samples at depths to 25 cm. The regression relationship between soil and vegetation concentrations of $sup 235$U and $sup 238$U at adjacent sampling locations is also examined using three different models. The applicability of stratified random sampling to the estimation of concentration contours of $sup 239-240$Pu in surface soil using computer algorithms is also investigated. Estimates of such contours are obtained using several different methods. The planning of field sampling plans for estimating inventory and distribution is discussed. (auth)« less

Leveraging Machine Learning to Estimate Soil Salinity through Satellite-Based Remote Sensing

NASA Astrophysics Data System (ADS)

Welle, P.; Ravanbakhsh, S.; Póczos, B.; Mauter, M.

2016-12-01

Human-induced salinization of agricultural soils is a growing problem which now affects an estimated 76 million hectares and causes billions of dollars of lost agricultural revenues annually. While there are indications that soil salinization is increasing in extent, current assessments of global salinity levels are outdated and rely heavily on expert opinion due to the prohibitive cost of a worldwide sampling campaign. A more practical alternative to field sampling may be earth observation through remote sensing, which takes advantage of the distinct spectral signature of salts in order to estimate soil conductivity. Recent efforts to map salinity using remote sensing have been met with limited success due to tractability issues of managing the computational load associated with large amounts of satellite data. In this study, we use Google Earth Engine to create composite satellite soil datasets, which combine data from multiple sources and sensors. These composite datasets contain pixel-level surface reflectance values for dates in which the algorithm is most confident that the surface contains bare soil. We leverage the detailed soil maps created and updated by the United States Geological Survey as label data and apply machine learning regression techniques such as Gaussian processes to learn a smooth mapping from surface reflection to noisy estimates of salinity. We also explore a semi-supervised approach using deep generative convolutional networks to leverage the abundance of unlabeled satellite images in producing better estimates for salinity values where we have relatively fewer measurements across the globe. The general method results in two significant contributions: (1) an algorithm that can be used to predict levels of soil salinity in regions without detailed soil maps and (2) a general framework that serves as an example for how remote sensing can be paired with extensive label data to generate methods for prediction of physical phenomenon.

Utilizing Calibrated GPS Reflected Signals to Estimate Soil Reflectivity and Dielectric Constant: Results from SMEX02

NASA Technical Reports Server (NTRS)

Katzberg, Stephen J.; Torres, Omar; Grant, Michael S.; Masters, Dallas

2006-01-01

Extensive reflected GPS data was collected using a GPS reflectometer installed on an HC130 aircraft during the Soil Moisture Experiment 2002 (SMEX02) near Ames, Iowa. At the same time, widespread surface truth data was acquired in the form of point soil moisture profiles, areal sampling of near-surface soil moisture, total green biomass and precipitation history, among others. Previously, there have been no reported efforts to calibrate reflected GPS data sets acquired over land. This paper reports the results of two approaches to calibration of the data that yield consistent results. It is shown that estimating the strength of the reflected signals by either (1) assuming an approximately specular surface reflection or (2) inferring the surface slope probability density and associated normalization constants give essentially the same results for the conditions encountered in SMEX02. The corrected data is converted to surface reflectivity and then to dielectric constant as a test of the calibration approaches. Utilizing the extensive in-situ soil moisture related data this paper also presents the results of comparing the GPS-inferred relative dielectric constant with the Wang-Schmugge model frequently used to relate volume moisture content to dielectric constant. It is shown that the calibrated GPS reflectivity estimates follow the expected dependence of permittivity with volume moisture, but with the following qualification: The soil moisture value governing the reflectivity appears to come from only the top 1-2 centimeters of soil, a result consistent with results found for other microwave techniques operating at L-band. Nevertheless, the experimentally derived dielectric constant is generally lower than predicted. Possible explanations are presented to explain this result.

A Comparison of Methods for a Priori Bias Correction in Soil Moisture Data Assimilation

NASA Technical Reports Server (NTRS)

Kumar, Sujay V.; Reichle, Rolf H.; Harrison, Kenneth W.; Peters-Lidard, Christa D.; Yatheendradas, Soni; Santanello, Joseph A.

2011-01-01

Data assimilation is being increasingly used to merge remotely sensed land surface variables such as soil moisture, snow and skin temperature with estimates from land models. Its success, however, depends on unbiased model predictions and unbiased observations. Here, a suite of continental-scale, synthetic soil moisture assimilation experiments is used to compare two approaches that address typical biases in soil moisture prior to data assimilation: (i) parameter estimation to calibrate the land model to the climatology of the soil moisture observations, and (ii) scaling of the observations to the model s soil moisture climatology. To enable this research, an optimization infrastructure was added to the NASA Land Information System (LIS) that includes gradient-based optimization methods and global, heuristic search algorithms. The land model calibration eliminates the bias but does not necessarily result in more realistic model parameters. Nevertheless, the experiments confirm that model calibration yields assimilation estimates of surface and root zone soil moisture that are as skillful as those obtained through scaling of the observations to the model s climatology. Analysis of innovation diagnostics underlines the importance of addressing bias in soil moisture assimilation and confirms that both approaches adequately address the issue.

Use of visible, near-infrared, and thermal infrared remote sensing to study soil moisture

NASA Technical Reports Server (NTRS)

Blanchard, M. B.; Greeley, R.; Goettelman, R.

1974-01-01

Two methods are described which are used to estimate soil moisture remotely using the 0.4- to 14.0 micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of soil temperature. The reflectance method is based on observations which show that directional reflectance decreases as soil moisture increases for a given material. The soil temperature method is based on observations which show that differences between daytime and nighttime soil temperatures decrease as moisture content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid soil moisture determination. In this combined approach, reflectance is used to estimate low moisture levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of soil moisture, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).

Use of visible, near-infrared, and thermal infrared remote sensing to study soil moisture

NASA Technical Reports Server (NTRS)

Blanchard, M. B.; Greeley, R.; Goettelman, R.

1974-01-01

Two methods are used to estimate soil moisture remotely using the 0.4- to 14.0-micron wavelength region: (1) measurement of spectral reflectance, and (2) measurement of soil temperature. The reflectance method is based on observations which show that directional reflectance decreases as soil moisture increases for a given material. The soil temperature method is based on observations which show that differences between daytime and nighttime soil temperatures decrease as moisture content increases for a given material. In some circumstances, separate reflectance or temperature measurements yield ambiguous data, in which case these two methods may be combined to obtain a valid soil moisture determination. In this combined approach, reflectance is used to estimate low moisture levels; and thermal inertia (or thermal diffusivity) is used to estimate higher levels. The reflectance method appears promising for surface estimates of soil moisture, whereas the temperature method appears promising for estimates of near-subsurface (0 to 10 cm).

Soil moisture content estimation using ground-penetrating radar reflection data

NASA Astrophysics Data System (ADS)

Lunt, I. A.; Hubbard, S. S.; Rubin, Y.

2005-06-01

Ground-penetrating radar (GPR) reflection travel time data were used to estimate changes in soil water content under a range of soil saturation conditions throughout the growing season at a California winery. Data were collected during three data acquisition campaigns over an 80 by 180 m area using 100 MHz surface GPR antennas. GPR reflections were associated with a thin, low permeability clay layer located 0.8-1.3 m below the ground surface that was identified from borehole information and mapped across the study area. Field infiltration tests and neutron probe logs suggest that the thin clay layer inhibited vertical water flow, and was coincident with high volumetric water content (VWC) values. The GPR reflection two-way travel time and the depth of the reflector at the borehole locations were used to calculate an average dielectric constant for soils above the reflector. A site-specific relationship between the dielectric constant and VWC was then used to estimate the depth-averaged VWC of the soils above the reflector. Compared to average VWC measurements from calibrated neutron probe logs over the same depth interval, the average VWC estimates obtained from GPR reflections had an RMS error of 0.018 m 3 m -3. These results suggested that the two-way travel time to a GPR reflection associated with a geological surface could be used under natural conditions to obtain estimates of average water content when borehole control is available and the reflection strength is sufficient. The GPR reflection method therefore, has potential for monitoring soil water content over large areas and under variable hydrological conditions.

InSAR analysis of surface deformation over permafrost to estimate active layer thickness based on one-dimensional heat transfer model of soils

PubMed Central

Li, Zhiwei; Zhao, Rong; Hu, Jun; Wen, Lianxing; Feng, Guangcai; Zhang, Zeyu; Wang, Qijie

2015-01-01

This paper presents a novel method to estimate active layer thickness (ALT) over permafrost based on InSAR (Interferometric Synthetic Aperture Radar) observation and the heat transfer model of soils. The time lags between the periodic feature of InSAR-observed surface deformation over permafrost and the meteorologically recorded temperatures are assumed to be the time intervals that the temperature maximum to diffuse from the ground surface downward to the bottom of the active layer. By exploiting the time lags and the one-dimensional heat transfer model of soils, we estimate the ALTs. Using the frozen soil region in southern Qinghai-Tibet Plateau (QTP) as examples, we provided a conceptual demonstration of the estimation of the InSAR pixel-wise ALTs. In the case study, the ALTs are ranging from 1.02 to 3.14 m and with an average of 1.95 m. The results are compatible with those sparse ALT observations/estimations by traditional methods, while with extraordinary high spatial resolution at pixel level (~40 meter). The presented method is simple, and can potentially be used for deriving high-resolution ALTs in other remote areas similar to QTP, where only sparse observations are available now. PMID:26480892

InSAR analysis of surface deformation over permafrost to estimate active layer thickness based on one-dimensional heat transfer model of soils.

PubMed

Li, Zhiwei; Zhao, Rong; Hu, Jun; Wen, Lianxing; Feng, Guangcai; Zhang, Zeyu; Wang, Qijie

2015-10-20

This paper presents a novel method to estimate active layer thickness (ALT) over permafrost based on InSAR (Interferometric Synthetic Aperture Radar) observation and the heat transfer model of soils. The time lags between the periodic feature of InSAR-observed surface deformation over permafrost and the meteorologically recorded temperatures are assumed to be the time intervals that the temperature maximum to diffuse from the ground surface downward to the bottom of the active layer. By exploiting the time lags and the one-dimensional heat transfer model of soils, we estimate the ALTs. Using the frozen soil region in southern Qinghai-Tibet Plateau (QTP) as examples, we provided a conceptual demonstration of the estimation of the InSAR pixel-wise ALTs. In the case study, the ALTs are ranging from 1.02 to 3.14 m and with an average of 1.95 m. The results are compatible with those sparse ALT observations/estimations by traditional methods, while with extraordinary high spatial resolution at pixel level (~40 meter). The presented method is simple, and can potentially be used for deriving high-resolution ALTs in other remote areas similar to QTP, where only sparse observations are available now.

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.

Documentation of a deep percolation model for estimating ground-water recharge

USGS Publications Warehouse

Bauer, H.H.; Vaccaro, J.J.

1987-01-01

A deep percolation model, which operates on a daily basis, was developed to estimate long-term average groundwater recharge from precipitation. It has been designed primarily to simulate recharge in large areas with variable weather, soils, and land uses, but it can also be used at any scale. The physical and mathematical concepts of the deep percolation model, its subroutines and data requirements, and input data sequence and formats are documented. The physical processes simulated are soil moisture accumulation, evaporation from bare soil, plant transpiration, surface water runoff, snow accumulation and melt, and accumulation and evaporation of intercepted precipitation. The minimum data sets for the operation of the model are daily values of precipitation and maximum and minimum air temperature, soil thickness and available water capacity, soil texture, and land use. Long-term average annual precipitation, actual daily stream discharge, monthly estimates of base flow, Soil Conservation Service surface runoff curve numbers, land surface altitude-slope-aspect, and temperature lapse rates are optional. The program is written in the FORTRAN 77 language with no enhancements and should run on most computer systems without modifications. Documentation has been prepared so that program modifications may be made for inclusions of additional physical processes or deletion of ones not considered important. (Author 's abstract)

On the use of radiative surface temperature to estimate sensible heat flux over sparse shrubs in Nevada

NASA Technical Reports Server (NTRS)

Chehbouni, A.; Nichols, W. D.; Qi, J.; Njoku, E. G.; Kerr, Y. H.; Cabot, F.

1994-01-01

The accurate partitioning of available energy into sensible and latent heat flux is crucial to the understanding of surface atmosphere interactions. This issue is more complicated in arid and semi arid regions where the relative contribution to surface fluxes from the soil and vegetation may vary significantly throughout the day and throughout the season. A three component model to estimate sensible heat flux over heterogeneous surfaces is presented. The surface was represented with two adjacent compartments. The first compartment is made up of two components, shrubs and shaded soil, the second of open 'illuminated' soil. Data collected at two different sites in Nevada (U.S.) during the Summers of 1991 and 1992 were used to evaluate model performance. The results show that the present model is sufficiently general to yield satisfactory results for both sites.

A simplified 137Cs transport model for estimating erosion rates in undisturbed soil.

PubMed

Zhang, Xinbao; Long, Yi; He, Xiubin; Fu, Jiexiong; Zhang, Yunqi

2008-08-01

(137)Cs is an artificial radionuclide with a half-life of 30.12 years which released into the environment as a result of atmospheric testing of thermo-nuclear weapons primarily during the period of 1950s-1970s with the maximum rate of (137)Cs fallout from atmosphere in 1963. (137)Cs fallout is strongly and rapidly adsorbed by fine particles in the surface horizons of the soil, when it falls down on the ground mostly with precipitation. Its subsequent redistribution is associated with movements of the soil or sediment particles. The (137)Cs nuclide tracing technique has been used for assessment of soil losses for both undisturbed and cultivated soils. For undisturbed soils, a simple profile-shape model was developed in 1990 to describe the (137)Cs depth distribution in profile, where the maximum (137)Cs occurs in the surface horizon and it exponentially decreases with depth. The model implied that the total (137)Cs fallout amount deposited on the earth surface in 1963 and the (137)Cs profile shape has not changed with time. The model has been widely used for assessment of soil losses on undisturbed land. However, temporal variations of (137)Cs depth distribution in undisturbed soils after its deposition on the ground due to downward transport processes are not considered in the previous simple profile-shape model. Thus, the soil losses are overestimated by the model. On the base of the erosion assessment model developed by Walling, D.E., He, Q. [1999. Improved models for estimating soil erosion rates from cesium-137 measurements. Journal of Environmental Quality 28, 611-622], we discuss the (137)Cs transport process in the eroded soil profile and make some simplification to the model, develop a method to estimate the soil erosion rate more expediently. To compare the soil erosion rates calculated by the simple profile-shape model and the simple transport model, the soil losses related to different (137)Cs loss proportions of the reference inventory at the Kaixian site of the Three Gorge Region, China are estimated by the two models. The over-estimation of the soil loss by using the previous simple profile-shape model obviously increases with the time period from the sampling year to the year of 1963 and (137)Cs loss proportion of the reference inventory. As to 20-80% of (137)Cs loss proportions of the reference inventory at the Kaixian site in 2004, the annual soil loss depths estimated by the new simplified transport process model are only 57.90-56.24% of the values estimated by the previous model.

DISAGGREGATION OF GOES LAND SURFACE TEMPERATURES USING SURFACE EMISSIVITY

USDA-ARS?s Scientific Manuscript database

Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfo...

Processes of Ammonia Air-Surface Exchange in a Fertilized Zea Mays Canopy

EPA Science Inventory

Recent incorporation of coupled soil biogeochemical and bi-directional NH3 air-surface exchange algorithms into regional air quality models holds promise for further reducing uncertainty in estimates of NH3 emissions from fertilized soils. While this advancement represents a sig...

An estimation of the main wetting branch of the soil water retention curve based on its main drying branch using the machine learning method

NASA Astrophysics Data System (ADS)

Lamorski, Krzysztof; Šimūnek, Jiří; Sławiński, Cezary; Lamorska, Joanna

2017-02-01

In this paper, we estimated using the machine learning methodology the main wetting branch of the soil water retention curve based on the knowledge of the main drying branch and other, optional, basic soil characteristics (particle size distribution, bulk density, organic matter content, or soil specific surface). The support vector machine algorithm was used for the models' development. The data needed by this algorithm for model training and validation consisted of 104 different undisturbed soil core samples collected from the topsoil layer (A horizon) of different soil profiles in Poland. The main wetting and drying branches of SWRC, as well as other basic soil physical characteristics, were determined for all soil samples. Models relying on different sets of input parameters were developed and validated. The analysis showed that taking into account other input parameters (i.e., particle size distribution, bulk density, organic matter content, or soil specific surface) than information about the drying branch of the SWRC has essentially no impact on the models' estimations. Developed models are validated and compared with well-known models that can be used for the same purpose, such as the Mualem (1977) (M77) and Kool and Parker (1987) (KP87) models. The developed models estimate the main wetting SWRC branch with estimation errors (RMSE = 0.018 m3/m3) that are significantly lower than those for the M77 (RMSE = 0.025 m3/m3) or KP87 (RMSE = 0. 047 m3/m3) models.

Estimation of effective hydrologic properties of soils from observations of vegetation density

NASA Technical Reports Server (NTRS)

Tellers, T. E.; Eagleson, P. S.

1980-01-01

A one-dimensional model of the annual water balance is reviewed. Improvements are made in the method of calculating the bare soil component of evaporation, and in the way surface retention is handled. A natural selection hypothesis, which specifies the equilibrium vegetation density for a given, water limited, climate soil system, is verified through comparisons with observed data. Comparison of CDF's of annual basin yield derived using these soil properties with observed CDF's provides verification of the soil-selection procedure. This method of parameterization of the land surface is useful with global circulation models, enabling them to account for both the nonlinearity in the relationship between soil moisture flux and soil moisture concentration, and the variability of soil properties from place to place over the Earth's surface.

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

PubMed

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

2018-01-01

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

A surface fuel classification for estimating fire effects

Treesearch

Duncan C. Lutes; Robert E. Keane; John F. Caratti

2009-01-01

We present a classification of duff, litter, fine woody debris, and logs that can be used to stratify a project area into sites with fuel loading that yield significantly different emissions and maximum soil surface temperature. Total particulate matter smaller than 2.5?m in diameter and maximum soil surface temperature were simulated using the First...

Estimating soil solution nitrate concentration from dielectric spectra using PLS analysis

USDA-ARS?s Scientific Manuscript database

Fast and reliable methods for in situ monitoring of soil nitrate-nitrogen concentration are vital for reducing nitrate-nitrogen losses to ground and surface waters from agricultural systems. While several studies have been done to indirectly estimate nitrate-nitrogen concentration from time domain s...

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Passive microwave sensing of soil moisture content: Soil bulk density and surface roughness

NASA Technical Reports Server (NTRS)

Wang, J. R.

1982-01-01

Microwave radiometric measurements over bare fields of different surface roughnesses were made at the frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence as well as the possible time variation of surface roughness. The presence of surface roughness was found to increase the brightness temperature of soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time series observation over a given field indicated that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. This time variation of surface roughness served to enhance the uncertainty in remote soil moisture estimate by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which turned out to be an important factor in the interpretation of radiometric data.

Passive microwave sensing of soil moisture content - The effects of soil bulk density and surface roughness

NASA Technical Reports Server (NTRS)

Wang, J. R.

1983-01-01

Microwave radiometric measurements over bare fields of different surface roughness were made at frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence, as well as the possible time variation, of surface roughness. An increase in surface roughness was found to increase the brightness temperature of soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time-series observations over a given field indicate that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. The variation of surface roughness increases the uncertainty of remote soil moisture estimates by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which is an important factor in the interpretation of radiometric data.

Influence of soil environmental parameters on thoron exhalation rate.

PubMed

Hosoda, M; Tokonami, S; Sorimachi, A; Ishikawa, T; Sahoo, S K; Furukawa, M; Shiroma, Y; Yasuoka, Y; Janik, M; Kavasi, N; Uchida, S; Shimo, M

2010-10-01

Field measurements of thoron exhalation rates have been carried out using a ZnS(Ag) scintillation detector with an accumulation chamber. The influence of soil surface temperature and moisture saturation on the thoron exhalation rate was observed. When the variation of moisture saturation was small, the soil surface temperature appeared to induce a strong effect on the thoron exhalation rate. On the other hand, when the variation of moisture saturation was large, the influence of moisture saturation appeared to be larger than the soil surface temperature. The number of data ranged over 405, and the median was estimated to be 0.79 Bq m(-2) s(-1). Dependence of geology on the thoron exhalation rate from the soil surface was obviously found, and a nationwide distribution map of the thoron exhalation rate from the soil surface was drawn by using these data. It was generally high in the southwest region than in the northeast region.

Emission-dominated gas exchange of elemental mercury vapor over natural surfaces in China

NASA Astrophysics Data System (ADS)

Wang, Xun; Lin, Che-Jen; Yuan, Wei; Sommar, Jonas; Zhu, Wei; Feng, Xinbin

2016-09-01

Mercury (Hg) emission from natural surfaces plays an important role in global Hg cycling. The present estimate of global natural emission has large uncertainty and remains unverified against field data, particularly for terrestrial surfaces. In this study, a mechanistic model is developed for estimating the emission of elemental mercury vapor (Hg0) from natural surfaces in China. The development implements recent advancements in the understanding of air-soil and air-foliage exchange of Hg0 and redox chemistry in soil and on surfaces, incorporates the effects of soil characteristics and land use changes by agricultural activities, and is examined through a systematic set of sensitivity simulations. Using the model, the net exchange of Hg0 between the atmosphere and natural surfaces of mainland China is estimated to be 465.1 Mg yr-1, including 565.5 Mg yr-1 from soil surfaces, 9.0 Mg yr-1 from water bodies, and -100.4 Mg yr-1 from vegetation. The air-surface exchange is strongly dependent on the land use and meteorology, with 9 % of net emission from forest ecosystems; 50 % from shrubland, savanna, and grassland; 33 % from cropland; and 8 % from other land uses. Given the large agricultural land area in China, farming activities play an important role on the air-surface exchange over farmland. Particularly, rice field shift from a net sink (3.3 Mg uptake) during April-October (rice planting) to a net source when the farmland is not flooded (November-March). Summing up the emission from each land use, more than half of the total emission occurs in summer (51 %), followed by spring (28 %), autumn (13 %), and winter (8 %). Model verification is accomplished using observational data of air-soil/air-water fluxes and Hg deposition through litterfall for forest ecosystems in China and Monte Carlo simulations. In contrast to the earlier estimate by Shetty et al. (2008) that reported large emission from vegetative surfaces using an evapotranspiration approach, the estimate in this study shows natural emissions are primarily from grassland and dry cropland. Such an emission pattern may alter the current understanding of Hg emission outflow from China as reported by Lin et al. (2010b) because a substantial natural Hg emission occurs in West China.

Visually assessing the level of development and soil surface stability of cyanobacterially dominated biological soil crusts

USGS Publications Warehouse

Belnap, J.; Phillips, S.L.; Witwicki, D.L.; Miller, M.E.

2008-01-01

Biological soil crusts (BSCs) are an integral part of dryland ecosystems and often included in long-term ecological monitoring programs. Estimating moss and lichen cover is fairly easy and non-destructive, but documenting cyanobacterial level of development (LOD) is more difficult. It requires sample collection for laboratory analysis, which causes soil surface disturbance. Assessing soil surface stability also requires surface disturbance. Here we present a visual technique to assess cyanobacterial LOD and soil surface stability. We define six development levels of cyanobacterially dominated soils based on soil surface darkness. We sampled chlorophyll a concentrations (the most common way of assessing cyanobacterial biomass), exopolysaccharide concentrations, and soil surface aggregate stability from representative areas of each LOD class. We found that, in the laboratory and field, LOD classes were effective at predicting chlorophyll a soil concentrations (R2=68-81%), exopolysaccharide concentrations (R2=71%), and soil aggregate stability (R2=77%). We took representative photos of these classes to construct a field guide. We then tested the ability of field crews to distinguish these classes and found this technique was highly repeatable among observers. We also discuss how to adjust this index for the different types of BSCs found in various dryland regions.

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.

Soil property effects on wind erosion of organic soils

NASA Astrophysics Data System (ADS)

Zobeck, Ted M.; Baddock, Matthew; Scott Van Pelt, R.; Tatarko, John; Acosta-Martinez, Veronica

2013-09-01

Histosols (also known as organic soils, mucks, or peats) are soils that are dominated by organic matter (OM > 20%) in half or more of the upper 80 cm. Forty two states have a total of 21 million ha of Histosols in the United States. These soils, when intensively cropped, are subject to wind erosion resulting in loss of crop productivity and degradation of soil, air, and water quality. Estimating wind erosion on Histosols has been determined by USDA-Natural Resources Conservation Service (NRCS) as a critical need for the Wind Erosion Prediction System (WEPS) model. WEPS has been developed to simulate wind erosion on agricultural land in the US, including soils with organic soil material surfaces. However, additional field measurements are needed to understand how soil properties vary among organic soils and to calibrate and validate estimates of wind erosion of organic soils using WEPS. Soil properties and sediment flux were measured in six soils with high organic contents located in Michigan and Florida, USA. Soil properties observed included organic matter content, particle density, dry mechanical stability, dry clod stability, wind erodible material, and geometric mean diameter of the surface aggregate distribution. A field portable wind tunnel was used to generate suspended sediment and dust from agricultural surfaces for soils ranging from 17% to 67% organic matter. The soils were tilled and rolled to provide a consolidated, friable surface. Dust emissions and saltation were measured using an isokinetic vertical slot sampler aspirated by a regulated suction source. Suspended dust was sampled using a Grimm optical particle size analyzer. Particle density of the saltation-sized material (>106 μm) was inversely related to OM content and varied from 2.41 g cm-3 for the soil with the lowest OM content to 1.61 g cm-3 for the soil with highest OM content. Wind erodible material and the geometric mean diameter of the surface soil were inversely related to dry clod stability. The effect of soil properties on sediment flux varied among flux types. Saltation flux was adequately predicted with simple linear regression models. Dry mechanical stability was the best single soil property linearly related to saltation flux. Simple linear models with soil properties as independent variables were not well correlated with PM10E values (mass flux). A second order polynomial equation with OM as the independent variable was found to be most highly correlated with PM10E values. These results demonstrate that variations in sediment and dust emissions can be linked to soil properties using simple models based on one or more soil properties to estimate saltation mass flux and PM10E values from organic and organic-rich soils.

Adhesion and enrichment of metals on human hands from contaminated soil at an Arctic urban brownfield.

PubMed

Siciliano, Steven D; James, K; Zhang, Guiyin; Schafer, Alexis N; Peak, J Derek

2009-08-15

Human exposure to contaminated soils drives clean up criteria at many urban brownfields. Current risk assessment guidelines assume that humans ingest some fraction of soil smaller than 4 mm but have no estimates of what fraction of soil is ingested by humans. Here, we evaluated soil adherence to human hands for 13 agricultural soils from Saskatchewan, Canada and 17 different soils from a brownfield located in Iqaluit, Nunavut, Canada. In addition, we estimated average particle size adhering to human hands for residents of a northern urban setting. Further, we estimated how metal concentrations differed between the adhered and bulk (< 4 mm) fraction of soil. The average particle size for adhered agricultural soils was 34 microm, adhered brownfield soils was 105 microm, and particles adhered to human residentswas 36 microm. Metals were significantly enriched in these adhered fractions with an average enrichment [(adhered-bulk)/bulk] in metal concentration of 184% (113% median) for 24 different elements. Enrichment was greater for key toxicological elements of concern such as chromium (140%), copper (140%), nickel (130%), lead (110%), and zinc (130%) and was highest for silver (810%), mercury (630%), selenium (500%), and arsenic (420%). Enrichment were positively correlated with carbonate complexation constants (but not bulk solubility products) and suggests that the dominant mechanism controlling metal enrichment in these samples is a precipitation of carbonate surfaces that subsequently adsorb metals. Our results suggest that metals of toxicological concern are selectively enriched in the fraction of soil that humans incidentally ingest. Investigators should likely process soil samples through a 45 microm sieve before estimating the risk associated with contaminated soils to humans. The chemical mechanisms resulting in metal enrichment likely differ between sites but at our site were linked to surface complexation with carbonates.

Estimates of amounts of soil removal for clean-up of transuranics at NAEG offsite safety-shot sites

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

Kinnison, R.R.; Gilbert, R.O.

Rough estimates are given for the amount of soil removal necessary to decontaminate five representative safety-shot areas. In order to decontaminate to levels of less than 160 pCi /sup 239/Pu per gram of surface soil, it is estimated that over one-half million tons of soil would have to be removed from the five areas. This is a preliminary estimate based on summary data and concentration contour maps readily available in NAEG publications. More accurate estimates could be obtained by applying Kriging techniques to available soil data if the need arises. The inclusion of /sup 241/Am and /sup 238/Pu activities domore » not significantly increase the soil tonnage estimates obtained for /sup 239/ /sup 240/Pu because of their relatively small contributions to total transuranic activity. The magnitude of the errors inherent in our use of summary data to obtain rough estimates also suggests that a revision of the tonnage estimates for /sup 239/ /sup 240/Pu to include /sup 241/Am and /sup 238/Pu is not warranted.« less

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.

A Comparison of Land Surface Model Soil Hydraulic Properties Estimated by Inverse Modeling and Pedotransfer Functions

NASA Technical Reports Server (NTRS)

Gutmann, Ethan D.; Small, Eric E.

2007-01-01

Soil hydraulic properties (SHPs) regulate the movement of water in the soil. This in turn plays an important role in the water and energy cycles at the land surface. At present, SHPS are commonly defined by a simple pedotransfer function from soil texture class, but SHPs vary more within a texture class than between classes. To examine the impact of using soil texture class to predict SHPS, we run the Noah land surface model for a wide variety of measured SHPs. We find that across a range of vegetation cover (5 - 80% cover) and climates (250 - 900 mm mean annual precipitation), soil texture class only explains 5% of the variance expected from the real distribution of SHPs. We then show that modifying SHPs can drastically improve model performance. We compare two methods of estimating SHPs: (1) inverse method, and (2) soil texture class. Compared to texture class, inverse modeling reduces errors between measured and modeled latent heat flux from 88 to 28 w/m(exp 2). Additionally we find that with increasing vegetation cover the importance of SHPs decreases and that the van Genuchten m parameter becomes less important, while the saturated conductivity becomes more important.

10Be inventories in Alpine soils and their potential for dating land surfaces

NASA Astrophysics Data System (ADS)

Egli, Markus; Brandová, Dagmar; Böhlert, Ralph; Favilli, Filippo; Kubik, Peter W.

2010-07-01

To exploit natural sedimentary archives and geomorphic landforms it is necessary to date them first. Landscape evolution of Alpine areas is often strongly related to the activities of glaciers in the Pleistocene and Holocene. At sites where no organic matter for radiocarbon dating exists and where suitable boulders for surface exposure dating (using in situ produced cosmogenic nuclides) are absent, dating of soils could give information about the timing of landscape evolution. This paper explores the applicability of soil dating using the inventory of meteoric 10Be in Alpine soils. For this purpose, a set of 6 soil profiles in the Swiss and Italian Alps was investigated. The surface at these sites had already been dated (using the radiocarbon technique or the surface exposure determination using in situ produced 10Be). Consequently, a direct comparison of the ages of the soils using meteoric 10Be and other dating techniques was made possible. The estimation of 10Be deposition rates is subject to severe limitations and strongly influences the obtained results. We tested three scenarios using a) the meteoric 10Be deposition rates as a function of the annual precipitation rate, b) a constant 10Be input for the Central Alps, and c) as b) but assuming a pre-exposure of the parent material. The obtained ages that are based on the 10Be inventory in soils and on scenario a) for the 10Be input agreed reasonably well with the age using surface exposure or radiocarbon dating. The ages obtained from soils using scenario b) produced ages that were mostly too old whereas the approach using scenario c) seemed to yield better results than scenario b). Erosion calculations can, in theory, be performed using the 10Be inventory and 10Be deposition rates. An erosion estimation was possible using scenario a) and c), but not using b). The calculated erosion rates using these scenarios seemed to be plausible with values in the range of 0-57 mm/ky. The dating of soils using 10Be has several potential error sources. Analytical errors as well as errors from other parameters such as bulk soil density and soil skeleton content have to be taken into account. The error range was from 8 up to 21%. Furthermore, uncertainties in estimating 10Be deposition rates substantially influence the calculated ages. Relative age estimates and, under optimal conditions, absolute dating can be carried out. Age determination of Alpine soils using 10Be gives another possibility to date surfaces when other methods fail or are not possible at all. It is, however, not straightforward, quite laborious and may consequently have some distinct limitations.

Relationship between specific surface area and the dry end of the water retention curve for soils with varying clay and organic carbon contents

NASA Astrophysics Data System (ADS)

Resurreccion, Augustus C.; Moldrup, Per; Tuller, Markus; Ferré, T. P. A.; Kawamoto, Ken; Komatsu, Toshiko; de Jonge, Lis Wollesen

2011-06-01

Accurate description of the soil water retention curve (SWRC) at low water contents is important for simulating water dynamics and biochemical vadose zone processes in arid environments. Soil water retention data corresponding to matric potentials of less than -10 MPa, where adsorptive forces dominate over capillary forces, have also been used to estimate soil specific surface area (SA). In the present study, the dry end of the SWRC was measured with a chilled-mirror dew point psychrometer for 41 Danish soils covering a wide range of clay (CL) and organic carbon (OC) contents. The 41 soils were classified into four groups on the basis of the Dexter number (n = CL/OC), and the Tuller-Or (TO) general scaling model describing water film thickness at a given matric potential (<-10 MPa) was evaluated. The SA estimated from the dry end of the SWRC (SA_SWRC) was in good agreement with the SA measured with ethylene glycol monoethyl ether (SA_EGME) only for organic soils with n > 10. A strong correlation between the ratio of the two surface area estimates and the Dexter number was observed and applied as an additional scaling function in the TO model to rescale the soil water retention curve at low water contents. However, the TO model still overestimated water film thickness at potentials approaching ovendry condition (about -800 MPa). The semi-log linear Campbell-Shiozawa-Rossi-Nimmo (CSRN) model showed better fits for all investigated soils from -10 to -800 MPa and yielded high correlations with CL and SA. It is therefore recommended to apply the empirical CSRN model for predicting the dry part of the water retention curve (-10 to -800 MPa) from measured soil texture or surface area. Further research should aim to modify the more physically based TO model to obtain better descriptions of the SWRC in the very dry range (-300 to -800 MPa).

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Using ARM Observations to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the U.S. Southern Great Plains

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

Phillips, Thomas J.; Klein, Stephen A.; Ma, Hsi -Yen

Several independent measurements of warm-season soil moisture and surface atmospheric variables recorded at the ARM Southern Great Plains (SGP) research facility are used to estimate the terrestrial component of land-atmosphere coupling (LAC) strength and its regional uncertainty. The observations reveal substantial variation in coupling strength, as estimated from three soil moisture measurements at a single site, as well as across six other sites having varied soil and land cover types. The observational estimates then serve as references for evaluating SGP terrestrial coupling strength in the Community Atmospheric Model coupled to the Community Land Model. These coupled model components are operatedmore » in both a free-running mode and in a controlled configuration, where the atmospheric and land states are reinitialized daily, so that they do not drift very far from observations. Although the controlled simulation deviates less from the observed surface climate than its free-running counterpart, the terrestrial LAC in both configurations is much stronger and displays less spatial variability than the SGP observational estimates. Preliminary investigation of vegetation leaf area index (LAI) substituted for soil moisture suggests that the overly strong coupling between model soil moisture and surface atmospheric variables is associated with too much evaporation from bare ground and too little from the vegetation cover. Lastly, these results imply that model surface characteristics such as LAI, as well as the physical parameterizations involved in the coupling of the land and atmospheric components, are likely to be important sources of the problematical LAC behaviors.« less

Using ARM Observations to Evaluate Climate Model Simulations of Land-Atmosphere Coupling on the U.S. Southern Great Plains

DOE PAGES

Phillips, Thomas J.; Klein, Stephen A.; Ma, Hsi -Yen; ...

2017-10-13

Several independent measurements of warm-season soil moisture and surface atmospheric variables recorded at the ARM Southern Great Plains (SGP) research facility are used to estimate the terrestrial component of land-atmosphere coupling (LAC) strength and its regional uncertainty. The observations reveal substantial variation in coupling strength, as estimated from three soil moisture measurements at a single site, as well as across six other sites having varied soil and land cover types. The observational estimates then serve as references for evaluating SGP terrestrial coupling strength in the Community Atmospheric Model coupled to the Community Land Model. These coupled model components are operatedmore » in both a free-running mode and in a controlled configuration, where the atmospheric and land states are reinitialized daily, so that they do not drift very far from observations. Although the controlled simulation deviates less from the observed surface climate than its free-running counterpart, the terrestrial LAC in both configurations is much stronger and displays less spatial variability than the SGP observational estimates. Preliminary investigation of vegetation leaf area index (LAI) substituted for soil moisture suggests that the overly strong coupling between model soil moisture and surface atmospheric variables is associated with too much evaporation from bare ground and too little from the vegetation cover. Lastly, these results imply that model surface characteristics such as LAI, as well as the physical parameterizations involved in the coupling of the land and atmospheric components, are likely to be important sources of the problematical LAC behaviors.« less

On the Soil Roughness Parameterization Problem in Soil Moisture Retrieval of Bare Surfaces from Synthetic Aperture Radar

PubMed Central

Verhoest, Niko E.C; Lievens, Hans; Wagner, Wolfgang; Álvarez-Mozos, Jesús; Moran, M. Susan; Mattia, Francesco

2008-01-01

Synthetic Aperture Radar has shown its large potential for retrieving soil moisture maps at regional scales. However, since the backscattered signal is determined by several surface characteristics, the retrieval of soil moisture is an ill-posed problem when using single configuration imagery. Unless accurate surface roughness parameter values are available, retrieving soil moisture from radar backscatter usually provides inaccurate estimates. The characterization of soil roughness is not fully understood, and a large range of roughness parameter values can be obtained for the same surface when different measurement methodologies are used. In this paper, a literature review is made that summarizes the problems encountered when parameterizing soil roughness as well as the reported impact of the errors made on the retrieved soil moisture. A number of suggestions were made for resolving issues in roughness parameterization and studying the impact of these roughness problems on the soil moisture retrieval accuracy and scale. PMID:27879932

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...

Surface compaction estimates and soil sensitivity in Aspen stands of the Great Lakes States

Treesearch

Aaron Steber; Ken Brooks; Charles H. Perry; Randy Kolka

2007-01-01

Aspen forests in the Great Lakes States support much of the regional timber industry. Management-induced soil compaction is a concern because it affects forest health and productivity and soil erosion. Soil compaction increases bulk density and soil strength and can also decrease air and water movement into and through the soil profile. Currently, most inventories, and...

A Smart Irrigation Approach Aided by Monitoring Surface Soil Moisture using Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Wienhold, K. J.; Li, D.; Fang, N. Z.

2017-12-01

Soil moisture is a critical component in the optimization of irrigation scheduling in water resources management. Unmanned Aerial Vehicles (UAV) equipped with multispectral sensors represent an emerging technology capable of detecting and estimating soil moisture for irrigation and crop management. This study demonstrates a method of using a UAV as an optical and thermal remote sensing platform combined with genetic programming to derive high-resolution, surface soil moisture (SSM) estimates. The objective is to evaluate the feasibility of spatially-variable irrigation management for a golf course (about 50 acres) in North Central Texas. Multispectral data is collected over the course of one month in the visible, near infrared and longwave infrared spectrums using a UAV capable of rapid and safe deployment for daily estimates. The accuracy of the model predictions is quantified using a time domain reflectometry (TDR) soil moisture sensor and a holdout validation test set. The model produces reasonable estimates for SSM with an average coefficient of correlation (r) = 0.87 and coefficient of determination of (R2) = 0.76. The study suggests that the derived SSM estimates be used to better inform irrigation scheduling decisions for lightly vegetated areas such as the turf or native roughs found on golf courses.

Surface Soil Moisture Memory Estimated from Models and SMAP Observations

NASA Astrophysics Data System (ADS)

He, Q.; Mccoll, K. A.; Li, C.; Lu, H.; Akbar, R.; Pan, M.; Entekhabi, D.

2017-12-01

Soil moisture memory(SMM), which is loosely defined as the time taken by soil to forget an anomaly, has been proved to be important in land-atmosphere interaction. There are many metrics to calculate the SMM timescale, for example, the timescale based on the time-series autocorrelation, the timescale ignoring the soil moisture time series and the timescale which only considers soil moisture increment. Recently, a new timescale based on `Water Cycle Fraction' (Kaighin et al., 2017), in which the impact of precipitation on soil moisture memory is considered, has been put up but not been fully evaluated in global. In this study, we compared the surface SMM derived from SMAP observations with that from land surface model simulations (i.e., the SMAP Nature Run (NR) provided by the Goddard Earth Observing System, version 5) (Rolf et al., 2014). Three timescale metrics were used to quantify the surface SMM as: T0 based on the soil moisture time series autocorrelation, deT0 based on the detrending soil moisture time series autocorrelation, and tHalf based on the Water Cycle Fraction. The comparisons indicate that: (1) there are big gaps between the T0 derived from SMAP and that from NR (2) the gaps get small for deT0 case, in which the seasonality of surface soil moisture was removed with a moving average filter; (3) the tHalf estimated from SMAP is much closer to that from NR. The results demonstrate that surface SMM can vary dramatically among different metrics, while the memory derived from land surface model differs from the one from SMAP observation. tHalf, with considering the impact of precipitation, may be a good choice to quantify surface SMM and have high potential in studies related to land atmosphere interactions. References McColl. K.A., S.H. Alemohammad, R. Akbar, A.G. Konings, S. Yueh, D. Entekhabi. The Global Distribution and Dynamics of Surface Soil Moisture, Nature Geoscience, 2017 Reichle. R., L. Qing, D.L. Gabrielle, A. Joe. The "SMAP_Nature_v03" Data Product, 2014

a Method Using Gnss Lh-Reflected Signals for Soil Roughness Estimation

NASA Astrophysics Data System (ADS)

Jia, Y.; Li, W.; Chen, Y.; Lv, H.; Pei, Y.

2018-04-01

Global Navigation Satellite System Reflectometry (GNSS-R) is based on the concept of receiving GPS signals reflected by the ground using a passive receiver. The receiver can be on the ground or installed on a small aircraft or UAV and collects the electromagnetic field scattered from the surface of the Earth. The received signals are then analyzed to determine the characteristics of the surface. Many research has been reported showing the capability of the GNSS-R technique. However, the roughness of the surface impacts the phase and amplitude of the received signals, which is still a worthwhile study. This paper presented a method can be used by GNSS-R to estimate the surface roughness. First, the data was calculated in the specular reflection with the assumption of a flat surface with different permittivity. Since the power reflectivity can be evaluated as the ratio of left-hand (LH) reflected signal to the direct right-hand (RH) signal. Then a semi-empirical roughness model was applied to the data for testing. The results showed the method can distinguish the water and the soil surface. The sensitivity of the parameters was also analyzed. It indicates this method for soil roughness estimation can be used by GNSS-R LH reflected signals. In the next step, several experiments need to be done for improving the model and exploring the way of the estimation.

A Three Component Model to Estimate Sensible Heat Flux Over Sparse Shrubs in Nevada

USGS Publications Warehouse

Chehbouni, A.; Nichols, W.D.; Njoku, E.G.; Qi, J.; Kerr, Y.H.; Cabot, F.

1997-01-01

It is now recognized that accurate partitioning of available energy into sensible and latent heat flux is crucial to understanding surface-atmosphere interactions. This issue is more complicated in arid and semi-arid regions where the relative contribution to surface fluxes from the soil and vegetation may vary significantly throughout the day and throughout the season. The objective of this paper is to present a three-component model to estimate sensible heat flux over heterogeneous surfaces. The surface was represented with two adjacent compartments. The first compartment is made up of two components, shrubs and shaded soil; the second compartment consists of bare, unshaded soil. Data collected at two different sites in Nevada during the summers of 1991 and 1992 were used to evaluate model performance. The results show that the present model is sufficiently general to yield satisfactory results for both sites.

Using lagged dependence to identify (de)coupled surface and subsurface soil moisture values

NASA Astrophysics Data System (ADS)

Carranza, Coleen D. U.; van der Ploeg, Martine J.; Torfs, Paul J. J. F.

2018-04-01

Recent advances in radar remote sensing popularized the mapping of surface soil moisture at different spatial scales. Surface soil moisture measurements are used in combination with hydrological models to determine subsurface soil moisture values. However, variability of soil moisture across the soil column is important for estimating depth-integrated values, as decoupling between surface and subsurface can occur. In this study, we employ new methods to investigate the occurrence of (de)coupling between surface and subsurface soil moisture. Using time series datasets, lagged dependence was incorporated in assessing (de)coupling with the idea that surface soil moisture conditions will be reflected at the subsurface after a certain delay. The main approach involves the application of a distributed-lag nonlinear model (DLNM) to simultaneously represent both the functional relation and the lag structure in the time series. The results of an exploratory analysis using residuals from a fitted loess function serve as a posteriori information to determine (de)coupled values. Both methods allow for a range of (de)coupled soil moisture values to be quantified. Results provide new insights into the decoupled range as its occurrence among the sites investigated is not limited to dry conditions.

Constraining the 2012-2014 growing season Alaskan methane budget using CARVE aircraft measurements

NASA Astrophysics Data System (ADS)

Hartery, S.; Chang, R. Y. W.; Commane, R.; Lindaas, J.; Miller, S. M.; Wofsy, S. C.; Karion, A.; Sweeney, C.; Miller, C. E.; Dinardo, S. J.; Steiner, N.; McDonald, K. C.; Watts, J. D.; Zona, D.; Oechel, W. C.; Kimball, J. S.; Henderson, J.; Mountain, M. E.

2015-12-01

Soil in northen latitudes contains rich carbon stores which have been historically preserved via permafrost within the soil bed; however, recent surface warming in these regions is allowing deeper soil layers to thaw, influencing the net carbon exchange from these areas. Due to the extreme nature of its climate, these eco-regions remain poorly understood by most global models. In this study we analyze methane fluxes from Alaska using in situ aircraft observations from the 2012-2014 Carbon in Arctic Reservoir Vulnerability Experiment (CARVE). These observations are coupled with an atmospheric particle transport model which quantitatively links surface emissions to atmospheric observations to make regional methane emission estimates. The results of this study are two-fold. First, the inter-annual variability of the methane emissions was found to be <1 Tg over the area of interest and is largely influenced by the length of time the deep soil remains unfrozen. Second, the resulting methane flux estimates and mean soil parameters were used to develop an empirical emissions model to help spatially and temporally constrain the methane exchange at the Alaskan soil surface. The empirical emissions model will provide a basis for exploring the sensitivity of methane emissions to subsurface soil temperature, soil moisture, organic carbon content, and other parameters commonly used in process-based models.

Near-surface turbulence as a missing link in modeling evapotranspiration-soil moisture relationships

NASA Astrophysics Data System (ADS)

Haghighi, Erfan; Kirchner, James W.

2017-07-01

Despite many efforts to develop evapotranspiration (ET) models with improved parametrizations of resistance terms for water vapor transfer into the atmosphere, estimates of ET and its partitioning remain prone to bias. Much of this bias could arise from inadequate representations of physical interactions near nonuniform surfaces from which localized heat and water vapor fluxes emanate. This study aims to provide a mechanistic bridge from land-surface characteristics to vertical transport predictions, and proposes a new physically based ET model that builds on a recently developed bluff-rough bare soil evaporation model incorporating coupled soil moisture-atmospheric controls. The newly developed ET model explicitly accounts for (1) near-surface turbulent interactions affecting soil drying and (2) soil-moisture-dependent stomatal responses to atmospheric evaporative demand that influence leaf (and canopy) transpiration. Model estimates of ET and its partitioning were in good agreement with available field-scale data, and highlight hidden processes not accounted for by commonly used ET schemes. One such process, nonlinear vegetation-induced turbulence (as a function of vegetation stature and cover fraction) significantly influences ET-soil moisture relationships. Our results are particularly important for water resources and land use planning of semiarid sparsely vegetated ecosystems where soil surface interactions are known to play a critical role in land-climate interactions. This study potentially facilitates a mathematically tractable description of the strength (i.e., the slope) of the ET-soil moisture relationship, which is a core component of models that seek to predict land-atmosphere coupling and its feedback to the climate system in a changing climate.

Estimation of bare soil evaporation using multifrequency airborne SAR

NASA Technical Reports Server (NTRS)

Soares, Joao V.; Shi, Jiancheng; Van Zyl, Jakob; Engman, E. T.

1992-01-01

It is shown that for homogeneous areas soil moisture can be derived from synthetic aperture radar (SAR) measurements, so that the use of microwave remote sensing can given realistic estimates of energy fluxes if coupled to a simple two-layer model repesenting the soil. The model simulates volumetric water content (Wg) using classical meterological data, provided that some of the soil thermal and hydraulic properties are known. Only four parameters are necessary: mean water content, thermal conductivity and diffusitivity, and soil resistance to evaporation. They may be derived if a minimal number of measured values of Wg and surface layer temperature (Tg) are available together with independent measurements of energy flux to compare with the estimated values. The estimated evaporation is shown to be realistic and in good agreement with drying stage theory in which the transfer of water in the soil is in vapor form.

The Soil Moisture Active and Passive (SMAP) Mission

NASA Technical Reports Server (NTRS)

Entekhabi, Dara; Nijoku, Eni G.; ONeill, Peggy E.; Kellogg, Kent H.; Crow, Wade T.; Edelstein, Wendy N.; Entin, Jared K.; Goodman, Shawn D.; Jackson, Thomas J.; Johnson, Joel;

Soil carbon storage in a small arid catchment in the Negev desert (Israel)

NASA Astrophysics Data System (ADS)

Hoffmann, Ulrike; Kuhn, Nikolaus

2010-05-01

The mineral soil represents a major pool in the global carbon cycle. The behavior of mineral soil as a carbon reservoir in global climate and environmental issues is far from fully understood and causes a serious lack of comparable data on mineral soil organic carbon (SOC) at regional scale. To improve our understanding of soil carbon sequestration, it is necessary to acquire regional estimates of soil carbon pools in different ecosystem types. So far, little attention has been given to Dryland ecosystems, but they are often considered as highly sensitive to environmental change, with large and rapid responses to even smallest changes of climate conditions. Due to this fact, Drylands, as an ecosystem with extensive surface area across the globe (6.15 billion ha), have been suggested as a potential component for major carbon storage. A priori reasoning suggests that regional spatial patterns of SOC density (kg/m²) in Drylands are mostly affected by vegetation, soil texture, landscape position, soil truncation, wind erosion/deposition and the effect of water supply. Particularly unassigned is the interaction between soil volume, geomorphic processes and SOC density on regional scale. This study aims to enhance our understanding of regional spatial variability in dependence on soil volume, topography and surface parameters in areas susceptible to environmental change. Soil samples were taken in small transects at different representative slope positions across a range of elevations, soil texture, vegetation types, and terrain positions in a small catchment (600 ha) in the Negev desert. Topographic variables were extracted from a high resolution (0.5m) digital elevation model. Subsequently, we estimated the soil volume by excavating the entire soil at the representative sampling position. The volume was then estimated by laser scanning before and after soil excavation. SOC concentration of the soil samples was determined by CHN-analyser. For each sample, carbon densities (in kg/m²) were estimated for the mineral soil layer. The results indicate a large spatial variability of the carbon contents, the soil volume and depths across the landscape. In general, topography exerts a strong control on the carbon contents and the soil depths in the study site. Lowest carbon contents are apparent at the hillslope tops with increasing contents downslope. Because of the significantly larger carbon content at the northern exposed slope, we suggest that solar radiation driven differences of soil moisture content major controls SOC. Regarding the soil depths, the differences are not that clear. Soil depths seem to be higher at the southern exposed slope, but differences with respect to the slope position are not significant. Concerning the total amount of carbon storage in the study area, the results show that soil carbon may not be neglected in arid areas. Our results should provide an indication that carbon contents in dynamic environments are more affected and controlled by surface properties (soil volume) than by climate. Concluding that hint, climate is less important than surface processes in dryland ecosystems.

Evapotranspiration and remote sensing

NASA Technical Reports Server (NTRS)

Schmugge, T. J.; Gurney, R.

1982-01-01

There are three things required for evapotranspiration to occur: (1) energy (580 cal/gm) for the change of phase of the water; (2) a source of the water, i.e., adequate soil moisture in the surface layer or in the root zone of the plant; and (3) a sink for the water, i.e., a moisture deficit in the air above the ground. Remote sensing can contribute information to the first two of these conditions by providing estimates of solar insolation, surface albedo, surface temperature, vegetation cover, and soil moisture content. In addition there have been attempts to estimate precipitation and shelter air temperature from remotely sensed data. The problem remains to develop methods for effectively using these sources of information to make large area estimates of evapotranspiration.

Estimation of factors from natural and anthropogenic radioactivity present in the surface soil and comparison with DCF values.

PubMed

Ranade, A K; Pandey, M; Datta, D

2013-01-01

A study was conducted to evaluate the absorbed rate coefficient of (238)U, (232)Th, (40)K and (137)Cs present in soil. A total of 31 soil samples and the corresponding terrestrial dose rates at 1 m from different locations were taken around the Anushaktinagar region, where the litho-logy is dominated by red soil. A linear regression model was developed for the estimation of these factors. The estimated coefficients (nGy h(-1) Bq(-1) kg(-1)) were 0.454, 0.586, 0.035 and 0.392, respectively. The factors calculated were in good agreement with the literature values.

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.

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

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Estimating root-zone soil moisture in the West Africa Sahel using remotely sensed rainfall and vegetation

NASA Astrophysics Data System (ADS)

McNally, Amy L.

Agricultural drought is characterized by shortages in precipitation, large differences between actual and potential evapotranspiration, and soil water deficits that impact crop growth and pasture productivity. Rainfall and other agrometeorological gauge networks in Sub-Saharan Africa are inadequate for drought early warning systems and hence, satellite-based estimates of rainfall and vegetation greenness provide the main sources of information. While a number of studies have described the empirical relationship between rainfall and vegetation greenness, these studies lack a process based approach that includes soil moisture storage. In Chapters I and II, I modeled soil moisture using satellite rainfall inputs and developed a new method for estimating soil moisture with NDVI calibrated to in situ and microwave soil moisture observations. By transforming both NDVI and rainfall into estimates of soil moisture I was able to easily compare these two datasets in a physically meaningful way. In Chapter II, I also show how the new NDVI derived soil moisture can be assimilated into a water balance model that calculates an index of crop water stress. Compared to the analogous rainfall derived estimates of soil moisture and crop stress the NDVI derived estimates were better correlated with millet yields. In Chapter III, I developed a metric for defining growing season drought events that negatively impact millet yields. This metric is based on the data and models used in the Chapters I and II. I then use this metric to evaluate the ability of a sophisticated land surface model to detect drought events. The analysis showed that this particular land surface model's soil moisture estimates do have the potential to benefit the food security and drought early warning communities. With a focus on soil moisture, this dissertation introduced new methods that utilized a variety of data and models for agricultural drought monitoring applications. These new methods facilitate a more quantitative, transparent `convergence of evidence' approach to identifying agricultural drought events that lead to food insecurity. Ideally, these new methods will contribute to better famine early warning and the timely delivery of food aid to reduce the human suffering caused by drought.

Black soiling of an architectural limestone during two-year term exposure to urban air in the city of Granada (S Spain).

PubMed

Urosevic, Maja; Yebra-Rodríguez, Africa; Sebastián-Pardo, Eduardo; Cardell, Carolina

2012-01-01

A two-year term aging test was carried out on a building limestone under different urban conditions in the city of Granada (Southern Spain) to assess its Cultural Heritage sustainability. For this purpose stone tablets were placed vertically at four sites with contrasting local pollution micro-environments and exposure conditions (rain-sheltered and unsheltered). The back (rain-sheltered) and the front (rain-unsheltered) faces of the stone tablets were studied for each site. The soiling process (surface blackening) was monitored through lightness (ΔL*) and chroma changes (ΔC*). Additionally atmospheric particles deposited on the stone surfaces and on PM10 filters during the exposure time were studied through a multianalytical approach including scanning electron microscopy (SEM-EDX), transmission electron microscopy (TEM) and micro-Raman spectroscopy. The identified atmospheric particles (responsible for stone soiling) were mainly soot and soil dust particles; also fly ash and aged salt particles were found. The soiling process was related to surface texture, exposure conditions and proximity to dense traffic streets. On the front faces of all stones, black soiling and surface roughness promoted by differential erosion between micritic and sparitic calcite were noticed. Moreover, it was found that surface roughness enhanced a feedback process that triggers further black soiling. The calculated effective area coverage (EAC) by light absorbing dust ranged from 10.2 to 20.4%, exceeding by far the established value of 2% EAC (limit perceptible to the human eye). Soiling coefficients (SC) were estimated based on square-root and bounded exponential fittings. Estimated black carbon (BC) concentration resulted in relatively similar SC for all studied sites and thus predicts the soiling process better than using particulate matter (PM10) concentration. Copyright © 2011 Elsevier B.V. All rights reserved.

Application of Data Assimilation with the Root Zone Water Quality Model for Soil Moisture Profile Estimation

USDA-ARS?s Scientific Manuscript database

Estimation of soil moisture has received considerable attention in the areas of hydrology, agriculture, meteorology and environmental studies because of its role in the partitioning water and energy at the land surface. In this study, the Ensemble Kalman Filter (EnKF), a popular data assimilation te...

Calibration of the Root Zone Water Quality Model and Application of Data Assimilation Techniques to Estimate Profile Soil Moisture

USDA-ARS?s Scientific Manuscript database

Estimation of soil moisture has received considerable attention in the areas of hydrology, agriculture, meteorology and environmental studies because of its role in the partitioning water and energy at the land surface. In this study, the USDA, Agricultural Research Service, Root Zone Water Quality ...

Wide-Area Soil Moisture Estimation Using the Propagation of Lightning Generated Low-Frequency Electromagnetic Signals 1977

USDA-ARS?s Scientific Manuscript database

Land surface moisture measurements are central to our understanding of the earth’s water system, and are needed to produce accurate model-based weather/climate predictions. Currently, there exists no in-situ network capable of estimating wide-area soil moisture. In this paper, we explore an alterna...

Efficacy of passive capillary samplers for estimating soil water drainage in the vadose zone

USDA-ARS?s Scientific Manuscript database

The efficacy and accuracy of PCAP samplers were evaluated for continuous estimating of soil water drainage and fluxes below the rootzone of a sugarbeet-potato-barley rotation under two irrigation frequencies. Twelve automated PCAPs with outside sampling surface dimensions of 91 cm length x 31 cm wid...

Aircraft active microwave measurements for estimating soil moisture

NASA Technical Reports Server (NTRS)

Jackson, T. J.; Chang, A.; Schmugge, T. J.

1981-01-01

Both active and passive microwave sensors are sensitive to variations in near-surface soil moisture. The principal advantage of active microwave systems for soil moisture applications is that high spatial resolution can be retained even at satellite attitudes. The considered investigation is concerned with the use of active microwave scatterometers for estimating near-surface soil moisture. Microwave scatterometer data were obtained during a series of three aircraft flights over a group of Oklahoma research watersheds during May 1978. Data were obtained for the C, L, and P bands at angles of incidence between 5 and 50 degrees. The best results were obtained using C band data at incidence angles of 10 and 15 degrees and soil moisture depth of 0 to 15 cm. These results were in excellent agreement with the conclusions of the truck-mounted scatterometer measurement program reported by Ulaby et al. (1978, 1979).

Discrepancy between Snowmelt and Soil Infiltration

NASA Astrophysics Data System (ADS)

Fassnacht, S. R.

2017-12-01

A majority of snowmelt enters the soil and is either transmitted through or stored in the soil. Snowmelt has been estimated from the decrease in snow mass of a snow pillow and soil infiltration has been estimated from near surface TDR probes. Here, these data are from a set of Snow Telemetry (SNOTEL) stations across Colorado. While seasonal totals are similar, it is shown that there is a disconnect between the amount of water melted in a day and the increased daily volume of water measured in the near sub-surface. It is surmised that these differences are a function of the data collection methods, the infiltration rate, and possible lateral flow. An examination of daily infiltration volumes at depth shows a further disconnect, as it is likely that lateral flow complicates the measurements to a true three dimensional problem. The data are informative to illustrate the transmission of meltwater into the soil; methods for improvement are explored.

Role of Subsurface Physics in the Assimilation of Surface Soil Moisture Observations

NASA Technical Reports Server (NTRS)

Reichle, R. H.

2010-01-01

Root zone soil moisture controls the land-atmosphere exchange of water and energy and exhibits memory that may be useful for climate prediction at monthly scales. Assimilation of satellite-based surface soil moisture observations into a land surface model is an effective way to estimate large-scale root zone soil moisture. The propagation of surface information into deeper soil layers depends on the model-specific representation of subsurface physics that is used in the assimilation system. In a suite of experiments we assimilate synthetic surface soil moisture observations into four different models (Catchment, Mosaic, Noah and CLM) using the Ensemble Kalman Filter. We demonstrate that identical twin experiments significantly overestimate the information that can be obtained from the assimilation of surface soil moisture observations. The second key result indicates that the potential of surface soil moisture assimilation to improve root zone information is higher when the surface to root zone coupling is stronger. Our experiments also suggest that (faced with unknown true subsurface physics) overestimating surface to root zone coupling in the assimilation system provides more robust skill improvements in the root zone compared with underestimating the coupling. When CLM is excluded from the analysis, the skill improvements from using models with different vertical coupling strengths are comparable for different subsurface truths. Finally, the skill improvements through assimilation were found to be sensitive to the regional climate and soil types.

Land Capability Potential Index (LCPI) for the Lower Missouri River Valley

USGS Publications Warehouse

Jacobson, Robert B.; Chojnacki, Kimberly A.; Reuter, Joanna M.

2007-01-01

The Land Capability Potential Index (LCPI) was developed to serve as a relatively coarse-scale index to delineate broad land capability classes in the valley of the Lower Missouri River. The index integrates fundamental factors that determine suitability of land for various uses, and may provide a useful mechanism to guide land-management decisions. The LCPI was constructed from integration of hydrology, hydraulics, land-surface elevations, and soil permeability (or saturated hydraulic conductivity) datasets for an area of the Lower Missouri River, river miles 423–670. The LCPI estimates relative wetness based on intersecting water-surface elevations, interpolated from measurements or calculated from hydraulic models, with a high-resolution land-surface elevation dataset. The potential for wet areas to retain or drain water is assessed using soil-drainage classes that are estimated from saturated hydraulic conductivity of surface soils. Terrain mapping that delineates areas with convex, concave, and flat parts of the landscape provides another means to assess tendency of landscape patches to retain surface water.

The Use of Indirect Estimates of Soil Moisture to Initialize Coupled Models and its Impact on Short-Term and Seasonal Simulations

NASA Technical Reports Server (NTRS)

Lapenta, William M.; Crosson, William; Dembek, Scott; Lakhtakia, Mercedes

1998-01-01

It is well known that soil moisture is a characteristic of the land surface that strongly affects the partitioning of outgoing radiation into sensible and latent heat which significantly impacts both weather and climate. Detailed land surface schemes are now being coupled to mesoscale atmospheric models in order to represent the effect of soil moisture upon atmospheric simulations. However, there is little direct soil moisture data available to initialize these models on regional to continental scales. As a result, a Soil Hydrology Model (SHM) is currently being used to generate an indirect estimate of the soil moisture conditions over the continental United States at a grid resolution of 36 Km on a daily basis since 8 May 1995. The SHM is forced by analyses of atmospheric observations including precipitation and contains detailed information on slope soil and landcover characteristics.The purpose of this paper is to evaluate the utility of initializing a detailed coupled model with the soil moisture data produced by SHM.

Chemical weathering rates of a soil chronosequence on granitic alluvium: I. Quantification of mineralogical and surface area changes and calculation of primary silicate reaction rates

USGS Publications Warehouse

White, A.F.; Blum, A.E.; Schulz, M.S.; Bullen, T.D.; Harden, J.W.; Peterson, M.L.

1996-01-01

Mineral weathering rates are determined for a series of soils ranging in age from 0.2-3000 Ky developed on alluvial terraces near Merced in the Central Valley of California. Mineralogical and elemental abundances exhibit time-dependent trends documenting the chemical evolution of granitic sand to residual kaolinite and quartz. Mineral losses with time occur in the order: hornblende > plagioclase > K-feldspar. Maximum volume decreases of >50% occur in the older soils. BET surface areas of the bulk soils increase with age, as do specific surface areas of aluminosilicate mineral fractions such as plagioclase, which increases from 0.4-1.5 m2 g-1 over 600 Ky. Quartz surface areas are lower and change less with time (0.11-0.23 m2 g-1). BET surface areas correspond to increasing external surface roughness (?? = 10-600) and relatively constant internal surface area (??? 1.3 m2 g-1). SEM observations confirm both surface pitting and development of internal porosity. A numerical model describes aluminosilicate dissolution rates as a function of changes in residual mineral abundance, grain size distributions, and mineral surface areas with time. A simple geometric treatment, assuming spherical grains and no surface roughness, predicts average dissolution rates (plagioclase, 10-17.4; K-feldspar, 10-17.8; and hornblende, 10-17.5 mol cm-1 s-1) that are constant with time and comparable to previous estimates of soil weathering. Average rates, based on BET surface area measurements and variable surface roughnesses, are much slower (plagioclase, 10-19.9; K-feldspar, 10-20.5; and hornblende 10-20.1 mol cm-2 s-1). Rates for individual soil horizons decrease by a factor of 101.5 over 3000 Ky indicating that the surface reactivities of minerals decrease as the physical surface areas increase. Rate constants based on BET estimates for the Merced soils are factors of 103-104 slower than reported experimental dissolution rates determined from freshly prepared silicates with low surface roughness (?? <10). This study demonstrates that the utility of experimental rate constants to predict weathering in soils is limited without consideration of variable surface areas and processes that control the evolution of surface reactivity with time.

Percolation behavior of tritiated water into a soil packed bed

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

Honda, T.; Katayama, K.; Uehara, K.

2015-03-15

A large amount of cooling water is used in a D-T fusion reactor. The cooling water will contain tritium with high concentration because tritium can permeate metal walls at high temperature easily. A development of tritium handling technology for confining tritiated water in the fusion facility is an important issue. In addition, it is also important to understand tritium behavior in environment assuming severe accidents. In this study, percolation experiments of tritiated water in soil packed bed were carried out and tritium behavior in soil was discussed. Six soil samples were collected in Hakozaki campus of Kyushu University. These particlemore » densities were of the same degree as that of general soils and moisture contents were related to BET surface area. For two soil samples used in the percolation experiment of tritiated water, saturated hydraulic conductivity agreed well with the estimating value by Creager. Tritium retention ratio in the soil packed bed was larger than water retention. This is considered to be due to an effect of tritium sorption on the surface of soil particles. The isotope exchange capacity estimated by assuming that H/T ratio of supplied tritiated water and H/T ratio of surface water of soil particle was equal was comparable to that on cement paste and mortar which were obtained by exposure of tritiated water vapor. (authors)« less

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.

New Physical Algorithms for Downscaling SMAP Soil Moisture

NASA Astrophysics Data System (ADS)

Sadeghi, M.; Ghafari, E.; Babaeian, E.; Davary, K.; Farid, A.; Jones, S. B.; Tuller, M.

2017-12-01

The NASA Soil Moisture Active Passive (SMAP) mission provides new means for estimation of surface soil moisture at the global scale. However, for many hydrological and agricultural applications the spatial SMAP resolution is too low. To address this scale issue we fused SMAP data with MODIS observations to generate soil moisture maps at 1-km spatial resolution. In course of this study we have improved several existing empirical algorithms and introduced a new physical approach for downscaling SMAP data. The universal triangle/trapezoid model was applied to relate soil moisture to optical/thermal observations such as NDVI, land surface temperature and surface reflectance. These algorithms were evaluated with in situ data measured at 5-cm depth. Our results demonstrate that downscaling SMAP soil moisture data based on physical indicators of soil moisture derived from the MODIS satellite leads to higher accuracy than that achievable with empirical downscaling algorithms. Keywords: Soil moisture, microwave data, downscaling, MODIS, triangle/trapezoid model.

Mapping Soil Surface Macropores Using Infrared Thermography: An Exploratory Laboratory Study

PubMed Central

de Lima, João L. M. P.; Abrantes, João R. C. B.; Silva, Valdemir P.; de Lima, M. Isabel P.; Montenegro, Abelardo A. A.

2014-01-01

Macropores and water flow in soils and substrates are complex and are related to topics like preferential flow, nonequilibrium flow, and dual-continuum. Hence, the quantification of the number of macropores and the determination of their geometry are expected to provide a better understanding on the effects of pores on the soil's physical and hydraulic properties. This exploratory study aimed at evaluating the potential of using infrared thermography for mapping macroporosity at the soil surface and estimating the number and size of such macropores. The presented technique was applied to a small scale study (laboratory soil flume). PMID:25371915

Assimilation of Freeze - Thaw Observations into the NASA Catchment Land Surface Model

NASA Technical Reports Server (NTRS)

Farhadi, Leila; Reichle, Rolf H.; DeLannoy, Gabrielle J. M.; Kimball, John S.

2014-01-01

The land surface freeze-thaw (F-T) state plays a key role in the hydrological and carbon cycles and thus affects water and energy exchanges and vegetation productivity at the land surface. In this study, we developed an F-T assimilation algorithm for the NASA Goddard Earth Observing System, version 5 (GEOS-5) modeling and assimilation framework. The algorithm includes a newly developed observation operator that diagnoses the landscape F-T state in the GEOS-5 Catchment land surface model. The F-T analysis is a rule-based approach that adjusts Catchment model state variables in response to binary F-T observations, while also considering forecast and observation errors. A regional observing system simulation experiment was conducted using synthetically generated F-T observations. The assimilation of perfect (error-free) F-T observations reduced the root-mean-square errors (RMSE) of surface temperature and soil temperature by 0.206 C and 0.061 C, respectively, when compared to model estimates (equivalent to a relative RMSE reduction of 6.7 percent and 3.1 percent, respectively). For a maximum classification error (CEmax) of 10 percent in the synthetic F-T observations, the F-T assimilation reduced the RMSE of surface temperature and soil temperature by 0.178 C and 0.036 C, respectively. For CEmax=20 percent, the F-T assimilation still reduces the RMSE of model surface temperature estimates by 0.149 C but yields no improvement over the model soil temperature estimates. The F-T assimilation scheme is being developed to exploit planned operational F-T products from the NASA Soil Moisture Active Passive (SMAP) mission.

Scaling up from field to region for wind erosion prediction using a field-scale wind erosion model and GIS

USGS Publications Warehouse

Zobeck, T.M.; Parker, N.C.; Haskell, S.; Guoding, K.

2000-01-01

Factors that affect wind erosion such as surface vegetative and other cover, soil properties and surface roughness usually change spatially and temporally at the field-scale to produce important field-scale variations in wind erosion. Accurate estimation of wind erosion when scaling up from fields to regions, while maintaining meaningful field-scale process details, remains a challenge. The objectives of this study were to evaluate the feasibility of using a field-scale wind erosion model with a geographic information system (GIS) to scale up to regional levels and to quantify the differences in wind erosion estimates produced by different scales of soil mapping used as a data layer in the model. A GIS was used in combination with the revised wind erosion equation (RWEQ), a field-scale wind erosion model, to estimate wind erosion for two 50 km2 areas. Landsat Thematic Mapper satellite imagery from 1993 with 30 m resolution was used as a base map. The GIS database layers included land use, soils, and other features such as roads. The major land use was agricultural fields. Data on 1993 crop management for selected fields of each crop type were collected from local government agency offices and used to 'train' the computer to classify land areas by crop and type of irrigation (agroecosystem) using commercially available software. The land area of the agricultural land uses was overestimated by 6.5% in one region (Lubbock County, TX, USA) and underestimated by about 21% in an adjacent region (Terry County, TX, USA). The total estimated wind erosion potential for Terry County was about four times that estimated for adjacent Lubbock County. The difference in potential erosion among the counties was attributed to regional differences in surface soil texture. In a comparison of different soil map scales in Terry County, the generalised soil map had over 20% more of the land area and over 15% greater erosion potential in loamy sand soils than did the detailed soil map. As a result, the wind erosion potential determined using the generalised soil map Was about 26% greater than the erosion potential estimated by using the detailed soil map in Terry County. This study demonstrates the feasibility of scaling up from fields to regions to estimate wind erosion potential by coupling a field-scale wind erosion model with GIS and identifies possible sources of error with this approach.

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.

Evaluation of HCMM data for assessing soil moisture and water table depth. [South Dakota

NASA Technical Reports Server (NTRS)

Moore, D. G.; Heilman, J. L.; Tunheim, J. A.; Westin, F. C.; Heilman, W. E.; Beutler, G. A.; Ness, S. D. (Principal Investigator)

1981-01-01

Soil moisture in the 0-cm to 4-cm layer could be estimated with 1-mm soil temperatures throughout the growing season of a rainfed barley crop in eastern South Dakota. Empirical equations were developed to reduce the effect of canopy cover when radiometrically estimating the soil temperature. Corrective equations were applied to an aircraft simulation of HCMM data for a diversity of crop types and land cover conditions to estimate the soil moisture. The average difference between observed and measured soil moisture was 1.6% of field capacity. Shallow alluvial aquifers were located with HCMM predawn data. After correcting the data for vegetation differences, equations were developed for predicting water table depths within the aquifer. A finite difference code simulating soil moisture and soil temperature shows that soils with different moisture profiles differed in soil temperatures in a well defined functional manner. A significant surface thermal anomaly was found to be associated with shallow water tables.

Landscape-Scale Soil Carbon Inventories by Microclimate Decomposition

NASA Astrophysics Data System (ADS)

Beaudette, D. E.; O'Geen, A. T.

2008-12-01

Estimation of carbon stocks in rangeland and foothill ecosystems is poised to become an important service once legislation regulating greenhouse gas emissions is passed. Trading of carbon credits and greenhouse gas emission/sequestration budgets for vegetated areas is largely dependent on an accurate and scale- dependent inventory of existing conditions. Soil survey presents one possible resource for surface carbon stocks, however these data are usually not mapped at the landscape-scale. Soil-landscape modeling techniques have been successfully used in several instances to predict the spatial variation in soil carbon. Most of these studies have used site exposure (aspect angle) as a categorical proxy for terrain-induced microclimate. Our objective was to model parameters related to soil microclimate (soil temperature and moisture) for the production of detailed maps of soil carbon and organic matter quality (i.e. C:N ratio). We used a solar radiation model and long-term monitoring of soil moisture and temperature to generate several models of soil microclimate. Parameterization of the ESRA (European Solar Radiation Atlas) solar radiation model (clear-sky version) was accomplished with daily estimates of the Linke turbidity factor, using local pyranometer measurements (11 year record). Our estimated daily radiance values correlated well with local weather station data (R2 = 0.965, p < 0.001). This model is included in the popular, open source GRASS GIS. A preliminary study based on 35 sites, spanning two contrasting landform types (and lithology), revealed a statistically significant relationship between annual radiation load and carbon (R2 = 0.75, p < 0.001). A highly significant relationship between C:N ratio and annual radiation load was identified as well (R2 = 0.99, p < 0.001). Solar radiation models are simple to use, and have the potential to refine previous soil-landscape modeling efforts that relied on aspect class or angle. Models linking surface processes with microclimate can be used to directly generate estimates of carbon, or used to down-scale soil survey-based estimates.

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.

Calcium depletion in a Southeastern United States forest ecosystem

USGS Publications Warehouse

Huntington, T.G.; Hooper, R.P.; Johnson, C.E.; Aulenbach, Brent T.; Cappellato, R.; Blum, A.E.

2000-01-01

Forest soil Ca depletion through leaching and vegetation uptake may threaten long-term sustainability of forest productivity in the southeastern USA. This study was conducted to assess Ca pools and fluxes in a representative southern Piedmont forest to determine the soil Ca depletion rate. Soil Ca storage, Ca inputs in atmospheric deposition, and outputs in soil leaching and vegetation uptake were investigated at the Panola Mountain Research Watershed (PMRW) near Atlanta, GA. Average annual outputs of 12.3 kg ha-1 yr-1 in uptake into merchantable wood and 2.71 kg ha-1 yr-1 soil leaching exceeded inputs in atmospheric deposition of 2.24 kg ha-1 yr-1. The annual rate of Ca uptake into merchantable wood exceeds soil leaching losses by a factor of more than five. The potential for primary mineral weathering to provide a substantial amount of Ca inputs is low. Estimates of Ca replenishment through mineral weathering in the surface 1 m of soil and saprolite was estimated to be 0.12 kg ha-1 yr-1. The weathering rate in saprolite and partially weathered bedrock below the surface 1 m is similarly quite low because mineral Ca is largely depleted. The soil Ca depletion rate at PMRW is estimated to be 12.7 kg ha-1 yr-1. At PMRW and similar hardwood-dominated forests in the Piedmont physiographic province, Ca depletion will probably reduce soil reserves to less than the requirement for a merchantable forest stand in ???80 yr. This assessment and comparable analyses at other southeastern USA forest sites suggests that there is a strong potential for a regional problem in forest nutrition in the long term.Forest soil Ca depletion through leaching and vegetation uptake may threaten long-term sustainability of forest productivity in the southeastern USA. This study was conducted to assess Ca pools and fluxes in a representative southern Piedmont forest to determine the soil Ca depletion rate. Soil Ca storage, Ca inputs in atmospheric deposition, and outputs in soil leaching and vegetation uptake were investigated at the Panola Mountain Research Watershed (PMRW) near Atlanta, GA. Average annual outputs of 12.3 kg ha-1 yr-1 in uptake into merchantable wood and 2.71 kg ha-1 yr-1 soil leaching exceeded inputs in atmospheric deposition of 2.24 kg ha-1 yr-1. The annual rate of Ca uptake into merchantable wood exceeds soil leaching losses by a factor of more than five. The potential for primary mineral weathering to provide a substantial amount of Ca inputs is low. Estimates of Ca replenishment through mineral weathering in the surface 1 m of soil and saprolite was estimated to be 0.12 kg ha-1 yr-1. The weathering rate in saprolite and partially weathered bedrock below the surface 1 m is similarly quite low because mineral Ca is largely depleted. The soil Ca depletion rate at PMRW is estimated to be 12.7 kg ha-1 yr-1. At PMRW and similar hardwood-dominated forests in the Piedmont physiographic province, Ca depletion will probably reduce soil reserves to less than the requirement for a merchantable forest stand in ???80 yr. This assessment and comparable analyses at other southeastern USA forest sites suggests that there is a strong potential for a regional problem in forest nutrition in the long term.

Assessment of Radioactive Materials and Heavy Metals in the Surface Soil around the Bayanwula Prospective Uranium Mining Area in China

PubMed Central

Bai, Haribala; Hu, Bitao; Wang, Chengguo; Bao, Shanhu; Sai, Gerilemandahu; Xu, Xiao; Zhang, Shuai; Li, Yuhong

2017-01-01

The present work is the first systematic and large scale study on radioactive materials and heavy metals in surface soil around the Bayanwula prospective uranium mining area in China. In this work, both natural and anthropogenic radionuclides and heavy metals in 48 surface soil samples were analyzed using High Purity Germanium (HPGe) γ spectrometry and inductively coupled plasma-mass spectrometry (ICP-MS). The obtained mean activity concentrations of 238U, 226Ra, 232Th, 40K, and 137Cs were 25.81 ± 9.58, 24.85 ± 2.77, 29.40 ± 3.14, 923.0 ± 47.2, and 5.64 ± 4.56 Bq/kg, respectively. The estimated average absorbed dose rate and annual effective dose rate were 76.7 ± 3.1 nGy/h and 83.1 ± 3.8 μSv, respectively. The radium equivalent activity, external hazard index, and internal hazard index were also calculated, and their mean values were within the acceptable limits. The estimated lifetime cancer risk was 3.2 × 10−4/Sv. The heavy metal contents of Cr, Ni, Cu, Zn, As, Cd, and Pb from the surface soil samples were measured and their health risks were then assessed. The concentrations of all heavy metals were much lower than the average backgrounds in China except for lead which was about three times higher than that of China’s mean. The non-cancer and cancer risks from the heavy metals were estimated, which are all within the acceptable ranges. In addition, the correlations between the radionuclides and the heavy metals in surface soil samples were determined by the Pearson linear coefficient. Strong positive correlations between radionuclides and the heavy metals at the 0.01 significance level were found. In conclusion, the contents of radionuclides and heavy metals in surface soil around the Bayanwula prospective uranium mining area are at a normal level. PMID:28335450

Assessment of Radioactive Materials and Heavy Metals in the Surface Soil around the Bayanwula Prospective Uranium Mining Area in China.

PubMed

Bai, Haribala; Hu, Bitao; Wang, Chengguo; Bao, Shanhu; Sai, Gerilemandahu; Xu, Xiao; Zhang, Shuai; Li, Yuhong

2017-03-14

The present work is the first systematic and large scale study on radioactive materials and heavy metals in surface soil around the Bayanwula prospective uranium mining area in China. In this work, both natural and anthropogenic radionuclides and heavy metals in 48 surface soil samples were analyzed using High Purity Germanium (HPGe) γ spectrometry and inductively coupled plasma-mass spectrometry (ICP-MS). The obtained mean activity concentrations of 238 U, 226 Ra, 232 Th, 40 K, and 137 Cs were 25.81 ± 9.58, 24.85 ± 2.77, 29.40 ± 3.14, 923.0 ± 47.2, and 5.64 ± 4.56 Bq/kg, respectively. The estimated average absorbed dose rate and annual effective dose rate were 76.7 ± 3.1 nGy/h and 83.1 ± 3.8 μ Sv, respectively. The radium equivalent activity, external hazard index, and internal hazard index were also calculated, and their mean values were within the acceptable limits. The estimated lifetime cancer risk was 3.2 × 10 -4 /Sv. The heavy metal contents of Cr, Ni, Cu, Zn, As, Cd, and Pb from the surface soil samples were measured and their health risks were then assessed. The concentrations of all heavy metals were much lower than the average backgrounds in China except for lead which was about three times higher than that of China's mean. The non-cancer and cancer risks from the heavy metals were estimated, which are all within the acceptable ranges. In addition, the correlations between the radionuclides and the heavy metals in surface soil samples were determined by the Pearson linear coefficient. Strong positive correlations between radionuclides and the heavy metals at the 0.01 significance level were found. In conclusion, the contents of radionuclides and heavy metals in surface soil around the Bayanwula prospective uranium mining area are at a normal level.

Use of geostatistics for remediation planning to transcend urban political boundaries.

PubMed

Milillo, Tammy M; Sinha, Gaurav; Gardella, Joseph A

2012-11-01

Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible. Copyright © 2012 Elsevier Ltd. All rights reserved.

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Estimating Surface Soil Moisture in a Mixed-Landscape using SMAP and MODIS/VIIRS Data

NASA Astrophysics Data System (ADS)

Tang, J.; Di, L.; Xiao, J.

2017-12-01

Soil moisture, a critical parameter of earth ecosystem linking land surface and atmosphere, has been widely applied in many application (Di, 1991; Njoku et al. 2003; Western 2002; Zhao et al. 2014; McColl et al. 2017) from regional to continental or even global scale. The advent of satellite-based remote sensing, particular in the last two decades, has proven successful for mapping the surface soil moisture (SSM) from space (Petropoulos et al. 2015; Kim et al. 2015; Molero et al. 2016). The current soil moisture products, however, is not able to fully characterize the spatial and temporal variability of soil moisture at mixed landscape types (Albergel et al. 2013; Zeng et al. 2015). In this research, we derived the SSM at 1-km spatial resolution by using sensor observation and high-level products from SMAP and MODIS/VIIRS as well as metrorological, landcover, and soil data. Specifically, we proposed a practicable method to produce the originally planned SMAP L3_SM_A with comparable quality by downscaling the SMAP L3_SM_P product through a proved method, the geographically weighted regression method at mixed landscape in southern New Hampshire. This estimated SSM was validated using the Soil Climate Analysis Network (SCAN) from Natural Resources Conservation Service (NRCS) of United States Department of Agriculture (USDA).

Application of the two-source energy balance model to partition evapotranspiration in an arid wine vineyard

NASA Astrophysics Data System (ADS)

Kool, Dilia; Kustas, William P.; Agam, Nurit

2016-04-01

The partitioning of evapotranspiration (ET) into transpiration (T), a productive water use, and soil water evaporation (E), which is generally considered a water loss, is highly relevant to agriculture in the light of increasing desertification and water scarcity. This task is challenged by the complexity of soil and plant interactions, coupled with changes in atmospheric and soil water content conditions. Many of the processes controlling water/energy exchange are not adequately modeled. The two-source energy balance model (TSEB) was evaluated and adapted for independent E and T estimations in an isolated drip-irrigated wine vineyard in the arid Negev desert. The TSEB model estimates ET by computing vegetation and soil energy fluxes using remotely sensed composite surface temperature, local weather data (solar radiation, air temperature and humidity, and wind speed), and vegetation metrics (row spacing, canopy height and width, and leaf area). The soil and vegetation energy fluxes are computed numerically using a system of temperature gradient and resistance equations; where soil and canopy temperatures are derived from the composite surface temperature. For estimation of ET, the TSEB model has been shown to perform well for various agricultural crops under a wide range of environmental conditions, but validation of T and E fluxes is limited to one study in a well-watered cotton crop. Extending the TSEB approach to water-limited vineyards demands careful consideration regarding how the complex canopy structure of vineyards will influence the accuracy of the partitioning between E and T. Data for evaluation of the TSEB model were collected over a season (bud break till harvest). Composite, canopy, and soil surface temperatures were measured using infrared thermometers. The composite vegetation and soil surface energy fluxes were assessed using independent measurements of net radiation, and soil, sensible and latent heat flux. The below canopy energy balance was assessed at the dry midrow position as well as the wet irrigated position directly underneath the vine row, where net radiation and soil heat flux were measured, sensible heat flux was computed indirectly, and E was calculated as the residual. While the below canopy energy balance approach used in this study allowed continuous assessment of E at daily intervals, instantaneous E fluxes could not be assessed due to vertical variability in shading below the canopy. Seasonal partitioning indicated that total E amounted to 9-11% of ET. Initial evaluation of the TSEB model indicated that discrepancies between modeled and measured fluxes can largely be attributed to net radiation partitioning. In addition, large diurnal variation at the soil surface requires adaptation of the soil heat flux formulations. Improved estimation of energy fluxes by accounting for the relatively complex canopy structure of vineyards will be highlighted.

Hydrologic data assimilation with a hillslope-scale-resolving model and L band radar observations: Synthetic experiments with the ensemble Kalman filter

NASA Astrophysics Data System (ADS)

Flores, Alejandro N.; Bras, Rafael L.; Entekhabi, Dara

2012-08-01

Soil moisture information is critical for applications like landslide susceptibility analysis and military trafficability assessment. Existing technologies cannot observe soil moisture at spatial scales of hillslopes (e.g., 100 to 102 m) and over large areas (e.g., 102 to 105 km2) with sufficiently high temporal coverage (e.g., days). Physics-based hydrologic models can simulate soil moisture at the necessary spatial and temporal scales, albeit with error. We develop and test a data assimilation framework based on the ensemble Kalman filter for constraining uncertain simulated high-resolution soil moisture fields to anticipated remote sensing products, specifically NASA's Soil Moisture Active-Passive (SMAP) mission, which will provide global L band microwave observation approximately every 2-3 days. The framework directly assimilates SMAP synthetic 3 km radar backscatter observations to update hillslope-scale bare soil moisture estimates from a physics-based model. Downscaling from 3 km observations to hillslope scales is achieved through the data assimilation algorithm. Assimilation reduces bias in near-surface soil moisture (e.g., top 10 cm) by approximately 0.05 m3/m3and expected root-mean-square errors by at least 60% in much of the watershed, relative to an open loop simulation. However, near-surface moisture estimates in channel and valley bottoms do not improve, and estimates of profile-integrated moisture throughout the watershed do not substantially improve. We discuss the implications of this work, focusing on ongoing efforts to improve soil moisture estimation in the entire soil profile through joint assimilation of other satellite (e.g., vegetation) and in situ soil moisture measurements.

The StreamCat Dataset: Accumulated Attributes for NHDPlusV2 Catchments (Version 2.1) for the Conterminous United States: Soil Erodibility (KFFACT)

EPA Pesticide Factsheets

This dataset represents the adjusted soil erodibility factor within individual, local NHDPlusV2 catchments and upstream, contributing watersheds. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. (See Supplementary Info for Glossary of Terms) The STATSGO Layer table specifies two soil erodibility factors for each component layer, KFFACT and KFACT. The STATSGO documentation describes KFFACT as a soil erodibility factor which quanitifies the susceptibility of soil particles to detachment and movement by water. This factor is used in the Universal Soil Loss Equation to caluculate soil loss by water. KFACT is described as a soil erodibility factor which is adjusted for the effect of rock fragments. The average value of each of these soil erodibility factors was determined for the top (surface) layer for each map unit of each state.The base-flow index (BFI) grid for the conterminous United States was developed to estimate (1) BFI values for ungaged streams, and (2) ground-water recharge throughout the conterminous United States (see Data Source). Estimates of BFI values at ungaged streams and BFI-based ground-water recharge estimates are useful for interpreting relations between land use and water quality in surface and ground water. The soil erodibility factor was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metri

Soil crusts to warm the planet

NASA Astrophysics Data System (ADS)

Garcia-Pichel, Ferran; Couradeau, Estelle; Karaoz, Ulas; da Rocha Ulisses, Nunes; Lim Hsiao, Chiem; Northen, Trent; Brodie, Eoin

2016-04-01

Soil surface temperature, an important driver of terrestrial biogeochemical processes, depends strongly on soil albedo, which can be significantly modified by factors such as plant cover. In sparsely vegetated lands, the soil surface can also be colonized by photosynthetic microbes that build biocrust communities. We used concurrent physical, biochemical and microbiological analyses to show that mature biocrusts can increase surface soil temperature by as much as 10 °C through the accumulation of large quantities of a secondary metabolite, the microbial sunscreen scytonemin, produced by a group of late-successional cyanobacteria. Scytonemin accumulation decreases soil albedo significantly. Such localized warming had apparent and immediate consequences for the crust soil microbiome, inducing the replacement of thermosensitive bacterial species with more thermotolerant forms. These results reveal that not only vegetation but also microorganisms are a factor in modifying terrestrial albedo, potentially impacting biosphere feedbacks on past and future climate, and call for a direct assessment of such effects at larger scales. Based on estimates of the global biomass of cyanobacteria in soil biocrusts, one can easily calculate that there must currently exist about 15 million metric tons of scytonemin at work, warming soil surfaces worldwide

Orbiting multi-beam microwave radiometer for soil moisture remote sensing

NASA Technical Reports Server (NTRS)

Shiue, J. C.; Lawrence, R. W.

1985-01-01

The effects of soil moisture and other factors on soil surface emissivity are reviewed and design concepts for a multibeam microwave radiometer with a 15 m antenna are described. Characteristic antenna gain and radiation patterns are shown and losses due to reflector roughness are estimated.

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.

Biases of chamber methods for measuring soil CO2 efflux demonstrated with a laboratory apparatus.

Treesearch

S. Mark Nay; Kim G. Mattson; Bernard T. Bormann

1994-01-01

Investigators have historically measured soil CO2 efflux as an indicator of soil microbial and root activity and more recently in calculations of carbon budgets. The most common methods estimate CO2 efflux by placing a chamber over the soil surface and quantifying the amount of CO2 entering the...

Non-destructive estimates of soil carbonic anhydrase activity and associated soil water oxygen isotope composition

NASA Astrophysics Data System (ADS)

Jones, Sam P.; Ogée, Jérôme; Sauze, Joana; Wohl, Steven; Saavedra, Noelia; Fernández-Prado, Noelia; Maire, Juliette; Launois, Thomas; Bosc, Alexandre; Wingate, Lisa

2017-12-01

The contribution of photosynthesis and soil respiration to net land-atmosphere carbon dioxide (CO2) exchange can be estimated based on the differential influence of leaves and soils on budgets of the oxygen isotope composition (δ18O) of atmospheric CO2. To do so, the activity of carbonic anhydrases (CAs), a group of enzymes that catalyse the hydration of CO2 in soils and plants, needs to be understood. Measurements of soil CA activity typically involve the inversion of models describing the δ18O of CO2 fluxes to solve for the apparent, potentially catalysed, rate of CO2 hydration. This requires information about the δ18O of CO2 in isotopic equilibrium with soil water, typically obtained from destructive, depth-resolved sampling and extraction of soil water. In doing so, an assumption is made about the soil water pool that CO2 interacts with, which may bias estimates of CA activity if incorrect. Furthermore, this can represent a significant challenge in data collection given the potential for spatial and temporal variability in the δ18O of soil water and limited a priori information with respect to the appropriate sampling resolution and depth. We investigated whether we could circumvent this requirement by inferring the rate of CO2 hydration and the δ18O of soil water from the relationship between the δ18O of CO2 fluxes and the δ18O of CO2 at the soil surface measured at different ambient CO2 conditions. This approach was tested through laboratory incubations of air-dried soils that were re-wetted with three waters of different δ18O. Gas exchange measurements were made on these soils to estimate the rate of hydration and the δ18O of soil water, followed by soil water extraction to allow for comparison. Estimated rates of CO2 hydration were 6.8-14.6 times greater than the theoretical uncatalysed rate of hydration, indicating that CA were active in these soils. Importantly, these estimates were not significantly different among water treatments, suggesting that this represents a robust approach to assay the activity of CA in soil. As expected, estimates of the δ18O of the soil water that equilibrates with CO2 varied in response to alteration to the δ18O of soil water. However, these estimates were consistently more negative than the composition of the soil water extracted by cryogenic vacuum distillation at the end of the gas measurements with differences of up to -3.94 ‰ VSMOW-SLAP. These offsets suggest that, at least at lower water contents, CO2-H2O isotope equilibration primarily occurs with water pools that are bound to particle surfaces and are depleted in 18O compared to bulk soil water.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Climate and the equilibrium state of land surface hydrology parameterizations

NASA Technical Reports Server (NTRS)

Entekhabi, Dara; Eagleson, Peter S.

1991-01-01

For given climatic rates of precipitation and potential evaporation, the land surface hydrology parameterizations of atmospheric general circulation models will maintain soil-water storage conditions that balance the moisture input and output. The surface relative soil saturation for such climatic conditions serves as a measure of the land surface parameterization state under a given forcing. The equilibrium value of this variable for alternate parameterizations of land surface hydrology are determined as a function of climate and the sensitivity of the surface to shifts and changes in climatic forcing are estimated.

Quantifying watershed surface depression storage: determination and application in a hydrologic model

Treesearch

Joseph K. O. Amoah; Devendra M. Amatya; Soronnadi Nnaji

2012-01-01

Hydrologic models often require correct estimates of surface macro-depressional storage to accurately simulate rainfall–runoff processes. Traditionally, depression storage is determined through model calibration or lumped with soil storage components or on an ad hoc basis. This paper investigates a holistic approach for estimating surface depressional storage capacity...

Evaluation of reanalysis datasets against observational soil temperature data over China

NASA Astrophysics Data System (ADS)

Yang, Kai; Zhang, Jingyong

2018-01-01

Soil temperature is a key land surface variable, and is a potential predictor for seasonal climate anomalies and extremes. Using observational soil temperature data in China for 1981-2005, we evaluate four reanalysis datasets, the land surface reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA-Interim/Land), the second modern-era retrospective analysis for research and applications (MERRA-2), the National Center for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), and version 2 of the Global Land Data Assimilation System (GLDAS-2.0), with a focus on 40 cm soil layer. The results show that reanalysis data can mainly reproduce the spatial distributions of soil temperature in summer and winter, especially over the east of China, but generally underestimate their magnitudes. Owing to the influence of precipitation on soil temperature, the four datasets perform better in winter than in summer. The ERA-Interim/Land and GLDAS-2.0 produce spatial characteristics of the climatological mean that are similar to observations. The interannual variability of soil temperature is well reproduced by the ERA-Interim/Land dataset in summer and by the CFSR dataset in winter. The linear trend of soil temperature in summer is well rebuilt by reanalysis datasets. We demonstrate that soil heat fluxes in April-June and in winter are highly correlated with the soil temperature in summer and winter, respectively. Different estimations of surface energy balance components can contribute to different behaviors in reanalysis products in terms of estimating soil temperature. In addition, reanalysis datasets can mainly rebuild the northwest-southeast gradient of soil temperature memory over China.

Estimation of small-scale soil erosion in laboratory experiments with Structure from Motion photogrammetry

NASA Astrophysics Data System (ADS)

Balaguer-Puig, Matilde; Marqués-Mateu, Ángel; Lerma, José Luis; Ibáñez-Asensio, Sara

2017-10-01

The quantitative estimation of changes in terrain surfaces caused by water erosion can be carried out from precise descriptions of surfaces given by means of digital elevation models (DEMs). Some stages of water erosion research efforts are conducted in the laboratory using rainfall simulators and soil boxes with areas less than 1 m2. Under these conditions, erosive processes can lead to very small surface variations and high precision DEMs are needed to account for differences measured in millimetres. In this paper, we used a photogrammetric Structure from Motion (SfM) technique to build DEMs of a 0.5 m2 soil box to monitor several simulated rainfall episodes in the laboratory. The technique of DEM of difference (DoD) was then applied using GIS tools to compute estimates of volumetric changes between each pair of rainfall episodes. The aim was to classify the soil surface into three classes: erosion areas, deposition areas, and unchanged or neutral areas, and quantify the volume of soil that was eroded and deposited. We used a thresholding criterion of changes based on the estimated error of the difference of DEMs, which in turn was obtained from the root mean square error of the individual DEMs. Experimental tests showed that the choice of different threshold values in the DoD can lead to volume differences as large as 60% when compared to the direct volumetric difference. It turns out that the choice of that threshold was a key point in this method. In parallel to photogrammetric work, we collected sediments from each rain episode and obtained a series of corresponding measured sediment yields. The comparison between computed and measured sediment yields was significantly correlated, especially when considering the accumulated value of the five simulations. The computed sediment yield was 13% greater than the measured sediment yield. The procedure presented in this paper proved to be suitable for the determination of sediment yields in rainfall-driven soil erosion experiments conducted in the laboratory.

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.

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...

Soil Temperature and Moisture Profile (STAMP) System Handbook

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

Cook, David R.

The soil temperature and moisture profile system (STAMP) provides vertical profiles of soil temperature, soil water content (soil-type specific and loam type), plant water availability, soil conductivity, and real dielectric permittivity as a function of depth below the ground surface at half-hourly intervals, and precipitation at one-minute intervals. The profiles are measured directly by in situ probes at all extended facilities of the SGP climate research site. The profiles are derived from measurements of soil energy conductivity. Atmospheric scientists use the data in climate models to determine boundary conditions and to estimate the surface energy flux. The data are alsomore » useful to hydrologists, soil scientists, and agricultural scientists for determining the state of the soil. The STAMP system replaced the SWATS system in early 2016.« less

VHF SoOp (Signal of Opportunity) Technology Demonstration for Soil Moisture Measurement Using Microwave Hydraulic Boom Truck Platform

NASA Technical Reports Server (NTRS)

Joseph, A. T.; Deshpande, M.; O'Neill, P. E.; Miles, L.

2017-01-01

A goal of this research is to test deployable VHF antennas for 6U Cubesat platforms to enable validation of root zone soil moisture (RZSM) estimation algorithms for signal of opportunity (SoOp) remote sensing over the 240-270 MHz frequency band. The proposed work provides a strong foundation for establishing a technology development path for maturing a global direct surface soil moisture (SM) and RZSM measurement system over a variety of land covers. Knowledge of RZSM up to a depth of 1 meter and surface SM up to a depth of 0.05 meter on a global scale, at a spatial resolution of 1-10 km through moderate-to-heavy vegetation, is critical to understanding global water resources and the vertical moisture gradient in the Earths surface layer which controls moisture interactions between the soil, vegetation, and atmosphere. Current observations of surface SM from space by L-band radiometers (1.4 GHz) and radars (1.26 GHz) are limited to measurements of surface SM up to a depth of 0.05 meter through moderate amounts of vegetation. This limitation is mainly due to the inability of L-band signals to penetrate through dense vegetation and deep into the soil column. Satellite observations of the surface moisture conditions are coupled to sophisticated models which extrapolate the surface SM into the root zone, thus providing an indirect estimate rather than a direct measurement of RZSM. To overcome this limitation, low-frequency airborne radars operating at 435 MHz and 118 MHz have been investigated, since these lower frequencies should penetrate denser vegetation and respond to conditions deeper in the soil.

Stochastic Analysis and Probabilistic Downscaling of Soil Moisture

NASA Astrophysics Data System (ADS)

Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.

2017-12-01

Soil moisture is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution soil-moisture estimates but also confidence limits on those estimates and soil-moisture patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution (9-40 km) soil moisture from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic soil-moisture estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed soil-moisture patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for soil moisture at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic soil-moisture estimate, and the pdf can be used to quantify the uncertainty in the soil-moisture estimates and to simulate soil-moisture patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed soil-moisture observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated soil-moisture patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce soil-moisture patterns with more realistic statistical properties than the deterministic model. Additionally, the results suggest that the variance and spatial correlation of the stochastic soil-moisture variations do not vary consistently with the spatial-average soil moisture.

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

Duran, S.; Gueray, R. T.; Yalcin, C.

In this study, 137Cs specific activities were measured in surface soil samples collected from undisturbed areas over the eastern part of the Black Sea coast line of Turkey, between Trabzon and Hopa, in August 2004 and January 2005. A total number of 41 surface soil samples were counted using an HpGe spectrometer system. The results indicate that 137Cs levels show a large variation over the coast ranging between 10 Bq/kg and 1000 Bq/kg. The soil activities are generally higher in the eastern part of the coast. Exposure rates above the ground surface due to 137Cs activity in soil estimated tomore » vary between 0.1mR/s and 9 mR/s.« less

Passive microwave soil moisture downscaling using vegetation index and skin surface temperature

USDA-ARS?s Scientific Manuscript database

Soil moisture satellite estimates are available from a variety of passive microwave satellite sensors, but their spatial resolution is frequently too coarse for use by land managers and other decision makers. In this paper, a soil moisture downscaling algorithm based on a regression relationship bet...

Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates

USDA-ARS?s Scientific Manuscript database

SMAP (Soil Moisture Active and Passive) radiometer observations at 40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9-km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to ...

Performance of MODIS satellite and mesoscale model based land surface temperature for soil moisture deficit estimation using Neural Network

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Petropoulos, George P.; Gupta, Manika; Islam, Tanvir

2015-04-01

Soil Moisture Deficit (SMD) is a key variable in the water and energy exchanges that occur at the land-surface/atmosphere interface. Monitoring SMD is an alternate method of irrigation scheduling and represents the use of the suitable quantity of water at the proper time by combining measurements of soil moisture deficit. In past it is found that LST has a strong relation to SMD, which can be estimated by MODIS or numerical weather prediction model such as WRF (Weather Research and Forecasting model). By looking into the importance of SMD, this work focused on the application of Artificial Neural Network (ANN) for evaluating its capabilities towards SMD estimation using the LST data estimated from MODIS and WRF mesoscale model. The benchmark SMD estimated from Probability Distribution Model (PDM) over the Brue catchment, Southwest of England, U.K. is used for all the calibration and validation experiments. The performances between observed and simulated SMD are assessed in terms of the Nash-Sutcliffe Efficiency (NSE), the Root Mean Square Error (RMSE) and the percentage of bias (%Bias). The application of the ANN confirmed a high capability WRF and MODIS LST for prediction of SMD. Performance during the ANN calibration and validation showed a good agreement between benchmark and estimated SMD with MODIS LST information with significantly higher performance than WRF simulated LST. The work presented showed the first comprehensive application of LST from MODIS and WRF mesoscale model for hydrological SMD estimation, particularly for the maritime climate. More studies in this direction are recommended to hydro-meteorological community, so that useful information will be accumulated in the technical literature domain for different geographical locations and climatic conditions. Keyword: WRF, Land Surface Temperature, MODIS satellite, Soil Moisture Deficit, Neural Network

Modelling Soil Heat and Water Flow as a Coupled Process in Land Surface Models

NASA Astrophysics Data System (ADS)

García González, Raquel; Verhoef, Anne; Vidale, Pier Luigi; Braud, Isabelle

2010-05-01

To improve model estimates of soil water and heat flow by land surface models (LSMs), in particular in the first few centimetres of the near-surface soil profile, we have to consider in detail all the relevant physical processes involved (see e.g. Milly, 1982). Often, thermal and iso-thermal vapour fluxes in LSMs are neglected and the simplified Richard's equation is used as a result. Vapour transfer may affect the water fluxes and heat transfer in LSMs used for hydrometeorological and climate simulations. Processes occurring in the top 50 cm soil may be relevant for water and heat flux dynamics in the deeper layers, as well as for estimates of evapotranspiration and heterotrophic respiration, or even for climate and weather predictions. Water vapour transfer, which was not incorporated in previous versions of the MOSES/JULES model (Joint UK Land Environment Simulator; Cox et al., 1999), has now been implemented. Furthermore, we also assessed the effect of the soil vertical resolution on the simulated soil moisture and temperature profiles and the effect of the processes occurring at the upper boundary, mainly in terms of infiltration rates and evapotranspiration. SiSPAT (Simple Soil Plant Atmosphere Transfer Model; Braud et al., 1995) was initially used to quantify the changes that we expect to find when we introduce vapour transfer in JULES, involving parameters such as thermal vapour conductivity and diffusivity. Also, this approach allows us to compare JULES to a more complete and complex numerical model. Water vapour flux varied with soil texture, depth and soil moisture content, but overall our results suggested that water vapour fluxes change temperature gradients in the entire soil profile and introduce an overall surface cooling effect. Increasing the resolution smoothed and reduced temperature differences between liquid (L) and liquid/vapour (LV) simulations at all depths, and introduced a temperature increase over the entire soil profile. Thermal gradients rather than soil water potential gradients seem to cause temporal and spatial (vertical) soil temperature variability. We conclude that a multi-soil layer configuration may improve soil water dynamics, heat transfer and coupling of these processes, as well as evapotranspiration estimates and land surface-atmosphere coupling. However, a compromise should be reached between numerical and process-simulation aspects. References: Braud I., A.C. Dantas-Antonino, M. Vauclin, J.L. Thony and P. Ruelle, 1995b: A Simple Soil Plant Atmo- sphere Transfer model (SiSPAT), Development and field verification, J. Hydrol, 166: 213-250 Cox, P.M., R.A. Betts, C.B. Bunton, R.L.H. Essery, P.R. Rowntree, and J. Smith (1999), The impact of new land surface physics on the GCM simulation of climate and climate sensitivity. Clim. Dyn., 15, 183-203. Milly, P.C.D., 1982. Moisture and heat transport in hysteric inhomogeneous porous media: a matric head- based formulation and a numerical model, Water Resour. Res., 18:489-498

Soil concentrations, occurrence, sources and estimation of air-soil exchange of polychlorinated biphenyls in Indian cities.

PubMed

Chakraborty, Paromita; Zhang, Gan; Li, Jun; Selvaraj, Sakthivel; Breivik, Knut; Jones, Kevin C

2016-08-15

Past studies have shown potentially increasing levels of polychlorinated biphenyls (PCBs) in the Indian environment. This is the first attempt to investigate the occurrence of PCBs in surface soil and estimate diffusive air-soil exchange, both on a regional scale as well as at local level within the metropolitan environment of India. From the north, New Delhi and Agra, east, Kolkata, west, Mumbai and Goa and Chennai and Bangalore in the southern India were selected for this study. 33 PCB congeners were quantified in surface soil and possible sources were derived using positive matrix factorization model. Net flux directions of PCBs were estimated in seven major metropolitan cities of India along urban-suburban-rural transects. Mean Σ33PCBs concentration in soil (12ng/g dry weight) was nearly twice the concentration found in global background soil, but in line with findings from Pakistan and urban sites of China. Higher abundance of the heavier congeners (6CB-8CB) was prevalent mostly in the urban centers. Cities like Chennai, Mumbai and Kolkata with evidence of ongoing PCB sources did not show significant correlation with soil organic carbon (SOC). This study provides evidence that soil is acting as sink for heavy weight PCB congeners and source for lighter congeners. Atmospheric transport is presumably a controlling factor for occurrence of PCBs in less polluted sites of India. Copyright © 2016 Elsevier B.V. All rights reserved.

Consumption of atmospheric methane by desert soils

USGS Publications Warehouse

Striegl, Robert G.; McConnaughey, T.A.; Thorstenson, D.C.; Weeks, E.P.; Woodward, J.C.

1992-01-01

ATMOSPHERIC concentrations of methane, a greenhouse gas, are increasing at a rate of about 1% yr-1 (refs 1-4). Oxidation by methylotrophic bacteria in soil is the largest terrestrial sink for atmospheric CH4, and is estimated to consume about 30?? 1012 g CH4 yr-1 (refs 4-6). Spatial and temporal variability in the rate of soil CH4 consumption are incompletely understood6-19, as are the apparent inhibitory12,13,18 or enhancing20 effects of changes in land use. Dry deserts, which constitute 20% of total land surface, are not currently included in global soil uptake estimates. Here we describe measurements of the rate of uptake of atmospheric CH4 by undisturbed desert soils. We observed rates as great as 4.38 mg CH4 m-2 day-1; 50% of the measured rates were between 0.24 and 0.92 mg CH4 m2 d-1. Uptake of CH4 by desert soil is enhanced by rainfall after an initial soil-drainage period - opposite to the response of temperate forest soils12. Methane is consumed to a depth of about 2 m, allowing for deep removal of atmospheric CH4 if near-surface conditions are unfavourable for consumption. On the basis of an annual average CH4 consumption rate of 0.66 mg CH4 m-2 d-1, we estimate that the global CH4 sink term needs to be increased by about 7 ?? 1012 g yr-1 to account for the contribution of desert soils.

Evaluating new SMAP soil moisture for drought monitoring in the rangelands of the US High Plains

USGS Publications Warehouse

Velpuri, Naga Manohar; Senay, Gabriel B.; Morisette, Jeffrey T.

2016-01-01

Level 3 soil moisture datasets from the recently launched Soil Moisture Active Passive (SMAP) satellite are evaluated for drought monitoring in rangelands.Validation of SMAP soil moisture (SSM) with in situ and modeled estimates showed high level of agreement.SSM showed the highest correlation with surface soil moisture (0-5 cm) and a strong correlation to depths up to 20 cm.SSM showed a reliable and expected response of capturing seasonal dynamics in relation to precipitation, land surface temperature, and evapotranspiration.Further evaluation using multi-year SMAP datasets is necessary to quantify the full benefits and limitations for drought monitoring in rangelands.

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.

Soil Water and Temperature System (SWATS) Instrument Handbook

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

Cook, David R.

2016-04-01

The soil water and temperature system (SWATS) provides vertical profiles of soil temperature, soil-water potential, and soil moisture as a function of depth below the ground surface at hourly intervals. The temperature profiles are measured directly by in situ sensors at the Central Facility and many of the extended facilities of the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility Southern Great Plains (SGP) site. The soil-water potential and soil moisture profiles are derived from measurements of soil temperature rise in response to small inputs of heat. Atmospheric scientists use the data in climate models tomore » determine boundary conditions and to estimate the surface energy flux. The data are also useful to hydrologists, soil scientists, and agricultural scientists for determining the state of the soil.« less

Estimating the Soil Temperature Profile from a Single Depth Observation: A Simple Empirical Heatflow Solution

NASA Technical Reports Server (NTRS)

Holmes, Thomas; Owe, Manfred; deJeu, Richard

2007-01-01

Two data sets of experimental field observations with a range of meteorological conditions are used to investigate the possibility of modeling near-surface soil temperature profiles in a bare soil. It is shown that commonly used heat flow methods that assume a constant ground heat flux can not be used to model the extreme variations in temperature that occur near the surface. This paper proposes a simple approach for modeling the surface soil temperature profiles from a single depth observation. This approach consists of two parts: 1) modeling an instantaneous ground flux profile based on net radiation and the ground heat flux at 5cm depth; 2) using this ground heat flux profile to extrapolate a single temperature observation to a continuous near surface temperature profile. The new model is validated with an independent data set from a different soil and under a range of meteorological conditions.

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.

Microwave soil moisture estimation in humid and semiarid watersheds

NASA Technical Reports Server (NTRS)

O'Neill, P. E.; Jackson, T. J.; Chauhan, N. S.; Seyfried, M. S.

1993-01-01

Land surface hydrologic-atmospheric interactions in humid and semi-arid watersheds were investigated. Active and passive microwave sensors were used to estimate the spatial and temporal distribution of soil moisture at the catchment scale in four areas. Results are presented and discussed. The eventual use of this information in the analysis and prediction of associated hydrologic processes is examined.

Maize and prairie root contributions to soil CO2 emissions in the field

USDA-ARS?s Scientific Manuscript database

Background and aims: A major hurdle in closing carbon budgets is partitioning soil-surface CO2 fluxes by source. This study aims to estimate CO2 resulting from root growth (RG) in the field. Methods: We used periodic 48-hour shading over two seasons to estimate and compare RG-derived CO2 in one annu...

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Evaluation of Modeling Schemes to Estimate Evapotranspiration and Root Zone Soil Water Content over Vineyard using a Scintillometer and Remotely Sensed Surface Energy Balance

NASA Astrophysics Data System (ADS)

Geli, H. M. E.; Gonzalez-Piqueras, J.; Isidro, C., Sr.

2016-12-01

Actual crop evapotranspiration (ETa) and root zone soil water content (SMC) are key operational variable to monitor water consumption and water stress condition for improve vineyard grapes productivity and quality. This analysis, evaluates the estimation of ETa and SMC based on two modeling approaches. The first approach is a hybrid model that couples a thermal-based two source energy balance (TSEB) model (Norman et al. 1995) and water balance model to estimate the two variable (Geli 2012). The second approach is based on Large Aperture Scintillometer (LAS)-based estimates of sensible heat flux. The LAS-based estimates of sensible heat fluxes were used to calculate latent heat flux as the residual of surface energy balance equation on hourly basis which was converted to daily ETa. The calculated ETa from the scintillometer was then couple with the water balance approach to provide updated ETa_LAS and SMC_LAS. Both estimates of ETa and SMC based on LAS (i.e. ETa_LAS and SMC_LAS) and TSEB (ETa_TSEB and SMC_TSEB) were compared with ground-based observation from eddy covariance and soil water content measurements at multiple depths. The study site is an irrigated vineyard located in Central Spain Primary with heterogeneous surface conditions in term of irrigation practices and the ground based observation over the vineyard were collected during the summer of 2007. Preliminary results of the inter-comparison of the two approaches suggests relatively good between both modeling approaches and ground-based observations with RMSE lower than 1.2 mm/day for ETa and lower than 20% for SMC. References Norman, J. M., Kustas, W. P., & Humes, K. S. (1995). A two-source approach for estimating soil and vegetation energy fluxes in observations of directional radiometric surface temperature. Agricultural and Forest Meteorology, 77, 263293. Geli, Hatim M. E. (2012). Modeling spatial surface energy fluxes of agricultural and riparian vegetation using remote sensing, Ph. D. dissertation, Department of Civil and Environmental Engineering, Utah State University.

Soil-Water Balance (SWB) model estimates of soil-moisture variability and groundwater recharge in the South Platte watershed, Colorado

NASA Astrophysics Data System (ADS)

Anderson, A. M.; Walker, E. L.; Hogue, T. S.; Ruybal, C. J.

2015-12-01

Unconventional energy production in semi-arid regions places additional stress on already over-allocated water systems. Production of shale gas and oil resources in northern Colorado has rapidly increased since 2010, and is expected to continue growing due to advances in horizontal drilling and hydraulic fracturing. This unconventional energy production has implications for the availability of water in the South Platte watershed, where water demand for hydraulic fracturing of unconventional shale resources reached ~16,000 acre-feet in 2014. Groundwater resources are often exploited to meet water demands for unconventional energy production in regions like the South Platte basin, where surface water supply is limited and allocated across multiple uses. Since groundwater is often a supplement to surface water in times of drought and peak demand, variability in modeled recharge estimates can significantly impact projected availability. In the current work we used the Soil-Water Balance Model (SWB) to assess the variability in model estimates of actual evapotranspiration (ET) and soil-moisture conditions utilized to derive estimates of groundwater recharge. Using both point source and spatially distributed data, we compared modeled actual ET and soil-moisture derived from several potential ET methods, such as Thornthwaite-Mather, Jense-Haise, Turc, and Hargreaves-Samani, to historic soil moisture conditions obtained through sources including the Gravity Recovery and Climate Experiment (GRACE). In addition to a basin-scale analysis, we divided the South Platte watershed into sub-basins according to land cover to evaluate model capabilities of estimating soil-moisture parameters with variations in land cover and topography. Results ultimately allow improved prediction of groundwater recharge under future scenarios of climate and land cover change. This work also contributes to complementary subsurface groundwater modeling and decision support modeling in the South Platte.

Estimating forest-grassland dynamics using soil phytolith assemblages and δ13C of soil organic matter

Treesearch

Becky K. Kerns; Margeret M. Moore; Stephen C. Hart

2001-01-01

Our objectives were to examine the relationship between contemporary vegetation and surface soil phytolith assemblages, and use phytoliths and δ13C of soil organic matter (SOM) to explore forest-grassland vegetation dynamics. We established plots within three canopy types (open, old-growth, and dense young pine) with different grass species compositions in a...

Rapid soil development after windthrow disturbance in pristine forests.

Treesearch

B.T. Bormann; H. Spaltenstein; M.H. McClellan; F.C. Ugolini; K. Cromack; S.M. Nay

1995-01-01

1. We examined how rapidly soils can change during secondary succession by observing soil development on 350-year chronosequences in three pristine forest ecosystems in south-east Alaska. 2. Soil surfaces, created by different windthrow events of known or estimated age, were examined within each of three forest stands (0.5-2.0 ha plots; i.e. a within-stand...

Detection of soil erosion within pinyon-juniper woodlands using Thematic Mapper (TM) data

NASA Technical Reports Server (NTRS)

Price, Kevin P.

1993-01-01

Multispectral measurements collected by Landsat Thematic Mapper (TM) were correlated with field measurements, direct soil loss estimates, and Universal Soil Loss Equation (USLE) estimates to determine the sensitivity of TM data to varying degrees of soil erosion in pinyon-juniper woodland in central Utah. TM data were also evaluated as a predictor of the USLE Crop Management C factor for pinyon-juniper woodlands. TM spectral data were consistently better predictors of soil erosion factors than any combination of field factors. TM data were more sensitive to vegetation variations than the USLE C factor. USLE estimates showed low annual rates of erosion which varied little among the study sites. Direct measurements of rate of soil loss using the SEDIMENT (Soil Erosion DIrect measureMENT) technique, indicated high and varying rates of soil loss among the sites since tree establishment. Erosion estimates from the USLE and SEDIMENT methods suggest that erosion rates have been severe in the past, but because significant amounts of soil have already been eroded, and the surface is now armored by rock debris, present erosion rates are lower. Indicators of accelerated erosion were still present on all sites, however, suggesting that the USLE underestimated erosion within the study area.

Microwave remote sensing of soil moisture, volume 1. [Guymon, Oklahoma and Dalhart, Texas

NASA Technical Reports Server (NTRS)

Mcfarland, M. J. (Principal Investigator); Theis, S. W.; Rosenthal, W. D.; Jones, C. L.

1982-01-01

Multifrequency sensor data from NASA's C-130 aircraft were used to determine which of the all weather microwave sensors demonstrated the highest correlation to surface soil moisture over optimal bare soil conditions, and to develop and test techniques which use visible/infrared sensors to compensate for the vegetation effect in this sensor's response to soil moisture. The L-band passive microwave radiometer was found to be the most suitable single sensor system to estimate soil moisture over bare fields. The perpendicular vegetation index (PVI) as determined from the visible/infrared sensors was useful as a measure of the vegetation effect on the L-band radiometer response to soil moisture. A linear equation was developed to estimate percent field capacity as a function of L-band emissivity and the vegetation index. The prediction algorithm improves the estimation of moisture significantly over predictions from L-band emissivity alone.

Surface vapor conductance derived from the ETRHEQ: Dependence on environmental variables and similarity to Oren's stomatal stress model for vapor pressure deficit

NASA Astrophysics Data System (ADS)

Salvucci, G.; Rigden, A. J.

2015-12-01

Daily time series of evapotranspiration and surface conductance to water vapor were estimated using the ETRHEQ method (Evapotranspiration from Relative Humidity at Equilibrium). ETRHEQ has been previously compared with ameriflux site-level measurements of ET at daily and seasonal time scales, with watershed water balance estimates, and with various benchmark ET data sets. The ETRHEQ method uses meteorological data collected at common weather stations and estimates the surface conductance by minimizing the vertical variance of the calculated relative humidity profile averaged over the day. The key advantage of the ETRHEQ method is that it does not require knowledge of the surface state (soil moisture, stomatal conductance, leaf are index, etc.) or site-specific calibration. The daily estimates of conductance from 229 weather stations for 53 years were analyzed for dependence on environmental variables known to impact stomatal conductance and soil diffusivity: surface temperature, surface vapor pressure deficit, solar radiation, antecedent precipitation (as a surrogate for soil moisture), and a seasonal vegetation greenness index. At each site the summertime (JJAS) conductance values estimated from ETRHEQ were fitted to a multiplicate Jarvis-type stress model. Functional dependence was not proscribed, but instead fitted using flexible piecewise-linear splines. The resulting stress functions reproduce the time series of conductance across a wide range of ecosystems and climates. The VPD stress term resembles that proposed by Oren (i.e., 1-m*log(VPD) ), with VPD measured in kilopascals. The equivalent value of m derived from our spline-fits at each station varied over a remarkably small range of 0.58 to 0.62, in agreement with Oren's original analysis based on leaf and tree-level measurements.

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.

Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions

NASA Technical Reports Server (NTRS)

Nearing, Grey S.; Mocko, David M.; Peters-Lidard, Christa D.; Kumar, Sujay V.; Xia, Youlong

2016-01-01

Model benchmarking allows us to separate uncertainty in model predictions caused 1 by model inputs from uncertainty due to model structural error. We extend this method with a large-sample approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in (i) forcing data, (ii) model parameters, and (iii) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in the North American Land Data Assimilation System Phase 2 (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of the NLDAS-2 system. In particular, continued work toward refining the parameter maps and look-up tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.

Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions

PubMed Central

Nearing, Grey S.; Mocko, David M.; Peters-Lidard, Christa D.; Kumar, Sujay V.; Xia, Youlong

2018-01-01

Model benchmarking allows us to separate uncertainty in model predictions caused by model inputs from uncertainty due to model structural error. We extend this method with a “large-sample” approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in (i) forcing data, (ii) model parameters, and (iii) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in the North American Land Data Assimilation System Phase 2 (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of the NLDAS-2 system. In particular, continued work toward refining the parameter maps and look-up tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances. PMID:29697706

Benchmarking NLDAS-2 Soil Moisture and Evapotranspiration to Separate Uncertainty Contributions.

PubMed

Nearing, Grey S; Mocko, David M; Peters-Lidard, Christa D; Kumar, Sujay V; Xia, Youlong

2016-03-01

Model benchmarking allows us to separate uncertainty in model predictions caused by model inputs from uncertainty due to model structural error. We extend this method with a "large-sample" approach (using data from multiple field sites) to measure prediction uncertainty caused by errors in (i) forcing data, (ii) model parameters, and (iii) model structure, and use it to compare the efficiency of soil moisture state and evapotranspiration flux predictions made by the four land surface models in the North American Land Data Assimilation System Phase 2 (NLDAS-2). Parameters dominated uncertainty in soil moisture estimates and forcing data dominated uncertainty in evapotranspiration estimates; however, the models themselves used only a fraction of the information available to them. This means that there is significant potential to improve all three components of the NLDAS-2 system. In particular, continued work toward refining the parameter maps and look-up tables, the forcing data measurement and processing, and also the land surface models themselves, has potential to result in improved estimates of surface mass and energy balances.

Crop moisture estimation over the southern Great Plains with dual polarization 1.66 centimeter passive microwave data from Nimbus 7

NASA Technical Reports Server (NTRS)

Mcfarland, M. J.; Harder, P. H., II; Wilke, G. D.; Huebner, G. L., Jr.

1984-01-01

Moisture content of snow-free, unfrozen soil is inferred using passive microwave brightness temperatures from the scanning multichannel microwave radiometer (SMMR) on Nimbus-7. Investigation is restricted to the two polarizations of the 1.66 cm wavelength sensor. Passive microwave estimates of soil moisture are of two basic categories; those based upon soil emissivity and those based upon the polarization of soil emission. The two methods are compared and contrasted through the investigation of 54 potential functions of polarized brightness temperatures and, in some cases, ground-based temperature measurements. Of these indices, three are selected for the estimated emissivity, the difference between polarized brightness temperatures, and the normalized polarization difference. Each of these indices is about equally effective for monitoring soil moisture. Using an antecedent precipitation index (API) as ground control data, temporal and spatial analyses show that emissivity data consistently give slightly better soil moisture estimates than depolarization data. The difference, however, is not statistically significant. It is concluded that polarization data alone can provide estimates of soil moisture in areas where the emissivity cannot be inferred due to nonavailability of surface temperature data.

Improved exposure estimation in soil screening and cleanup criteria for volatile organic chemicals.

PubMed

DeVaull, George E

2017-09-01

Soil cleanup criteria define acceptable concentrations of organic chemical constituents for exposed humans. These criteria sum the estimated soil exposure over multiple pathways. Assumptions for ingestion, dermal contact, and dust exposure generally presume a chemical persists in surface soils at a constant concentration level for the entire exposure duration. For volatile chemicals, this is an unrealistic assumption. A calculation method is presented for surficial soil criteria that include volatile depletion of chemical for these uptake pathways. The depletion estimates compare favorably with measured concentration profiles and with field measurements of soil concentration. Corresponding volatilization estimates compare favorably with measured data for a wide range of volatile and semivolatile chemicals, including instances with and without the presence of a mixed-chemical residual phase. Selected examples show application of the revised factors in estimating screening levels for benzene in surficial soils. Integr Environ Assess Manag 2017;13:861-869. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC). © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Recharge at the Hanford Site: Status report

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

Gee, G.W.

A variety of field programs designed to evaluate recharge and other water balance components including precipitation, infiltration, evaporation, and water storage changes, have been carried out at the Hanford Site since 1970. Data from these programs have indicated that a wide range of recharge rates can occur depending upon specific site conditions. Present evidence suggests that minimum recharge occurs where soils are fine-textured and surfaces are vegetated with deep-rooted plants. Maximum recharge occurs where coarse soils or gravels exist at the surface and soils are kept bare. Recharge can occur in areas where shallow-rooted plants dominate the surface, particularly wheremore » soils are coarse-textured. Recharge estimates have been made for the site using simulation models. A US Geological Survey model that attempts to account for climate variability, soil storage parameters, and plant factors has calculated recharge values ranging from near zero to an average of about 1 cm/yr for the Hanford Site. UNSAT-H, a deterministic model developed for the site, appears to be the best code available for estimating recharge on a site-specific basis. Appendix I contains precipitation data from January 1979 to June 1987. 42 refs., 11 figs., 11 tabs.« less

Improving Soil Moisture Estimation through the Joint Assimilation of SMOS and GRACE Satellite Observations

NASA Technical Reports Server (NTRS)

Girotto, Manuela

2018-01-01

Observations from recent soil moisture dedicated missions (e.g. SMOS or SMAP) have been used in innovative data assimilation studies to provide global high spatial (i.e., approximately10-40 km) and temporal resolution (i.e., daily) soil moisture profile estimates from microwave brightness temperature observations. These missions are only sensitive to near-surface soil moisture 0-5 cm). In contrast, the Gravity Recovery and Climate Experiment (GRACE) mission provides accurate measurements of the entire vertically integrated terrestrial water storage (TWS) column but, it is characterized by low spatial (i.e., 150,000 km2) and temporal (i.e., monthly) resolutions. Data assimilation studies have shown that GRACE-TWS primarily affects (in absolute terms) deeper moisture storages (i.e., groundwater). In this presentation I will review benefits and drawbacks associated to the assimilation of both types of observations. In particular, I will illustrate the benefits and drawbacks of their joint assimilation for the purpose of improving the entire profile of soil moisture (i.e., surface and deeper water storages).

Multi Seasonal and Diurnal Characterization of Sensible Heat Flux in an Arid Land Environment

NASA Astrophysics Data System (ADS)

Al-Mashharawi, S.; Aragon, B.; McCabe, M.

2017-12-01

In sparsely vegetated arid and semi-arid regions, the available energy is transformed primarily into sensible heat, with little to no energy partitioned into latent heat. The characterization of bare soil arid environments are rather poorly understood in the context of both local, regional and global energy budgets. Using data from a long-term surface layer scintillometer and co-located meteorological installation, we examine the diurnal and seasonal patterns of sensible heat flux and the net radiation to soil heat flux ratio. We do this over a bare desert soil located adjacent to an irrigated agricultural field in the central region of Saudi Arabia. The results of this exploratory analysis can be used to inform upon remote sensing techniques for surface flux estimation, to derive and monitor soil heat flux dynamics, estimate the heat transfer resistance and the thermal roughness length over bare soils, and to better inform efforts that model the advective effects that complicate the accurate representation of agricultural energy budgets in the arid zone.

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.

A Novel Optical Model for Remote Sensing of Near-Surface Soil Moisture

NASA Astrophysics Data System (ADS)

Babaeian, E.; Sadeghi, M.; Jones, S. B.; Tuller, M.

2016-12-01

Common triangle and trapezoid methods that are based on both optical and thermal remote sensing (RS) information have been widely applied in the past to estimate near-surface soil moisture from the soil temperature - vegetation index space (e.g., LST-NDVI). For most cases, this approach assumes a linear relationship between soil moisture and temperature. Though this linearity assumption yields reasonable moisture estimates, it is not always justified as evidenced by laboratory and field measurements. Furthermore, this approach requires optical as well as thermal RS data for definition of the land surface temperature (LST) - vegetation index space, therefore, it is not applicable to satellites that do not provide thermal output such as the ESA Sentinel-2. To overcome these limitations, we propose a novel trapezoid model that only relies on optical NIR and SWIR data. The new model was validated using Sentinel-2 and Landsat-8 data for the semiarid Walnut Gulch (AZ) and sub humid Little Washita (OK) watersheds that vastly differ in land use and surface cover and provide excellent ground-truth moisture information from extensive sensor networks. Preliminary results for 2015-2016 indicate significant potential of the new model with a RMSE smaller than 4% volumetric near-surface moisture content and also confirm the enhanced utility of the high spatially and temporally resolved Sentinel-2 data.

On the spatial distribution of the transpiration and soil moisture of a Mediterranean heterogeneous ecosystem in water-limited conditions.

NASA Astrophysics Data System (ADS)

Curreli, Matteo; Corona, Roberto; Montaldo, Nicola; Albertson, John D.; Oren, Ram

2014-05-01

Mediterranean ecosystems are characterized by a strong heterogeneity, and often by water-limited conditions. In these conditions contrasting plant functional types (PFT, e.g. grass and woody vegetation) compete for the water use. Both the vegetation cover spatial distribution and the soil properties impact the soil moisture (SM) spatial distribution. Indeed, vegetation cover density and type affects evapotranspiration (ET), which is the main lack of the soil water balance in these ecosystems. With the objective to carefully estimate SM and ET spatial distribution in a Mediterranean water-limited ecosystem and understanding SM and ET relationships, an extended field campaign is carried out. The study was performed in a heterogeneous ecosystem in Orroli, Sardinia (Italy). The experimental site is a typical Mediterranean ecosystem where the vegetation is distributed in patches of woody vegetation (wild olives mainly) and grass. Soil depth is low and spatially varies between 10 cm and 40 cm, without any correlation with the vegetation spatial distribution. ET, land-surface fluxes and CO2 fluxes are estimated by an eddy covariance technique based micrometeorological tower. But in heterogeneous ecosystems a key assumption of the eddy covariance theory, the homogeneity of the surface, is not preserved and the ET estimate may be not correct. Hence, we estimate ET of the woody vegetation using the thermal dissipation method (i.e. sap flow technique) for comparing the two methodologies. Due the high heterogeneity of the vegetation and soil properties of the field a total of 54 sap flux sensors were installed. 14 clumps of wild olives within the eddy covariance footprint were identified as the most representative source of flux and they were instrumented with the thermal dissipation probes. Measurements of diameter at the height of sensor installation (height of 0.4 m above ground) were recorded in all the clumps. Bark thickness and sapwood depth were measured on several trees to obtain a generalized estimates of sapwood depth. The known of allometric relationships between sapwood area, diameter and canopy cover area within the eddy covariance footprint helped for the application of a reliable scaling procedure of the local sap flow estimates which are in a good agreement with the estimates of ET eddy covariance based. Soil moisture were also extensively monitored through 25 probes installed in the eddy covariance footprint. Results show that comparing eddy covariance and sap flow ET estimates eddy covariance technique is still accurate in this heterogeneous field, whereas the key assumption, surface homogeneity, is not preserved. Furthermore, interestingly wild olives still transpire at higher rates for the driest soil moisture conditions, confirming the hydraulic redistribution from soil below the roots, and from roots penetrating deep cracks in the underlying basalt parent rock.

Minimising methodological biases to improve the accuracy of partitioning soil respiration using natural abundance 13C.

PubMed

Snell, Helen S K; Robinson, David; Midwood, Andrew J

2014-11-15

Microbial degradation of soil organic matter (heterotrophic respiration) is a key determinant of net ecosystem exchange of carbon, but it is difficult to measure because the CO2 efflux from the soil surface is derived not only from heterotrophic respiration, but also from plant root and rhizosphere respiration (autotrophic). Partitioning total CO2 efflux can be achieved using the different natural abundance stable isotope ratios (δ(13)C) of root and soil CO2. Successful partitioning requires very accurate measurements of total soil efflux δ(13)CO2 and the δ(13)CO2 of the autotrophic and heterotrophic sources, which typically differ by just 2-8‰. In Scottish moorland and grass mesocosm studies we systematically tested some of the most commonly used techniques in order to identify and minimise methodological errors. Typical partitioning methods are to sample the total soil-surface CO2 efflux using a chamber, then to sample CO2 from incubated soil-free roots and root-free soil. We investigated the effect of collar depth on chamber measurements of surface efflux δ(13)CO2 and the effect of incubation time on estimates of end-member δ(13)CO2. (1) a 5 cm increase in collar depth affects the measurement of surface efflux δ(13)CO2 by -1.5‰ and there are fundamental inconsistencies between modelled and measured biases; (2) the heterotrophic δ(13)CO2 changes by up to -4‰ within minutes of sampling; we recommend using regression to estimate the in situ δ(13)CO2 values; (3) autotrophic δ(13)CO2 measurements are reliable if root CO2 is sampled within an hour of excavation; (4) correction factors should be used to account for instrument drift of up to 3‰ and concentration-dependent non-linearity of CRDS (cavity ringdown spectroscopy) analysis. Methodological biases can lead to large inaccuracies in partitioning estimates. The utility of stable isotope partitioning of soil CO2 efflux will be enhanced by consensus on the optimum measurement protocols and by minimising disturbance, particularly during chamber measurements. Copyright © 2014 John Wiley & Sons, Ltd.

Critical evaluation of 13C natural abundance techniques to partition soil-surface CO2 efflux

NASA Astrophysics Data System (ADS)

Snell, H.; Midwood, A. J.; Robinson, D.

2013-12-01

Soil is the largest terrestrial store of carbon and the flux of CO2 from soils to the atmosphere is estimated at around 98 Pg (98 billion tonnes) of carbon per year. The CO2 efflux from the soil surface is derived from plant root and rhizosphere respiration (autotrophically fuelled) and microbial degradation of soil organic matter (heterotrophic respiration). Heterotrophic respiration is a key determinant of an ecosystem's long-term C balance, but one that is difficult to measure in the field. One approach involves partitioning the total soil-surface CO2 efflux between heterotrophic and autotrophic components; this can be done using differences in the natural abundance stable isotope ratios (δ13C) of autotrophic and heterotrophic CO2 as the end-members of a simple mixing model. In most natural, temperate ecosystems, current and historical vegetation cover (and therefore also plant-derived soil organic matter) is produced from C3 photosynthesis so the difference in δ13C between the autotrophic and heterotrophic CO2 sources is small. Successful partitioning therefore requires accurate and precise measurements of the δ13CO2 of the autotrophic and heterotrophic end-members (obtained by measuring the δ13CO2 of soil-free roots and root-free soil) and of total soil CO2 efflux. There is currently little consensus on the optimum measurement protocols. Here we systematically tested some of the most commonly used techniques to identify and minimise methodological errors. Using soil-surface chambers to sample total CO2 efflux and a cavity ring-down spectrometer to measure δ13CO2 in a partitioning study on a Scottish moorland, we found that: using soil-penetrating collars leads to a more depleted chamber measurement of total soil δ13CO2 as a result of severing roots and fungal hyphae or equilibrating with δ13CO2 at depth or both; root incubations provide an accurate estimate of in-situ root respired δ13CO2 provided they are sampled within one hour; the δ13CO2 from root-free soil changes rapidly during incubation and even CO2 sampled very soon after excavation is unlikely to give an accurate estimate of the heterotrophic isotope end-member, to solve this we applied non-linear regressions to the change in δ13CO2 with time to derive the heterotrophic end-member in undisturbed soil.

Evaluation of a simple, point-scale hydrologic model in simulating soil moisture using the Delaware environmental observing system

NASA Astrophysics Data System (ADS)

Legates, David R.; Junghenn, Katherine T.

2018-04-01

Many local weather station networks that measure a number of meteorological variables (i.e. , mesonetworks) have recently been established, with soil moisture occasionally being part of the suite of measured variables. These mesonetworks provide data from which detailed estimates of various hydrological parameters, such as precipitation and reference evapotranspiration, can be made which, when coupled with simple surface characteristics available from soil surveys, can be used to obtain estimates of soil moisture. The question is Can meteorological data be used with a simple hydrologic model to estimate accurately daily soil moisture at a mesonetwork site? Using a state-of-the-art mesonetwork that also includes soil moisture measurements across the US State of Delaware, the efficacy of a simple, modified Thornthwaite/Mather-based daily water balance model based on these mesonetwork observations to estimate site-specific soil moisture is determined. Results suggest that the model works reasonably well for most well-drained sites and provides good qualitative estimates of measured soil moisture, often near the accuracy of the soil moisture instrumentation. The model exhibits particular trouble in that it cannot properly simulate the slow drainage that occurs in poorly drained soils after heavy rains and interception loss, resulting from grass not being short cropped as expected also adversely affects the simulation. However, the model could be tuned to accommodate some non-standard siting characteristics.

Estimation of global soil respiration by accounting for land-use changes derived from remote sensing data.

PubMed

Adachi, Minaco; Ito, Akihiko; Yonemura, Seiichiro; Takeuchi, Wataru

2017-09-15

Soil respiration is one of the largest carbon fluxes from terrestrial ecosystems. Estimating global soil respiration is difficult because of its high spatiotemporal variability and sensitivity to land-use change. Satellite monitoring provides useful data for estimating the global carbon budget, but few studies have estimated global soil respiration using satellite data. We provide preliminary insights into the estimation of global soil respiration in 2001 and 2009 using empirically derived soil temperature equations for 17 ecosystems obtained by field studies, as well as MODIS climate data and land-use maps at a 4-km resolution. The daytime surface temperature from winter to early summer based on the MODIS data tended to be higher than the field-observed soil temperatures in subarctic and temperate ecosystems. The estimated global soil respiration was 94.8 and 93.8 Pg C yr -1 in 2001 and 2009, respectively. However, the MODIS land-use maps had insufficient spatial resolution to evaluate the effect of land-use change on soil respiration. The spatial variation of soil respiration (Q 10 ) values was higher but its spatial variation was lower in high-latitude areas than in other areas. However, Q 10 in tropical areas was more variable and was not accurately estimated (the values were >7.5 or <1.0) because of the low seasonal variation in soil respiration in tropical ecosystems. To solve these problems, it will be necessary to validate our results using a combination of remote sensing data at higher spatial resolution and field observations for many different ecosystems, and it will be necessary to account for the effects of more soil factors in the predictive equations. Copyright © 2017 Elsevier Ltd. All rights reserved.

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...

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...

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

USDA-ARS?s Scientific Manuscript database

his study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA’s long lasting AMSR-E mission. Additionally three other products we...

Use of LANDSAT images of vegetation cover to estimate effective hydraulic properties of soils

NASA Technical Reports Server (NTRS)

Eagleson, Peter S.; Jasinski, Michael F.

1988-01-01

The estimation of the spatially variable surface moisture and heat fluxes of natural, semivegetated landscapes is difficult due to the highly random nature of the vegetation (e.g., plant species, density, and stress) and the soil (e.g., moisture content, and soil hydraulic conductivity). The solution to that problem lies, in part, in the use of satellite remotely sensed data, and in the preparation of those data in terms of the physical properties of the plant and soil. The work was focused on the development and testing of a stochastic geometric canopy-soil reflectance model, which can be applied to the physically-based interpretation of LANDSAT images. The model conceptualizes the landscape as a stochastic surface with bulk plant and soil reflective properties. The model is particularly suited for regional scale investigations where the quantification of the bulk landscape properties, such as fractional vegetation cover, is important on a pixel by pixel basis. A summary of the theoretical analysis and the preliminary testing of the model with actual aerial radiometric data is provided.

Version 3 of the SMAP Level 4 Soil Moisture Product

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Liu, Qing; Ardizzone, Joe; Crow, Wade; De Lannoy, Gabrielle; Kolassa, Jana; Kimball, John; Koster, Randy

2017-01-01

The NASA Soil Moisture Active Passive (SMAP) Level 4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root zone (0-100 cm) soil moisture as well as related land surface states and fluxes from 31 March 2015 to present with a latency of 2.5 days. The ensemble-based L4_SM algorithm is a variant of the Goddard Earth Observing System version 5 (GEOS-5) land data assimilation system and ingests SMAP L-band (1.4 GHz) Level 1 brightness temperature observations into the Catchment land surface model. The soil moisture analysis is non-local (spatially distributed), performs downscaling from the 36-km resolution of the observations to that of the model, and respects the relative uncertainties of the modeled and observed brightness temperatures. Prior to assimilation, a climatological rescaling is applied to the assimilated brightness temperatures using a 6 year record of SMOS observations. A new feature in Version 3 of the L4_SM data product is the use of 2 years of SMAP observations for rescaling where SMOS observations are not available because of radio frequency interference, which expands the impact of SMAP observations on the L4_SM estimates into large regions of northern Africa and Asia. This presentation investigates the performance and data assimilation diagnostics of the Version 3 L4_SM data product. The L4_SM soil moisture estimates meet the 0.04 m3m3 (unbiased) RMSE requirement. We further demonstrate that there is little bias in the soil moisture analysis. Finally, we illustrate where the assimilation system overestimates or underestimates the actual errors in the system.

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.

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

NASA Technical Reports Server (NTRS)

Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

2010-01-01

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

Soil Moisture (SMAP) and Vapor Pressure Deficit Controls on Evaporative Fraction over the Continental U.S.

NASA Astrophysics Data System (ADS)

Salvucci, G.; Rigden, A. J.; Gianotti, D.; Entekhabi, D.

2017-12-01

We analyze the control over evapotranspiration (ET) imposed by soil moisture limitations and stomatal closure due to vapor pressure deficit (VPD) across the United States using estimates of satellite-derived soil moisture from SMAP and a meteorological, data-driven ET estimate over a two year period at over 1000 locations. The ET data are developed independent of soil moisture using the emergent relationship between the diurnal cycle of the relative humidity profile and ET based on ETRHEQ (Salvucci and Gentine (2013), PNAS, 110(16): 6287-6291, Rigden and Salvucci, 2015, WRR, 51(4): 2951-2973; Rigden and Salvucci, 2017, GCB, 23(3) 1140-1151). The key advantage of using this approach to estimate ET is that no measurements of surface limiting factors (soil moisture, leaf area, canopy conductance) are required; instead, ET is estimated from only meteorological data. The combination of these two independent datasets allows for a unique spatial analysis of the control on ET imposed by the availability of soil moisture vs. VPD. Spatial patterns of limitations are inferred by fitting the ETRHEQ-inferred surface conductance to a weighted sum of a Jarvis type stomatal conductance model and bare soil evaporation conductance model, with separate moisture-dependent evaporation efficiency relations for bare soil and vegetation. Spatial patterns are visualized by mapping the optimal curve fitting coefficients and by conducting sensitivity analyses of the resulting fitted model across the Unites States. Results indicate regional variations in rate-limiting factors, and suggest that in some areas the VPD effect on stomatal closure is strong enough to induce a decrease in ET under projected climate change, despite an increase in atmospheric drying (and thus evaporative demand).

Inspection Method for Contact Condition of Soil on the Surface of Underground Pipe Utilizing Resonance of Transverse Lamb Wave

NASA Astrophysics Data System (ADS)

Tanigawa, Hiroshi; Seno, Hiroaki; Watanabe, Yoshiaki; Nakajima, Koshiro

1998-05-01

A nondestructive inspection method to estimate the contact condition of soil on the surface of an underground pipe, utilizing the resonance of a transverse Lamb wave circulating along the pipe wall is proposed.The Q factor of the resonance is considered and measured under some contact conditions by sweeping the vibrating frequency in a 150-mm-inner diameter Fiberglass Reinforced Plastic Mortar (FRPM) pipe. It is confirmed that the Q factor shows a clear response to the change in the contact conditions. For example, the Q factor is 8.4 when the pipe is in ideal contact with the soil plane and goes up to 19.2 when a 100-mm-diameter void is located at the contact surface of the soil.The spatial resolution of the proposed inspection method is also measured by moving the sensing point along the direction of laying the length of the pipe into a 85-mm-diameter void. The resolution of the proposed method is estimated at about 50 mm.

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.

HCMM energy budget data as a model input for assessing regions of high potential groundwater pollution. [South Dakota

NASA Technical Reports Server (NTRS)

Moore, D. G. (Principal Investigator); Heilman, J. L.

1980-01-01

The author has identified the following significant results. Day thermal data were analyzed to assess depth to groundwater in the test site. HCMM apparent temperature was corrected for atmospheric effects using lake temperature of the Oahe Reservoir in central South Dakota. Soil surface temperatures were estimated using an equation developed for ground studies. A significant relationship was found between surface soil temperature and depth to groundwater, as well as between the surface soil-maximum air temperature differential and soil water content (% of field capacity) in the 0 cm and 4 cm layer of the profile. Land use for the data points consisted of row crops, small grains, stubble, and pasture.

Microbial Signatures of Cadaver Gravesoil During Decomposition.

PubMed

Finley, Sheree J; Pechal, Jennifer L; Benbow, M Eric; Robertson, B K; Javan, Gulnaz T

2016-04-01

Genomic studies have estimated there are approximately 10(3)-10(6) bacterial species per gram of soil. The microbial species found in soil associated with decomposing human remains (gravesoil) have been investigated and recognized as potential molecular determinants for estimates of time since death. The nascent era of high-throughput amplicon sequencing of the conserved 16S ribosomal RNA (rRNA) gene region of gravesoil microbes is allowing research to expand beyond more subjective empirical methods used in forensic microbiology. The goal of the present study was to evaluate microbial communities and identify taxonomic signatures associated with the gravesoil human cadavers. Using 16S rRNA gene amplicon-based sequencing, soil microbial communities were surveyed from 18 cadavers placed on the surface or buried that were allowed to decompose over a range of decomposition time periods (3-303 days). Surface soil microbial communities showed a decreasing trend in taxon richness, diversity, and evenness over decomposition, while buried cadaver-soil microbial communities demonstrated increasing taxon richness, consistent diversity, and decreasing evenness. The results show that ubiquitous Proteobacteria was confirmed as the most abundant phylum in all gravesoil samples. Surface cadaver-soil communities demonstrated a decrease in Acidobacteria and an increase in Firmicutes relative abundance over decomposition, while buried soil communities were consistent in their community composition throughout decomposition. Better understanding of microbial community structure and its shifts over time may be important for advancing general knowledge of decomposition soil ecology and its potential use during forensic investigations.

Surface runoff and soil erosion by difference of surface cover characteristics using by an oscillating rainfall simulator

NASA Astrophysics Data System (ADS)

Kim, J. K.; Kim, M. S.; Yang, D. Y.

2017-12-01

Sediment transfer within hill slope can be changed by the hydrologic characteristics of surface material on hill slope. To better understand sediment transfer of the past and future related to climate changes, studies for the changes of soil erosion due to hydrological characteristics changes by surface materials on hill slope are needed. To do so, on-situ rainfall simulating test was conducted on three different surface conditions, i.e. well covered with litter layer condition (a), undisturbed bare condition (b), and disturbed bare condition (c) and these results from rainfall simulating test were compared with that estimated using the Limburg Soil Erosion Model (LISEM). The result from the rainfall simulating tests showed differences in the infiltration rate (a > b > c) and the highest soil erosion rate was occurred on c condition. The result from model also was similar to those from rainfall simulating tests, however, the difference from the value of soil erosion rate between two results was quite large on b and c conditions. These results implied that the difference of surface conditions could change the surface runoff and soil erosion and the result from the erosion model might significantly underestimate on bare surface conditions rather than that from rainfall simulating test.

A new method of fully three dimensional analysis of stress field in the soil layer of a soil-mantled hillslope

NASA Astrophysics Data System (ADS)

Wu, Y. H.; Nakakita, E.

2017-12-01

Hillslope stability is highly related to stress equilibrium near the top surface of soil-mantled hillslopes. Stress field in a hillslope can also be significantly altered by variable groundwater motion under the rainfall influence as well as by different vegetation above and below the slope. The topographic irregularity, biological effects from vegetation and variable rainfall patterns couple with others to make the prediction of shallow landslide complicated and difficult. In an increasing tendency of extreme rainfall, the mountainous area in Japan has suffered more and more shallow landslides. To better assess shallow landslide hazards, we would like to develop a new mechanically-based method to estimate the fully three-dimensional stress field in hillslopes. The surface soil-layer of hillslope is modelled as a poroelastic medium, and the tree surcharge on the slope surface is considered as a boundary input of stress forcing. The modelling of groundwater motion is involved to alter effective stress state in the soil layer, and the tree root-reinforcement estimated by allometric equations is taken into account for influencing the soil strength. The Mohr-Coulomb failure theory is then used for locating possible yielding surfaces, or says for identifying failure zones. This model is implemented by using the finite element method. Finally, we performed a case study of the real event of massive shallow landslides occurred in Hiroshima in August, 2014. The result shows good agreement with the field condition.

Spatial and seasonal dynamics of surface soil carbon in the Luquillo Experimental Forest, Puerto Rico.

Treesearch

Hongqing Wang; Joseph D. Cornell; Charles A.S. Hall; David P. Marley

2002-01-01

We developed a spatially-explicit version of the CENTURY soil model to characterize the storage and flux of soil organic carbon (SOC, 0–30 cm depth) in the Luquillo Experimental Forest (LEF), Puerto Rico as a function of climate, vegetation, and soils. The model was driven by monthly estimates of average air temperature, precipitation, and potential evapotranspiration...

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.

Improving Evapotranspiration Estimates Using Multi-Platform Remote Sensing

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Assessment of long-term erosion in a mountain vineyard, Aosta Valley (NW Italy)

NASA Astrophysics Data System (ADS)

Biddoccu, Marcella; Zecca, Odoardo; Barmaz, Andrea; Godone, Franco; Cavallo, Eugenio

2015-04-01

Tillage and chemical weeding are common soil management techniques adopted in mountain vineyards, with high slope gradient, to maintain bare soil. Both techniques exposes the soil to degradation, favoring runoff and soil losses, that may cause relevant on-site and off-site damage. Steep mountain slopes makes optimum conditions for grape-growing. In the mountain region of Aosta Valley, NW Italy, the vineyards were, in the past, traditionally grown on terraces supported by dry stone walls. Since the 1960s the plantation of vines in the direction of the slope became more and more widespread, also on very steep slopes. Generally, no particular measure to channel and control surface water is adopted in this area due to the low rainfall (560 mm/year). Nevertheless in steep mountain slope rainfall events can cause important runoff erosion. In order to evaluate the long-term effect of vineyard management techniques on soil erosion, a study was carried out on a mountain slope vineyard located near Aosta, at about 900 m above the sea level. The vineyard was planted at the end of 1960s and is managed by the Institut Agricole Régional. The rows are accommodated oriented along the slope, which is about 45%. The inter-rows' soil management of the vineyard included chemical weeding and, in first year after plantation, the adoption of irrigation (by fixed overhead sprinklers) and hilling-up/taking-out the soil around the vine plants, to protect them from cold weather. The long-term soil erosion rate was determined adopting the technique of botanical benchmark (Casalí et al.,2009). The grafting callus was used as a marker to identify the paleo-surface at the time of planting. A detailed topographic survey was carried out to determine the present surface of the vineyard while the current position of the grafting callus was recorded for a number of plants. The original position of the callus was estimated by data obtained by farmers and by a survey on reference vineyards. Two digital elevation models (DEMs) were generated: the first depicting, the present vineyard surface and the second representing the topography of the vineyard at time of vineyard plantation, based on the height of the grafting callus above the soil. The difference between the DEMs represents the local soil loss/gain over the vineyard surface from the plantation to today. According to this calculation the estimated total soil lost across 46 years was about 800 Mg, with average annual soil loss of 58.6 Mg ha-1year -1. The long-term erosion rate estimated by the study is consistent with values reported for vineyards by other studies considering shorter periods of time. The estimated erosion rate dramatically exceeds the upper limit of the tolerable soil erosion rates (1.4 Mg ha-1 year-1) proposed for Europe by Verheijen et al. (2009). It is likely that the water and soil management practices adopted in the vineyard, besides the high slope gradient, have played a relevant role in determining the high erosion rate.

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.

Global estimation of evapotranspiration using a leaf area index-based surface energy and water balance model

USDA-ARS?s Scientific Manuscript database

Studies of global hydrologic cycles, carbon cycles and climate change are greatly facilitated when global estimates of evapotranspiration (E) are available. We have developed an air-relative-humidity-based two-source (ARTS) E model that simulates the surface energy balance, soil water balance, and e...

The desorptivity model of bulk soil-water evaporation

NASA Technical Reports Server (NTRS)

Clapp, R. B.

1983-01-01

Available models of bulk evaporation from a bare-surfaced soil are difficult to apply to field conditions where evaporation is complicated by two main factors: rate-limiting climatic conditions and redistribution of soil moisture following infiltration. Both factors are included in the "desorptivity model', wherein the evaporation rate during the second stage (the soil-limiting stage) of evaporation is related to the desorptivity parameter, A. Analytical approximations for A are presented. The approximations are independent of the surface soil moisture. However, calculations using the approximations indicate that both soil texture and soil moisture content at depth significantly affect A. Because the moisture content at depth decreases in time during redistribution, it follows that the A parameter also changes with time. Consequently, a method to calculate a representative value of A was developed. When applied to field data, the desorptivity model estimated cumulative evaporation well. The model is easy to calculate, but its usefulness is limited because it requires an independent estimate of the time of transition between the first and second stages of evaporation. The model shows that bulk evaporation after the transition to the second stage is largely independent of climatic conditions.

Estimating persistence of brominated and chlorinated organic pollutants in air, water, soil, and sediments with the QSPR-based classification scheme.

PubMed

Puzyn, T; Haranczyk, M; Suzuki, N; Sakurai, T

2011-02-01

We have estimated degradation half-lives of both brominated and chlorinated dibenzo-p-dioxins (PBDDs and PCDDs), furans (PBDFs and PCDFs), biphenyls (PBBs and PCBs), naphthalenes (PBNs and PCNs), diphenyl ethers (PBDEs and PCDEs) as well as selected unsubstituted polycyclic aromatic hydrocarbons (PAHs) in air, surface water, surface soil, and sediments (in total of 1,431 compounds in four compartments). Next, we compared the persistence between chloro- (relatively well-studied) and bromo- (less studied) analogs. The predictions have been performed based on the quantitative structure-property relationship (QSPR) scheme with use of k-nearest neighbors (kNN) classifier and the semi-quantitative system of persistence classes. The classification models utilized principal components derived from the principal component analysis of a set of 24 constitutional and quantum mechanical descriptors as input variables. Accuracies of classification (based on an external validation) were 86, 85, 87, and 75% for air, surface water, surface soil, and sediments, respectively. The persistence of all chlorinated species increased with increasing halogenation degree. In the case of brominated organic pollutants (Br-OPs), the trend was the same for air and sediments. However, we noticed that the opposite trend for persistence in surface water and soil. The results suggest that, due to high photoreactivity of C-Br chemical bonds, photolytic processes occurring in surface water and soil are able to play significant role in transforming and removing Br-OPs from these compartments. This contribution is the first attempt of classifying together Br-OPs and Cl-OPs according to their persistence, in particular, environmental compartments.

Remotely monitoring evaporation rate and soil water status using thermal imaging and "three-temperatures model (3T Model)" under field-scale conditions.

PubMed

Qiu, Guo Yu; Zhao, Ming

2010-03-01

Remote monitoring of soil evaporation and soil water status is necessary for water resource and environment management. Ground based remote sensing can be the bridge between satellite remote sensing and ground-based point measurement. The primary object of this study is to provide an algorithm to estimate evaporation and soil water status by remote sensing and to verify its accuracy. Observations were carried out in a flat field with varied soil water content. High-resolution thermal images were taken with a thermal camera; soil evaporation was measured with a weighing lysimeter; weather data were recorded at a nearby meteorological station. Based on the thermal imaging and the three-temperatures model (3T model), we developed an algorithm to estimate soil evaporation and soil water status. The required parameters of the proposed method were soil surface temperature, air temperature, and solar radiation. By using the proposed method, daily variation in soil evaporation was estimated. Meanwhile, soil water status was remotely monitored by using the soil evaporation transfer coefficient. Results showed that the daily variation trends of measured and estimated evaporation agreed with each other, with a regression line of y = 0.92x and coefficient of determination R(2) = 0.69. The simplicity of the proposed method makes the 3T model a potentially valuable tool for remote sensing.

Surface disturbances: their role in accelerating desertification

USGS Publications Warehouse

Belnap, Jayne

1995-01-01

Maintaining soil stability and normal water and nutrient cycles in desert systems is critical to avoiding desertification. These particular ecosystem processes are threatened by trampling of livestock and people, and by off-road vehicle use. Soil compaction and disruption of cryptobiotic soil surfaces (composed of cyanobacteria, lichens, and mosses) can result in decreased water availability to vascular plants through decreased water infiltration and increased albedo with possible decreased precipitation. Surface disturbance may also cause accelerated soil loss through wind and water erosion and decreased diversity and abundance of soil biota. In addition, nutrient cycles can be altered through lowered nitrogen and carbon inputs and slowed decomposition of soil organic matter, resulting in lower nutrient levels in associated vascular plants. Some cold desert systems may be especially susceptible to these disruptions due to the paucity of surface-rooting vascular plants for soil stabilization, fewer nitrogen-fixing higher plants, and lower soil temperatures, which slow nutrient cycles. Desert soils may recover slowly from surface disturbances, resulting in increased vulnerability to desertification. Recovery from compaction and decreased soil stability is estimated to take several hundred years. Re-establishment rates for soil bacterial and fungal populations are not known. The nitrogen fixation capability of soil requires at least 50 years to recover. Recovery of crusts can be hampered by large amounts of moving sediment, and re-establishment can be extremely difficult in some areas. Given the sensitivity of these resources and slow recovery times, desertification threatens million of hectares of semiarid lands in the United States.

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

Estimating Agricultural Nitrous Oxide Emissions

USDA-ARS?s Scientific Manuscript database

Nitrous oxide emissions are highly variable in space and time and different methodologies have not agreed closely, especially at small scales. However, as scale increases, so does the agreement between estimates based on soil surface measurements (bottom up approach) and estimates derived from chang...

Wading bird guano contributes to Hg accumulation in tree island soils in the Florida Everglades.

PubMed

Zhu, Yingjia; Gu, Binhe; Irick, Daniel L; Ewe, Sharon; Li, Yuncong; Ross, Michael S; Ma, Lena Q

2014-01-01

Tree islands are habitat for wading birds and a characteristic landscape feature in the Everglades. A total of 93 surface soil and 3 soil core samples were collected from 7 degraded/ghost and 34 live tree islands. The mean Hg concentration in surface soils of ghost tree islands was low and similar to marsh soil. For live tree islands, Hg concentrations in the surface head region were considerably greater than those in mid and tail region, and marsh soils. Hg concentrations in bird guano (286 μg kg(-1)) were significantly higher than those in mammal droppings (105 μg kg(-1)) and plant leaves (53 μg kg(-1)). In addition, Hg concentrations and δ(15)N values displayed positive correlation in soils influenced by guano. During 1998-2010, estimated annual Hg deposition by guano was 148 μg m(-2) yr(-1) and ~8 times the atmospheric deposition. Published by Elsevier Ltd.

Satellite microwave observations of soil moisture variations. [by the microwave radiometer on the Nimbus 5 satellite

NASA Technical Reports Server (NTRS)

Schmugge, T. J.; Rango, A.; Neff, R.

1975-01-01

The electrically scanning microwave radiometer (ESMR) on the Nimbus 5 satellite was used to observe microwave emissions from vegetated and soil surfaces over an Illinois-Indiana study area, the Mississippi Valley, and the Great Salt Lake Desert in Utah. Analysis of microwave brightness temperatures (T sub B) and antecedent rainfall over these areas provided a way to monitor variations of near-surface soil moisture. Because vegetation absorbs microwave emission from the soil at the 1.55 cm wavelength of ESMR, relative soil moisture measurements can only be obtained over bare or sparsely vegetated soil. In general T sub B increased during rainfree periods as evaporation of water and drying of the surface soil occurs, and drops in T sub B are experienced after significant rainfall events wet the soil. Microwave observations from space are limited to coarse resolutions (10-25 km), but it may be possible in regions with sparse vegetation cover to estimate soil moisture conditions on a watershed or agricultural district basis, particularly since daily observations can be obtained.

Orbiting passive microwave sensor simulation applied to soil moisture estimation

NASA Technical Reports Server (NTRS)

Newton, R. W. (Principal Investigator); Clark, B. V.; Pitchford, W. M.; Paris, J. F.

1979-01-01

A sensor/scene simulation program was developed and used to determine the effects of scene heterogeneity, resolution, frequency, look angle, and surface and temperature relations on the performance of a spaceborne passive microwave system designed to estimate soil water information. The ground scene is based on classified LANDSAT images which provide realistic ground classes, as well as geometries. It was determined that the average sensitivity of antenna temperature to soil moisture improves as the antenna footprint size increased. Also, the precision (or variability) of the sensitivity changes as a function of resolution.

Chamber measurement of surface-atmosphere trace gas exchange: Numerical evaluation of dependence on soil, interfacial layer, and source/sink properties

NASA Astrophysics Data System (ADS)

Hutchinson, G. L.; Livingston, G. P.; Healy, R. W.; Striegl, R. G.

2000-04-01

We employed a three-dimensional finite difference gas diffusion model to simulate the performance of chambers used to measure surface-atmosphere trace gas exchange. We found that systematic errors often result from conventional chamber design and deployment protocols, as well as key assumptions behind the estimation of trace gas exchange rates from observed concentration data. Specifically, our simulations showed that (1) when a chamber significantly alters atmospheric mixing processes operating near the soil surface, it also nearly instantaneously enhances or suppresses the postdeployment gas exchange rate, (2) any change resulting in greater soil gas diffusivity, or greater partitioning of the diffusing gas to solid or liquid soil fractions, increases the potential for chamber-induced measurement error, and (3) all such errors are independent of the magnitude, kinetics, and/or distribution of trace gas sources, but greater for trace gas sinks with the same initial absolute flux. Finally, and most importantly, we found that our results apply to steady state as well as non-steady-state chambers, because the slow rate of gas diffusion in soil inhibits recovery of the former from their initial non-steady-state condition. Over a range of representative conditions, the error in steady state chamber estimates of the trace gas flux varied from -30 to +32%, while estimates computed by linear regression from non-steady-state chamber concentrations were 2 to 31% too small. Although such errors are relatively small in comparison to the temporal and spatial variability characteristic of trace gas exchange, they bias the summary statistics for each experiment as well as larger scale trace gas flux estimates based on them.

Chamber measurement of surface-atmosphere trace gas exchange--Numerical evaluation of dependence on soil interfacial layer, and source/sink products

USGS Publications Warehouse

Hutchinson, G.L.; Livingston, G.P.; Healy, R.W.; Striegl, Robert G.

2000-01-01

We employed a three-dimensional finite difference gas diffusion model to simulate the performance of chambers used to measure surface-atmosphere tace gas exchange. We found that systematic errors often result from conventional chamber design and deployment protocols, as well as key assumptions behind the estimation of trace gas exchange rates from observed concentration data. Specifically, our simulationshowed that (1) when a chamber significantly alters atmospheric mixing processes operating near the soil surface, it also nearly instantaneously enhances or suppresses the postdeployment gas exchange rate, (2) any change resulting in greater soil gas diffusivity, or greater partitioning of the diffusing gas to solid or liquid soil fractions, increases the potential for chamber-induced measurement error, and (3) all such errors are independent of the magnitude, kinetics, and/or distribution of trace gas sources, but greater for trace gas sinks with the same initial absolute flux. Finally, and most importantly, we found that our results apply to steady state as well as non-steady-state chambers, because the slow rate of gas diffusion in soil inhibits recovery of the former from their initial non-steady-state condition. Over a range of representative conditions, the error in steady state chamber estimates of the trace gas flux varied from -30 to +32%, while estimates computed by linear regression from non-steadystate chamber concentrations were 2 to 31% too small. Although such errors are relatively small in comparison to the temporal and spatial variability characteristic of trace gas exchange, they bias the summary statistics for each experiment as well as larger scale trace gas flux estimates based on them.

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.

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

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

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

2008-11-15

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

A comparison between evapotranspiration estimates based on remotely sensed surface energy balance and ground-based soil water balance analyses

USDA-ARS?s Scientific Manuscript database

Remotely sensed and in-situ data were used to investigate dynamics of root zone soil moisture and evapotranspiration (ET) at four Mesonet stations in north-central Oklahoma over an 11-year period (2000-2010). Two moisture deficit indicators based on soil matric potential had spatial and temporal pat...

A standardized approach for estimating the permeability of plastic films to soil fumigants under various field and environmental conditions

USDA-ARS?s Scientific Manuscript database

Minimizing atmospheric emissions of soil fumigants is critical for protecting human and environmental health. Covering the soil surface with a plastic tarp is a common approach to restrict fumigant emissions. The mass transfer of the fumigant vapors through the tarp is often the rate-limiting factor...

Enhancing the soil organic matter pool through biomass incorporation

Treesearch

Felipe G. Sanchez; Emily A. Carter; John F. Klepac

2003-01-01

A study was installed in the Upper Coastal Plain of South Carolina, USA that sought to examine the impact of incorporating downed slash materials into subsoil layers on soil chemical and physical properties as compared with the effect of slash materials left on the soil surface. Baseline levels of slash were estimated by establishing transects within harvested stands...

Modeling Environmental Controls on Tree Water Use at Different Temporal scales

NASA Astrophysics Data System (ADS)

Guan, H.; Wang, H.; Simmons, C. T.

2014-12-01

Vegetation covers 70% of land surface, significantly influencing water and carbon exchange between land surface and the atmosphere. Vegetation transpiration (Et) contributes 80% of the global terrestrial evapotranspiration, making an adequate illustration of how important vegetation is to any hydrological or climatological applications. Transpiration can be estimated through upscaling from sap flow measurements on selected trees. Alternatively, transpiration (or tree water use for forests) can be correlated with environmental variables or estimated in land surface simulations in which a canopy conductance (gc) model is often used. Transpiration and canopy conductance are constrained by supply and demand control factors. Some previous studies estimated Et and gc considering the stresses from both the supply (soil water condition) and demand (e.g. temperature, vapor pressure deficit, solar radiation) factors, while some only considered the demand controls. In this study, we examined the performance of two types of models at daily and half-hourly scales for transpiration and canopy conductance modelling based on a native species in South Australia. The results show that the significance of soil water condition for Et and gc modelling varies with time scales. The model parameter values also vary across time scales. This result calls for attention in choosing models and parameter values for soil-plant-atmosphere continuum and land surface modeling.

On the use of the GRACE normal equation of inter-satellite tracking data for estimation of soil moisture and groundwater in Australia

NASA Astrophysics Data System (ADS)

Tangdamrongsub, Natthachet; Han, Shin-Chan; Decker, Mark; Yeo, In-Young; Kim, Hyungjun

2018-03-01

An accurate estimation of soil moisture and groundwater is essential for monitoring the availability of water supply in domestic and agricultural sectors. In order to improve the water storage estimates, previous studies assimilated terrestrial water storage variation (ΔTWS) derived from the Gravity Recovery and Climate Experiment (GRACE) into land surface models (LSMs). However, the GRACE-derived ΔTWS was generally computed from the high-level products (e.g. time-variable gravity fields, i.e. level 2, and land grid from the level 3 product). The gridded data products are subjected to several drawbacks such as signal attenuation and/or distortion caused by a posteriori filters and a lack of error covariance information. The post-processing of GRACE data might lead to the undesired alteration of the signal and its statistical property. This study uses the GRACE least-squares normal equation data to exploit the GRACE information rigorously and negate these limitations. Our approach combines GRACE's least-squares normal equation (obtained from ITSG-Grace2016 product) with the results from the Community Atmosphere Biosphere Land Exchange (CABLE) model to improve soil moisture and groundwater estimates. This study demonstrates, for the first time, an importance of using the GRACE raw data. The GRACE-combined (GC) approach is developed for optimal least-squares combination and the approach is applied to estimate the soil moisture and groundwater over 10 Australian river basins. The results are validated against the satellite soil moisture observation and the in situ groundwater data. Comparing to CABLE, we demonstrate the GC approach delivers evident improvement of water storage estimates, consistently from all basins, yielding better agreement on seasonal and inter-annual timescales. Significant improvement is found in groundwater storage while marginal improvement is observed in surface soil moisture estimates.

Soil Water Balance and Vegetation Dynamics in two Water-limited Mediterranean Ecosystem on Sardinia under past and future climate change

NASA Astrophysics Data System (ADS)

Corona, R.; Montaldo, N.; Albertson, J. D.

2016-12-01

Water limited conditions strongly impacts soil and vegetation dynamics in Mediterranean regions, which are commonly heterogeneous ecosystems, characterized by inter-annual rainfall variability, topography variability and contrasting plant functional types (PFTs) competing for water use. Historical human influences (e.g., deforestation, urbanization) further altered these ecosystems. Sardinia island is a representative region of Mediterranean ecosystems. It is low urbanized except some plan areas close to the main cities where main agricultural activities are concentrated. Two contrasting case study sites are within the Flumendosa river basin (1700 km2). The first site is a typical grassland on an alluvial plan valley (soil depth > 2m) while the second is a patchy mixture of Mediterranean vegetation species (mainly wild olive trees and C3 herbaceous) that grow in a soil bounded from below by a rocky layer of basalt, partially fractured (soil depth 15 - 40 cm). In both sites land-surface fluxes and CO2 fluxes are estimated by the eddy correlation technique while soil moisture was continuously estimated with water content reflectometers, and periodically leaf area index (LAI) was estimated. The following objectives are addressed:1) pointing out the dynamics of land surface fluxes, soil moisture, CO2 and vegetation cover for two contrasting water-limited ecosystems; 2) assess the impact of the soil depth and type on the CO2 and water balance dynamics; 3) evaluate the impact of past and future climate change scenarios on the two contrasting ecosystems. For reaching the objectives an ecohydrologic model that couples a vegetation dynamic model (VDM), and a 3-component (bare soil, grass and woody vegetation) land surface model (LSM) has been used. Historical meteorological data are available from 1922 and hydro-meteorological scenarios are then generated using a weather generator. The VDM-LSM model predict soil water balance and vegetation dynamics for the generated hydrometeorological scenarios in the two contrasting ecosystems. Results demonstrate that vegetation dynamics are influenced by the inter-annual variability of atmospheric forcing, with vegetation density changing significantly according to seasonal rainfall amount. At the same time the vegetation dynamics affect the soil water balance.

Evaluating RGB photogrammetry and multi-temporal digital surface models for detecting soil erosion

NASA Astrophysics Data System (ADS)

Anders, Niels; Keesstra, Saskia; Seeger, Manuel

2013-04-01

Photogrammetry is a widely used tool for generating high-resolution digital surface models. Unmanned Aerial Vehicles (UAVs), equipped with a Red Green Blue (RGB) camera, have great potential in quickly acquiring multi-temporal high-resolution orthophotos and surface models. Such datasets would ease the monitoring of geomorphological processes, such as local soil erosion and rill formation after heavy rainfall events. In this study we test a photogrammetric setup to determine data requirements for soil erosion studies with UAVs. We used a rainfall simulator (5 m2) and above a rig with attached a Panasonic GX1 16 megapixel digital camera and 20mm lens. The soil material in the simulator consisted of loamy sand at an angle of 5 degrees. Stereo pair images were taken before and after rainfall simulation with 75-85% overlap. Acquired images were automatically mosaicked to create high-resolution orthorectified images and digital surface models (DSM). We resampled the DSM to different spatial resolutions to analyze the effect of cell size to the accuracy of measured rill depth and soil loss estimations, and determined an optimal cell size (thus flight altitude). Furthermore, the high spatial accuracy of the acquired surface models allows further analysis of rill formation and channel initiation related to e.g. surface roughness. We suggest implementing near-infrared and temperature sensors to combine soil moisture and soil physical properties with surface morphology for future investigations.

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.

Soils of eagle crater and Meridiani Planum at the opportunity Rover landing site

USGS Publications Warehouse

Soderblom, L.A.; Anderson, R.C.; Arvidson, R. E.; Bell, J.F.; Cabrol, N.A.; Calvin, W.; Christensen, P.R.; Clark, B. C.; Economou, T.; Ehlmann, B.L.; Farrand, W. H.; Fike, D.; Gellert, Ralf; Glotch, T.D.; Golombek, M.P.; Greeley, R.; Grotzinger, J.P.; Herkenhoff, K. E.; Jerolmack, D.J.; Johnson, J. R.; Jolliff, B.; Klingelhofer, C.; Knoll, A.H.; Learner, Z.A.; Li, R.; Malin, M.C.; McLennan, S.M.; McSween, H.Y.; Ming, D. W.; Morris, R.V.; Rice, J. W.; Richter, L.; Rieder, R.; Rodionov, D.; Schroder, C.; Seelos, F.P.; Soderblom, J.M.; Squyres, S. W.; Sullivan, R.; Watters, W.A.; Weitz, C.M.; Wyatt, M.B.; Yen, A.; Zipfel, J.

2004-01-01

The soils at the Opportunity site are fine-grained basaltic sands mixed with dust and sulfate-rich outcrop debris. Hematite is concentrated in spherules eroded from the strata. Ongoing saltation exhumes the spherules and their fragments, concentrating them at the surface. Spherules emerge from soils coated, perhaps from subsurface cementation, by salts. Two types of vesicular clasts may represent basaltic sand sources. Eolian ripples, armored by well-sorted hematite-rich grains, pervade Meridiani Planum. The thickness of the soil on the plain is estimated to be about a meter. The flatness and thin cover suggest that the plain may represent the original sedimentary surface.

Estimating Vertical Stress on Soil Subjected to Vehicular Loading

DTIC Science & Technology

2009-02-01

specified surface area of the tire . The silt and sand samples were both estimated to be 23.7-in. thick over a base of much harder soil. The pressures...study in which highway tread tires were used as opposed to the all-terrain tread currently on the vehicle. If the pressure pads are functioning...Vertical force versus time (front right CIV tire )....................................................................... 14 Tables Table 1. Testing

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.

Plot-scale soil loss estimation with laser scanning and photogrammetry methods

NASA Astrophysics Data System (ADS)

Szabó, Boglárka; Szabó, Judit; Jakab, Gergely; Centeri, Csaba; Szalai, Zoltán; Somogyi, Árpád; Barsi, Árpád

2017-04-01

Structure from Motion (SfM) is an automatic feature-matching algorithm, which nowadays is widely used tool in photogrammetry for geoscience applications. SfM method and parallel terrestrial laser scanning measurements are widespread and they can be well accomplished for quantitative soil erosion measurements as well. Therefore, our main scope was soil erosion characterization quantitatively and qualitatively, 3D visualization and morphological characterization of soil-erosion-dynamics. During the rainfall simulation, the surface had been measured and compared before and after the rainfall event by photogrammetry (SfM - Structure from Motion) and laser scanning (TLS - Terrestrial Laser Scanning) methods. The validation of the given results had been done by the caught runoff and the measured soil-loss value. During the laboratory experiment, the applied rainfall had 40 mm/h rainfall intensity. The size of the plot was 0.5 m2. The laser scanning had been implemented with Faro Focus 3D 120 S type equipment, while the SfM shooting had been carried out by 2 piece SJCAM SJ4000+ type, 12 MP resolution and 4K action cams. The photo-reconstruction had been made with Agisoft Photoscan software, while evaluation of the resulted point-cloud from laser scanning and photogrammetry had been implemented partly in CloudCompare and partly in ArcGIS. The resulted models and the calculated surface changes didn't prove to be suitable for estimating soil-loss, only for the detection of changes in the vertical surface. The laser scanning resulted a quite precise surface model, while the SfM method is affected by errors at the surface model due to other factors. The method needs more adequate technical laboratory preparation.

Two-Layer Variable Infiltration Capacity Land Surface Representation for General Circulation Models

NASA Technical Reports Server (NTRS)

Xu, L.

1994-01-01

A simple two-layer variable infiltration capacity (VIC-2L) land surface model suitable for incorporation in general circulation models (GCMs) is described. The model consists of a two-layer characterization of the soil within a GCM grid cell, and uses an aerodynamic representation of latent and sensible heat fluxes at the land surface. The effects of GCM spatial subgrid variability of soil moisture and a hydrologically realistic runoff mechanism are represented in the soil layers. The model was tested using long-term hydrologic and climatalogical data for Kings Creek, Kansas to estimate and validate the hydrological parameters. Surface flux data from three First International Satellite Land Surface Climatology Project Field Experiments (FIFE) intensive field compaigns in the summer and fall of 1987 in central Kansas, and from the Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) in Brazil were used to validate the mode-simulated surface energy fluxes and surface temperature.

Soil moisture and evapotranspiration predictions using Skylab data

NASA Technical Reports Server (NTRS)

Myers, V. I. (Principal Investigator); Moore, D. G.; Horton, M. L.; Russell, M. J.

1975-01-01

The author has identified the following significant results. Multispectral reflectance and emittance data from the Skylab workshop were evaluated for prediction of evapotranspiration and soil moisture for an irrigated region of southern Texas. Wavelengths greater than 2.1 microns were required to spectrally distinguish between wet and dry fallow surfaces. Thermal data provided a better estimate of soil moisture than did data from the reflective bands. Thermal data were dependent on soil moisture but not on the type of agricultural land use. The emittance map, when used in conjunction with existing models, did provide an estimate of evapotranspiration rates. Surveys of areas of high soil moisture can be accomplished with space altitude thermal data. Thermal data will provide a reliable input into irrigation scheduling.

Surface and downhole shear wave seismic methods for thick soil site investigations

USGS Publications Warehouse

Hunter, J.A.; Benjumea, B.; Harris, J.B.; Miller, R.D.; Pullan, S.E.; Burns, R.A.; Good, R.L.

2002-01-01

Shear wave velocity-depth information is required for predicting the ground motion response to earthquakes in areas where significant soil cover exists over firm bedrock. Rather than estimating this critical parameter, it can be reliably measured using a suite of surface (non-invasive) and downhole (invasive) seismic methods. Shear wave velocities from surface measurements can be obtained using SH refraction techniques. Array lengths as large as 1000 m and depth of penetration to 250 m have been achieved in some areas. High resolution shear wave reflection techniques utilizing the common midpoint method can delineate the overburden-bedrock surface as well as reflecting boundaries within the overburden. Reflection data can also be used to obtain direct estimates of fundamental site periods from shear wave reflections without the requirement of measuring average shear wave velocity and total thickness of unconsolidated overburden above the bedrock surface. Accurate measurements of vertical shear wave velocities can be obtained using a seismic cone penetrometer in soft sediments, or with a well-locked geophone array in a borehole. Examples from thick soil sites in Canada demonstrate the type of shear wave velocity information that can be obtained with these geophysical techniques, and show how these data can be used to provide a first look at predicted ground motion response for thick soil sites. ?? 2002 Published by Elsevier Science Ltd.

Efforts to estimate pesticide degradation rates in subsurface ...

EPA Pesticide Factsheets

When pesticides are used in real-world settings, the objective is to be effective in pest eradication at the site of application, but also it is desired that the pesticide have minimal persistence and mobility as it migrates away from the application site. At the site of application, sorption on soil and surface-soil degradation rates both factor into the pesticides' persistence. But once it migrates to the subsurface vadose zone and/or aquifers, subsurface degradation rate is a factor as well. Unfortunately, numerous soil properties that might affect pesticide degradation rate vary by orders of magnitude in the subsurface environment, both spatially and temporally, e.g., organic-carbon concentration, oxygen concentration, redox conditions, pH and soil mineralogy. Consequently, estimation of subsurface pesticide degradation rates and, in tum, pesticide persistence and mobility in the environment, has remained a challenge. To address this intransigent uncertainty, we surveyed peer-reviewed literature to identify > 100 data pairs in which investigators reported pesticide degradation rates in both surface and subsurface soils, using internally consistent experimental methods. These > 100 data pairs represented >30 separate pesticides. When the > 100 subsurface half-lives were plotted against surface half-lives, a limiting line could be defined for which all subsurface half-lives but three fe ll below the line. Of the three data points plotting above the limiting li

Estimation of runoff mitigation by morphologically different cover crop root systems

NASA Astrophysics Data System (ADS)

Yu, Yang; Loiskandl, Willibald; Kaul, Hans-Peter; Himmelbauer, Margarita; Wei, Wei; Chen, Liding; Bodner, Gernot

2016-07-01

Hydrology is a major driver of biogeochemical processes underlying the distinct productivity of different biomes, including agricultural plantations. Understanding factors governing water fluxes in soil is therefore a key target for hydrological management. Our aim was to investigate changes in soil hydraulic conductivity driven by morphologically different root systems of cover crops and their impact on surface runoff. Root systems of twelve cover crop species were characterized and the corresponding hydraulic conductivity was measured by tension infiltrometry. Relations of root traits to Gardner's hydraulic conductivity function were determined and the impact on surface runoff was estimated using HYDRUS 2D. The species differed in both rooting density and root axes thickness, with legumes distinguished by coarser axes. Soil hydraulic conductivity was changed particularly in the plant row where roots are concentrated. Specific root length and median root radius were the best predictors for hydraulic conductivity changes. For an intensive rainfall simulation scenario up to 17% less rainfall was lost by surface runoff in case of the coarsely rooted legumes Melilotus officinalis and Lathyrus sativus, and the densely rooted Linum usitatissimum. Cover crops with coarse root axes and high rooting density enhance soil hydraulic conductivity and effectively reduce surface runoff. An appropriate functional root description can contribute to targeted cover crop selection for efficient runoff mitigation.

Subsurface soil carbon losses offset surface carbon accumulation in abandoned agricultural fields

NASA Astrophysics Data System (ADS)

Yang, Y.; Knops, J. M. H.

2017-12-01

Soil carbon is widely understood to accumulate after agricultural abandonment. However, most of the studies have been focused on shallow depths (10 to 30 cm), and there is a lack of deeper soil carbon data. It was reported that in temperate grasslands, 58% of the soil organic carbon in the first meter was stored between 20 and 100 cm, and organic matter in deeper soil might also be susceptible to agricultural disturbance. We used repeated sampling in 2001 and 2014 to directly measure rates of soil carbon change in both surface and subsurface soil in 21 abandoned agricultural fields at Cedar Creek Ecosystem Science Reserve, MN. Congruent with many other studies, we found carbon accumulated 384.2 C g/m2 in surface soil (0 - 20 cm) over the 13 years. However, we also found carbon pool declined 688.1 C g/m2 in the subsurface soil (40-100 cm), which resulted in a net total loss of soil carbon. We investigated the ecosystem carbon pools and fluxes to explore the mechanisms of the observed soil carbon changes. We found root carbon was not significantly correlated with soil carbon in any of the depth. In situ soil incubation showed nitrogen mineralization rates in subsurface soil are lower than that of surface soil. However, the estimated nitrogen and carbon output through decomposition is higher than inputs from roots, therefore leading to carbon loss in subsurface soil. These results suggest that the decomposition of soil organic matter by microorganisms in subsurface soil is significant, and should be incorporated in ecosystem carbon budget models.

Preliminary evaluation of the SIR-B response to soil moisture, surface roughness, and crop canopy cover

NASA Technical Reports Server (NTRS)

Dobson, M. C.; Ulaby, F. T.

1986-01-01

Two predawn ascending data-takes by the Shuttle Imaging Radar-B (SIR-B) were used to evaluate the effects of surface roughness, crop canopy, and soil moisture on radar backscatter. The two images, separated by three days, were both obtained at 30-deg local angle of incidence, but with opposite azimuth viewing directions. The imagery was externally calibrated with respect to the radar backscattering coefficient sigma(0) via response to arrays of point and area-extended targets of known radar cross section. Three land-cover classes: (1) corn, (2) corn stubble and plowed bare soil, and (3) disked bare soil, soybeans, soybean stubble, alfalfa, and clover could be readily separated for either observation date on the basis of image tone alone. The dependence of sigma(0) on the surface roughness and canopy brightness inhibits the capability of SIR to globally estimate the near-surface soil moisture from the value of sigma(0) for single date observations, unless the surface roughness or canopy cover conditions are accounted for. However, within given ranges of these conditions, the sigma(0) was found to be highly correlated with the soil moisture.

Seasonal patterns in the soil water balance of a Spartina marsh site at North Inlet, South Carolina, USA

USGS Publications Warehouse

Gardner, L.R.; Reeves, H.W.

2002-01-01

Time series of ground-water head at a mid-marsh site near North Inlet, South Carolina, USA can be classified into five types of forcing signatures based on the dominant water flux governing water-level dynamics during a given time interval. The fluxes that can be recognized are recharge by tides and rain, evapotranspiration (ET), seepage into the near surface soil from below, and seepage across the soil surface to balance either ET losses or seepage influxes from below. Minimal estimates for each flux can be made by multiplying the head change induced by it by the measured specific yield of the soil. These flux estimates are provide minimal values because ET fluxes resulting from this method are about half as large as those estimated from calculated potential evapotranspiration (PET), which place an upper limit on the actual ET. As evapotranspiration is not moisture-limited at this regularly submerged site, the actual ET is probably nearly equal to PET. Thus, all of the other fluxes are probably twice as large as those given by this method. Application of this method shows that recharge by tides and rain only occurs during spring and summer when ET exceeds upward seepage from below and is thereby able to draw down the water table below the marsh surface occasionally. During fall and winter, seepage of fresh water from below is largely balanced by seepage out of the soil into overlying tidal water or into sheet flow during tidal exposure. The resulting reduction in soil water salinity may thereby enhance the growth of Spartina in the following spring. ?? 2002, The Society of Wetland Scientists.

Land cover controls on depression-focused recharge: an example from southern Ontario

NASA Astrophysics Data System (ADS)

Buttle, J. M.; Greenwood, W. J.

2015-12-01

The Oak Ridges Moraine (ORM) is a critical hydrogeologic feature in southern Ontario. Although previous research has highlighted the implications of spatially-focused recharge in closed topographic depressions for regional groundwater resources, such depression-focused recharge (DFR) has not been empirically demonstrated on the ORM. Permeable surficial sands and gravels mantling much of the ORM imply that water fluxes will largely be vertical recharge rather than lateral downslope transfer into depressions. Nevertheless, lateral fluxes may occur in winter and spring, when concrete frost development encourages surface runoff of rainfall and snowmelt. The potential for DFR was examined under forest and agricultural land cover with similar soils and surficial geology. Soil water contents, soil temperatures and ground frost thickness were measured at the crest and base of closed depressions in two agricultural fields and two forest stands on permeable ORM outcrops. Recharge from late-fall to the end of spring snowmelt was estimated via 1-d water balances and surface-applied bromide tracing. Both forest and agricultural sites experienced soil freezing; however, greater soil water contents prior to freeze-up at the latter led to concrete soil frost development. This resulted in lateral movement of snowmelt and rainfall into topographic depressions and surface ponding, which did not occur in forest depressions. Water balance recharge exceeded estimates from the bromide tracer approach at all locations; nevertheless, both methods indicated DRF exceeded recharge at the depression crest in agricultural areas with little difference in forest areas. Water balance estimates suggest winter-spring DFR (1300 - 2000 mm) is 3-5× recharge on level agricultural sites. Differences in the potential for DFR between agricultural and forest land covers have important implications for the spatial variability of recharge fluxes and the quality of recharging water on the ORM.

Directional Canopy Emissivity Estimation Based on Spectral Invariants

NASA Astrophysics Data System (ADS)

Guo, M.; Cao, B.; Ren, H.; Yongming, D.; Peng, J.; Fan, W.

2017-12-01

Land surface emissivity is a crucial parameter for estimating land surface temperature from remote sensing data and also plays an important role in the physical process of surface energy and water balance from local to global scales. To our knowledge, the emissivity varies with surface type and cover. As for the vegetation, its canopy emissivity is dependent on vegetation types, viewing zenith angle and structure that changes in different growing stages. Lots of previous studies have focused on the emissivity model, but few of them are analytic and suited to different canopy structures. In this paper, a new physical analytic model is proposed to estimate the directional emissivity of homogenous vegetation canopy based on spectral invariants. The initial model counts the directional absorption in six parts: the direct absorption of the canopy and the soil, the absorption of the canopy and soil after a single scattering and after multiple scattering within the canopy-soil system. In order to analytically estimate the emissivity, the pathways of photons absorbed in the canopy-soil system are traced using the re-collision probability in Fig.1. After sensitive analysis on the above six absorptions, the initial complicated model was further simplified as a fixed mathematic expression to estimate the directional emissivity for vegetation canopy. The model was compared with the 4SAIL model, FRA97 model, FRA02 model and DART model in Fig.2, and the results showed that the FRA02 model is significantly underestimated while the FRA97 model is a little underestimated, on basis of the new model. On the contrary, the emissivity difference between the new model with the 4SAIL model and DART model was found to be less than 0.002. In general, since the new model has the advantages of mathematic expression with accurate results and clear physical meaning, the model is promising to be extended to simulate the directional emissivity for the discrete canopy in further study.

Estimation of nocturnal CO2 and N2O soil emissions from changes in surface boundary layer mass storage

NASA Astrophysics Data System (ADS)

Grant, Richard H.; Omonode, Rex A.

2018-04-01

Annual budgets of greenhouse and other trace gases require knowledge of the emissions throughout the year. Unfortunately, emissions into the surface boundary layer during stable, calm nocturnal periods are not measurable using most micrometeorological methods due to non-stationarity and uncoupled flow. However, during nocturnal periods with very light winds, carbon dioxide (CO2) and nitrous oxide (N2O) frequently accumulate near the surface and this mass accumulation can be used to determine emissions. Gas concentrations were measured at four heights (one within and three above canopy) and turbulence was measured at three heights above a mature 2.5 m maize canopy from 23 July to 10 September 2015. Nocturnal CO2 and N2O fluxes from the canopy were determined using the accumulation of mass within a 6.3 m control volume and out the top of the control volume within the nocturnal surface boundary layer. Diffusive fluxes were estimated by flux gradient method. The total accumulative and diffusive fluxes during near-calm nights (friction velocities < 0.05 ms-1) averaged 1.16 µmol m-2 s-1 CO2 and 0.53 nmol m-2 s-1 N2O. Fluxes were also measured using chambers. Daily mean CO2 fluxes determined by the accumulation method were 90 to 130 % of those determined using soil chambers. Daily mean N2O fluxes determined by the accumulation method were 60 to 80 % of that determined using soil chambers. The better signal-to-noise ratios of the chamber method for CO2 over N2O, non-stationary flow, assumed Schmidt numbers, and anemometer tilt were likely contributing reasons for the differences in chambers versus accumulated nocturnal mass flux estimates. Near-surface N2O accumulative flux measurements in more homogeneous regions and with greater depth are needed to confirm the conclusion that mass accumulation can be effectively used to estimate soil emissions during nearly calm nights.

Implications of climate change for evaporation from bare soils in a Mediterranean environment.

PubMed

Aydin, Mehmet; Yano, Tomohisa; Evrendilek, Fatih; Uygur, Veli

2008-05-01

The purpose of this study was to predict quantitative changes in evaporation from bare soils in the Mediterranean climate region of Turkey in response to the projections of a regional climate model developed in Japan (hereafter RCM). Daily RCM data for the estimation of reference evapotranspiration (ETr) and soil evaporation were obtained for the periods of 1994--2003 and 2070--2079. Potential evaporation (Ep) from bare soils was calculated using the Penman-Monteith equation with a surface resistance of zero. Simulation of actual soil evaporation (Ea) was carried out using Aydin model (Aydin et al., Ecological Modelling 182:91-105, 2005) combined with Aydin and Uygur (2006, A model for estimating soil water potential of bare fields. In Proceedings of the 18th International Soil Meeting (ISM) on Soils Sustaining Life on Earth, Managing Soil and Technology, Sanliurfa, 477-480pp.) model of predicting soil water potential at the top surface layer of a bare soil, after performances of Aydin model (R2 = 94.0%) and Aydin and Uygur model (R2 = 97.6) were tested. The latter model is based on the relations among potential soil evaporation, hydraulic diffusivity, and soil wetness, with some simplified assumptions. Input parameters of the model are simple and easily obtainable such as climatic parameters used to compute the potential soil evaporation, average diffusivity for the drying soil, and volumetric water content at field capacity. The combination of Aydin and Aydin and Uygur models appeared to be useful in estimating water potential of soils and Ea from bare soils, with only a few parameters. Unlike ETr and Ep projected to increase by 92 and 69 mm (equivalent to 8.0 and 7.3% increases) due to the elevated evaporative demand of the atmosphere, respectively, Ea from bare soils is projected to reduce by 50 mm (equivalent to a 16.5% decrease) in response to a decrease in rainfall by 46% in the Mediterranean region of Turkey by the 2070s predicted by RCM, and consequently, to decreased soil wetness in the future.

Soil Moisture Active Passive (SMAP) Mission Level 4 Surface and Root Zone Soil Moisture (L4_SM) Product Specification Document

NASA Technical Reports Server (NTRS)

Reichle, Rolf H.; Ardizzone, Joseph V.; Kim, Gi-Kong; Lucchesi, Robert A.; Smith, Edmond B.; Weiss, Barry H.

2015-01-01

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project. The Soil Moisture Active Passive (SMAP) mission will enhance the accuracy and the resolution of space-based measurements of terrestrial soil moisture and freeze-thaw state. SMAP data products will have a noteworthy impact on multiple relevant and current Earth Science endeavors. These include: Understanding of the processes that link the terrestrial water, the energy and the carbon cycles, Estimations of global water and energy fluxes over the land surfaces, Quantification of the net carbon flux in boreal landscapes Forecast skill of both weather and climate, Predictions and monitoring of natural disasters including floods, landslides and droughts, and Predictions of agricultural productivity. To provide these data, the SMAP mission will deploy a satellite observatory in a near polar, sun synchronous orbit. The observatory will house an L-band radiometer that operates at 1.40 GHz and an L-band radar that operates at 1.26 GHz. The instruments will share a rotating reflector antenna with a 6 meter aperture that scans over a 1000 km swath.

A New Scheme for Considering Soil Water-Heat Transport Coupling Based on Community Land Model: Model Description and Preliminary Validation

NASA Astrophysics Data System (ADS)

Wang, Chenghai; Yang, Kai

2018-04-01

Land surface models (LSMs) have developed significantly over the past few decades, with the result that most LSMs can generally reproduce the characteristics of the land surface. However, LSMs fail to reproduce some details of soil water and heat transport during seasonal transition periods because they neglect the effects of interactions between water movement and heat transfer in the soil. Such effects are critical for a complete understanding of water-heat transport within a soil thermohydraulic regime. In this study, a fully coupled water-heat transport scheme (FCS) is incorporated into the Community Land Model (version 4.5) to replaces its original isothermal scheme, which is more complete in theory. Observational data from five sites are used to validate the performance of the FCS. The simulation results at both single-point and global scale show that the FCS improved the simulation of soil moisture and temperature. FCS better reproduced the characteristics of drier and colder surface layers in arid regions by considering the diffusion of soil water vapor, which is a nonnegligible process in soil, especially for soil surface layers, while its effects in cold regions are generally inverse. It also accounted for the sensible heat fluxes caused by liquid water flow, which can contribute to heat transfer in both surface and deep layers. The FCS affects the estimation of surface sensible heat (SH) and latent heat (LH) and provides the details of soil heat and water transportation, which benefits to understand the inner physical process of soil water-heat migration.

Soil Water Balance and Vegetation Dynamics in two Contrasting Water-limited Mediterranean Ecosystems on Sardinia, Italy

NASA Astrophysics Data System (ADS)

Montaldo, N.; Albertson, J. D.; Corona, R.

2011-12-01

Water limited conditions strongly impacts soil and vegetation dynamics in Mediterranean regions, which are commonly heterogeneous ecosystems, characterized by inter-annual rainfall variability, topography variability and contrasting plant functional types (PFTs) competing for water use. Mediterranean regions are characterized by two main ecosystems, grassland and woodland, which for both natural and anthropogenic causes can grow in soils with different characteristics, highly impacting water resources. Water resources and forestal planning need a deep understanding of the dynamics between PFTs, soil and atmosphere and their impacts on water and CO2 distributions of these two main ecosystems. The first step is the monitoring of land surface fluxes, soil moisture, and vegetation dynamics of the two contrasting ecosystems. Moreover, due to the large percentage of soils with low depth (< 50 cm), and due to the quick hydrologic answer to atmospheric forcing in these soils, there is also the need to understand the impact of the soil depth in the vegetation dynamics, and make measurements in these types of soils. Sardinia island is a very interesting and representative region of Mediterranean ecosystems. It is low urbanized, and is not irrigated, except some plan areas close to the main cities where main agricultural activities are concentrated. The case study sites are within the Flumendosa river basin on Sardinia. Two sites, both in the Flumendosa river and with similar height a.s.l., are investigated. The distance between the sites is around 4 km but the first is a typically grass site located on an alluvial plan valley with a soil depth more than 2m, while the second site is a patchy mixture of Mediterranean vegetation types Oaks, creepers of the wild olive trees and C3 herbaceous species and the soil thickness varies from 15-40 cm, bounded from below by a rocky layer of basalt, partially fractured. In both sites land-surface fluxes and CO2 fluxes are estimated by eddy correlation technique based micrometeorological towers. Soil moisture profiles were also continuously estimated using water content reflectometers and gravimetric method, and periodically leaf area index PFTs are estimated during the Spring-Summer 2005. The following objectives are addressed:1) pointing out the dynamics of land surface fluxes, soil moisture, CO2 and vegetation cover for two contrasting water-limited ecosystems; 2) assess the impact of the soil depth and type on the CO2 and water balance dynamics. For reaching the objectives an ecohydrologic model is also successfully used and applied to the case studies. It couples a vegetation dynamic model, which computes the change in biomass over time for the PFTs, and a 3-component (bare soil, grass and woody vegetation) land surface model.

Development of a Distributed Source Containment Transport, Transformation, and Fate (CTT&F) Sub-Model for Military Installations

DTIC Science & Technology

2007-08-01

includes soil erodibility terms from the Universal Soil Lass Equation ( USLE ) for estimating the overland sediment transport capacity (for both the x and y...q = unit flow rate of water = va h [L2/T] vc = critical velocity for erosion overland [L/T] K = USLE soil erodibility factor C = USLE soil ...cover factor P = USLE soil management practice factor Be = width of eroding surface in flow direction [L]. In channels, sediment particles can be

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

NASA Astrophysics Data System (ADS)

Gupta, Manika; Bolten, John; Lakshmi, Venkat

2016-04-01

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

Evaluation of a bioluminescence method, contact angle measurements and topography for testing the cleanability of plastic surfaces under laboratory conditions

NASA Astrophysics Data System (ADS)

Redsven, I.; Kymäläinen, H.-R.; Pesonen-Leinonen, E.; Kuisma, R.; Ojala-Paloposki, T.; Hautala, M.; Sjöberg, A.-M.

2007-04-01

Detection of adenosine triphosphate (ATP) by bioluminescence is used, for instance, in the food industry and in hospitals to assess the hygiene status of surfaces. The aim of this laboratory study was to investigate the feasibility of the ATP method for estimating the cleanability of resilient floor coverings from biological soil. The surfaces were worn using a Soiling and Wearing Drum Tester, and soiled and cleaned with an Erichsen Washability and Scrubbing Resistance Tester. In the laboratory test carried out with the bioluminescence method, most of the new and worn floor coverings that were biologically soiled were cleaned efficiently. According to this study, the semiquantitative ATP screening method can be used for hygiene monitoring of flooring materials. No correlation was found between cleanability and contact angles or surface topography measured using a profilometer. However, by revealing local irregularities and damage on surfaces, scanning electron micrographs appeared useful in explaining differences in cleanability.

Soil surface CO2 flux in a boreal black spruce fire chronosequence

NASA Astrophysics Data System (ADS)

Wang, Chuankuan; Bond-Lamberty, Ben; Gower, Stith T.

2003-02-01

Understanding the effects of wildfire on the carbon (C) cycle of boreal forests is essential to quantifying the role of boreal forests in the global carbon cycle. Soil surface CO2 flux (Rs), the second largest C flux in boreal forests, is directly and indirectly affected by fire and is hypothesized to change during forest succession following fire. The overall objective of this study was to measure and model Rs for a black spruce (Picea mariana [Mill.] BSP) postfire chronosequence in northern Manitoba, Canada. The experiment design was a nested factorial that included two soil drainage classes (well and poorly drained) × seven postfire aged stands. Specific objectives were (1) to quantify the relationship between Rs and soil temperature for different aged boreal black spruce forests in well-drained and poorly drained soil conditions, (2) to examine Rs dynamics along postfire successional stands, and (3) to estimate annual soil surface CO2 flux for these ecosystems. Soil surface CO2 flux was significantly affected by soil drainage class (p = 0.014) and stand age (p = 0.006). Soil surface CO2 flux was positively correlated to soil temperature (R2 = 0.78, p < 0.001), but different models were required for each drainage class × aged stand combination. Soil surface CO2 flux was significantly greater at the well-drained than the poorly drained stands (p = 0.007) during growing season. Annual soil surface CO2 flux for the 1998, 1995, 1989, 1981, 1964, 1930, and 1870 burned stands averaged 226, 412, 357, 413, 350, 274, and 244 g C m-2 yr-1 in the well-drained stands and 146, 380, 300, 303, 256, 233, and 264 g C m-2 yr-1 in the poorly drained stands. Soil surface CO2 flux during the winter (from 1 November to 30 April) comprised from 5 to 19% of the total annual Rs. We speculate that the smaller soil surface CO2 flux in the recently burned than the older stands is mainly caused by decreased root respiration.

Soil surface CO2 flux in a boreal black spruce fire chronosequence

NASA Astrophysics Data System (ADS)

Wang, Chuankuan; Bond-Lamberty, Ben; Gower, Stith T.

2002-02-01

Understanding the effects of wildfire on the carbon (C) cycle of boreal forests is essential to quantifying the role of boreal forests in the global carbon cycle. Soil surface CO2 flux (Rs), the second largest C flux in boreal forests, is directly and indirectly affected by fire and is hypothesized to change during forest succession following fire. The overall objective of this study was to measure and model Rs for a black spruce (Picea mariana [Mill.] BSP) postfire chronosequence in northern Manitoba, Canada. The experiment design was a nested factorial that included two soil drainage classes (well and poorly drained) × seven postfire aged stands. Specific objectives were (1) to quantify the relationship between Rs and soil temperature for different aged boreal black spruce forests in well-drained and poorly drained soil conditions, (2) to examine Rs dynamics along postfire successional stands, and (3) to estimate annual soil surface CO2 flux for these ecosystems. Soil surface CO2 flux was significantly affected by soil drainage class (p = 0.014) and stand age (p = 0.006). Soil surface CO2 flux was positively correlated to soil temperature (R2 = 0.78, p < 0.001), but different models were required for each drainage class × aged stand combination. Soil surface CO2 flux was significantly greater at the well-drained than the poorly drained stands (p = 0.007) during growing season. Annual soil surface CO2 flux for the 1998, 1995, 1989, 1981, 1964, 1930, and 1870 burned stands averaged 226, 412, 357, 413, 350, 274, and 244 g C m-2 yr-1 in the well-drained stands and 146, 380, 300, 303, 256, 233, and 264 g C m-2 yr-1 in the poorly drained stands. Soil surface CO2 flux during the winter (from 1 November to 30 April) comprised from 5 to 19% of the total annual Rs. We speculate that the smaller soil surface CO2 flux in the recently burned than the older stands is mainly caused by decreased root respiration.

Above- and below-ground methane fluxes and methanotrophic activity in a landfill-cover soil

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

Schroth, M.H., E-mail: martin.schroth@env.ethz.ch; Eugster, W.; Gomez, K.E.

2012-05-15

Highlights: Black-Right-Pointing-Pointer We quantify above- and below-ground CH{sub 4} fluxes in a landfill-cover soil. Black-Right-Pointing-Pointer We link methanotrophic activity to estimates of CH{sub 4} loading from the waste body. Black-Right-Pointing-Pointer Methane loading and emissions are highly variable in space and time. Black-Right-Pointing-Pointer Eddy covariance measurements yield largest estimates of CH{sub 4} emissions. Black-Right-Pointing-Pointer Potential methanotrophic activity is high at a location with substantial CH{sub 4} loading. - Abstract: Landfills are a major anthropogenic source of the greenhouse gas methane (CH{sub 4}). However, much of the CH{sub 4} produced during the anaerobic degradation of organic waste is consumed by methanotrophic microorganismsmore » during passage through the landfill-cover soil. On a section of a closed landfill near Liestal, Switzerland, we performed experiments to compare CH{sub 4} fluxes obtained by different methods at or above the cover-soil surface with below-ground fluxes, and to link methanotrophic activity to estimates of CH{sub 4} ingress (loading) from the waste body at selected locations. Fluxes of CH{sub 4} into or out of the cover soil were quantified by eddy-covariance and static flux-chamber measurements. In addition, CH{sub 4} concentrations at the soil surface were monitored using a field-portable FID detector. Near-surface CH{sub 4} fluxes and CH{sub 4} loading were estimated from soil-gas concentration profiles in conjunction with radon measurements, and gas push-pull tests (GPPTs) were performed to quantify rates of microbial CH{sub 4} oxidation. Eddy-covariance measurements yielded by far the largest and probably most representative estimates of overall CH{sub 4} emissions from the test section (daily mean up to {approx}91,500 {mu}mol m{sup -2} d{sup -1}), whereas flux-chamber measurements and CH{sub 4} concentration profiles indicated that at the majority of locations the cover soil was a net sink for atmospheric CH{sub 4} (uptake up to -380 {mu}mol m{sup -2} d{sup -1}) during the experimental period. Methane concentration profiles also indicated strong variability in CH{sub 4} loading over short distances in the cover soil, while potential methanotrophic activity derived from GPPTs was high (v{sub max} {approx} 13 mmol L{sup -1}(soil air) h{sup -1}) at a location with substantial CH{sub 4} loading. Our results provide a basis to assess spatial and temporal variability of CH{sub 4} dynamics in the complex terrain of a landfill-cover soil.« less

Global Soil Moisture from the Aquarius/SAC-D Satellite: Description and Initial Assessment

NASA Technical Reports Server (NTRS)

Bindlish, Rajat; Jackson, Thomas; Cosh, Michael; Zhao, Tianjie; O'Neil, Peggy

2015-01-01

Aquarius satellite observations over land offer a new resource for measuring soil moisture from space. 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 focused on using only the radiometer data because of the extensive heritage of passive microwave retrieval of soil moisture. The single channel algorithm (SCA) was implemented using the Aquarius observations to estimate surface soil moisture. Aquarius radiometer observations from three beams (after bias/gain modification) along with the National Centers for Environmental Prediction model forecast surface temperatures were then used to retrieve soil moisture. Ancillary data inputs required for using the SCA are vegetation water content, land surface temperature, and several soil and vegetation parameters based on land cover classes. The resulting global spatial patterns of soil moisture were consistent with the precipitation climatology and with soil moisture from other satellite missions (Advanced Microwave Scanning Radiometer for the Earth Observing System and Soil Moisture Ocean Salinity). Initial assessments were performed using in situ observations from the U.S. Department of Agriculture Little Washita and Little River watershed soil moisture networks. Results showed good performance by the algorithm for these land surface conditions for the period of August 2011-June 2013 (rmse = 0.031 m(exp 3)/m(exp 3), Bias = -0.007 m(exp 3)/m(exp 3), and R = 0.855). This radiometer-only soil moisture product will serve as a baseline for continuing research on both active and combined passive-active soil moisture algorithms. The products are routinely available through the National Aeronautics and Space Administration data archive at the National Snow and Ice Data Center.

Assessment of radioactive materials and heavy metals in the surface soil around uranium mining area of Tongliao, China.

PubMed

Haribala; Hu, Bitao; Wang, Chengguo; Gerilemandahu; Xu, Xiao; Zhang, Shuai; Bao, Shanhu; Li, Yuhong

2016-08-01

Natural and artificial radionuclides and heavy metals in the surface soil of the uranium mining area of Tongliao, China, were measured using gamma spectrometry, flame atomic absorption spectrophotometry, graphite furnace atomic absorption spectrophotometry and microwave dissolution atomic fluorescence spectrometry respectively. The estimated average activity concentrations of (238)U, (232)Th, (226)Ra, (40)K and (137)Cs are 27.53±16.01, 15.89±5.20, 12.64±4.27, 746.84±38.24 and 4.23±4.76Bq/kg respectively. The estimated average absorbed dose rate in the air and annual effective dose rate are 46.58±5.26nGy/h and 57.13±6.45μSv, respectively. The radium equivalent activity, external and internal hazard indices were also calculated and their mean values are within the acceptable limits. The heavy metal concentrations of Pb, Cd, Cu, Zn, Hg and As from the surface soil were measured and their health risks were then determined. Although the content of Cd is much higher than the average background in China, its non-cancer and cancer risk indices are all within the acceptable ranges. These calculated hazard indices to estimate the potential radiological health risk in soil and the dose rate are well below their permissible limit. In addition the correlations between the radioactivity concentrations of the radionuclides and the heavy metals in soil were determined by the Pearson linear coefficient. Copyright © 2016 Elsevier Inc. All rights reserved.

Stream Flow Prediction by Remote Sensing and Genetic Programming

NASA Technical Reports Server (NTRS)

Chang, Ni-Bin

2009-01-01

A genetic programming (GP)-based, nonlinear modeling structure relates soil moisture with synthetic-aperture-radar (SAR) images to present representative soil moisture estimates at the watershed scale. Surface soil moisture measurement is difficult to obtain over a large area due to a variety of soil permeability values and soil textures. Point measurements can be used on a small-scale area, but it is impossible to acquire such information effectively in large-scale watersheds. This model exhibits the capacity to assimilate SAR images and relevant geoenvironmental parameters to measure soil moisture.

Estimation of Regional Evapotranspiration Using Remotely Sensed Land Surface Temperature. Part 2: Application of Equilibrium Evaporation Model to Estimate Evapotranspiration by Remote Sensing Technique. [Japan

NASA Technical Reports Server (NTRS)

Kotoda, K.; Nakagawa, S.; Kai, K.; Yoshino, M. M.; Takeda, K.; Seki, K.

1985-01-01

In a humid region like Japan, it seems that the radiation term in the energy balance equation plays a more important role for evapotranspiration then does the vapor pressure difference between the surface and lower atmospheric boundary layer. A Priestley-Taylor type equation (equilibrium evaporation model) is used to estimate evapotranspiration. Net radiation, soil heat flux, and surface temperature data are obtained. Only temperature data obtained by remotely sensed techniques are used.

Partitioning Evapotranspiration in Semiarid Grassland and Shrubland Ecosystems Using Diurnal Surface Temperature Variation

NASA Technical Reports Server (NTRS)

Moran, M. Susan; Scott, Russell L.; Keefer, Timothy O.; Paige, Ginger B.; Emmerich, William E.; Cosh, Michael H.; O'Neill, Peggy E.

2007-01-01

The encroachment of woody plants in grasslands across the Western U.S. will affect soil water availability by altering the contributions of evaporation (E) and transpiration (T) to total evapotranspiration (ET). To study this phenomenon, a network of flux stations is in place to measure ET in grass- and shrub-dominated ecosystems throughout the Western U.S. A method is described and tested here to partition the daily measurements of ET into E and T based on diurnal surface temperature variations of the soil and standard energy balance theory. The difference between the mid-afternoon and pre-dawn soil surface temperature, termed Apparent Thermal Inertia (I(sub A)), was used to identify days when E was negligible, and thus, ET=T. For other days, a three-step procedure based on energy balance equations was used to estimate Qe contributions of daily E and T to total daily ET. The method was tested at Walnut Gulch Experimental Watershed in southeast Arizona based on Bowen ratio estimates of ET and continuous measurements of surface temperature with an infrared thermometer (IRT) from 2004- 2005, and a second dataset of Bowen ratio, IRT and stem-flow gage measurements in 2003. Results showed that reasonable estimates of daily T were obtained for a multi-year period with ease of operation and minimal cost. With known season-long daily T, E and ET, it is possible to determine the soil water availability associated with grass- and shrub-dominated sites and better understand the hydrologic impact of regional woody plant encroachment.

Organic Carbon and Nitrogen Storages of Soils Overlying Yedoma Deposits in the Lena River Delta

NASA Astrophysics Data System (ADS)

Zubrzycki, Sebastian; Kutzbach, Lars; Desiatkin, Aleksei; Pfeiffer, Eva-Maria

2016-04-01

The Lena River Delta (LRD) is located in northeast Siberia and extends over a soil covered area of around 21,500 km2. LRD likely stores more than half of the entire soil organic carbon (SOC) mass stored in deltas affected by permafrost. LRD consists of several geomorphic units. Recent studies showed that the spatially dominating Holocene units of the LRD (61 % of the area) store around 240 Tg of SOC and 12 Tg of nitrogen (N) within the first meter of ground. These units are a river terrace dominated by wet sedge polygons and the active floodplains. About 50 % of these reported storages are located in the perennially frozen ground below 50 cm depth and are excluded from intense biogeochemical exchange with the atmosphere today. However, these storages are likely to be mineralised in near future due to the projected temperature increases in this region. A substantial part of the LRD (1,712 km2) belongs to the so-called Yedoma Region, which formed during the Late Pleistocene. This oldest unit of the LRD is characterised by extensive plains incised by thermo-erosional valleys and large thermokarst depressions. Such depressions are called Alases and cover around 20 % of the area. Yedoma deposits in the LDR are known to store high amounts of SOC. However, within the LRD no detailed spatial studies on SOC and N in the soils overlying Yedoma and thermokarst depressions were carried out so far. We present here our "investigation in progress" on soils in these landscape units of the LRD. Our first estimates, based on 69 pedons sampled in 2008, show that the mean SOC stocks for the upper 30 cm of soils on both units were estimated at 13.0 kg m2 ± 4.8 kg m2 on the Yedoma surfaces and at 13.1 kg m2 ± 3.8 kg m2 in the Alases. The stocks of N were estimated at 0.69 kg m2 ± 0.25 kg m2and at 0.70 kg m2 ± 0.18 kg m2 on the Yedoma surfaces and in the Alases, respectively. The estimated SOC and N pools for the depth of 30 cm within the investigated part of the LRD add to 20.9 Tg and 1.1 Tg, respectively. The Yedoma surfaces (1,313 km2) store 17.1 ± 6.3 Tg SOC and 0.9 ± 0.3 Tg N, whereas the Alases (287 km2) store 3.8 ± 1.1 Tg SOC and 0.2 ± 0.05 Tg N within the investigated depth of 30 cm. Further analyses of the soil core material collected in 2013 will provide SOC and N pool estimates for a depth of 100 cm including both, the seasonally active layer and the perennially frozen ground. With continuing advanced analyses of an available digital elevation model, slopes will be designated with their extents and inclinations since the planar extents of slopes derived from satellite imagery do not correspond to the actual slope soil surface area, which is vital for spatial SOC and N storage calculations as well as trace gas release estimates. The actual soil surface area of slopes will be calculated prior to result extrapolations.

Organic Carbon Deposits of Soils Overlying the Ice Complex in the Lena River Delta

NASA Astrophysics Data System (ADS)

Zubrzycki, Sebastian; Pfeiffer, Eva-Maria; Kutzbach, Lars; Desiatkin, Aleksei

2017-04-01

The Lena River Delta (LRD) is located in northeast Siberia and extends over a soil covered area of around 21,500 km2. LRD likely stores more than half of the entire soil organic carbon (SOC) mass stored in deltas affected by permafrost. LRD consists of several geomorphic units. Recent studies showed that the spatially dominating Holocene units of the LRD (61 % of the area) store around 240 Tg of SOC and 12 Tg of nitrogen (N) within the first meter of ground. These units are a river terrace dominated by wet sedge polygons and the active floodplains. About 50 % of these reported storages are located in the perennially frozen ground below 50 cm depth and are excluded from intense biogeochemical exchange with the atmosphere today. However, these storages are likely to be mineralized in near future due to the projected temperature increases in this region. A substantial part of the LRD (1,712 km2) belongs to the so-called Ice Complex (Yedoma) Region, which formed during the Late Pleistocene. This oldest unit of the LRD is characterized by extensive plains incised by thermo-erosional valleys and large thermokarst depressions. Such depressions are called Alases and cover around 20 % of the area. Ice Complex deposits in the LDR are known to store high amounts of SOC. However, within the LRD no detailed spatial studies on SOC and N in the soils overlying Ice Complex and thermokarst depressions were carried out so far. We present here our "investigation in progress" on soils in these landscape units of the LRD. Our first estimates, based on 69 pedons sampled in 2008, show that the mean SOC stocks for the upper 30 cm of soils on both units were estimated at 13.0 kg m2 ± 4.8 kg m2 on the Ice Complex surfaces and at 13.1 kg m2 ± 3.8 kg m2 in the Alases. The stocks of N were estimated at 0.69 kg m2 ± 0.25 kg m2 and at 0.70 kg m2 ± 0.18 kg m2 on the Ice Complex surfaces and in the Alases, respectively. The estimated SOC and N pools for the depth of 30 cm within the investigated part of the LRD add to 20.9 Tg and 1.1 Tg, respectively. The Ice Complex surfaces (1,313 km2) store 17.1 ± 6.3 Tg SOC and 0.9 ± 0.3 Tg N, whereas the Alases (287 km2) store 3.8 ± 1.1 Tg SOC and 0.2 ± 0.05 Tg N within the investigated depth of 30 cm. Further analyses of the soil core material collected in 2015 will provide SOC and N pool estimates for a depth of 100 cm including both, the seasonally active layer and the perennially frozen ground. With continuing advanced analyses of an available digital elevation model, slopes will be designated with their extents and inclinations since the planar extents of slopes derived from satellite imagery do not correspond to the actual slope soil surface area, which is vital for spatial SOC and N storage calculations as well as trace gas release estimates. The actual soil surface area of slopes will be calculated prior to result extrapolations.

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.

Soil conservation applications with C-band SAR

NASA Technical Reports Server (NTRS)

Brisco, B.; Brown, R. J.; Naunheimer, J.; Bedard, D.

1992-01-01

Soil conservation programs are becoming more important as the growing human population exerts greater pressure on this non-renewable resource. Indeed, soil degradation affects approximately 10 percent of Canada's agricultural land with an estimated loss of 6,000 hectares of topsoil annually from Ontario farmland alone. Soil loss not only affects agricultural productivity but also decreases water quality and can lead to siltation problems. Thus, there is a growing demand for soil conservation programs and a need to develop an effective monitoring system. Topography and soil type information can easily be handled within a geographic information system (GIS). Information about vegetative cover type and surface roughness, which both experience considerable temporal change, can be obtained from remote sensing techniques. For further development of the technology to produce an operational soil conservation monitoring system, an experiment was conducted in Oxford County, Ontario which investigated the separability of fall surface cover type using C-band Synthetic Aperture Radar (SAR) data.

Evaluation of gravimetric ground truth soil moisture data collected for the agricultural soil moisture experiment, 1978 Colby, Kansas, aircraft mission

NASA Technical Reports Server (NTRS)

Arya, L. M.; Phinney, D. E. (Principal Investigator)

1980-01-01

Soil moisture data acquired to support the development of algorithms for estimating surface soil moisture from remotely sensed backscattering of microwaves from ground surfaces are presented. Aspects of field uniformity and variability of gravimetric soil moisture measurements are discussed. Moisture distribution patterns are illustrated by frequency distributions and contour plots. Standard deviations and coefficients of variation relative to degree of wetness and agronomic features of the fields are examined. Influence of sampling depth on observed moisture content an variability are indicated. For the various sets of measurements, soil moisture values that appear as outliers are flagged. The distribution and legal descriptions of the test fields are included along with examinations of soil types, agronomic features, and sampling plan. Bulk density data for experimental fields are appended, should analyses involving volumetric moisture content be of interest to the users of data in this report.

Deriving the suction stress of unsaturated soils from water retention curve, based on wetted surface area in pores

NASA Astrophysics Data System (ADS)

Greco, Roberto; Gargano, Rudy

2016-04-01

The evaluation of suction stress in unsaturated soils has important implications in several practical applications. Suction stress affects soil aggregate stability and soil erosion. Furthermore, the equilibrium of shallow unsaturated soil deposits along steep slopes is often possible only thanks to the contribution of suction to soil effective stress. Experimental evidence, as well as theoretical arguments, shows that suction stress is a nonlinear function of matric suction. The relationship expressing the dependence of suction stress on soil matric suction is usually indicated as Soil Stress Characteristic Curve (SSCC). In this study, a novel equation for the evaluation of the suction stress of an unsaturated soil is proposed, assuming that the exchange of stress between soil water and solid particles occurs only through the part of the surface of the solid particles which is in direct contact with water. The proposed equation, based only upon geometric considerations related to soil pore-size distribution, allows to easily derive the SSCC from the water retention curve (SWRC), with the assignment of two additional parameters. The first parameter, representing the projection of the external surface area of the soil over a generic plane surface, can be reasonably estimated from the residual water content of the soil. The second parameter, indicated as H0, is the water potential, below which adsorption significantly contributes to water retention. For the experimental verification of the proposed approach such a parameter is considered as a fitting parameter. The proposed equation is applied to the interpretation of suction stress experimental data, taken from the literature, spanning over a wide range of soil textures. The obtained results show that in all cases the proposed relationships closely reproduces the experimental data, performing better than other currently used expressions. The obtained results also show that the adopted values of the parameter H0, allowing for a good fitting of the experimental data, are in agreement with the values of water potential marking the limit between capillary and adsorptive soil water retention, which can be estimated from the shape of the water retention curve. Therefore, with the proposed approach, at least in principle it is possible to derive the SSSC directly from the knowledge of the SWRC.

Soil Moisture Active Passive (SMAP) Mission Level 4 Carbon (L4_C) Product Specification Document

NASA Technical Reports Server (NTRS)

Glassy, Joe; Kimball, John S.; Jones, Lucas; Reichle, Rolf H.; Ardizzone, Joseph V.; Kim, Gi-Kong; Lucchesi, Robert A.; Smith, Edmond B.; Weiss, Barry H.

2015-01-01

This is the Product Specification Document (PSD) for Level 4 Surface and Root Zone Soil Moisture (L4_SM) data for the Science Data System (SDS) of the Soil Moisture Active Passive (SMAP) project. The L4_SM data product provides estimates of land surface conditions based on the assimilation of SMAP observations into a customized version of the NASA Goddard Earth Observing System, Version 5 (GEOS-5) land data assimilation system (LDAS). This document applies to any standard L4_SM data product generated by the SMAP Project.

Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief

NASA Astrophysics Data System (ADS)

Cowley, Garret S.; Niemann, Jeffrey D.; Green, Timothy R.; Seyfried, Mark S.; Jones, Andrew S.; Grazaitis, Peter J.

2017-02-01

Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil data to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model performs well at catchments with low topographic relief (≤124 m), but it has not been applied to regions with larger ranges of elevation. Large relief can produce substantial variations in precipitation and potential evapotranspiration (PET), which might affect the fine-resolution patterns of soil moisture. In this research, simple methods to downscale temporal average precipitation and PET are developed and included in the EMT+VS model, and the effects of spatial variations in these variables on the surface soil moisture estimates are investigated. The methods are tested against ground truth data at the 239 km2 Reynolds Creek watershed in southern Idaho, which has 1145 m of relief. The precipitation and PET downscaling methods are able to capture the main features in the spatial patterns of both variables. The space-time Nash-Sutcliffe coefficients of efficiency of the fine-resolution soil moisture estimates improve from 0.33 to 0.36 and 0.41 when the precipitation and PET downscaling methods are included, respectively. PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling likely because the PET pattern is more persistent through time, and thus more predictable, than the precipitation pattern.

Multifrequency passive microwave observations of soil moisture in an arid rangeland environment

NASA Technical Reports Server (NTRS)

Jackson, T. J.; Schmugge, T. J.; Parry, R.; Kustas, W. P.; Ritchie, J. C.; Shutko, A. M.; Khaldin, A.; Reutov, E.; Novichikhin, E.; Liberman, B.

1992-01-01

A cooperative experiment was conducted by teams from the U.S. and U.S.S.R. to evaluate passive microwave instruments and algorithms used to estimate surface soil moisture. Experiments were conducted as part of an interdisciplinary experiment in an arid rangeland watershed located in the southwest United States. Soviet microwave radiometers operating at wavelengths of 2.25, 21 and 27 cm were flown on a U.S. aircraft. Radio frequency interference limited usable data to the 2.25 and 21 cm systems. Data have been calibrated and compared to ground observations of soil moisture. These analyses showed that the 21 cm system could produce reliable and useful soil moisture information and that the 2.25 cm system was of no value for soil moisture estimation in this experiment.

An Innovative Method for Estimating Soil Retention at a ...

EPA Pesticide Factsheets

Planning for a sustainable future should include an accounting of services currently provided by ecosystems such as erosion control. Retention of soil improves fertility, increases water retention, and decreases sedimentation in streams and rivers. Landscapes patterns that facilitate these services could help reduce costs for flood control, dredging of reservoirs and waterways, while maintaining habitat for fish and other species important to recreational and tourism industries. Landscape scale geospatial data available for the continental United States was leveraged to estimate sediment erosion (RUSLE-based, Renard, et al. 1997) employing recent geospatial techniques of sediment delivery ratio (SDR) estimation (Cavalli, et al. 2013). The approach was designed to derive a quantitative approximation of the ecological services provided by vegetative cover, management practices, and other surface features with respect to protecting soils from the erosion processes of detachment, transport, and deposition. Quantities of soil retained on the landscape and potential erosion for multiple land cover scenarios relative to current (NLCD 2011) conditions were calculated for each calendar month, and summed to yield annual estimations at a 30-meter grid cell. Continental-scale data used included MODIS NDVI data (2000-2014) to estimate monthly USLE C-factors, gridded soil survey geographic (gSSURGO) soils data (annual USLE K factor), PRISM rainfall data (monthly USLE

Estimating Sources and Sinks of Methane from Soils in the Contiguous United States (CONUS)

NASA Astrophysics Data System (ADS)

Shu, S.; Jain, A. K.; Kheshgi, H. S.

2017-12-01

The global methane (CH4) budget estimated based on state-of-the-art models remains highly uncertain. Sources and sinks of CH4 from soils, including wetlands, are the most important source of uncertainty. Soils are estimated to account for about 45% of global CH4 emissions. At the same time oxidation of CH4 by soils is a significant sink, representing about 10% of the total sink. However, most regional and global scale modeling studies of soil CH4 fluxes have ignored the sink through soil oxidation and the source of CH4 emissions from the wet soils with shallow water tables. In this study, we link a bottom-up soil gas diffusion and CH4 biogeochemistry model to a land surface model, ISAM, to calculate the sources, emissions from both wetlands and non-wetlands, and sinks, soil oxidation, of CH4 from soils for the CONUS over the period 1900-2100. The newly developed soil CH4 model framework consists of a gas diffusion module with the vertically resolved soil hydrology (depth up to 3.5 m soil) and soil organic carbon (SOC) and CH4 biogeochemistry module. SOC profile is estimated by modeling vertical soil mixing and thus can represent the deep SOC content and estimate CH4 production from the deep non-wetland soil. For the diffusion calculations, we separately consider both the dissolved and gaseous O2 and CH4 at each soil layer. For CH4 biogeochemistry, we parameterize the production, soil oxidation, ebullition and aerenchyma transportation of CH4 for both seasonal/permanent wetland and wet soil. The SWAMP inundated fraction dataset with 8-day temporal resolution is incorporated to prescribe the extent of permanent and seasonal wetland extent for the recent decade. The model is first evaluated using a compilation of published CH4 site measurement data for CONUS. We then perform two different model experiments: 1) forced by the CRUNCEP climate data from 1900 to 2010 to estimate the contemporary CH4 emission and 2) forced by a climate projection of IPCC's highest representative concentration pathway (RCP8.5) from 2011 to 2100. Our study shows that soil oxidation has an important role attenuating the estimated natural CH4 source. We also find a wetter and warmer climate affects the dry soil CH4 sink and wet soil CH4 emissions and increases the estimated CH4 source over the CONUS.

Evaluating Microbial Purification during Soil Treatment of Wastewater with Multicomponent Tracer and Surrogate Tests

USGS Publications Warehouse

Van Cuyk, S.; Siegrist, R.L.; Lowe, K.; Harvey, R.W.

2004-01-01

Soil treatment of wastewater has the potential to achieve high purification efficiency, yet the understanding and predictability of purification with respect to removal of viruses and other pathogens is limited. Research has been completed to quantify the removal of virus and bacteria through the use of microbial surrogates and conservative tracers during controlled experiments with three-dimensional pilot-scale soil treatment systems in the laboratory and during the testing of full-scale systems under field conditions. The surrogates and tracers employed included two viruses (MS-2 and PRID-1 bacteriophages), one bacterium (ice-nucleating active Pseudomonas), and one conservative tracer (bromide ion). Efforts have also been made to determine the relationship between viruses and fecal coliform bacteria in soil samples below the wastewater infiltrative surface, and the correlation between Escherichia coil concentrations measured in percolating soil solution as compared with those estimated from analyses of soil solids. The results suggest episodic breakthrough of virus and bacteria during soil treatment of wastewater and a 2 to 3 log (99-99.9%) removal of virus and near complete removal of fecal coliform bacteria during unsaturated flow through 60 to 90 cm of sandy medium. Results also suggest that the fate of fecal coliform bacteria may be indicative of that of viruses in soil media near the infiltrative surface receiving wastewater effluent. Concentrations of fecal coliform in percolating soil solution may be conservatively estimated from analysis of extracted soil solids.

Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models

NASA Technical Reports Server (NTRS)

Gillies, Robert R.; Carlson, Toby N.

1995-01-01

This study outlines a method for the estimation of regional patterns of surface moisture availability (M(sub 0)) and fractional vegetation (Fr) in the presence of spatially variable vegetation cover. The method requires relating variations in satellite-derived (NOAA, Advanced Very High Resolution Radiometer (AVHRR)) surface radiant temperature to a vegetation index (computed from satellite visible and near-infrared data) while coupling this association to an inverse modeling scheme. More than merely furnishing surface soil moisture values, the method constitues a new conceptual and practical approach for combining thermal infrared and vegetation index measurements for incorporating the derived values of M(sub 0) into hydrologic and atmospheric prediction models. Application of the technique is demonstrated for a region in and around the city of Newcastle upon Tyne situated in the northeast of England. A regional estimate of M(sub 0) is derived and is probabbly good for fractional vegetation cover up to 80% before errors in the estimated soil water content become unacceptably large. Moreover, a normalization scheme is suggested from which a nomogram, `universal triangle,' is constructed and is seen to fit the observed data well. The universal triangle also simplifies the inclusion of remotely derived M(sub 0) in hydrology and meteorological models and is perhaps a practicable step toward integrating derived data from satellite measurements in weather forecasting.

Carbon and nutrient contents in soils from the Kings River Experimental Watersheds, Sierra Nevada Mountains, California

Treesearch

D.W. Johnson; C.T. Hunsaker; D.W. Glass; B.M. Rau; B.A. Roath

2011-01-01

Soil C and nutrient contents were estimated for eight watersheds in two sites (one high elevation, Bull, and one low elevation, Providence) in the Kings River Experimental Watersheds in the western Sierra Nevada Mountains of California. Eighty-seven quantitative pits were dug to measure soil bulk density and total rock content, while three replicate surface samples...

Redistribution of pyrogenic carbon from hillslopes to stream corridors following a large montane wildfire

Treesearch

M. Francesca Cotrufo; Claudia M. Boot; Stephanie Kampf; Peter A. Nelson; Daniel J. Brogan; Tim Covino; Michelle L. Haddix; Lee H. MacDonald; Sarah Rathburn; Sandra Ryan-Burkett; Sarah Schmeer; Ed Hall

2016-01-01

Pyrogenic carbon (PyC) constitutes a significant fraction of organic carbon in most soils. However, PyC soil stocks are generally smaller than what is expected from estimates of PyC produced from fire and decomposition losses, implying that other processes cause PyC loss from soils. Surface erosion has been previously suggested as one such process. To address this,...

Estimated smoldering probability: a new tool for predicting ground fire in the organic soils on the North Carolina Coastal Plain

Treesearch

James Reardon; Gary Curcio

2011-01-01

In the Southeastern United States, fires in pocosin wetlands and other similar vegetation communities with deep organic soils are a serious concern to fire managers. Highly flammable shrubs, such as gallberry and fetterbush, and small evergreen trees, such as red and loblolly bay, create the potential for extreme surface fire behavior. Moreover, deep organic soils...

Strengths and weaknesses of temporal stability analysis for monitoring and estimating grid-mean soil moisture in a high-intensity irrigated agricultural landscape

NASA Astrophysics Data System (ADS)

Ran, Youhua; Li, Xin; Jin, Rui; Kang, Jian; Cosh, Michael H.

2017-01-01

Monitoring and estimating grid-mean soil moisture is very important for assessing many hydrological, biological, and biogeochemical processes and for validating remotely sensed surface soil moisture products. Temporal stability analysis (TSA) is a valuable tool for identifying a small number of representative sampling points to estimate the grid-mean soil moisture content. This analysis was evaluated and improved using high-quality surface soil moisture data that were acquired by a wireless sensor network in a high-intensity irrigated agricultural landscape in an arid region of northwestern China. The performance of the TSA was limited in areas where the representative error was dominated by random events, such as irrigation events. This shortcoming can be effectively mitigated by using a stratified TSA (STSA) method, proposed in this paper. In addition, the following methods were proposed for rapidly and efficiently identifying representative sampling points when using TSA. (1) Instantaneous measurements can be used to identify representative sampling points to some extent; however, the error resulting from this method is significant when validating remotely sensed soil moisture products. Thus, additional representative sampling points should be considered to reduce this error. (2) The calibration period can be determined from the time span of the full range of the grid-mean soil moisture content during the monitoring period. (3) The representative error is sensitive to the number of calibration sampling points, especially when only a few representative sampling points are used. Multiple sampling points are recommended to reduce data loss and improve the likelihood of representativeness at two scales.

Enhancing a Remote-Sensing Method for Soil Moisture by Accounting for Regional Soil, Vegetation, and Climatic Characteristics

NASA Astrophysics Data System (ADS)

Sahaar, A. S.; Niemann, J. D.

2016-12-01

Accurate knowledge of root-zone soil moisture is critical for understanding the perpetuation of droughts and managing agricultural water systems. A remote-sensing method based on optical and thermal satellite imagery has been previously proposed to estimate fine-resolution (30 m) root-zone soil moisture over large regions. This method uses Landsat imagery to calculate all the components of the surface energy balance and then calculates the evaporative fraction (Λ) as the ratio of the latent heat flux to the sum of the sensible and latent heat fluxes. Root-zone soil moisture (θ) is then estimated from an empirical relationship with Λ. A similar approach has also been proposed to estimate the degree of saturation. Previous testing of this method for a semiarid region of southeastern Colorado has shown that a single relationship between θ and Λ does not apply universally. The primary objective of this study is to evaluate the impact of regional soil, vegetation, and climatic conditions on the form and strength of the Λ- θ relationship. To accomplish this goal, a global sensitivity analysis is performed using the Extended Fourier Amplitude Sensitivity Test (FAST) and a physically-based model (Hydrus-1D) that simulates both the land-surface energy balance and soil moisture dynamics. The modeling results show that, within a given climatic region, soil characteristics are very important in determining the shape of the Λ-θ relationship, while vegetation characteristics have the largest effect on the strength of the relationship. The modeling results also indicate that the annual average rainfall, which helps determine the climatic region, has a strong effect on both the form and strength of the relationship. From this analysis, the constants that define the Λ-θ relationships are estimated using regional characteristics. This approach allows the remote-sensing method to be adapted to local conditions and has the potential to greatly improve its performance.

Remote sensing for mapping soil moisture and drainage potential in semi-arid regions: Applications to the Campidano plain of Sardinia, Italy.

PubMed

Filion, Rébecca; Bernier, Monique; Paniconi, Claudio; Chokmani, Karem; Melis, Massimo; Soddu, Antonino; Talazac, Manon; Lafortune, Francois-Xavier

2016-02-01

The aim of this study is to investigate the potential of radar (ENVISAT ASAR and RADARSAT-2) and LANDSAT data to generate reliable soil moisture maps to support water management and agricultural practice in Mediterranean regions, particularly during dry seasons. The study is based on extensive field surveys conducted from 2005 to 2009 in the Campidano plain of Sardinia, Italy. A total of 12 small bare soil fields were sampled for moisture, surface roughness, and texture values. From field scale analysis with ENVISAT ASAR (C-band, VV polarized, descending mode, incidence angle from 15.0° to 31.4°), an empirical model for estimating bare soil moisture was established, with a coefficient of determination (R(2)) of 0.85. LANDSAT TM5 images were also used for soil moisture estimation using the TVX slope (temperature/vegetation index), and in this case the best linear relationship had an R(2) of 0.81. A cross-validation on the two empirical models demonstrated the potential of C-band SAR data for estimation of surface moisture, with and R(2) of 0.76 (bias +0.3% and RMSE 7%) for ENVISAT ASAR and 0.54 (bias +1.3% and RMSE 5%) for LANDSAT TM5. The two models developed at plot level were then applied over the Campidano plain and assessed via multitemporal and spatial analyses, in the latter case against soil permeability data from a pedological map of Sardinia. Encouraging estimated soil moisture (ESM) maps were obtained for the SAR-based model, whereas the LANDSAT-based model would require a better field data set for validation, including ground data collected on vegetated fields. ESM maps showed sensitivity to soil drainage qualities or drainage potential, which could be useful in irrigation management and other agricultural applications. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of meteorological models on the solution of the surface energy balance and soil temperature variations in bare soils

NASA Astrophysics Data System (ADS)

Saito, Hirotaka; Šimůnek, Jiri

2009-07-01

SummaryA complete evaluation of the soil thermal regime can be obtained by evaluating the movement of liquid water, water vapor, and thermal energy in the subsurface. Such an evaluation requires the simultaneous solution of the system of equations for the surface water and energy balance, and subsurface heat transport and water flow. When only daily climatic data is available, one needs not only to estimate diurnal cycles of climatic data, but to calculate the continuous values of various components in the energy balance equation, using different parameterization methods. The objective of this study is to quantify the impact of the choice of different estimation and parameterization methods, referred together to as meteorological models in this paper, on soil temperature predictions in bare soils. A variety of widely accepted meteorological models were tested on the dataset collected at a proposed low-level radioactive-waste disposal site in the Chihuahua Desert in West Texas. As the soil surface was kept bare during the study, no vegetation effects were evaluated. A coupled liquid water, water vapor, and heat transport model, implemented in the HYDRUS-1D program, was used to simulate diurnal and seasonal soil temperature changes in the engineered cover installed at the site. The modified version of HYDRUS provides a flexible means for using various types of information and different models to evaluate surface mass and energy balance. Different meteorological models were compared in terms of their prediction errors for soil temperatures at seven observation depths. The results obtained indicate that although many available meteorological models can be used to solve the energy balance equation at the soil-atmosphere interface in coupled water, vapor, and heat transport models, their impact on overall simulation results varies. For example, using daily average climatic data led to greater prediction errors, while relatively simple meteorological models may significantly improve soil temperature predictions. On the other hand, while models for the albedo and soil emissivity had little impact on soil temperature predictions, the choice of the atmospheric emissivity models had a greater impact. A comparison of all the different models indicates that the error introduced at the soil atmosphere interface propagates to deeper layers. Therefore, attention needs to be paid not only to the precise determination of the soil hydraulic and thermal properties, but also to the selection of proper meteorological models for the components involved in the surface energy balance calculations.

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.

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Mahanama, Sarith; Koster, Randal; Lettenmaier, Dennis

2009-01-01

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill.

Influence of selected physicochemical parameters on microbiological activity of mucks.

NASA Astrophysics Data System (ADS)

Całka, A.; Sokołowska, Z.; Warchulska, P.; Dąbek-Szreniawska, M.

2009-04-01

One of the basic factor decided about soil fertility are microorganisms that together with flora, determine trend and character of biochemical processes as well totality of fundamental transformations connected with biogeochemistry and physicochemical properties of soil. Determination of general bacteria number, quantity of selected groups of microorganisms and investigation of respiration intensity let estimate microbiological activity of soil. Intensity of microbiological processes is directly connected with physicochemical soil parameters. In that case, such structural parameters as bulk density, porosity, surface or carbon content play significant role. Microbiological activity also changes within the bounds of mucks with different stage of humification and secondary transformation. Knowledge of relations between structural properties, microorganism activity and degree of transformation and humification can lead to better understanding microbiological processes as well enable to estimate microbiological activity at given physicochemical conditions and at progressing process of soil transformation. The study was carried out on two peaty-moorsh (muck) soils at different state of secondary transformation and humification degree. Soil samples were collected from Polesie Lubelskie (layer depth: 5 - 25 cm). Investigated mucks originated from soils formed from low peatbogs. Soil sample marked as I belonged to muck group weakly secondary transformed. Second sample (II) represented soil group with middle stage of secondary transformation. The main purpose of the research was to examine the relations between some physicochemical and surface properties and their biological activity. Total number and respiration activity of microorganisms were determined. The effectiveness of utilizing the carbon substances from the soil by the bacteria increased simultaneously with the transformation state of the peat-muck soils. Quantity of organic carbon decreased distinctly in the soil at the higher stage of secondary transformation and it influenced quantity and activity of soil microorganisms. Bulk density and surface increased with increasing secondary transformation degree. On the other hand, porosity decreased with increasing secondary transformation index. Process of secondary transformation influenced the soil environment for the microbes by changing the physicochemical properties. This way it influenced the number of microorganisms and caused changes of biological activity in the soils.

Persistence of Escherichia coli and Salmonella in surface soil following application of liquid hog manure for production of pickling cucumbers.

PubMed

Côté, Caroline; Quessy, Sylvain

2005-05-01

Liquid hog manure is routinely applied to farm land as a crop fertilizer. However, this practice raises food safety concerns, especially when manure is used on fruit and vegetable crops. The objectives of this project were to evaluate the persistence of Escherichia coli and Salmonella in surface soil after application of liquid hog manure to fields where pickling cucumbers were grown and to verify the microbiological quality of harvested cucumbers. Mineral fertilizers were replaced by liquid hog manure at various ratios in the production of pickling cucumbers in a 3-year field study. The experimental design was a randomized complete block comprising four replicates in sandy loam (years 1, 2, and 3) and loamy sand (year 3). Soil samples were taken at a depth of 20 cm every 2 weeks after June application of organic and inorganic fertilizers. Vegetable samples were also taken at harvest time. Liquid hog manure, soil, and vegetable (washed and unwashed) samples were analyzed for the presence of Salmonella and E. coli. An exponential decrease of E. coli populations was observed in surface soil after the application of manure. The estimated average time required to reach undetectable concentrations of E. coli in sandy loam varied from 56 to 70 days, whereas the absence of E. coli was estimated at 77 days in loamy sand. The maximal Salmonella persistence in soil was 54 days. E. coli and Salmonella were not detected in any vegetable samples.

Global Scale Simultaneous Retrieval of Smoothened Vegetation Optical Depth and Surface Roughness Parameter using AMSR-E X-band Observations

NASA Astrophysics Data System (ADS)

Lanka, Karthikeyan; Pan, Ming; Konings, Alexandra; Piles, María; D, Nagesh Kumar; Wood, Eric

2017-04-01

Traditionally, passive microwave retrieval algorithms such as Land Parameter Retrieval Model (LPRM) estimate simultaneously soil moisture and Vegetation Optical Depth (VOD) using brightness temperature (Tb) data. The algorithm requires a surface roughness parameter which - despite implications - is generally assumed to be constant at global scale. Due to inherent noise in the satellite data and retrieval algorithm, the VOD retrievals are usually observed to be highly fluctuating at daily scale which may not occur in reality. Such noisy VOD retrievals along with spatially invariable roughness parameter may affect the quality of soil moisture retrievals. The current work aims to smoothen the VOD retrievals (with an assumption that VOD remains constant over a period of time) and simultaneously generate, for the first time, global surface roughness map using multiple descending X-band Tb observations of AMSR-E. The methodology utilizes Tb values under a moving-time-window-setup to estimate concurrently the soil moisture of each day and a constant VOD in the window. Prior to this step, surface roughness parameter is estimated using the complete time series of Tb record. Upon carrying out the necessary sensitivity analysis, the smoothened VOD along with soil moisture retrievals is generated for the 10-year duration of AMSR-E (2002-2011) with a 7-day moving window using the LPRM framework. The spatial patterns of resulted global VOD maps are in coherence with vegetation biomass and climate conditions. The VOD results also exhibit a smoothening effect in terms of lower values of standard deviation. This is also evident from time series comparison of VOD and LPRM VOD retrievals without optimization over moving windows at several grid locations across the globe. The global surface roughness map also exhibited spatial patterns that are strongly influenced by topography and land use conditions. Some of the noticeable features include high roughness over mountainous regions and heavily vegetated tropical rainforests, low roughness in desert areas and moderate roughness value over higher latitudes. The new datasets of VOD and surface roughness can help improving the quality of soil moisture retrievals. Also, the methodology proposed is generic by nature and can be implemented over currently operating AMSR2, SMOS, and SMAP soil moisture missions.

The potential of using thermoluminescence to date buried soils developed on colluvial and fluvial sediments from Utah and Colorado, U.S.A.: Preliminary results

NASA Astrophysics Data System (ADS)

Forman, S. L.; Jackson, M. E.; McCalpin, J.; Maat, P.

The natural TL intensity for surface and buried Holocene and Pleistocene A horizons developed on flood-plain silts, near Denver, Colorado exponentially decreases with time. This signal is approaching saturation by ca. 130 ka. The A horizon of the modern flood-plain soil is not fully light bleached. The TL properties and age estimates are presented for radiocarbon dated, eolian-enriched buried-A horizons developed on fault-derived colluvium from the American Fork segment of the Wasatch fault zone, Utah. Dating of these buried soils provide a close age estimate on paleoearthquake events. Mean TL age estimates by regeneration and total bleach techniques for buried A horizons are 0.5 ± 0.1 ka and 2.7 ± 0.4 ka which are in agreement with corresponding radiocarbon dates of 980 ± 70 years BP and 2620 ± 70 years BP. A surface sag pond mud formed within an antithetic grabben is well light bleached and yielded a TL age estimate by the total bleach method of 240 ± 60 years BP, in agreement with its known age of <300 years BP. This study indicates that relatively brief periods of pedogenesis are not sufficient to light-bleach sediment and that eolian additions enhance the reduction of TL in soils.

A complex permittivity model for field estimation of soil water contents using time domain reflectometry

USDA-ARS?s Scientific Manuscript database

Accurate electromagnetic sensing of soil water contents (') under field conditions is complicated by the dependence of permittivity on specific surface area, temperature, and apparent electrical conductivity, all which may vary across space or time. We present a physically-based mixing model to pred...

A thermal-based remote sensing modeling system for estimating evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a robust but relatively simple thermal-based energy balance model that parameterizes the key soil/s...

Reduction of soil erosion on forest roads

Treesearch

Edward R. Burroughs; John G. King

1989-01-01

Presents the expected reduction in surface erosion from selected treatments applied to forest road traveledways, cutslopes, fillslopes, and ditches. Estimated erosion reduction is expressed as functions of ground cover, slope gradient, and soil properties whenever possible. A procedure is provided to select rock riprap size for protection of the road ditch.

Estimating raindrop kinetic energy: evaluation of a low-cost method

USDA-ARS?s Scientific Manuscript database

The Loess Plateau of China is regarded as the most intensively eroded region in the world and soil erosion caused by raindrop action is a common occurrence on agricultural land within this region. Therefore, understanding the influence of rainfall energy on the soil surface is needed for modeling pu...

Benchmarking LSM root-zone soil mositure predictions using satellite-based vegetation indices

USDA-ARS?s Scientific Manuscript database

The application of modern land surface models (LSMs) to agricultural drought monitoring is based on the premise that anomalies in LSM root-zone soil moisture estimates can accurately anticipate the subsequent impact of drought on vegetation productivity and health. In addition, the water and energy ...

Soil carbon dynamics

NASA Astrophysics Data System (ADS)

Trumbore, Susan; Barbosa de Camargo, Plínio

The amount of organic carbon (C) stored in the upper meter of mineral soils in the Amazon Basin (˜40 Pg C) represents ˜3% of the estimated global store of soil carbon. Adding surface detrital C stocks and soil carbon deeper than 1 m can as much as quadruple this estimate. The potential for Amazon soil carbon to respond to changes in land use, climate, or atmospheric composition depends on the form and dynamics of soil carbon. Much (˜30% in the top ˜10 cm but >85% in soils to 1 m depth) of the carbon in mineral soils of the Oxisols and Ultisols that are the predominant soil types in the Amazon Basin is in forms that are strongly stabilized, with mean ages of centuries to thousands of years. Measurable changes in soil C stocks that accompany land use/land cover change occur in the upper meter of soil, although the presence of deep roots in forests systems drives an active C cycle at depths >1 m. Credible estimates of the potential for changes in Amazon soil C stocks with future land use and climate change are much smaller than predictions of aboveground biomass change. Soil organic matter influences fertility and other key soil properties, and thus is important independent of its role in the global C cycle. Most work on C dynamics is limited to upland soils, and more is needed to investigate C dynamics in poorly drained soils. Work is also needed to relate cycles of C with water, N, P, and other elements.

Nonlinear Spectral Mixture Modeling to Estimate Water-Ice Abundance of Martian Regolith

NASA Astrophysics Data System (ADS)

Gyalay, Szilard; Chu, Kathryn; Zeev Noe Dobrea, Eldar

2017-10-01

We present a novel technique to estimate the abundance of water-ice in the Martian permafrost using Phoenix Surface Stereo Imager multispectral data. In previous work, Cull et al. (2010) estimated the abundance of water-ice in trenches dug by the Mars Phoenix lander by modeling the spectra of the icy regolith using the radiative transfer methods described in Hapke (2008) with optical constants for Mauna Kea palagonite (Clancy et al., 1995) as a substitute for unknown Martian regolith optical constants. Our technique, which uses the radiative transfer methods described in Shkuratov et al. (1999), seeks to eliminate the uncertainty that stems from not knowing the composition of the Martian regolith by using observations of the Martian soil before and after the water-ice has sublimated away. We use observations of the desiccated regolith sample to estimate its complex index of refraction from its spectrum. This removes any a priori assumptions of Martian regolith composition, limiting our free parameters to the estimated real index of refraction of the dry regolith at one specific wavelength, ice grain size, and regolith porosity. We can then model mixtures of regolith and water-ice, fitting to the original icy spectrum to estimate the ice abundance. To constrain the uncertainties in this technique, we performed laboratory measurements of the spectra of known mixtures of water-ice and dry soils as well as those of soils after desiccation with controlled viewing geometries. Finally, we applied the technique to Phoenix Surface Stereo Imager observations and estimated water-ice abundances consistent with pore-fill in the near-surface ice. This abundance is consistent with atmospheric diffusion, which has implications to our understanding of the history of water-ice on Mars and the role of the regolith at high latitudes as a reservoir of atmospheric H2O.

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.

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.

[A review on research of land surface water and heat fluxes].

PubMed

Sun, Rui; Liu, Changming

2003-03-01

Many field experiments were done, and soil-vegetation-atmosphere transfer(SVAT) models were stablished to estimate land surface heat fluxes. In this paper, the processes of experimental research on land surface water and heat fluxes are reviewed, and three kinds of SVAT model(single layer model, two layer model and multi-layer model) are analyzed. Remote sensing data are widely used to estimate land surface heat fluxes. Based on remote sensing and energy balance equation, different models such as simplified model, single layer model, extra resistance model, crop water stress index model and two source resistance model are developed to estimate land surface heat fluxes and evapotranspiration. These models are also analyzed in this paper.

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2014-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

Integrating Multi-Sensor Remote Sensing and In-situ Measurements for Africa Drought Monitoring and Food Security Assessment

NASA Astrophysics Data System (ADS)

Hao, X.; Qu, J. J.; Motha, R. P.; Stefanski, R.; Malherbe, J.

2015-12-01

Drought is one of the most complicated natural hazards, and causes serious environmental, economic and social consequences. Agricultural production systems, which are highly susceptible to weather and climate extremes, are often the first and most vulnerable sector to be affected by drought events. In Africa, crop yield potential and grazing quality are already nearing their limit of temperature sensitivity, and, rapid population growth and frequent drought episodes pose serious complications for food security. It is critical to promote sustainable agriculture development in Africa under conditions of climate extremes. Soil moisture is one of the most important indicators for agriculture drought, and is a fundamentally critical parameter for decision support in crop management, including planting, water use efficiency and irrigation. While very significant technological advances have been introduced for remote sensing of surface soil moisture from space, in-situ measurements are still critical for calibration and validation of soil moisture estimation algorithms. For operational applications, synergistic collaboration is needed to integrate measurements from different sensors at different spatial and temporal scales. In this presentation, a collaborative effort is demonstrated for drought monitoring in Africa, supported and coordinated by WMO, including surface soil moisture and crop status monitoring. In-situ measurements of soil moisture, precipitation and temperature at selected sites are provided by local partners in Africa. Measurements from the Soil Moisture and Ocean Salinity (SMOS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) are integrated with in-situ observations to derive surface soil moisture at high spatial resolution. Crop status is estimated through temporal analysis of current and historical MODIS measurements. Integrated analysis of soil moisture data and crop status provides both in-depth understanding of drought conditions and potential impacts on crop yield. This information is extremely useful in local decision support for agricultural management.

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

NASA Astrophysics Data System (ADS)

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

2010-12-01

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

Measurement of surface energy balance components in dryland wheat/fallow and limited-irrigation corn

USDA-ARS?s Scientific Manuscript database

Water evaporation from soil and plant surfaces and plant transpiration comprise land surface/canopy evapotranspiration (ET), which is essential to estimate for land-atmosphere interaction and crop water use. There are no direct measurements of ET, and the most direct methods (e.g., weighing lysimet...

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.

Estimating historical atmospheric mercury concentrations from silver mining and their legacies in present-day surface soil in Potosí, Bolivia

NASA Astrophysics Data System (ADS)

Hagan, Nicole; Robins, Nicholas; Hsu-Kim, Heileen; Halabi, Susan; Morris, Mark; Woodall, George; Zhang, Tong; Bacon, Allan; Richter, Daniel De B.; Vandenberg, John

2011-12-01

Detailed Spanish records of mercury use and silver production during the colonial period in Potosí, Bolivia were evaluated to estimate atmospheric emissions of mercury from silver smelting. Mercury was used in the silver production process in Potosí and nearly 32,000 metric tons of mercury were released to the environment. AERMOD was used in combination with the estimated emissions to approximate historical air concentrations of mercury from colonial mining operations during 1715, a year of relatively low silver production. Source characteristics were selected from archival documents, colonial maps and images of silver smelters in Potosí and a base case of input parameters was selected. Input parameters were varied to understand the sensitivity of the model to each parameter. Modeled maximum 1-h concentrations were most sensitive to stack height and diameter, whereas an index of community exposure was relatively insensitive to uncertainty in input parameters. Modeled 1-h and long-term concentrations were compared to inhalation reference values for elemental mercury vapor. Estimated 1-h maximum concentrations within 500 m of the silver smelters consistently exceeded present-day occupational inhalation reference values. Additionally, the entire community was estimated to have been exposed to levels of mercury vapor that exceed present-day acute inhalation reference values for the general public. Estimated long-term maximum concentrations of mercury were predicted to substantially exceed the EPA Reference Concentration for areas within 600 m of the silver smelters. A concentration gradient predicted by AERMOD was used to select soil sampling locations along transects in Potosí. Total mercury in soils ranged from 0.105 to 155 mg kg-1, among the highest levels reported for surface soils in the scientific literature. The correlation between estimated air concentrations and measured soil concentrations will guide future research to determine the extent to which the current community of Potosí and vicinity is at risk of adverse health effects from historical mercury contamination.

Barley root hair growth and morphology in soil, sand, and water solution media and relationship with nickel toxicity.

PubMed

Lin, Yanqing; Allen, Herbert E; Di Toro, Dominic M

2016-08-01

Barley, Hordeum vulgare (Doyce), was grown in the 3 media of soil, hydroponic sand solution (sand), and hydroponic water solution (water) culture at the same environmental conditions for 4 d. Barley roots were scanned, and root morphology was analyzed. Plants grown in the 3 media had different root morphology and nickel (Ni) toxicity response. Root elongations and total root lengths followed the sequence soil > sand > water. Plants grown in water culture were more sensitive to Ni toxicity and had greater root hair length than those from soil and sand cultures, which increased root surface area. The unit root surface area as root surface area per centimeter of length of root followed the sequence water > sand > soil and was found to be related with root elongation. Including the unit root surface area, the difference in root elongation and 50% effective concentration were diminished, and percentage of root elongations can be improved with a root mean square error approximately 10% for plants grown in different media. Because the unit root surface area of plants in sand culture is closer to that in soil culture, the sand culture method, not water culture, is recommended for toxicity parameter estimation. Environ Toxicol Chem 2016;35:2125-2133. © 2016 SETAC. © 2016 SETAC.

Simulation using HYDRUS-2D for Soil Water and Heat Transfer under Drip Irrigation with 95oC Hot Water

NASA Astrophysics Data System (ADS)

Ito, Y.; Noborio, K.

2015-12-01

In Japan, soil disinfection with hot water has been popular since the use of methyl bromide was restricted in 2005. Decreasing the amount of hot water applied may make farmers reduce the operation cost. To determine the appropriate amount of hot water needed for soil disinfection, HYDRUS-2D was evaluated. A field experiment was conducted and soil water content and soil temperature were measured at 5, 10, 20, 40, 60, 80 and 100 cm deep when 95oC hot water was applied. Irrigation tubing equipped with drippers every 30 cm were laid at the soil surface, z=0 cm. An irrigation rate for each dripper was 0.83 cm min-1 between t=0 and 120 min, and thereafter it was zero. Temperature of irrigation water was 95oC. Total simulation time with HYDRUS-2D was 720 min for a homogeneous soil. A simulating domain was selected as x=60 cm and z=100 cm. A potential evaporation rate was assumed to be 0 cm min-1 because the soil surface was covered with a plastic sheet. The boundary condition at the bottom was free drainage and those of both sides were no-flux conditions. Hydraulic properties and bulk densities measured at each depth were used for simulation. It was assumed that there was no organic matter contained. Soil thermal properties were adopted from previous study and HYDRUS 2D. Simulated temperatures at 5, 10, 20 and 40 cm deep agreed well with those measured although simulated temperatures at 60, 80, and 100 cm deep were overly estimated. Estimates of volumetric water content at 5 cm deep agreed well with measured values. Simulated values at 10 to 100 cm deep were overly estimated by 0.1 to 0.3 (m3 m-3). The deeper the soil became, the more the simulated wetting front lagged behind the measured one. It was speculated that water viscosity estimated smaller at high temperature might attributed to the slower advances of wetting front simulated with HYDRUS 2-D.

Testing an Energy Balance Model for Estimating Actual Evapotranspiration Using Remotely Sensed Data. [Hannover, West Germany barley and wheat fields

NASA Technical Reports Server (NTRS)

Gurney, R. J.; Camillo, P. J.

1985-01-01

An energy-balance model is used to estimate daily evapotranspiration for 3 days for a barley field and a wheat field near Hannover, Federal Republic of Germany. The model was calibrated using once-daily estimates of surface temperatures, which may be remotely sensed. The evaporation estimates were within the 95% error bounds of independent eddy correlation estimates for the daytime periods for all three days for both sites, but the energy-balance estimates are generally higher; it is unclear which estimate is biassed. Soil moisture in the top 2 cm of soil, which may be remotely sensed, may be used to improve these evaporation estimates under partial ground cover. Sensitivity studies indicate the amount of ground data required is not excessive.

Analysis of the NASA AirMOSS Root Zone Soil Water and Soil Temperature from Three North American Ecosystems

NASA Astrophysics Data System (ADS)

Hagimoto, Y.; Cuenca, R. H.

2015-12-01

Root zone soil water and temperature are controlling factors for soil organic matter accumulation and decomposition which contribute significantly to the CO2 flux of different ecosystems. An in-situ soil observation protocol developed at Oregon State University has been deployed to observe soil water and temperature dynamics in seven ecological research sites in North America as part of the NASA AirMOSS project. Three instrumented profiles defining a transect of less than 200 m are installed at each site. All three profiles collect data for in-situ water and temperature dynamics employing seven soil water and temperature sensors installed at seven depth levels and one infrared surface temperature sensor monitoring the top of the profile. In addition, two soil heat flux plates and associated thermocouples are installed at one of three profiles at each site. At each profile, a small 80 cm deep access hole is typically made, and all below ground sensors are installed into undisturbed soil on the side of the hole. The hole is carefully refilled and compacted so that root zone soil water and temperature dynamics can be observed with minimum site disturbance. This study focuses on the data collected from three sites: a) Tonzi Ranch, CA; b) Metolius, OR and c) BERMS Old Jack Pine Site, Saskatchewan, Canada. The study describes the significantly different seasonal root zone water and temperature dynamics under the various physical and biological conditions at each site. In addition, this study compares the soil heat flux values estimated by the standard installation using the heat flux plates and thermocouples installed near the surface with those estimated by resolving the soil heat storage based on the soil water and temperature data collected over the total soil profile.

Desorption of polycyclic aromatic hydrocarbons from field-contaminated soil to a two-dimensional hydrophobic surface before and after bioremediation.

PubMed

Hu, Jing; Aitken, Michael D

2012-10-01

Dermal exposure can represent a significant health risk in settings involving potential contact with soil contaminated with polycyclic aromatic hydrocarbons (PAHs). However, there is limited work on the ability of PAHs in contaminated soil to reach the skin surface via desorption from the soil. We evaluated PAH desorption from a field-contaminated soil to a two-dimensional hydrophobic surface (C18 extraction disk) as a measure of potential dermal exposure as a function of soil loading (5-100 mg dry soil cm(-2)), temperature (20-40°C), and soil moisture content (2-40%) over periods up to 16d. The efficacy of bioremediation in removing the most readily desorbable PAH fractions was also evaluated. Desorption kinetics were described well by an empirical two-compartment kinetic model. PAH mass desorbed to the C18 disk kept increasing at soil loadings well above the estimated monolayer coverage, suggesting mechanisms for PAH transport to the surface other than by direct contact. Such mechanisms were reinforced by observations that desorption occurred even with dry or moist glass microfiber filters placed between the C18 disk and the soil. Desorption of all PAHs was substantially reduced at a soil moisture content corresponding to field capacity, suggesting that transport through pore air contributed to PAH transport to the C18 disk. The lower molecular weight PAHs had greater potential to desorb from soil than higher molecular weight PAHs. Biological treatment of the soil in a slurry-phase bioreactor completely eliminated PAH desorption to the C18 disks. Copyright © 2012 Elsevier Ltd. All rights reserved.

Desorption of polycyclic aromatic hydrocarbons from field-contaminated soil to a two-dimensional hydrophobic surface before and after bioremediation

PubMed Central

Hu, Jing; Aitken, Michael D.

2012-01-01

Dermal exposure can represent a significant health risk in settings involving potential contact with soil contaminated with polycyclic aromatic hydrocarbons (PAHs). However, there is limited work on the ability of PAHs in contaminated soil to reach the skin surface via desorption from the soil. We evaluated PAH desorption from a field-contaminated soil to a two-dimensional hydrophobic surface (C18 extraction disk) as a measure of potential dermal exposure as a function of soil loading (5 to 100 mg dry soil/cm2), temperature (20 °C to 40 °C), and soil moisture content (2% to 40%) over periods up to 16 d. The efficacy of bioremediation in removing the most readily desorbable PAH fractions was also evaluated. Desorption kinetics were described well by an empirical two-compartment kinetic model. PAH mass desorbed to the C18 disk kept increasing at soil loadings well above the estimated monolayer coverage, suggesting mechanisms for PAH transport to the surface other than by direct contact. Such mechanisms were reinforced by observations that desorption occurred even with dry or moist glass microfiber filters placed between the C18 disk and the soil. Desorption of all PAHs was substantially reduced at a soil moisture content corresponding to field capacity, suggesting that transport through pore air contributed to PAH transport to the C18 disk. The lower molecular weight PAHs had greater potential to desorb from soil than higher molecular weight PAHs. Biological treatment of the soil in a slurry-phase bioreactor completely eliminated PAH desorption to the C18 disks. PMID:22704210

Estimation of Apollo Lunar Dust Transport using Optical Extinction Measurements

NASA Astrophysics Data System (ADS)

Lane, John E.; Metzger, Philip T.

2015-04-01

A technique to estimate mass erosion rate of surface soil during landing of the Apollo Lunar Module (LM) and total mass ejected due to the rocket plume interaction is proposed and tested. The erosion rate is proportional to the product of the second moment of the lofted particle size distribution N(D), and third moment of the normalized soil size distribution S(D), divided by the integral of S(D)ṡD2/v(D), where D is particle diameter and v(D) is the vertical component of particle velocity. The second moment of N(D) is estimated by optical extinction analysis of the Apollo cockpit video. Because of the similarity between mass erosion rate of soil as measured by optical extinction and rainfall rate as measured by radar reflectivity, traditional NWS radar/rainfall correlation methodology can be applied to the lunar soil case where various S(D) models are assumed corresponding to specific lunar sites.

Untangling the biological contributions to soil stability in semiarid shrublands

USGS Publications Warehouse

Chaudhary, V. Bala; Bowker, Matthew A.; O'Dell, Thomas E.; Grace, James B.; Redman, Andrea E.; Rillig, Matthias C.; Johnson, Nancy C.

2009-01-01

Communities of plants, biological soil crusts (BSCs), and arbuscular mycorrhizal (AM) fungi are known to influence soil stability individually, but their relative contributions, interactions, and combined effects are not well understood, particularly in arid and semiarid ecosystems. In a landscape-scale field study we quantified plant, BSC, and AM fungal communities at 216 locations along a gradient of soil stability levels in southern Utah, USA. We used multivariate modeling to examine the relative influences of plants, BSCs, and AM fungi on surface and subsurface stability in a semiarid shrubland landscape. Models were found to be congruent with the data and explained 35% of the variation in surface stability and 54% of the variation in subsurface stability. The results support several tentative conclusions. While BSCs, plants, and AM fungi all contribute to surface stability, only plants and AM fungi contribute to subsurface stability. In both surface and subsurface models, the strongest contributions to soil stability are made by biological components of the system. Biological soil crust cover was found to have the strongest direct effect on surface soil stability (0.60; controlling for other factors). Surprisingly, AM fungi appeared to influence surface soil stability (0.37), even though they are not generally considered to exist in the top few millimeters of the soil. In the subsurface model, plant cover appeared to have the strongest direct influence on soil stability (0.42); in both models, results indicate that plant cover influences soil stability both directly (controlling for other factors) and indirectly through influences on other organisms. Soil organic matter was not found to have a direct contribution to surface or subsurface stability in this system. The relative influence of AM fungi on soil stability in these semiarid shrublands was similar to that reported for a mesic tallgrass prairie. Estimates of effects that BSCs, plants, and AM fungi have on soil stability in these models are used to suggest the relative amounts of resources that erosion control practitioners should devote to promoting these communities. This study highlights the need for system approaches in combating erosion, soil degradation, and arid-land desertification.

X-Ray Fluorescence to Estimate the Maximum Temperature Reached at Soil Surface during Experimental Slash-and-Burn Fires.

PubMed

Melquiades, Fábio L; Thomaz, Edivaldo L

2016-05-01

An important aspect for the evaluation of fire effects in slash-and-burn agricultural system, as well as in wildfire, is the soil burn severity. The objective of this study is to estimate the maximum temperature reached in real soil burn events using energy dispersive X-ray fluorescence (EDXRF) as an analytical tool, combined with partial least square (PLS) regression. Muffle-heated soil samples were used for PLS regression model calibration and two real slash-and-burn soils were tested as external samples in the model. It was possible to associate EDXRF spectra alterations to the maximum temperature reached in the heat affected soils with about 17% relative standard deviation. The results are promising since the analysis is fast, nondestructive, and conducted after the burn event, although local calibration for each type of burned soil is necessary. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

Effects of a clear-cut harvest on soil respiration in a jack pine - Lichen woodland

USGS Publications Warehouse

Striegl, Robert G.; Wickland, K.P.

1998-01-01

Quantification of the components of ecosystem respiration is essential to understanding carbon (C) cycling of natural and disturbed landscapes. Soil respiration, which includes autotrophic and heterotrophic respiration from throughout the soil profile, is the second largest flux in the global carbon cycle. We measured soil respiration (soil CO2 emission) at an undisturbed mature jack pine (Pinus banksiana Lamb.) stand in Saskatchewan (old jack pine, OJP), and at a formerly continuous portion of the stand that was clear-cut during the previous winter (clear-cut, CC). Tree harvesting reduced soil CO2 emission from ???22.5 to ???9.1 mol CO2??m2 for the 1994 growing season. OJP was a small net sink of atmospheric CO2, while CC was a net source of CO2. Winter emissions were similar at both sites. Reduction of soil respiration was attributed to disruption of the soil surface and to the death of tree roots. Flux simulations for CC and OJP identify 40% of CO2 emission at the undisturbed OJP site as near-surface respiration, 25% as deep-soil respiration, and 35% as tree-root respiration. The near-surface component was larger than the estimated annual C input to soil, suggesting fast C turnover and no net C accumulation in these boreal uplands in 1994.

Digging a Little Deeper: Microbial Communities, Molecular Composition and Soil Organic Matter Turnover along Tropical Forest Soil Depth Profiles

NASA Astrophysics Data System (ADS)

Pett-Ridge, J.; McFarlane, K. J.; Heckman, K. A.; Reed, S.; Green, E. A.; Nico, P. S.; Tfaily, M. M.; Wood, T. E.; Plante, A. F.

2016-12-01

Tropical forest soils store more carbon (C) than any other terrestrial ecosystem and exchange vast amounts of CO2, water, and energy with the atmosphere. Much of this C is leached and stored in deep soil layers where we know little about its fate or the microbial communities that drive deep soil biogeochemistry. Organic matter (OM) in tropical soils appears to be associated with mineral particles, suggesting deep soils may provide greater C stabilization. However, few studies have evaluated sub-surface soils in tropical ecosystems, including estimates of the turnover times of deep soil C, the sensitivity of this C to global environmental change, and the microorganisms involved. We quantified bulk C pools, microbial communities, molecular composition of soil organic matter, and soil radiocarbon turnover times from surface soils to 1.5m depths in multiple soil pits across the Luquillo Experimental Forest, Puerto Rico. Soil C, nitrogen, and root and microbial biomass all declined exponentially with depth; total C concentrations dropped from 5.5% at the surface to <0.5% at 140cm depth. High-throughput sequencing highlighted distinct microbial communities in surface soils (Acidobacteria and Proteobacteria) versus those below the active rooting zone (Verrucomicrobia and Thaumarchaea). High resolution mass spectrometry (FTICR-MS) analyses suggest a shift in the composition of OM with depth (especially in the water soluble fraction), an increase in oxidation, and decreasing H/C with depth (indicating higher aromaticity). Additionally, surface samples were rich in lignin-like compounds of plant origin that were absent with depth. Soil OM 14C and mean turnover times were variable across replicate horizons, ranging from 3-1500 years at the surface, to 5000-40,000 years at depth. In comparison to temperate deciduous forests, these 14C values reflect far older soil C. Particulate organic matter (free light fraction), with a relatively modern 14C was found in low but measureable concentration in even the deepest soil horizons. Our results indicate these tropical subsoils contain small but metabolically active microbial communities that are highly OM limited and may persist via degradation of recent inputs.

An improved Rosetta pedotransfer function and evaluation in earth system models

NASA Astrophysics Data System (ADS)

Zhang, Y.; Schaap, M. G.

2017-12-01

Soil hydraulic parameters are often difficult and expensive to measure, leading to the pedotransfer functions (PTFs) an alternative to predict those parameters. Rosetta (Schaap et al., 2001, denoted as Rosetta1) are widely used PTFs, which is based on artificial neural network (ANN) analysis coupled with the bootstrap re-sampling method, allowing the estimation of van Genuchten water retention parameters (van Genuchten, 1980, abbreviated here as VG), saturated hydraulic conductivity (Ks), as well as their uncertainties. We present an improved hierarchical pedotransfer functions (Rosetta3) that unify the VG water retention and Ks submodels into one, thus allowing the estimation of uni-variate and bi-variate probability distributions of estimated parameters. Results show that the estimation bias of moisture content was reduced significantly. Rosetta1 and Posetta3 were implemented in the python programming language, and the source code are available online. Based on different soil water retention equations, there are diverse PTFs used in different disciplines of earth system modelings. PTFs based on Campbell [1974] or Clapp and Hornberger [1978] are frequently used in land surface models and general circulation models, while van Genuchten [1980] based PTFs are more widely used in hydrology and soil sciences. We use an independent global scale soil database to evaluate the performance of diverse PTFs used in different disciplines of earth system modelings. PTFs are evaluated based on different soil characteristics and environmental characteristics, such as soil textural data, soil organic carbon, soil pH, as well as precipitation and soil temperature. This analysis provides more quantitative estimation error information for PTF predictions in different disciplines of earth system modelings.

Operational Mapping of Soil Moisture Using Synthetic Aperture Radar Data: Application to the Touch Basin (France)

PubMed Central

Baghdadi, Nicolas; Aubert, Maelle; Cerdan, Olivier; Franchistéguy, Laurent; Viel, Christian; Martin, Eric; Zribi, Mehrez; Desprats, Jean François

2007-01-01

Soil moisture is a key parameter in different environmental applications, such as hydrology and natural risk assessment. In this paper, surface soil moisture mapping was carried out over a basin in France using satellite synthetic aperture radar (SAR) images acquired in 2006 and 2007 by C-band (5.3 GHz) sensors. The comparison between soil moisture estimated from SAR data and in situ measurements shows good agreement, with a mapping accuracy better than 3%. This result shows that the monitoring of soil moisture from SAR images is possible in operational phase. Moreover, moistures simulated by the operational Météo-France ISBA soil-vegetation-atmosphere transfer model in the SIM-Safran-ISBA-Modcou chain were compared to radar moisture estimates to validate its pertinence. The difference between ISBA simulations and radar estimates fluctuates between 0.4 and 10% (RMSE). The comparison between ISBA and gravimetric measurements of the 12 March 2007 shows a RMSE of about 6%. Generally, these results are very encouraging. Results show also that the soil moisture estimated from SAR images is not correlated with the textural units defined in the European Soil Geographical Database (SGDBE) at 1:1000000 scale. However, dependence was observed between texture maps and ISBA moisture. This dependence is induced by the use of the texture map as an input parameter in the ISBA model. Even if this parameter is very important for soil moisture estimations, radar results shown that the textural map scale at 1:1000000 is not appropriate to differentiate moistures zones. PMID:28903238

Using satellite observations to improve model estimates of CO2 and CH4 flux: a Metropolis Hastings Markov Chain Monte Carlo approach

NASA Astrophysics Data System (ADS)

MacBean, Natasha; Disney, Mathias; Lewis, Philip; Ineson, Phil

2010-05-01

Peatlands are wetlands with an organic soil layer of >30cm (Limpens et al., 2008) that occur beneath a living plant layer as a result of the waterlogged nature of the soil restricting complete decay of the biomass (Charman, 2002). Peatlands are important ecosystems; boreal and subarctic peatlands are estimated to contain 455Pg of carbon (Gorham, 1991), about 15-30% of the world's soil carbon (Limpens et al., 2008), and yet constitute less than 3% of the world's total land area (Lai, 2009). Peatlands not only sequester CO2 through photosynthesis and the partial decomposition of organic matter but they release methane (CH4) due to anaerobic microbial activity under waterlogged conditions. The balance is even more complex, as microbial consumption of CH4 can result in additional CO2 being emitted to the atmosphere. Wetlands are the main source of natural CH4 (Le Mer and Roger, 2001). Northern wetlands contribute about 35Tgyr-1 (Bubier and Moore, 1994). The uncertainty on this estimate is large 1 mgm-2yr-1 to 2200 mgm-2yr-1. Given that CH4 is 20 to 30 times more efficient at absorbing infrared radiation than CO2 there is a need to better quantify CH4 emissions and their role in the net carbon balance of peatlands. Two of the key variables in the calculation of CH4 production are water table depth and soil temperature. Water table depth is important as methanogenic bacteria are predominantly active in the anoxic zone. In order to accurately model the water table depth a correct representation of the whole soil moisture profile is important. Soil moisture and soil temperature are important variables in model calculations, as they affect the decomposition of carbon in the soil, as well as influencing the water and energy fluxes at the surface - atmosphere boundary. Microwave measurements of surface soil moisture and thermal measurements of land surface temperature from satellites can theoretically be used to improve the representation of the hydrology and soil temperature profile as a whole. We present results from an Observing System Simulation Experiment (OSSE) designed to investigate the impact of management and climate change on peatland carbon fluxes, as well as how observations from satellites may be able to constrain modeled carbon fluxes. We use an adapted version of the Carnegie-Ames-Stanford Approach (CASA) model (Potter et al., 1993) that includes a representation of methane dynamics (Potter, 1997). The model formulation is further modified to allow for assimilation of satellite observations of surface soil moisture and land surface temperature. The observations are used to update model estimates using a Metropolis Hastings Markov Chain Monte Carlo (MCMC) approach. We examine the effect of temporal frequency and precision of satellite observations with a view to establishing how, and at what level, such observations would make a significant improvement in model uncertainty. We compare this with the system characteristics of existing and future satellites. We believe this is the first attempt to assimilate surface soil moisture and land surface temperature into an ecosystem model that includes a full representation of CH4 flux. Bubier, J., and T. Moore (1994), An ecological perspective on methane emissions from northern wetlands, TREE, 9, 460-464. Charman, D. (2002), Peatlands and Environmental Change, JohnWiley and Sons, Ltd, England. Gorham, E. (1991), Northern peatlands: Role in the carbon cycle and probable responses to climatic warming, Ecological Applications, 1, 182-195. Lai, D. (2009), Methane dynamics in northern peatlands: A review, Pedosphere, 19, 409-421. Le Mer, J., and P. Roger (2001), Production, oxidation, emission and consumption of methane by soils: A review, European Journal of Soil Biology, 37, 25-50. Limpens, J., F. Berendse, J. Canadell, C. Freeman, J. Holden, N. Roulet, H. Rydin, and Potter, C. (1997), An ecosystem simulation model for methane production and emission from wetlands, Global Biogeochemical Cycles, 11, 495-506. Potter, C., J. Randerson, C. Field, P. Matson, P. Vitousek, H. Mooney, and S. Klooster (1993), Terrestrial ecosystem production: A process model based on global satellite and surface data, Global Biogeochemical Cycles, 7, 811-841.

Multi-model perspectives and inter-comparison of soil moisture and evapotranspiration in East Africa—an application of Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS)

NASA Astrophysics Data System (ADS)

Pervez, M. S.; McNally, A.; Arsenault, K. R.

2017-12-01

Convergence of evidence from different agro-hydrologic sources is particularly important for drought monitoring in data sparse regions. In Africa, a combination of remote sensing and land surface modeling experiments are used to evaluate past, present and future drought conditions. The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) routinely simulates daily soil moisture, evapotranspiration (ET) and other variables over Africa using multiple models and inputs. We found that Noah 3.3, Variable Infiltration Capacity (VIC) 4.1.2, and Catchment Land Surface Model based FLDAS simulations of monthly soil moisture percentile maps captured concurrent drought and water surplus episodes effectively over East Africa. However, the results are sensitive to selection of land surface model and hydrometeorological forcings. We seek to identify sources of uncertainty (input, model, parameter) to eventually improve the accuracy of FLDAS outputs. In absence of in situ data, previous work used European Space Agency Climate Change Initiative Soil Moisture (CCI-SM) data measured from merged active-passive microwave remote sensing to evaluate FLDAS soil moisture, and found that during the high rainfall months of April-May and November-December Noah-based soil moisture correlate well with CCI-SM over the Greater Horn of Africa region. We have found good correlations (r>0.6) for FLDAS Noah 3.3 ET anomalies and Operational Simplified Surface Energy Balance (SSEBop) ET over East Africa. Recently, SSEBop ET estimates (version 4) were improved by implementing a land surface temperature correction factor. We re-evaluate the correlations between FLDAS ET and version 4 SSEBop ET. To further investigate the reasons for differences between models we evaluate FLDAS soil moisture with Advanced Scatterometer and SMAP soil moisture and FLDAS outputs with MODIS and AVHRR normalized difference vegetation index. By exploring longer historic time series and near-real time products we will be aiding convergence of evidence for better understanding of historic drought, improved monitoring and forecasting, and better understanding of uncertainties of water availability estimation over Africa

Calculation of fast neutron removal cross sections for different lunar soils

NASA Astrophysics Data System (ADS)

Tellili, B.; Elmahroug, Y.; Souga, C.

2014-01-01

The interaction of galactic cosmic rays (GCRs) and solar energetic particles (SEPs) with the lunar surface produces secondary radiations as neutrons. The study of the production and attenuation of these neutrons in the lunar soil is very important to estimate the annual ambient dose equivalent on the lunar surface and for lunar nuclear spectroscopy. Also, understanding the attenuation of fast neutrons in lunar soils can help in measuring of the lunar neutron density profile and to measure the neutron flux on the lunar surface. In this paper, the attenuation of fast neutrons in different lunar soils is investigated. The macroscopic effective removal cross section (ΣR) of fast neutrons was theoretically calculated from the mass removal cross-section values (ΣR/ρ) for various elements in soils. The obtained values of (ΣR) were discussed according to the density. The results show that the attenuation of fast neutrons is more important in the landing sites of Apollo 12 and Luna 16 than the other landing sites of Apollo and Luna missions.

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.

Estimating regional-scale methane flux and budgets using CARVE aircraft measurements over Alaska

NASA Astrophysics Data System (ADS)

Hartery, Sean; Commane, Róisín; Lindaas, Jakob; Sweeney, Colm; Henderson, John; Mountain, Marikate; Steiner, Nicholas; McDonald, Kyle; Dinardo, Steven J.; Miller, Charles E.; Wofsy, Steven C.; Chang, Rachel Y.-W.

2018-01-01

Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions are still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012-2014 as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Net surface CH4 fluxes are estimated using a Lagrangian particle dispersion model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the mixed layer. We estimate that between May and September, net CH4 emissions from the region of interest were 2.2 ± 0.5 Tg, 1.9 ± 0.4 Tg, and 2.3 ± 0.6 Tg of CH4 for 2012, 2013, and 2014, respectively. If emissions are only attributed to two biogenic eco-regions within our domain, then tundra regions were the predominant source, accounting for over half of the overall budget despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that, overall, CH4 fluxes were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, suggesting deeper methanogenesis in drier soils. Although similar environmental drivers have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional-scale net CH4 budget.

Lunar studies

NASA Technical Reports Server (NTRS)

Gold, T.

1979-01-01

Experimental and theoretical research, concerning lunar surface processes and the nature, origin and derivation of the lunar surface cover, conducted during the period of February 1, 1971 through January 31, 1976 is presented. The principle research involved were: (1) electrostatic dust motion and transport process; (2) seismology properties of fine rock powders in lunar conditions; (3) surface processes that darken the lunar soil and affect the surface chemical properties of the soil grains; (4) laser simulation of micrometeorite impacts (estimation of the erosion rate caused by the microemeteorite flux); (5) the exposure history of the lunar regolith; and (6) destruction of amino acids by exposure to a simulation of the solar wind at the lunar surface. Research papers are presented which cover these general topics.

Native Plant Uptake Model for Radioactive Waste Disposal Areas at the Nevada Test Site

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

BROWN,THERESA J.; WIRTH,SHARON

1999-09-01

This report defines and defends the basic framework, methodology, and associated input parameters for modeling plant uptake of radionuclides for use in Performance Assessment (PA) activities of Radioactive Waste Management Sites (RWMS) at the Nevada Test Site (NTS). PAs are used to help determine whether waste disposal configurations meet applicable regulatory standards for the protection of human health, the environment, or both. Plants adapted to the arid climate of the NTS are able to rapidly capture infiltrating moisture. In addition to capturing soil moisture, plant roots absorb nutrients, minerals, and heavy metals, transporting them within the plant to the above-groundmore » biomass. In this fashion, plant uptake affects the movement of radionuclides. The plant uptake model presented reflects rooting characteristics important to plant uptake, biomass turnover rates, and the ability of plants to uptake radionuclides from the soil. Parameters are provided for modeling plant uptake and estimating surface contaminant flux due to plant uptake under both current and potential future climate conditions with increased effective soil moisture. The term ''effective moisture'' is used throughout this report to indicate the soil moisture that is available to plants and is intended to be inclusive of all the variables that control soil moisture at a site (e.g., precipitation, temperature, soil texture, and soil chemistry). Effective moisture is a concept used to simplify a number of complex, interrelated soil processes for which there are too little data to model actual plant available moisture. The PA simulates both the flux of radionuclides across the land surface and the potential dose to humans from that flux. Surface flux is modeled here as the amount of soil contamination that is transferred from the soil by roots and incorporated into aboveground biomass. Movement of contaminants to the surface is the only transport mechanism evaluated with the model presented here. Parameters necessary for estimating surface contaminant flux due to native plants expected to inhabit the NTS RWMSS are developed in this report. The model is specific to the plant communities found at the NTS and is designed for both short-term (<1,000 years) and long-term (>1,000 years) modeling efforts. While the model has been crafted for general applicability to any NTS PA, the key radionuclides considered are limited to the transuranic (TRU) wastes disposed of at the NTS.« less

Direct estimation of mass flow and diffusion of nitrogen compounds in solution and soil.

PubMed

Oyewole, Olusegun Ayodeji; Inselsbacher, Erich; Näsholm, Torgny

2014-02-01

Plant nutrient uptake from soil is mainly governed by diffusion and transpirationally induced mass flow, but the current methods for assessing the relative importance of these processes are indirect. We developed a microdialysis method using solutions of different osmotic potentials as perfusates to simulate diffusion and mass flow processes, and assessed how induced mass flow affected fluxes of nitrogen (N) compounds in solution and in boreal forest soil. Varying the osmotic potential of perfusates induced vertical fluxes in the direction of the dialysis membranes at rates of between 1 × 10(-8) and 3 × 10(-7)  m s(-1) , thus covering the estimated range of water velocities perpendicular to root surfaces and induced by transpiration. Mass flow increased N fluxes in solution but even more so in soil. This effect was explained by an indirect effect of mass flow on rates of diffusive fluxes, possibly caused by the formation of steeper gradients in concentrations of N compounds from membrane surfaces out in the soil. Our results suggest that transpiration may be an essential driver of plant N acquisition. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

AccuCLASS - an Enhancement of the Canadian Land Surface Scheme for Climate Assessment Over the Prairies

NASA Astrophysics Data System (ADS)

Loukili, Y.; Woodbury, A. D.; Snelgrove, K. R.

2006-12-01

The Canadian Land Surface Scheme (CLASS) is a numerical model developed at the Canadian Atmospheric Environment Service by Verseghy et al. [1991, 1993, 2000] and used to evaluate the vertical transfer of energy and water between the land surface and three soil layers. Among the features of CLASS its treatment of the land surface as a composite of four primary subareas: canopy and snow covered ground, snow-covered ground, canopy covered soil, and bare soil. The vegetation properties are also related via weighted averages to four types: needleaf trees, broadleaf trees, grass and crops. The incorporation of meteorological data as forcing inputs drives the model through advanced formulae describing the earth surface physics. These include canopy radiation and evapotranspiration, sensible and latent heat fluxes, rainfall interception, infiltration and ponding, snow melt and soil freezing. Such treatment allows for a realistic estimation of the surface energy balance. In this work, a major revision of CLASS, called AccuCLASS, is introduced, which permits a user specified depth and as many soil layers as needed. Almost all the physically based calculations of heat and moisture transfer in CLASS are kept and adequately extended to fit the desired refined mesh. In the resolution of soil temperature and heat flux terms, the GMRES iterative method replaced the explicit algebraic manipulation. Moreover, in the moisture regime, a water table lower boundary condition is added for the future coupling with groundwater models. The results of AccuCLASS are extensively validated for some synthetic runs under real-like seasonal weather conditions and different soil types, through inter-comparing to simulation outputs from SHAW [Flerchinger and Saxon, 1989], HYDRUS-1D [Simunek et al., 1998] and HELP [Schroeder et al., 1994] models. We find that AccuCLASS and SHAW accurately predict moisture and bottom drainage amounts; and that the original CLASS code does not have sufficient grid refinement to track precisely the unsaturated flow below the soil surface. On the other hand, when considering short time scale responses, HELP overestimates the recharge for sandy soils and underestimates it for clayey soils. An improvement of surface energy terms estimation is also carried out by AccuCLASS. Furthermore, some stand-alone tests forced by actual meteorological data over two land squares representative of the Assiniboine Delta Aquifer (ADA) show the importance of our contributions and the ability to provide a more accurate forecast of water mass balance terms. The coupling of this novel version of CLASS to other GCM components will help study objectively the cyclic drought phenomenon on the Canadian Prairies as well as its medium and long term ecological and socio-economic impacts in the region.

Modelling of the long-term fate of pesticide residues in agricultural soils and their surface exchange with the atmosphere: Part II. Projected long-term fate of pesticide residues.

PubMed

Scholtz, M T; Bidleman, T F

2007-05-01

In the first part of this paper, a simple coupled dynamic soil-atmosphere model for studying the gaseous exchange of pesticide soil residues with the atmosphere is described and evaluated by comparing model results with published measurements of pesticide concentrations in air and soil. In Part II, the model is used to study the concentration profiles of pesticide residues in both undisturbed and annually tilled agricultural soils. Future trends are estimated for the measured air and soil concentrations of lindane and six highly persistent pesticides (toxaphene, p,p'-DDE, dieldrin, cis- and trans-chlordane and trans-nonachlor) over a twenty-year period due to volatilization and leaching into the deeper soil. Wet deposition and particle associated pesticide deposition (that increase soil residue concentrations) and soil erosion, degradation in the soil (other than for lindane) and run-off in precipitation are not considered in this study. Estimates of the rain deposition fluxes are reported that show that, other than for lindane, net volatilization fluxes greatly exceed rain deposition fluxes. The model shows that the persistent pesticides studied are highly immobile in soil and that loss of these highly persistent residues from the soil is by volatilization rather than leaching into the deeper soil. The soil residue levels of these six pesticides are currently sources of net volatilization to the atmosphere and will remain so for many years. The maximum rate of volatilization from the soil was simulated by setting the atmospheric background concentration to zero; these simulations show that the rates of volatilization will not be significantly increased since soil resistance rather than the atmospheric concentration controls the volatilization rates. Annual tilling of the soils increases the volatilization loss to the atmosphere. Nonetheless, the model predicts that, if only air-soil exchange is considered, more than 76% of current persistent pesticide residues will remain after 20 years in the top 7 cm of annually tilled soils. In contrast, lindane is relatively mobile in soil due to weaker binding to soil carbon and leaching of lindane into soil is the main removal route for current lindane residues near the soil surface. The model predicts that the soil is a sink for lindane in the atmosphere and that soil residue levels of lindane in the surface soil are determined by a balance between dry gaseous deposition to the soil from the atmosphere and leaching from the surface soil into the deeper soil where degradation is the dominant loss route. The model suggests that deposition of lindane from the atmosphere will sustain residues in the soil and, in the absence of fresh applications of lindane to the soil, eliminating lindane from the atmosphere would lead to a rapid decline of lindane residues in agricultural soils of the southern U.S.

Multitemporal analysis of estimated soil loss for the river Mourão watershed, Paraná - Brazil.

PubMed

Graça, C H; Passig, F H; Kelniar, A R; Piza, M A; Carvalho, K Q; Arantes, E J

2015-12-01

The multitemporal behavior of soil loss by surface water erosion in the hydrographic basin of the river Mourão in the center-western region of the Paraná state, Brazil, is analyzed. Forecast was based on the application of the Universal Soil Loss Equation (USLE) with the data integration and estimates within an Geography Information System (GIS) environment. Results had shown high mean annual rain erosivity (10,000 MJ.mm.ha(-1).h(-1).year(-1)), with great concentration in January and December. As a rule, soils have average erodibilities, exception of Dystroferric Red Latisol (low class) and Dystrophic Red Argisol (high class). Although the topographic factor was high (>20), rates lower than 1 were predominant. Main land uses comprise temporal crops and pasture throughout the years. The watershed showed a natural potential for low surface erosion. When related to usage types, yearly soil loss was also low (<50 ton.ha(-1).year(-1)), with more critical scores that reach rates higher than 150 ton.ha(-1).year(-1). Soil loss over the years did not provide great distinctions in distribution standards, although it becames rather intensified in some sectors, especially in the center-eastern and southwestern sections of the watershed.

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.

Estimating plant available water content from remotely sensed evapotranspiration

NASA Astrophysics Data System (ADS)

van Dijk, A. I. J. M.; Warren, G.; Doody, T.

2012-04-01

Plant available water content (PAWC) is an emergent soil property that is a critical variable in hydrological modelling. PAWC determines the active soil water storage and, in water-limited environments, is the main cause of different ecohydrological behaviour between (deep-rooted) perennial vegetation and (shallow-rooted) seasonal vegetation. Conventionally, PAWC is estimated for a combination of soil and vegetation from three variables: maximum rooting depth and the volumetric water content at field capacity and permanent wilting point, respectively. Without elaborate local field observation, large uncertainties in PAWC occur due to the assumptions associated with each of the three variables. We developed an alternative, observation-based method to estimate PAWC from precipitation observations and CSIRO MODIS Reflectance-based Evapotranspiration (CMRSET) estimates. Processing steps include (1) removing residual systematic bias in the CMRSET estimates, (2) making spatially appropriate assumptions about local water inputs and surface runoff losses, (3) using mean seasonal patterns in precipitation and CMRSET to estimate the seasonal pattern in soil water storage changes, (4) from these, calculating the mean seasonal storage range, which can be treated as an estimate of PAWC. We evaluate the resulting PAWC estimates against those determined in field experiments for 180 sites across Australia. We show that the method produces better estimates of PAWC than conventional techniques. In addition, the method provides detailed information with full continental coverage at moderate resolution (250 m) scale. The resulting maps can be used to identify likely groundwater dependent ecosystems and to derive PAWC distributions for each combination of soil and vegetation type.

Comparison of cellulose vs. plastic cigarette filter decomposition under distinct disposal environments.

PubMed

Joly, François-Xavier; Coulis, Mathieu

2018-02-01

It is estimated that 4.5 trillion cigarette butts are discarded annually, making them numerically the most common type of litter on Earth. To accelerate their disappearance after disposal, a new type of cigarette filters made of cellulose, a readily biodegradable compound, has been introduced in the market. Yet, the advantage of these cellulose filters over the conventional plastic ones (cellulose acetate) for decomposition, remains unknown. Here, we compared the decomposition of cellulose and plastic cigarettes filters, either intact or smoked, on the soil surface or within a composting bin over a six-month field decomposition experiment. Within the compost, cellulose filters decomposed faster than plastic filters, but this advantage was strongly reduced when filters had been used for smoking. This indicates that the accumulation of tars and other chemicals during filter use can strongly affect its subsequent decomposition. Strikingly, on the soil surface, we observed no difference in mass loss between cellulose and plastic filters throughout the incubation. Using a first order kinetic model for mass loss of for used filters over the short period of our experiment, we estimated that conventional plastic filters take 7.5-14 years to disappear, in the compost and on the soil surface, respectively. In contrast, we estimated that cellulose filters take 2.3-13 years to disappear, in the compost and on the soil surface, respectively. Our data clearly showed that disposal environments and the use of cellulose filters must be considered when assessing their advantage over plastic filters. In light of our results, we advocate that the shift to cellulose filters should not exempt users from disposing their waste in appropriate collection systems. Copyright © 2017 Elsevier Ltd. All rights reserved.

Measuring surface energy and evapotranspiration across Caribbean mangrove forests

NASA Astrophysics Data System (ADS)

Lagomasino, D.; Fatoyinbo, T. E.; Price, R.

2014-12-01

Coastal mangroves lose large amounts of water through evapotranspiration (ET) that can be equivalent to the amount of annual rainfall in certain years. Satellite remote sensing has been used to estimate surface energy and ET variability in many forested ecosystems, yet has been widely overlooked in mangrove forests. Using a combination of long-term datasets (30-year) acquired from the NASA Landsat 5 and 7 satellite databases, the present study investigated ET and surface energy balance variability between two mangrove forest sites in the Caribbean: 1) Everglades National Park (ENP; Florida, USA) and 2) Sian Ka'an Biosphere Reserve (SKBR; Quintana Roo, Mexico). A satellite-derived surface energy balance model was used to estimate ET in tall and scrub mangroves environments at ENP and SKBR. Results identified significant differences in soil heat flux measurements and ET between the tall and scrub mangrove environments. Scrub mangroves exhibited the highest soil heat flux coincident with the lowest biophysical indices (i.e., Fractional Vegetation Cover, Normalized Difference Vegetation Index, and Soil-Adjusted Vegetation Index) and ET rates. Mangrove damage and mortality was observed on the satellite images following strong tropical storms and associated with anthropogenic modifications and resulted in low values in spectral vegetation indices, higher soil heat flux, and higher ET. Recovery of the spectral characteristics, soil heat flux and ET was within 1-2 years following hurricane disturbance while, degradation caused by human disturbance persisted for many years. Remotely sensed ET of mangrove forests can provide estimates over a few decades and provide us with some understanding of how these environments respond to disturbances to the landscape in periods where no ground data exists or in locations that are difficult to access. Moreover, relationships between energy and water balance components developed for the coastal mangroves of Florida and Mexico could be extrapolated to other mangroves systems in the Caribbean to measure changes caused by natural events and human modifications.

Integrated Processing of High Resolution Topographic Data for Soil Erosion Assessment Considering Data Acquisition Schemes and Surface Properties

NASA Astrophysics Data System (ADS)

Eltner, A.; Schneider, D.; Maas, H.-G.

2016-06-01

Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.

Airborne gamma radiation soil moisture measurements over short flight lines

NASA Technical Reports Server (NTRS)

Peck, Eugene L.; Carrol, Thomas R.; Lipinski, Daniel M.

1990-01-01

Results are presented on airborne gamma radiation measurements of soil moisture condition, carried out along short flight lines as part of the First International Satellite Land Surface Climatology Project Field Experiment (FIFE). Data were collected over an area in Kansas during the summers of 1987 and 1989. The airborne surveys, together with ground measurements, provide the most comprehensive set of airborne and ground truth data available in the U.S. for calibrating and evaluating airborne gamma flight lines. Analysis showed that, using standard National Weather Service weights for the K, Tl, and Gc radiation windows, the airborne soil moisture estimates for the FIFE lines had a root mean square error of no greater than 3.0 percent soil moisture. The soil moisture estimates for sections having acquisition time of at least 15 sec were found to be reliable.

Spatial variability of specific surface area of arable soils in Poland

NASA Astrophysics Data System (ADS)

Sokolowski, S.; Sokolowska, Z.; Usowicz, B.

2012-04-01

Evaluation of soil spatial variability is an important issue in agrophysics and in environmental research. Knowledge of spatial variability of physico-chemical properties enables a better understanding of several processes that take place in soils. In particular, it is well known that mineralogical, organic, as well as particle-size compositions of soils vary in a wide range. Specific surface area of soils is one of the most significant characteristics of soils. It can be not only related to the type of soil, mainly to the content of clay, but also largely determines several physical and chemical properties of soils and is often used as a controlling factor in numerous biological processes. Knowledge of the specific surface area is necessary in calculating certain basic soil characteristics, such as the dielectric permeability of soil, water retention curve, water transport in the soil, cation exchange capacity and pesticide adsorption. The aim of the present study is two-fold. First, we carry out recognition of soil total specific surface area patterns in the territory of Poland and perform the investigation of features of its spatial variability. Next, semivariograms and fractal analysis are used to characterize and compare the spatial variability of soil specific surface area in two soil horizons (A and B). Specific surface area of about 1000 samples was determined by analyzing water vapor adsorption isotherms via the BET method. The collected data of the values of specific surface area of mineral soil representatives for the territory of Poland were then used to describe its spatial variability by employing geostatistical techniques and fractal theory. Using the data calculated for some selected points within the entire territory and along selected directions, the values of semivariance were determined. The slope of the regression line of the log-log plot of semi-variance versus the distance was used to estimate the fractal dimension, D. Specific surface area in A and B horizons was space-dependent, with the range of spatial dependence of about 2.5°. Variogram surfaces showed anisotropy of the specific surface area in both horizons with a trend toward the W to E directions. The smallest fractal dimensions were obtained for W to E directions and the highest values - for S to N directions. * The work was financially supported in part by the ESA Programme for European Cooperating States (PECS), No.98084 "SWEX-R, Soil Water and Energy Exchange/Research", AO3275.

Ground-based Remote Sensing for Quantifying Subsurface and Surface Co-variability to Scale Arctic Ecosystem Functioning

NASA Astrophysics Data System (ADS)

Oktem, R.; Wainwright, H. M.; Curtis, J. B.; Dafflon, B.; Peterson, J.; Ulrich, C.; Hubbard, S. S.; Torn, M. S.

2016-12-01

Predicting carbon cycling in Arctic requires quantifying tightly coupled surface and subsurface processes including permafrost, hydrology, vegetation and soil biogeochemistry. The challenge has been a lack of means to remotely sense key ecosystem properties in high resolution and over large areas. A particular challenge has been characterizing soil properties that are known to be highly heterogeneous. In this study, we exploit tightly-coupled above/belowground ecosystem functioning (e.g., the correlations among soil moisture, vegetation and carbon fluxes) to estimate subsurface and other key properties over large areas. To test this concept, we have installed a ground-based remote sensing platform - a track-mounted tram system - along a 70 m transect in the ice-wedge polygonal tundra near Barrow, Alaska. The tram carries a suite of near-surface remote sensing sensors, including sonic depth, thermal IR, NDVI and multispectral sensors. Joint analysis with multiple ground-based measurements (soil temperature, active layer soil moisture, and carbon fluxes) was performed to quantify correlations and the dynamics of above/belowground processes at unprecedented resolution, both temporally and spatially. We analyzed the datasets with particular focus on correlating key subsurface and ecosystem properties with surface properties that can be measured by satellite/airborne remote sensing over a large area. Our results provided several new insights about system behavior and also opens the door for new characterization approaches. We documented that: (1) soil temperature (at >5 cm depth; critical for permafrost thaw) was decoupled from soil surface temperature and was influenced strongly by soil moisture, (2) NDVI and greenness index were highly correlated with both soil moisture and gross primary productivity (based on chamber flux data), and (3) surface deformation (which can be measured by InSAR) was a good proxy for thaw depth dynamics at non-inundated locations.

Smap: A Hydrologist Goes Crazy with a New High-Quality Dataset

NASA Technical Reports Server (NTRS)

Koster, Randal

2018-01-01

By providing global measurements of near-surface soil moisture (down to about 5 cm) with unprecedented accuracy, the Soil Moisture Active/Passive (SMAP) satellite mission has opened the door to new and (in my opinion) exciting hydrological science. In this seminar, I present the results of a recent series of analyses performed with SMAP soil moisture data, covering a wide range of topics: (a) the characterization of the dynamics of near-surface soil moisture, with implications for forecasting soil moisture days into the future; (b) the multi-faceted character of the SMAP data, in the sense that different, established analysis approaches can extract information from the data that is largely (and perhaps unexpectedly) complementary; and (c) the interpretation of the data in the context of large-scale water fluxes. This final analysis is particularly exciting to me because it shows that, once the relevant algorithms are calibrated, precipitation and streamflow rates in hydrological basins can be estimated from the SMAP data alone - a reflection of the fact that the near-surface soil is a critical gateway between the atmospheric and subsurface branches of the hydrological cycle.

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

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Estimated stocks of circumpolar permafrost carbon with quantified uncertainty ranges and identified data gaps

DOE PAGES

Hugelius, Gustaf; Strauss, J.; Zubrzycki, S.; ...

2014-12-01

Soils and other unconsolidated deposits in the northern circumpolar permafrost region store large amounts of soil organic carbon (SOC). This SOC is potentially vulnerable to remobilization following soil warming and permafrost thaw, but SOC stock estimates were poorly constrained and quantitative error estimates were lacking. This study presents revised estimates of permafrost SOC stocks, including quantitative uncertainty estimates, in the 0–3 m depth range in soils as well as for sediments deeper than 3 m in deltaic deposits of major rivers and in the Yedoma region of Siberia and Alaska. Revised estimates are based on significantly larger databases compared tomore » previous studies. Despite this there is evidence of significant remaining regional data gaps. Estimates remain particularly poorly constrained for soils in the High Arctic region and physiographic regions with thin sedimentary overburden (mountains, highlands and plateaus) as well as for deposits below 3 m depth in deltas and the Yedoma region. While some components of the revised SOC stocks are similar in magnitude to those previously reported for this region, there are substantial differences in other components, including the fraction of perennially frozen SOC. Upscaled based on regional soil maps, estimated permafrost region SOC stocks are 217 ± 12 and 472 ± 27 Pg for the 0–0.3 and 0–1 m soil depths, respectively (±95% confidence intervals). Storage of SOC in 0–3 m of soils is estimated to 1035 ± 150 Pg. Of this, 34 ± 16 Pg C is stored in poorly developed soils of the High Arctic. Based on generalized calculations, storage of SOC below 3 m of surface soils in deltaic alluvium of major Arctic rivers is estimated as 91 ± 52 Pg. In the Yedoma region, estimated SOC stocks below 3 m depth are 181 ± 54 Pg, of which 74 ± 20 Pg is stored in intact Yedoma (late Pleistocene ice- and organic-rich silty sediments) with the remainder in refrozen thermokarst deposits. Total estimated SOC storage for the permafrost region is ∼1300 Pg with an uncertainty range of ∼1100 to 1500 Pg. Of this, ∼500 Pg is in non-permafrost soils, seasonally thawed in the active layer or in deeper taliks, while ∼800 Pg is perennially frozen. In conclusion, this represents a substantial ∼300 Pg lowering of the estimated perennially frozen SOC stock compared to previous estimates.« less

Thermal separation of soil particles from thermal conductivity measurement under various air pressures.

PubMed

Lu, Sen; Ren, Tusheng; Lu, Yili; Meng, Ping; Zhang, Jinsong

2017-01-05

The thermal conductivity of dry soils is related closely to air pressure and the contact areas between solid particles. In this study, the thermal conductivity of two-phase soil systems was determined under reduced and increased air pressures. The thermal separation of soil particles, i.e., the characteristic dimension of the pore space (d), was then estimated based on the relationship between soil thermal conductivity and air pressure. Results showed that under both reduced and increased air pressures, d estimations were significantly larger than the geometrical mean separation of solid particles (D), which suggested that conductive heat transfer through solid particles dominated heat transfer in dry soils. The increased air pressure approach gave d values lower than that of the reduced air pressure method. With increasing air pressure, more collisions between gas molecules and solid surface occurred in micro-pores and intra-aggregate pores due to the reduction of mean free path of air molecules. Compared to the reduced air pressure approach, the increased air pressure approach expressed more micro-pore structure attributes in heat transfer. We concluded that measuring thermal conductivity under increased air pressure procedures gave better-quality d values, and improved soil micro-pore structure estimation.

Estimation of organic carbon loss potential in north of Iran

NASA Astrophysics Data System (ADS)

Shahriari, A.; Khormali, F.; Kehl, M.; Welp, G.; Scholz, Ch.

2009-04-01

The development of sustainable agricultural systems requires techniques that accurately monitor changes in the amount, nature and breakdown rate of soil organic matter and can compare the rate of breakdown of different plant or animal residues under different management systems. In this research, the study area includes the southern alluvial and piedmont plains of Gorgan River extended from east to west direction in Golestan province, Iran. Samples from 10 soil series and were collected from cultivation depth (0-30 cm). Permanganate-oxidizable carbon (POC) an index of soil labile carbon, was used to show soil potential loss of organic carbon. In this index shows the maximum loss of OC in a given soil. Maximum loss of OC for each soil series was estimated through POC and bulk density (BD). The potential loss of OC were estimated between 1253263 and 2410813 g/ha Carbon. Stable organic constituents in the soil include humic substances and other organic macromolecules that are intrinsically resistant against microbial attack, or that are physically protected by adsorption on mineral surfaces or entrapment within clay and mineral aggregates. However, the (Clay + Silt)/OC ratio had a negative significant (p < 0.001) correlation with POC content, confirming the preserving effect of fine particle.

Satellite Mapping of Rain-Induced Nitric Oxide Emissions from Soils

NASA Technical Reports Server (NTRS)

Jaegle, L.; Martin, R. V.; Chance, K.; Steinberger, L.; Kurosu, T. P.; Jacob, D. J.; Modi, A. I.; Yoboue, V.; Sigha-Nkamdjou, L.; Galy-Lacaux, C.

2004-01-01

We use space-based observations of NO2 columns from the Global Ozone Monitoring Experiment (GOME) to map the spatial and seasonal variations of NOx emissions over Africa during 2000. The GOME observations show not only enhanced tropospheric NO2 columns from biomass burning during the dry season but also comparable enhancements from soil emissions during the rainy season over the Sahel. These soil emissions occur in strong pulses lasting 1-3 weeks following the onset of rain, and affect 3 million sq km of semiarid sub-Saharan savanna. Surface observations of NO2 from the International Global Atmospheric Chemistry (IGAC)/Deposition of Biochemically Important Trace Species (DEBITS)/Africa (IDAF) network over West Africa provide further evidence for a strong role for microbial soil sources. By combining inverse modeling of GOME NO2 columns with space-based observations of fires, we estimate that soils contribute 3.3+/-1.8 TgN/year, similar to the biomass burning source (3.8+/-2.1 TgN/year), and thus account for 40% of surface NO(x) emissions over Africa. Extrapolating to all the tropics, we estimate a 7.3 TgN/year biogenic soil source, which is a factor of 2 larger compared to model-based inventories but agrees with observation-based inventories. These large soil NO(x) emissions are likely to significantly contribute to the ozone enhancement originating from tropical Africa.

Estimation of the degree of soil P saturation from Brazilian Mehlich-1 P data and field investigations on P losses from agricultural sites in Minas Gerais.

PubMed

Fischer, P; Pöthig, R; Gücker, B; Venohr, M

The degree of phosphorus saturation (DPS) of agricultural soils is studied worldwide for risk assessment of phosphorus (P) losses. In previous studies, DPS could be reliably estimated from water-soluble P (WSP) for European and Brazilian soils. In the present study, we correlated measured WSP and Mehlich-1 P (M1P) from soils of Minas Gerais (MG) and Pernambuco (PE) (R(2) = 0.94, n = 59) to create a DPS map from monitoring data. The resulting DPS map showed high spatial variability and low values of DPS (54 ± 22%, mean and standard deviation; n = 1,827). Measured soil DPS values amounted to 63 ± 14% and resulted in relatively low dissolved P concentrations measured in a surface runoff study in MG. However, fertilizer grains on the soil surface led to high WSP values (>30 mg/kg) indicating high risks of dissolved P losses. We suppose that small Oxisol particles with Fe and Al hydroxides sorbed most of the dissolved fertilizer P in runoff so that P was mainly exported in particulate form. In soils with lower contents of P sorption and binding partners, e.g. Entisols in PE, this effect may be less dominant. Consequently, superficial fertilizer effects have to be considered in addition to DPS in risk assessment of P losses from agricultural areas in Brazil.

Multispectral Passive Microwave Correlations with an Antecedent Precipitation Index Using the Nimbus 7 SMMR.

DTIC Science & Technology

1984-08-01

M.D. Smallwood , 1981: An evaluation of the spatial resolution of soil moisture information. NASA CR-166724, Goddard Space Flight Center, Greenbelt, MD...Thesis, Dept of Meteor- ology, Univ. of Okla., Norman , 59 pp. Theis, S.W., 1979: Surface soil moisture estimation with the Electri- cally Scanning

Relations between surface conductance and spectral vegetation indices at intermediate (100 m sq to 15 km sq) length scales

NASA Technical Reports Server (NTRS)

Sellers, Piers J.; Heiser, Mark D.; Hall, Forrest G.

1992-01-01

The relationship between surface conductance and spectral vegetation indices is investigated utilizing the FIFE data set, principally the surface flux station data and images from the TM instrument. It is found that the unstressed canopy conductance for a given site for a given day is near-linearly related to the incident PAR flux. Estimates of unstressed canopy conductance were acquired via a model inversion that separated the soil and vegetation contributions to evapotranspiration and made adjustments for the effects of vapor pressure deficit and soil moisture stress.

Simulation of Soil Frost and Thaw Fronts Dynamics with Community Land Model 4.5

NASA Astrophysics Data System (ADS)

Gao, J.; Xie, Z.

2016-12-01

Freeze-thaw processes in soils, including changes in frost and thaw fronts (FTFs) , are important physical processes. The movement of FTFs affects soil water and thermal characteristics, as well as energy and water exchanges between land surface and the atmosphere, and then the land surface hydrothermal process. In this study, a two-directional freeze and thaw algorithm for simulating FTFs is incorporated into the community land surface model CLM4.5, which is called CLM4.5-FTF. The simulated FTFs depth and soil temperature of CLM4.5-FTF compared well with the observed data both in D66 station (permafrost) and Hulugou station (seasonally frozen soil). Because the soil temperature profile within a soil layer can be estimated according to the position of FTFs, CLM4.5 performed better in soil temperature simulation. Permafrost and seasonally frozen ground conditions in China from 1980 to 2010 were simulated using the CLM4.5-FTF. Numerical experiments show that the spatial distribution of simulated maximum frost depth by CLM4.5-FTF has seasonal variation obviously. Significant positive active-layer depth trends for permafrost regions and negative maximum freezing depth trends for seasonal frozen soil regions are simulated in response to positive air temperature trends except west of Black Sea.

Soil classification for seismic site effect using MASW and ReMi methods: A case study from western Anatolia (Dikili -İzmir)

NASA Astrophysics Data System (ADS)

Karabulut, Savaş

2018-03-01

The study area is located in the northern part of Izmir, Western Turkey, prone to an active tectonic extensional regime and includes typical features of sedimentary basins, horst-grabens surrounded by a series of normal and strike-slip faults. In September 1939 the Dikili (Kabakum) earthquake with a magnitude of Mw: 6.6 occurred and after this phenomenon, residents moved from the west of Dikili to the east (i.d. soft sediments to relative to rock area). A proper estimate of the earthquake-related hazard for the area is the main objective of this study. The site effect and soil engineering problems for estimating hazard parameters at the soil surface need to be carefully analyzed for seismic site classification and geo-engineering problems like soil liquefaction, soil settlement, soil bearing capacity and soil amplification. To solve the soil static and dynamic problems, shear-wave velocities have been used in a joint interpretation process; Multichannel Analysis of Surface Waves (MASW) and Refraction Microtremor (ReMi) analyses were conducted on 121 sites with 300 × 300 m grid size in an area of 60 km2. It has been proposed that the probability of an earthquake with a magnitude of Mw: 6 occurring within 10 years is 64%, when considering the Gutenberg-Richter model. This puts the region under an important earthquake risk. The estimated Vs30 values are ≤180 m/s in the central and the northernmost part of the study area are showing an E type soil after the classification of NEHRP, where alluvial deposits are dominant. Vs30 values in the north and central part are between 180 ≤ Vs ≤ 360 m/s suggesting a D type soil. In the southernmost part of the study area where volcanic rocks are widely distributed, Vs30 values range between 360 and 908 m/s, corresponding to a C type and B type soil. The results show that soil liquefaction induced settlement and soil amplification are the most important problems in the south and the northernmost part of the study area, which is densely populated and encompasses the urbanized part of the study region.

Estimation of effective soil hydraulic properties at field scale via ground albedo neutron sensing

NASA Astrophysics Data System (ADS)

Rivera Villarreyes, C. A.; Baroni, G.; Oswald, S. E.

2012-04-01

Upscaling of soil hydraulic parameters is a big challenge in hydrological research, especially in model applications of water and solute transport processes. In this contest, numerous attempts have been made to optimize soil hydraulic properties using observations of state variables such as soil moisture. However, in most of the cases the observations are limited at the point-scale and then transferred to the model scale. In this way inherent small-scale soil heterogeneities and non-linearity of dominate processes introduce sources of error that can produce significant misinterpretation of hydrological scenarios and unrealistic predictions. On the other hand, remote-sensed soil moisture over large areas is also a new promising approach to derive effective soil hydraulic properties over its observation footprint, but it is still limited to the soil surface. In this study we present a new methodology to derive soil moisture at the intermediate scale between point-scale observations and estimations at the remote-sensed scale. The data are then used for the estimation of effective soil hydraulic parameters. In particular, ground albedo neutron sensing (GANS) was used to derive non-invasive soil water content in a footprint of ca. 600 m diameter and a depth of few decimeters. This approach is based on the crucial role of hydrogen compared to other landscape materials as neutron moderator. As natural neutron measured aboveground depends on soil water content, the vertical footprint of the GANS method, i.e. its penetration depth, does also. Firstly, this study was designed to evaluate the dynamics of GANS vertical footprint and derive a mathematical model for its prediction. To test GANS-soil moisture and its penetration depth, it was accompanied by other soil moisture measurements (FDR) located at 5, 20 and 40 cm depths over the GANS horizontal footprint in a sunflower field (Brandenburg, Germany). Secondly, a HYDRUS-1D model was set up with monitored values of crop height and meteorological variables as input during a four-month period. Parameter estimation (PEST) software was coupled to HYDRUS-1D in order to calibrate soil hydraulic properties based on soil water content data. Thirdly, effective soil hydraulic properties were derived from GANS-soil moisture. Our observations show the potential of GANS to compensate the lack of information at the intermediate scale, soil water content estimation and effective soil properties. Despite measurement volumes, GANS-derived soil water content compared quantitatively to FDRs at several depths. For one-hour estimations, root mean square error was estimated as 0.019, 0.029 and 0.036 m3/m3 for 5 cm, 20 cm and 40 cm depths, respectively. In the context of soil hydraulic properties, this first application of GANS method succeed and its estimations were comparable to those derived by other approaches.

The contribution of space observations to water resources management; Proceedings of the Symposium, Bangalore, India, May 29-June 9, 1979

NASA Technical Reports Server (NTRS)

Salomonson, V. V. (Editor); Bhavsar, P. D.

1980-01-01

The symposium focused on hydrology, soil moisture estimation and ground water exploration, wetlands monitoring and water quality estimation, hydrometeorology, snow and ice monitoring, and evapotranspiration estimation. Other problems discussed include surface water and flood mapping, watershed runoff estimation and prediction, and new space systems contributing to water resources management.

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

Extension of coupled multispecies metal transport and speciation (TRANSPEC) model to soil.

PubMed

Bhavsar, Satyendra P; Gandhi, Nilima; Diamond, Miriam L

2008-01-01

Atmospheric deposition of metals emitted from mining operations has raised metal concentrations in the surrounding soils. This repository may be remobilized and act as a source of metals to nearby surface aquatic systems. It is important to understand metal dynamics and the impact of various chemistry and fate parameters on metal movement in the soil environment in order to evaluate risk associated with metals in terrestrial ecosystems and accurately establish critical discharge limits that are protective of aquatic biota. Here we extend our previously developed coupled multispecies metal fate-TRANsport and SPECiation/complexation (TRANSPEC) model, which was applicable to surface aquatic systems. The extended TRANSPEC, termed TRANSPEC-II, estimates the partition coefficient, K(d), between the soil-solid and -soluble phases using site-specific data and a semi-empirical regression model obtained from literature. A geochemical model calculates metal and species fractions in the dissolved and colloidal phases of the soil solution. The multispecies fugacity/aquivalence based fate-transport model then estimates inter-media transport rates such as leaching from soil, soil runoff, and water-sediment exchanges of each metal species. The model is illustratively applied to Ni in the Kelly Lake watershed (Sudbury, Ontario, Canada), where several mining operations are located. The model results suggest that the current atmospheric fallout supplies only 4% of Ni removed from soil through soil runoff and leaching. Soil runoff contributes about 20% of Ni entering into Kelly Lake with the rest coming from other sources. Leaching to groundwater, apart from runoff, is also a major loss process for Ni in the soil. A sensitivity analysis indicates that raising soil pH to above 6 may substantially reduce metal runoff and improve water quality of nearby water bodies that are impacted by runoff.

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.

The distribution of soil phosphorus for global biogeochemical modeling

DOE PAGES

Yang, Xiaojuan; Post, Wilfred M.; Thornton, Peter E.; ...

2013-04-16

We discuss that phosphorus (P) is a major element required for biological activity in terrestrial ecosystems. Although the total P content in most soils can be large, only a small fraction is available or in an organic form for biological utilization because it is bound either in incompletely weathered mineral particles, adsorbed on mineral surfaces, or, over the time of soil formation, made unavailable by secondary mineral formation (occluded). In order to adequately represent phosphorus availability in global biogeochemistry–climate models, a representation of the amount and form of P in soils globally is required. We develop an approach that buildsmore » on existing knowledge of soil P processes and databases of parent material and soil P measurements to provide spatially explicit estimates of different forms of naturally occurring soil P on the global scale. We assembled data on the various forms of phosphorus in soils globally, chronosequence information, and several global spatial databases to develop a map of total soil P and the distribution among mineral bound, labile, organic, occluded, and secondary P forms in soils globally. The amount of P, to 50cm soil depth, in soil labile, organic, occluded, and secondary pools is 3.6 ± 3, 8.6 ± 6, 12.2 ± 8, and 3.2 ± 2 Pg P (Petagrams of P, 1 Pg = 1 × 10 15g) respectively. The amount in soil mineral particles to the same depth is estimated at 13.0 ± 8 Pg P for a global soil total of 40.6 ± 18 Pg P. The large uncertainty in our estimates reflects our limited understanding of the processes controlling soil P transformations during pedogenesis and a deficiency in the number of soil P measurements. In spite of the large uncertainty, the estimated global spatial variation and distribution of different soil P forms presented in this study will be useful for global biogeochemistry models that include P as a limiting element in biological production by providing initial estimates of the available soil P for plant uptake and microbial utilization.« less

Space Weathering Effects in Lunar Soils: The Roles of Surface Exposure Time and Bulk Chemical Composition

NASA Technical Reports Server (NTRS)

Zhang, Shouliang; Keller, Lindsay P.

2011-01-01

Space weathering effects on lunar soil grains result from both radiation-damaged and deposited layers on grain surfaces. Typically, solar wind irradiation forms an amorphous layer on regolith silicate grains, and induces the formation of surficial metallic Fe in Fe-bearing minerals [1,2]. Impacts into the lunar regolith generate high temperature melts and vapor. The vapor component is largely deposited on the surfaces of lunar soil grains [3] as is a fraction of the melt [4, this work]. Both the vapor-deposits and the deposited melt typically contain nanophase Fe metal particles (npFe0) as abundant inclusions. The development of these rims and the abundance of the npFe0 in lunar regolith, and thus the optical properties, vary with the soil mineralogy and the length of time the soil grains have been exposed to space weathering effects [5]. In this study, we used the density of solar flare particle tracks in soil grains to estimate exposure times for individual grains and then perform nanometer-scale characterization of the rims using transmission electron microscopy (TEM). The work involved study of lunar soil samples with different mineralogy (mare vs. highland) and different exposure times (mature vs. immature).

Estimation of carbon emission from peatland fires using Landsat-8 OLI imagery in Siak District, Riau Province

NASA Astrophysics Data System (ADS)

Aisyah Fadhillah Hafni, Dinda; Syaufina, Lailan; Puspaningsih, Nining; Prasasti, Indah

2018-05-01

The study was conducted in three land cover conditions (secondary peat forest, shrub land, and palm plantation) that were burned in the Siak District, Riau Province, Indonesia year 2015. Measurement and calculation carbon emission from soil and vegetation of peatland should be done accurately to be implemented on climate change mitigation or greenhouse gases mitigation. The objective of the study was to estimate the carbon emission caused peatland fires in the Siak District, Riau Province, Indonesia year 2015. Estimated carbon emissions were performed using visual method and digital method. The visual method was a method that uses on-screen digitization assisted by hotspot data, the presence of smoke, and fire suppression data. The digital method was a method that uses the Normalized Burn Ratio (NBR) index. The estimated carbon emissions were calculated using the equation that was developed from IPCC 2006 in Verified Carbon Standard 2015. The results showed that the estimation of carbon emissions from fires from above the peat soil surface were higher than the carbon emissions from the peat soil. Carbon emissions above the peat soil surface of 1376.51 ton C/ha were obtained by visual method while 3984.33 ton C/ha were obtained by digital method. Peatland carbon emissions of 6.6 x 10-4 ton C/ha were obtained by visual method, whereas 2.84 x 10-3 ton C/ha was obtained by digital method. Visual method and digital method using remote sensing must be combined and developed in order to carbon emission values will be more accurate.

Comparing nocturnal eddy covariance measurements to estimates of ecosystem respiration made by scaling chamber measurements at six coniferous boreal sites

USGS Publications Warehouse

Lavigne, M.B.; Ryan, M.G.; Anderson, D.E.; Baldocchi, D.D.; Crill, P.M.; Fitzjarrald, D.R.; Goulden, M.L.; Gower, S.T.; Massheder, J.M.; McCaughey, J.H.; Rayment, M.; Striegl, Robert G.

1997-01-01

During the growing season, nighttime ecosystem respiration emits 30–100% of the daytime net photosynthetic uptake of carbon, and therefore measurements of rates and understanding of its control by the environment are important for understanding net ecosystem exchange. Ecosystem respiration can be measured at night by eddy covariance methods, but the data may not be reliable because of low turbulence or other methodological problems. We used relationships between woody tissue, foliage, and soil respiration rates and temperature, with temperature records collected on site to estimate ecosystem respiration rates at six coniferous BOREAS sites at half-hour or 1-hour intervals, and then compared these estimates to nocturnal measurements of CO2 exchange by eddy covariance. Soil surface respiration was the largest source of CO2 at all sites (48–71%), and foliar respiration made a large contribution to ecosystem respiration at all sites (25–43%). Woody tissue respiration contributed only 5–15% to ecosystem respiration. We estimated error for the scaled chamber predictions of ecosystem respiration by using the uncertainty associated with each respiration parameter and respiring biomass value. There was substantial uncertainty in estimates of foliar and soil respiration because of the spatial variability of specific respiration rates. In addition, more attention needs to be paid to estimating foliar respiration during the early part of the growing season, when new foliage is growing, and to determining seasonal trends of soil surface respiration. Nocturnal eddy covariance measurements were poorly correlated to scaled chamber estimates of ecosystem respiration (r2=0.06–0.27) and were consistently lower than scaled chamber predictions (by 27% on average for the six sites). The bias in eddy covariance estimates of ecosystem respiration will alter estimates of gross assimilation in the light and of net ecosystem exchange rates over extended periods.

Vs30 mapping at selected sites within the Greater Accra Metropolitan Area

NASA Astrophysics Data System (ADS)

Nortey, Grace; Armah, Thomas K.; Amponsah, Paulina

2018-06-01

A large part of Accra is underlain by a complex distribution of shallow soft soils. Within seismically active zones, these soils hold the most potential to significantly amplify seismic waves and cause severe damage, especially to structures sited on soils lacking sufficient stiffness. This paper presents preliminary site classification for the Greater Accra Metropolitan Area of Ghana (GAMA), using experimental data from two-dimensional (2-D) Multichannel Analysis of Surface Wave (MASW) technique. The dispersive characteristics of fundamental mode Rayleigh type surface waves were utilized for imaging the shallow subsurface layers (approx. up to 30 m depth) by estimating the 1D (depth) and 2D (depth and surface location) shear wave velocities at 5 selected sites. The average shear wave velocity for 30 m depth (Vs30), which is critical in evaluating the site response of the upper 30 m, was estimated and used for the preliminary site classification of the GAM area, as per NEHRP (National Earthquake Hazards Reduction Program). Based on the Vs30 values obtained in the study, two common site types C, and D corresponding to shallow (>6 m < 30 m) weathered rock and deep (up 30 m thick) stiff soils respectively, have been identified within the study area. Lower velocity profiles are inferred for the residual soils (sandy to silty clays), derived from the Accraian Formation that lies mainly within Accra central. Stiffer soil sites lie to the north of Accra, and to the west near Nyanyano. The seismic response characteristics over the residual soils in the GAMA have become apparent using the MASW technique. An extensive site effect map and a more robust probabilistic seismic hazard analysis can now be efficiently built for the metropolis, by considering the site classes and design parameters obtained from this study.

Evolution of Indian land surface biases in the seasonal hindcasts from the Met Office Global Seasonal Forecasting System GloSea5

NASA Astrophysics Data System (ADS)

Chevuturi, Amulya; Turner, Andrew G.; Woolnoug, Steve J.; Martin, Gill

2017-04-01

In this study we investigate the development of biases over the Indian region in summer hindcasts of the UK Met Office coupled initialised global seasonal forecasting system, GloSea5-GC2. Previous work has demonstrated the rapid evolution of strong monsoon circulation biases over India from seasonal forecasts initialised in early May, together with coupled strong easterly wind biases on the equator. These mean state biases lead to strong precipitation errors during the monsoon over the subcontinent. We analyse a set of three springtime start dates for the 20-year hindcast period (1992-2011) and fifteen total ensemble members for each year. We use comparisons with variety of observations to assess the evolution of the mean state biases over the Indian land surface. All biases within the model develop rapidly, particularly surface heat and radiation flux biases. Strong biases are present within the model climatology from pre-monsoon (May) in the surface heat fluxes over India (higher sensible / lower latent heat fluxes) when compared to observed estimates. The early evolution of such biases prior to onset rains suggests possible problems with the land surface scheme or soil moisture errors. Further analysis of soil moisture over the Indian land surface shows a dry bias present from the beginning of the hindcasts during the pre-monsoon. This lasts until the after the monsoon develops (July) after which there is a wet bias over the region. Soil moisture used for initialization of the model also shows a dry bias when compared against the observed estimates, which may lead to the same in the model. The early dry bias in the model may reduce local moisture availability through surface evaporation and thus may possibly limit precipitation recycling. On this premise, we identify and test the sensitivity of the monsoon in the model against higher soil moisture forcing. We run sensitivity experiments initiated using gridpoint-wise annual soil moisture maxima over the Indian land surface as input for experiments in the atmosphere-only version of the model. We plan to analyse the response of the sensitivity experiments on seasonal forecasting of surface heat fluxes and subsequently monsoon precipitation.

Seasonal Variability in Vadose zone biodegradation at a crude oil pipeline rupture site

USGS Publications Warehouse

Sihota, Natasha J.; Trost, Jared J.; Bekins, Barbara; Berg, Andrew M.; Delin, Geoffrey N.; Mason, Brent E.; Warren, Ean; Mayer, K. Ulrich

2016-01-01

Understanding seasonal changes in natural attenuation processes is critical for evaluating source-zone longevity and informing management decisions. The seasonal variations of natural attenuation were investigated through measurements of surficial CO2 effluxes, shallow soil CO2 radiocarbon contents, subsurface gas concentrations, soil temperature, and volumetric water contents during a 2-yr period. Surficial CO2 effluxes varied seasonally, with peak values of total soil respiration (TSR) occurring in the late spring and summer. Efflux and radiocarbon data indicated that the fractional contributions of natural soil respiration (NSR) and contaminant soil respiration (CSR) to TSR varied seasonally. The NSR dominated in the spring and summer, and CSR dominated in the fall and winter. Subsurface gas concentrations also varied seasonally, with peak values of CO2 and CH4 occurring in the fall and winter. Vadose zone temperatures and subsurface CO2 concentrations revealed a correlation between contaminant respiration and temperature. A time lag of 5 to 7 mo between peak subsurface CO2 concentrations and peak surface efflux is consistent with travel-time estimates for subsurface gas migration. Periods of frozen soils coincided with depressed surface CO2 effluxes and elevated CO2 concentrations, pointing to the temporary presence of an ice layer that inhibited gas transport. Quantitative reactive transport simulations demonstrated aspects of the conceptual model developed from field measurements. Overall, results indicated that source-zone natural attenuation (SZNA) rates and gas transport processes varied seasonally and that the average annual SZNA rate estimated from periodic surface efflux measurements is 60% lower than rates determined from measurements during the summer.

An evaluation of models of bare soil evaporation formulated with different land surface boundary conditions and assumptions

NASA Astrophysics Data System (ADS)

Smits, Kathleen M.; Ngo, Viet V.; Cihan, Abdullah; Sakaki, Toshihiro; Illangasekare, Tissa H.

2012-12-01

Bare soil evaporation is a key process for water exchange between the land and the atmosphere and an important component of the water balance. However, there is no agreement on the best modeling methodology to determine evaporation under different atmospheric boundary conditions. Also, there is a lack of directly measured soil evaporation data for model validation to compare these methods to establish the validity of their mathematical formulations. Thus, a need exists to systematically compare evaporation estimates using existing methods to experimental observations. The goal of this work is to test different conceptual and mathematical formulations that are used to estimate evaporation from bare soils to critically investigate various formulations and surface boundary conditions. Such a comparison required the development of a numerical model that has the ability to incorporate these boundary conditions. For this model, we modified a previously developed theory that allows nonequilibrium liquid/gas phase change with gas phase vapor diffusion to better account for dry soil conditions. Precision data under well-controlled transient heat and wind boundary conditions were generated, and results from numerical simulations were compared with experimental data. Results demonstrate that the approaches based on different boundary conditions varied in their ability to capture different stages of evaporation. All approaches have benefits and limitations, and no one approach can be deemed most appropriate for every scenario. Comparisons of different formulations of the surface boundary condition validate the need for further research on heat and vapor transport processes in soil for better modeling accuracy.

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.

Carbon black retention in saturated natural soils: Effects of flow conditions, soil surface roughness and soil organic matter.

PubMed

Lohwacharin, J; Takizawa, S; Punyapalakul, P

2015-10-01

We evaluated factors affecting the transport, retention, and re-entrainment of carbon black nanoparticles (nCBs) in two saturated natural soils under different flow conditions and input concentrations using the two-site transport model and Kelvin probe force microscopy (KPFM). Soil organic matter (SOM) was found to create unfavorable conditions for the retention. Despite an increased flow velocity, the relative stability of the estimated maximum retention capacity in soils may suggest that flow-induced shear stress forces were insufficient to detach nCB. The KPFM observation revealed that nCBs were retained at the grain boundary and on surface roughness, which brought about substantial discrepancy between theoretically-derived attachment efficiency factors and the ones obtained by the experiments using the two-site transport model. Thus, decreasing ionic strength and increasing solution pH caused re-entrainment of only a small fraction of retained nCB in the soil columns. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating soil erosion in Natura 2000 areas located on three semi-arid Mediterranean Islands.

PubMed

Zaimes, George N; Emmanouloudis, Dimitris; Iakovoglou, Valasia

2012-03-01

A major initiative in Europe is the protection of its biodiversity. To accomplish this, specific areas from all countries of the European Union are protected by the establishment of the "Natura 2000" network. One of the major threats to these areas and in general to ecosystems is soil erosion. The objective of this study was to quantitatively estimate surface soil losses for three of these protected areas that are located on semi-arid islands of the Mediterranean. One Natura 2000 area was selected from each of the following islands: Sicily in Italy, Cyprus and Rhodes in Greece. To estimate soil losses, Gerlach troughs were used. These troughs were established on slopes that ranged from 35-40% in four different vegetation types: i) Quercus ilex and Quercus rotundifolia forests, ii) Pinus brutia forests, iii) "Phrygana" shrublands and iv) vineyards. The shrublands had the highest soil losses (270 kg ha(-1) yr(-1)) that were 5-13 times more than the other three vegetation types. Soil losses in these shrublands should be considered a major concern. However, the other vegetation types also had high soil losses (21-50 kg ha(-1) yr(-1)). Conclusively, in order to enhance and conserve the biodiversity of these Natura 2000 areas protective management measures should be taken into consideration to decrease soil losses.

Soil seal development under simulated rainfall: Structural, physical and hydrological dynamics

NASA Astrophysics Data System (ADS)

Armenise, Elena; Simmons, Robert W.; Ahn, Sujung; Garbout, Amin; Doerr, Stefan H.; Mooney, Sacha J.; Sturrock, Craig J.; Ritz, Karl

2018-01-01

This study delivers new insights into rainfall-induced seal formation through a novel approach in the use of X-ray Computed Tomography (CT). Up to now seal and crust thickness have been directly quantified mainly through visual examination of sealed/crusted surfaces, and there has been no quantitative method to estimate this important property. X-ray CT images were quantitatively analysed to derive formal measures of seal and crust thickness. A factorial experiment was established in the laboratory using open-topped microcosms packed with soil. The factors investigated were soil type (three soils: silty clay loam - ZCL, sandy silt loam - SZL, sandy loam - SL) and rainfall duration (2-14 min). Surface seal formation was induced by applying artificial rainfall events, characterised by variable duration, but constant kinetic energy, intensity, and raindrop size distribution. Soil porosities derived from CT scans were used to quantify the thickness of the rainfall-induced surface seals and reveal temporal seal micro-morphological variations with increasing rainfall duration. In addition, the water repellency and infiltration dynamics of the developing seals were investigated by measuring water drop penetration time (WDPT) and unsaturated hydraulic conductivity (Kun). The range of seal thicknesses detected varied from 0.6 to 5.4 mm. Soil textural characteristics and OM content played a central role in the development of rainfall-induced seals, with coarser soil particles and lower OM content resulting in thicker seals. Two different trends in soil porosity vs. depth were identified: i) for SL soil porosity was lowest at the immediate soil surface, it then increased constantly with depth till the median porosity of undisturbed soil was equalled; ii) for ZCL and SL the highest reduction in porosity, as compared to the median porosity of undisturbed soil, was observed in a well-defined zone of maximum porosity reduction c. 0.24-0.48 mm below the soil surface. This contrasting behaviour was related to different dynamics and processes of seal formation which depended on the soil properties. The impact of rainfall-induced surface sealing on the hydrological behaviour of soil (as represented by WDTP and Kun) was rapid and substantial: an average 60% reduction in Kun occurred for all soils between 2 and 9 min rainfall, and water repellent surfaces were identified for SZL and ZCL. This highlights that the condition of the immediate surface of agricultural soils involving rainfall-induced structural seals has a strong impact in the overall ability of soil to function as water reservoir.

Use of anthropogenic radioisotopes to estimate rates of soil redistribution by wind II: The potential for future use of 239+240Pu

NASA Astrophysics Data System (ADS)

Van Pelt, R. Scott; Ketterer, Michael E.

2013-06-01

In the previous paper, the use of soilborne 137Cs from atmospheric fallout to estimate rates of soil redistribution, particularly by wind, was reviewed. This method relies on the assumption that the source of 137Cs in the soil profile is from atmospheric fallout following the period of atmospheric weapons testing so that the temporal and, to a certain extent, the spatial patterns of 137Cs deposition are known. One of the major limitations occurs when local or regional sources of 137Cs contamination mask the pulse from global fallout, making temporal estimates of redistribution difficult or impossible. Like 137Cs, Pu exhibits strong affinity for binding to soil particle surfaces, and therefore, re-distribution of Pu inventory indicates inferred soil re-distribution. Compared to 137Cs, 239Pu and 240Pu offer several important advantages: (a) the two major Pu isotopes have much longer half-lives than 137Cs and (b) the ratio 240Pu/239Pu is used to examine whether the Pu is from stratospheric fallout. In this paper, we review the literature concerning Pu in soil and of current attempts to use this tracer to estimate rates of soil redistribution. We also present preliminary, unpublished data from a pilot study designed to test whether or not 239+240Pu can be used to estimate rates of soil redistribution by wind. Based on similarities of profile distribution and relative inventories between 137Cs measurements and 239+240Pu measurements of split samples from a series of fields with documented wind erosion histories, we conclude that 239+240Pu may well be the anthropogenic radioisotope of choice for future soil redistribution investigations.

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.

GLEAM v3: updated land evaporation and root-zone soil moisture datasets

NASA Astrophysics Data System (ADS)

Martens, Brecht; Miralles, Diego; Lievens, Hans; van der Schalie, Robin; de Jeu, Richard; Fernández-Prieto, Diego; Verhoest, Niko

2016-04-01

Evaporation determines the availability of surface water resources and the requirements for irrigation. In addition, through its impacts on the water, carbon and energy budgets, evaporation influences the occurrence of rainfall and the dynamics of air temperature. Therefore, reliable estimates of this flux at regional to global scales are of major importance for water management and meteorological forecasting of extreme events. However, the global-scale magnitude and variability of the flux, and the sensitivity of the underlying physical process to changes in environmental factors, are still poorly understood due to the limited global coverage of in situ measurements. Remote sensing techniques can help to overcome the lack of ground data. However, evaporation is not directly observable from satellite systems. As a result, recent efforts have focussed on combining the observable drivers of evaporation within process-based models. The Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu) estimates terrestrial evaporation based on daily satellite observations of meteorological drivers of terrestrial evaporation, vegetation characteristics and soil moisture. Since the publication of the first version of the model in 2011, GLEAM has been widely applied for the study of trends in the water cycle, interactions between land and atmosphere and hydrometeorological extreme events. A third version of the GLEAM global datasets will be available from the beginning of 2016 and will be distributed using www.gleam.eu as gateway. The updated datasets include separate estimates for the different components of the evaporative flux (i.e. transpiration, bare-soil evaporation, interception loss, open-water evaporation and snow sublimation), as well as variables like the evaporative stress, potential evaporation, root-zone soil moisture and surface soil moisture. A new dataset using SMOS-based input data of surface soil moisture and vegetation optical depth will also be distributed. The most important updates in GLEAM include the revision of the soil moisture data assimilation system, the evaporative stress functions and the infiltration of rainfall. In this presentation, we will highlight the changes of the methodology and present the new datasets, their validation against in situ observations and the comparisons against alternative datasets of terrestrial evaporation, such as GLDAS-Noah, ERA-Interim and previous GLEAM datasets. Preliminary results indicate that the magnitude and the spatio-temporal variability of the evaporation estimates have been slightly improved upon previous versions of the datasets.

Geographical trends in 137Cs fallout from the Chernobyl accident and leaching from natural surface soil in Norway.

PubMed

Gjelsvik, Runhild; Steinnes, Eiliv

2013-12-01

In order to follow the turnover of (137)Cs in natural soils and estimate future trends in exposure of livestock, samples of natural surface soils were collected at 0-3 cm depth at 464 sites in 1995 and 463 sites in 2005 covering the country. In both cases the geographical pattern observed was similar to the original distribution from 1986, but the decline of (137)Cs activity in the surface soil was not the same everywhere. In 1995 the (137)Cs reduction since 1986 was found to be considerably greater in coastal areas than farther inland. The main reason for this appears to be the much greater deposition of marine cations such as Mg(2+) and Na(+) in the coastal areas, replacing Cs ions fixed on soil particle surfaces. This cation exchange appeared to be particularly strong near the southern coast where deposition of NH4(+) from transboundary air pollution is evident in addition to the marine cations. During 1995-2005 the (137)Cs decline in the surface soil was more uniform over the country than in the preceding 10-year period but still significantly higher in coastal areas than inland. Differences in precipitation chemistry may have influenced the uptake of (137)Cs in terrestrial food chains. Copyright © 2013 Elsevier Ltd. All rights reserved.

Soil respiration and aboveground litter dynamics of a tropical transitional forest in northwest Mato Grosso, Brazil

NASA Astrophysics Data System (ADS)

Valentini, Carla Maria Abido; Sanches, Luciana; de Paula, Sérgio Roberto; Vourlitis, George Louis; de Souza Nogueira, José; Pinto, Osvaldo Borges; de Almeida Lobo, Francisco

2008-12-01

Measurements of soil CO2 efflux, litter production, and the surface litter pool biomass were made over a 1 year period in a tropical transitional forest near Sinop, Mato Grosso, Brazil with the aim of quantifying the seasonal variation in soil respiration and litter decomposition and the annual contribution of litter decomposition to soil CO2 efflux. Average annual soil CO2 efflux (+/-95% confidence interval (CI)) was 7.91 +/- 1.16 g C m-2 d-1. Soil CO2 efflux was highest during the November-February wet season (9.15 +/- 0.90 g C m-2 d-1) and lowest during the May-September dry season (6.19 +/- 1.40 g C m-2 d-1), and over 60% of the variation in seasonal soil CO2 efflux was explained by seasonal variations in soil temperature and moisture. Mass balance estimates of mean (+/-95% CI) decomposition rates were statistically different between the wet and dry seasons (0.66 +/- 0.08 and 1.65 +/- 0.10 g C m-2 d-1, respectively), and overall, decomposition of leaf litter comprised 16% of the average annual soil respiration. Leaf litter production was higher during the dry season, and mean (+/-95% CI) leaf litter fall (5.6 +/- 1.7 Mg ha-1) comprised 73% of the total litter fall (7.8 +/- 2.3 Mg ha-1). Average (+/-95% CI) annual litter pool biomass was estimated to be 5.5 +/- 0.3 Mg ha-1, which was similar to the measured pool size (5.7 +/- 2.2 Mg ha-1). Overall, seasonal variations in environmental variables, specifically water availability (soil moisture and rainfall), had a profound influence on litter production, soil respiration, and surface litter decomposition.

The influence of annual precipitation, topography, and vegetative cover on soil moisture and summer drought in southern California.

PubMed

Miller, P C; Poole, D K

1983-02-01

The influence of annual precipitation and vegetation cover on soil moisture and on the length of the summer drought was estimated quantitatively using 9 years of soil moisture data collected at Echo Valley in southern California. The measurements support the conclusions that in the semi-arid mediterranean climate a soil drought will occur regardless of vegetation cover and annual precipitation, but the length of the drought is greatly dependent on soil depth and rockiness. Evergreen species which can survive this drought tend to accentuate the drought, especially in deep soil levels, by developing a canopy with a large transpiring surface.

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

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Improvment of the Trapezoid Method Using Raw Landsat Image Digital Count Data for Soil Moisture Estimation in the Texas (usa) High Plains

NASA Astrophysics Data System (ADS)

Shafian, S.; Maas, S. J.

2015-12-01

Variations in soil moisture strongly affect surface energy balances, regional runoff, land erosion and vegetation productivity (i.e., potential crop yield). Hence, the estimation of soil moisture is very valuable in the social, economic, humanitarian (food security) and environmental segments of society. Extensive efforts to exploit the potential of remotely sensed observations to help quantify this complex variable are ongoing. This study aims at developing a new index, the Thermal Ground cover Moisture Index (TGMI), for estimating soil moisture content. This index is based on empirical parameterization of the relationship between raw image digital count (DC) data in the thermal infrared spectral band and ground cover (determined from raw image digital count data in the red and near-infrared spectral bands).The index uses satellite-derived information only, and the potential for its operational application is therefore great. This study was conducted in 18 commercial agricultural fields near Lubbock, TX (USA). Soil moisture was measured in these fields over two years and statistically compared to corresponding values of TGMI determined from Landsat image data. Results indicate statistically significant correlations between TGMI and field measurements of soil moisture (R2 = 0.73, RMSE = 0.05, MBE = 0.17 and AAE = 0.049), suggesting that soil moisture can be estimated using this index. It was further demonstrated that maps of TGMI developed from Landsat imagery could be constructed to show the relative spatial distribution of soil moisture across a region.

Impacts of leaves, roots, and earthworms on soil organic matter composition and distribution in sycamore maple stands

NASA Astrophysics Data System (ADS)

Rivera, N.; Mueller, K. E.; Mueller, C. W.; Oleksyn, J.; Hale, C.; Freeman, K. H.; Eissenstat, D.

2009-12-01

The relative contributions of leaf and root material to soil organic matter (SOM) are poorly understood despite the importance of constraining SOM sources to conceptual and numeric models of SOM dynamics. Selective ingestion and bioturbation of litter and soil by earthworms can alter the fate and spatial distribution of OM in soils, including stabilization pathways of leaf and root litter. However, studies on the contributions of leaves, roots, and earthworms to SOM dynamics are rare. In 3 stands of sycamore maple (Acer pseudoplatanus) with minimal O horizon development and high earthworm activity, we sampled surface litter (> 2 mm) from the Oi horizon, fine roots (< 2 mm), bulk mineral soils (0-20 cm depth), and earthworm casts from Lumbricus terrestris middens. The chemical composition of these samples was estimated by wet-chemical degradation followed by GC-MS analysis. In addition, elemental analyses (C and N) were performed on bulk soils and earthworm casts, before and after physical fractionation by means of particle size and density. Relative to bulk soils, earthworm casts were highly enriched in organic matter, dominated by large particulate OM, and had lower acid to aldehyde ratios among lignin monomers (a proxy for extent of decomposition), confirming that L. terrestris casts stabilize recent plant litter inputs. Maple fine roots and surface litter were distinguished by different profiles of carboxylic acids estimated by GC-MS, facilitating interpretation of OM sources in bulk soil and earthworm casts. Earthworm casts were characterized by a distribution of carboxylic acids similar to that of surface litter while bulk soils had a carboxylic acid profile much closer to that of roots. These results confirm that L. terrestris is primarily a surface, leaf feeder and suggest that OM in the bulk soil may be dominated by root inputs. In bulk soils, the ratio of lignin to hydroxy- and diacids derived from suberin and cutin was low relative to plant litter, confirming the often-observed selective preservation of aliphatic over aromatic biomolecules. The ratio of lignin to cutin/suberin acids in earthworm casts was also low; based on the minimal extent of decomposition in casts evident by lignin acid to aldehyde ratios, we attribute this to selective ingestion by L. terrestris of leaf litter rich in aliphatic biomolecules at the expense of woody debris and petioles rich in lignin, rather than selective preservation.

Remote sensing-based estimation of annual soil respiration at two contrasting forest sites

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

Huang, Ni; Gu, Lianhong; Black, T. Andrew

Here, soil respiration (R s), an important component of the global carbon cycle, can be estimated using remotely sensed data, but the accuracy of this technique has not been thoroughly investigated. In this study, we proposed a methodology for the remote estimation of annual R s at two contrasting FLUXNET forest sites (a deciduous broadleaf forest and an evergreen needleleaf forest). A version of the Akaike's information criterion was used to select the best model from a range of models for annual R s estimation based on the remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer and root-zonemore » soil moisture product derived from assimilation of the NASA Advanced Microwave Scanning Radiometer soil moisture products and a two-layer Palmer water balance model. We found that the Arrhenius-type function based on nighttime land surface temperature (LST-night) was the best model by comprehensively considering the model explanatory power and model complexity at the Missouri Ozark and BC-Campbell River 1949 Douglas-fir sites.« less

Using Unmanned Aerial Vehicle (UAV) for spatio-temporal monitoring of soil erosion and roughness in Chania, Crete, Greece

NASA Astrophysics Data System (ADS)

Alexakis, Dimitrios; Seiradakis, Kostas; Tsanis, Ioannis

2016-04-01

This article presents a remote sensing approach for spatio-temporal monitoring of both soil erosion and roughness using an Unmanned Aerial Vehicle (UAV). Soil erosion by water is commonly known as one of the main reasons for land degradation. Gully erosion causes considerable soil loss and soil degradation. Furthermore, quantification of soil roughness (irregularities of the soil surface due to soil texture) is important and affects surface storage and infiltration. Soil roughness is one of the most susceptible to variation in time and space characteristics and depends on different parameters such as cultivation practices and soil aggregation. A UAV equipped with a digital camera was employed to monitor soil in terms of erosion and roughness in two different study areas in Chania, Crete, Greece. The UAV followed predicted flight paths computed by the relevant flight planning software. The photogrammetric image processing enabled the development of sophisticated Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimeter level. The DTMs were developed using photogrammetric processing of more than 500 images acquired with the UAV from different heights above the ground level. As the geomorphic formations can be observed from above using UAVs, shadowing effects do not generally occur and the generated point clouds have very homogeneous and high point densities. The DTMs generated from UAV were compared in terms of vertical absolute accuracies with a Global Navigation Satellite System (GNSS) survey. The developed data products were used for quantifying gully erosion and soil roughness in 3D as well as for the analysis of the surrounding areas. The significant elevation changes from multi-temporal UAV elevation data were used for estimating diachronically soil loss and sediment delivery without installing sediment traps. Concerning roughness, statistical indicators of surface elevation point measurements were estimated and various parameters such as standard deviation of DTM, deviation of residual and standard deviation of prominence were calculated directly from the extracted DTM. Sophisticated statistical filters and elevation indices were developed to quantify both soil erosion and roughness. The applied methodology for monitoring both soil erosion and roughness provides an optimum way of reducing the existing gap between field scale and satellite scale. Keywords : UAV, soil, erosion, roughness, DTM

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

PubMed

Fujimori, Takashi; Takigami, Hidetaka

2014-02-01

We studied distribution of heavy metals [lead (Pb), copper (Cu) and zinc (Zn)] in surface soil at an electronic-waste (e-waste) recycling workshop near Metro Manila in the Philippines to evaluate the pollution size (spot size, small area or the entire workshop), as well as to assess heavy metal transport into the surrounding soil environment. On-site length-of-stride-scale (~70 cm) measurements were performed at each surface soil point using field-portable X-ray fluorescence (FP-XRF). The surface soil at the e-waste recycling workshop was polluted with Cu, Zn and Pb, which were distributed discretely in surface soil. The site was divided into five areas based on the distance from an entrance gate (y-axis) of the e-waste recycling workshop. The three heavy metals showed similar concentration gradients in the y-axis direction. Zn, Pb and Cu concentrations were estimated to decrease to half of their maximum concentrations at ~3, 7 and 7 m from the pollution spot, respectively, inside the informal e-waste recycling workshop. Distance from an entrance may play an important role in heavy metal transport at the soil surface. Using on-site FP-XRF, we evaluated the metal ratio to characterise pollution features of the solid surface. Variability analysis of heavy metals revealed vanishing surficial autocorrelation over metre ranges. Also, the possibility of concentration prediction at unmeasured points using geostatistical kriging was evaluated, and heavy metals had a relative "small" pollution scales and remained inside the original workshop compared with toxic organohalogen compounds. Thus, exposure to heavy metals may directly influence the health of e-waste workers at the original site rather than the surrounding habitat and environmental media.

A practical method to detect the freezing/thawing onsets of seasonal frozen ground in Alaska

NASA Astrophysics Data System (ADS)

Chen, Xiyu; Liu, Lin

2017-04-01

Microwave remote sensing can provide useful information about freeze/thaw state of soil at the Earth surface. An edge detection method is applied in this study to estimate the onsets of soil freeze/thaw state transition using L band space-borne radiometer data. The Soil Moisture Active Passive (SMAP) mission has a L band radiometer and can provide daily brightness temperature (TB) with horizontal/vertical polarizations. We use the normalized polarization ratios (NPR) calculated based on the Level-1C TB product of SMAP (spatial resolution: 36 km) as the indicator for soil freeze/thaw state, to estimate the freezing and thawing onsets in Alaska in the year of 2015 and 2016. NPR is calculated based on the difference between TB at vertical and horizontal polarizations. Therefore, it is strongly sensitive to liquid water content change in the soil and independent with the soil temperature. Onset estimation is based on the detection of abrupt changes of NPR in transition seasons using edge detection method, and the validation is to compare estimated onsets with the onsets derived from in situ measurement. According to the comparison, the estimated onsets were generally 15 days earlier than the measured onsets in 2015. However, in 2016 there were 4 days in average for the estimation earlier than the measured, which may be due to the less snow cover. Moreover, we extended our estimation to the entire state of Alaska. The estimated freeze/thaw onsets showed a reasonable latitude-dependent distribution although there are still some outliers caused by the noisy variation of NPR. At last, we also try to remove these outliers and improve the performance of the method by smoothing the NPR time series.

Ectomycorrhizal mats alter forest soil biogeochemistry

Treesearch

Laurel A. Kluber; Kathryn M. Tinnesand; Bruce A. Caldwell; Susie M. Dunham; Rockie R. Yarwood; Peter J. Bottomley; David D. Myrold

2010-01-01

Dense hyphal mats formed by ectomycorrhizal (EcM) fungi are prominent features in Douglas-fir forest ecosystems, and have been estimated to cover up to 40% of the soil surface in some forest stands. Two morphotypes of EcM mats have been previously described: rhizomorphic mats, which have thick hyphal rhizomorphs and are found primarily in the organic horizon, and...

Carbon Cycling in Wetland Forest Soils

Treesearch

Carl C. Trettin; Martin F. Jurgensen

2003-01-01

Wetlands comprise a small proportion (i.e., 2 to 3%) of earth's terrestrial surface, yet they contain a significant proportion of the terrestrial carbon (C) pool. Soils comprise the largest terrestrial C pool (ca. 1550 Pg C in upper 100 cm; Eswaran et al., 1993; Batjes, 1996), and wetlands contain the single largest component, with estimates ranging between 18...

Evaluation of CO2 Efflux From Soils: A New Method Using Streamwater CO2 Measurements, Field Data and a Watershed Model

NASA Astrophysics Data System (ADS)

Sullivan, A. B.; Mulholland, P. J.; Jones, J. B.

2001-05-01

Headwater streams are almost always supersaturated with CO2 compared to concentrations expected in equilibrium with atmospheric CO2. Direct measurements of CO2 in two streams in eastern Tennessee with different bedrock lithologies (Walker Branch, Upper Gum Hollow Branch) over a year revealed levels of supersaturation of two to five times atmospheric CO2. Highest levels were generally found during the summer months. Springs discharging into the stream had dissolved CO2 concentration up to an order of magnitude higher than that in streamwater. These levels of supersaturation are a reflection of the high concentrations of CO2 in soil produced by root respiration and organic matter decomposition. The hydrologic connection between soil CO2 and streamwater CO2 forms the basis of our method to determine soil CO2 concentrations and efflux from the soil to the atmosphere. The method starts with streamwater measurements of CO2. Then corrections are made for evasion from the stream surface using injections of a conservative solute tracer and volatile gas, and for instream metabolism using a dissolved oxygen change technique. The approach then works backward along the hydrologic flowpath and evaluates the contribution of bedrock weathering, which consumes CO2, by examining the changes in major ion chemistry between precipitation and the stream. This produces estimates of CO2 concentration in soil water and soil atmosphere, which when coupled with soil porosity, allows estimation of CO2 efflux from soil. The hydrologic integration of CO2 signals from whole watersheds into streamwater allows calculation of soil CO2 efflux at large scales. These estimates are at scales larger than current chamber or tower methods, and can provide broad estimates of soil CO2 efflux with easily collected stream chemistry data.

Modeling Surface Roughness to Estimate Surface Moisture Using Radarsat-2 Quad Polarimetric SAR Data

NASA Astrophysics Data System (ADS)

Nurtyawan, R.; Saepuloh, A.; Budiharto, A.; Wikantika, K.

2016-08-01

Microwave backscattering from the earth's surface depends on several parameters such as surface roughness and dielectric constant of surface materials. The two parameters related to water content and porosity are crucial for estimating soil moisture. The soil moisture is an important parameter for ecological study and also a factor to maintain energy balance of land surface and atmosphere. Direct roughness measurements to a large area require extra time and cost. Heterogeneity roughness scale for some applications such as hydrology, climate, and ecology is a problem which could lead to inaccuracies of modeling. In this study, we modeled surface roughness using Radasat-2 quad Polarimetric Synthetic Aperture Radar (PolSAR) data. The statistical approaches to field roughness measurements were used to generate an appropriate roughness model. This modeling uses a physical SAR approach to predicts radar backscattering coefficient in the parameter of radar configuration (wavelength, polarization, and incidence angle) and soil parameters (surface roughness and dielectric constant). Surface roughness value is calculated using a modified Campbell and Shepard model in 1996. The modification was applied by incorporating the backscattering coefficient (σ°) of quad polarization HH, HV and VV. To obtain empirical surface roughness model from SAR backscattering intensity, we used forty-five sample points from field roughness measurements. We selected paddy field in Indramayu district, West Java, Indonesia as the study area. This area was selected due to intensive decreasing of rice productivity in the Northern Coast region of West Java. Third degree polynomial is the most suitable data fitting with coefficient of determination R2 and RMSE are about 0.82 and 1.18 cm, respectively. Therefore, this model is used as basis to generate the map of surface roughness.

Computer simulation of a space SAR using a range-sequential processor for soil moisture mapping

NASA Technical Reports Server (NTRS)

Fujita, M.; Ulaby, F. (Principal Investigator)

1982-01-01

The ability of a spaceborne synthetic aperture radar (SAR) to detect soil moisture was evaluated by means of a computer simulation technique. The computer simulation package includes coherent processing of the SAR data using a range-sequential processor, which can be set up through hardware implementations, thereby reducing the amount of telemetry involved. With such a processing approach, it is possible to monitor the earth's surface on a continuous basis, since data storage requirements can be easily met through the use of currently available technology. The Development of the simulation package is described, followed by an examination of the application of the technique to actual environments. The results indicate that in estimating soil moisture content with a four-look processor, the difference between the assumed and estimated values of soil moisture is within + or - 20% of field capacity for 62% of the pixels for agricultural terrain and for 53% of the pixels for hilly terrain. The estimation accuracy for soil moisture may be improved by reducing the effect of fading through non-coherent averaging.

A surface complexation and ion exchange model of Pb and Cd competitive sorption on natural soils

NASA Astrophysics Data System (ADS)

Serrano, Susana; O'Day, Peggy A.; Vlassopoulos, Dimitri; García-González, Maria Teresa; Garrido, Fernando

2009-02-01

The bioavailability and fate of heavy metals in the environment are often controlled by sorption reactions on the reactive surfaces of soil minerals. We have developed a non-electrostatic equilibrium model (NEM) with both surface complexation and ion exchange reactions to describe the sorption of Pb and Cd in single- and binary-metal systems over a range of pH and metal concentration. Mineralogical and exchange properties of three different acidic soils were used to constrain surface reactions in the model and to estimate surface densities for sorption sites, rather than treating them as adjustable parameters. Soil heterogeneity was modeled with >FeOH and >SOH functional groups, representing Fe- and Al-oxyhydroxide minerals and phyllosilicate clay mineral edge sites, and two ion exchange sites (X - and Y -), representing clay mineral exchange. An optimization process was carried out using the entire experimental sorption data set to determine the binding constants for Pb and Cd surface complexation and ion exchange reactions. Modeling results showed that the adsorption of Pb and Cd was distributed between ion exchange sites at low pH values and specific adsorption sites at higher pH values, mainly associated with >FeOH sites. Modeling results confirmed the greater tendency of Cd to be retained on exchange sites compared to Pb, which had a higher affinity than Cd for specific adsorption on >FeOH sites. Lead retention on >FeOH occurred at lower pH than for Cd, suggesting that Pb sorbs to surface hydroxyl groups at pH values at which Cd interacts only with exchange sites. The results from the binary system (both Pb and Cd present) showed that Cd retained in >FeOH sites decreased significantly in the presence of Pb, while the occupancy of Pb in these sites did not change in the presence of Cd. As a consequence of this competition, Cd was shifted to ion exchange sites, where it competes with Pb and possibly Ca (from the background electrolyte). Sorption on >SOH functional groups increased with increasing pH but was small compared to >FeOH sites, with little difference between single- and binary-metal systems. Model reactions and conditional sorption constants for Pb and Cd sorption were tested on a fourth soil that was not used for model optimization. The same reactions and constants were used successfully without adjustment by estimating surface site concentrations from soil mineralogy. The model formulation developed in this study is applicable to acidic mineral soils with low organic matter content. Extension of the model to soils of different composition may require selection of surface reactions that account for differences in clay and oxide mineral composition and organic matter content.

Estimating Surface Soil Moisture in Simulated AVIRIS Spectra

NASA Technical Reports Server (NTRS)

Whiting, Michael L.; Li, Lin; Ustin, Susan L.

2004-01-01

Soil albedo is influenced by many physical and chemical constituents, with moisture being the most influential on the spectra general shape and albedo (Stoner and Baumgardner, 1981). Without moisture, the intrinsic or matrix reflectance of dissimilar soils varies widely due to differences in surface roughness, particle and aggregate sizes, mineral types, including salts, and organic matter contents. The influence of moisture on soil reflectance can be isolated by comparing similar soils in a study of the effects that small differences in moisture content have on reflectance. However, without prior knowledge of the soil physical and chemical constituents within every pixel, it is nearly impossible to accurately attribute the reflectance variability in an image to moisture or to differences in the physical and chemical constituents in the soil. The effect of moisture on the spectra must be eliminated to use hyperspectral imagery for determining minerals and organic matter abundances of bare agricultural soils. Accurate soil mineral and organic matter abundance maps from air- and space-borne imagery can improve GIS models for precision farming prescription, and managing irrigation and salinity. Better models of soil moisture and reflectance will also improve the selection of soil endmembers for spectral mixture analysis.

Evaluation of WES One-Dimensional Dynamic Soil Testing Procedures.

DTIC Science & Technology

1983-06-01

relations: 18 AC - (2K +4G (6)a so I so r (6) Dividing the stress reduction Aa by a from equation (5), we obtain ana r estimate of the fractional error in...walls, the radial expansion of the soil caused by the expansion of the steel side walls, and the nonuniform stress and strain states in the sample...is applied rapidly, the stress state in the soil at the steel base may be very different from that at the top surface of the soil. With such

Estimating net rainfall, evaporation and water storage of a bare soil from sequential L-band emissivities

NASA Technical Reports Server (NTRS)

Stroosnijder, L.; Lascano, R. J.; Newton, R. W.; Vanbavel, C. H. M.

1984-01-01

A general method to use a time series of L-band emissivities as an input to a hydrological model for continuously monitoring the net rainfall and evaporation as well as the water content over the entire soil profile is proposed. The model requires a sufficiently accurate and general relation between soil emissivity and surface moisture content. A model which requires the soil hydraulic properties as an additional input, but does not need any weather data was developed. The method is shown to be numerically consistent.

A model for estimating time-variant rainfall infiltration as a function of antecedent surface moisture and hydrologic soil type

NASA Technical Reports Server (NTRS)

Wilkening, H. A.; Ragan, R. M.

1982-01-01

Recent research indicates that the use of remote sensing techniques for the measurement of near surface soil moisture could be practical in the not too distant future. Other research shows that infiltration rates, especially for average or frequent rainfall events, are extremely sensitive to the proper definition and consideration of the role of the soil moisture at the beginning of the rainfall. Thus, it is important that an easy to use, but theoretically sound, rainfall infiltration model be available if the anticipated remotely sensed soil moisture data is to be optimally utilized for hydrologic simulation. A series of numerical experiments with the Richards' equation for an array of conditions anticipated in watershed hydrology were used to develop functional relationships that describe temporal infiltration rates as a function of soil type and initial moisture conditions.

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

Reconstructing daily clear-sky land surface temperature for cloudy regions from MODIS data

USDA-ARS?s Scientific Manuscript database

Land surface temperature (LST) is a critical parameter in environmental studies and resource management. The MODIS LST data product has been widely used in various studies, such as drought monitoring, evapotranspiration mapping, soil moisture estimation and forest fire detection. However, cloud cont...

Using Data Assimilation Diagnostics to Assess the SMAP Level-4 Soil Moisture Product

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

2018-01-01

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx.2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Global Assessment of the SMAP Level-4 Soil Moisture Product Using Assimilation Diagnostics

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Liu, Qing; De Lannoy, Gabrielle; Crow, Wade; Kimball, John; Koster, Randy; Ardizzone, Joe

2018-01-01

The Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides 3-hourly, 9-km resolution, global estimates of surface (0-5 cm) and root-zone (0-100 cm) soil moisture and related land surface variables from 31 March 2015 to present with approx. 2.5-day latency. The ensemble-based L4_SM algorithm assimilates SMAP brightness temperature (Tb) observations into the Catchment land surface model. This study describes the spatially distributed L4_SM analysis and assesses the observation-minus-forecast (O-F) Tb residuals and the soil moisture and temperature analysis increments. Owing to the climatological rescaling of the Tb observations prior to assimilation, the analysis is essentially unbiased, with global mean values of approx. 0.37 K for the O-F Tb residuals and practically zero for the soil moisture and temperature increments. There are, however, modest regional (absolute) biases in the O-F residuals (under approx. 3 K), the soil moisture increments (under approx. 0.01 cu m/cu m), and the surface soil temperature increments (under approx. 1 K). Typical instantaneous values are approx. 6 K for O-F residuals, approx. 0.01 (approx. 0.003) cu m/cu m for surface (root-zone) soil moisture increments, and approx. 0.6 K for surface soil temperature increments. The O-F diagnostics indicate that the actual errors in the system are overestimated in deserts and densely vegetated regions and underestimated in agricultural regions and transition zones between dry and wet climates. The O-F auto-correlations suggest that the SMAP observations are used efficiently in western North America, the Sahel, and Australia, but not in many forested regions and the high northern latitudes. A case study in Australia demonstrates that assimilating SMAP observations successfully corrects short-term errors in the L4_SM rainfall forcing.

Surface wind accuracy for modeling mineral dust emissions: Comparing two regional models in a Bodélé case study

NASA Astrophysics Data System (ADS)

Laurent, B.; Heinold, B.; Tegen, I.; Bouet, C.; Cautenet, G.

2008-05-01

After a decade of research on improving the description of surface and soil features in desert regions to accurately model mineral dust emissions, we now emphasize the need for deeper evaluating the accuracy of modeled 10-m surface wind speeds U 10 . Two mesoscale models, the Lokal-Modell (LM) and the Regional Atmospheric Modeling System (RAMS), coupled with an explicit dust emission model have previously been used to simulate mineral dust events in the Bodélé region. We compare LM and RAMS U 10 , together with measurements at the Chicha site (BoDEx campaign) and Faya-Largeau meteorological station. Surface features and soil schemes are investigated to correctly simulate U 10 intensity and diurnal variability. The uncertainties in dust emissions computed with LM and RAMS U 10 and different soil databases are estimated. This sensitivity study shows the importance of accurate computation of surface winds to improve the quantification of regional dust emissions from the Bodélé

Stochastic modeling of macrodispersion in unsaturated heterogeneous porous media. Final report

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

Yeh, T.C.J.

1995-02-01

Spatial heterogeneity of geologic media leads to uncertainty in predicting both flow and transport in the vadose zone. In this work an efficient and flexible, combined analytical-numerical Monte Carlo approach is developed for the analysis of steady-state flow and transient transport processes in highly heterogeneous, variably saturated porous media. The approach is also used for the investigation of the validity of linear, first order analytical stochastic models. With the Monte Carlo analysis accurate estimates of the ensemble conductivity, head, velocity, and concentration mean and covariance are obtained; the statistical moments describing displacement of solute plumes, solute breakthrough at a compliancemore » surface, and time of first exceedance of a given solute flux level are analyzed; and the cumulative probability density functions for solute flux across a compliance surface are investigated. The results of the Monte Carlo analysis show that for very heterogeneous flow fields, and particularly in anisotropic soils, the linearized, analytical predictions of soil water tension and soil moisture flux become erroneous. Analytical, linearized Lagrangian transport models also overestimate both the longitudinal and the transverse spreading of the mean solute plume in very heterogeneous soils and in dry soils. A combined analytical-numerical conditional simulation algorithm is also developed to estimate the impact of in-situ soil hydraulic measurements on reducing the uncertainty of concentration and solute flux predictions.« less

Use of geostationary satellite imagery in optical and thermal bands for the estimation of soil moisture status and land evapotranspiration

NASA Astrophysics Data System (ADS)

Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.

2009-04-01

For water and agricultural management, there is an increasing demand to monitor the soil water status and the land evapotranspiration. In the framework of the LSA-SAF project (http://landsaf.meteo.pt), we are developing an energy balance model forced by remote sensing products, i.e. radiation components and vegetation parameters, to monitor in quasi real-time the evapotranspiration rate over land (Gellens-Meulenberghs et al, 2007; Ghilain et al, 2008). The model is applied over the full MSG disk, i.e. including Europe and Africa. Meteorological forcing, as well as the soil moisture status, is provided by the forecasts of the ECMWF model. Since soil moisture is computed by a forecast model not dedicated to the monitoring of the soil water status, inadequate soil moisture input can occur, and can cause large effects on evapotranspiration rates, especially over semi-arid or arid regions. In these regions, a remotely sensed-based method for the soil moisture retrieval can therefore be preferable, to avoid too strong dependency in ECMWF model estimates. Among different strategies, remote sensing offers the advantage of monitoring large areas. Empirical methods of soil moisture assessment exist using remotely sensed derived variables either from the microwave bands or from the thermal bands. Mainly polar orbiters are used for this purpose, and little attention has been paid to the new possibilities offered by geosynchronous satellites. In this contribution, images of the SEVIRI instrument on board of MSG geosynchronous satellites are used. Dedicated operational algorithms were developed for the LSA-SAF project and now deliver images of land surface temperature (LST) every 15-minutes (Trigo et al, 2008) and vegetations indices (leaf area index, LAI; fraction of vegetation cover, FVC; fraction of absorbed photosynthetically active radiation, FAPAR) every day (Garcia-Haro et al, 2005) over Africa and Europe. One advantage of using products derived from geostationary satellites is the close monitoring of the diurnal variation of the land surface temperature. This feature reinforced the statistical strength of empirical methods. An empirical method linking land surface morning heating rates and the fraction of the vegetation cover, also known as a ‘Triangle method' (Gillies et al, 1997) is examined. This method is expected to provide an estimation of a root-zone soil moisture index. The sensitivity of the method to wind speed, soil type, vegetation type and climatic region is explored. Moreover, the impact of the uncertainty of LST and FVC on the resulting soil moisture estimates is assessed. A first impact study of using remotely sensed soil moisture index in the energy balance model is shown and its potential benefits for operational monitoring of evapotranspiration are outlined. References García-Haro, F.J., F. Camacho-de Coca, J. Meliá, B. Martínez (2005) Operational derivation of vegetation products in the framework of the LSA SAF project. Proceedings of the EUMETSAT Meteorological Satellite Conference Dubrovnik (Croatia) 19-23 Septembre. Gellens-Meulenberghs, F., Arboleda, A., Ghilain, N. (2007) Towards a continuous monitoring of evapotranspiration based on MSG data. Proceedings of the symposium on Remote Sensing for Environmental Monitoring and Change Detection. IAHS series. IUGG, Perugia, Italy, July 2007, 7 pp. Ghilain, N., Arboleda, A. and Gellens-Meulenberghs, F., (2008) Improvement of a surface energy balance model by the use of MSG-SEVIRI derived vegetation parameters. Proceedings of the 2008 EUMETSAT meteorological satellite data user's conference, Darmstadt, Germany, 8th-12th September, 7 pp. Gillies R.R., Carlson T.N., Cui J., Kustas W.P. and Humes K. (1997), Verification of the triangle method for obtaining surface soil water content and energy fluxes from remote measurements of Normalized Difference Vegetation Index (NDVI) and surface radiant temperature, International Journal of Remote Sensing, 18, pp. 3145-3166. Trigo, I.F., Monteiro I.T., Olesen F. and Kabsch E. (2008) An assessment of remotely sensed land surface temperature. Journal of Geophysical Research, 113, D17108, doi:10.1029/2008JD010035.

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%.

Analysis of the sorption properties of different soils using water vapour adsorption and potentiometric titration methods

NASA Astrophysics Data System (ADS)

Skic, Kamil; Boguta, Patrycja; Sokołowska, Zofia

2016-07-01

Parameters of specific surface area as well as surface charge were used to determine and compare sorption properties of soils with different physicochemical characteristics. The gravimetric method was used to obtain water vapour isotherms and then specific surface areas, whereas surface charge was estimated from potentiometric titration curves. The specific surface area varied from 12.55 to 132.69 m2 g-1 for Haplic Cambisol and Mollic Gleysol soil, respectively, and generally decreased with pH (R=0.835; α = 0.05) and when bulk density (R=-0.736; α = 0.05) as well as ash content (R=-0.751; α = 0.05) increased. In the case of surface charge, the values ranged from 63.00 to 844.67 μmol g-1 Haplic Fluvisol and Mollic Gleysol, respecively. Organic matter gave significant contributions to the specific surface area and cation exchange capacity due to the large surface area and numerous surface functional groups, containing adsorption sites for water vapour molecules and for ions. The values of cation exchange capacity and specific surface area correlated linearly at the level of R=0.985; α = 0.05.

Detection of changes in soil moisture content using GNSS SNR signals

NASA Astrophysics Data System (ADS)

Roussel, Nicolas; Frappart, Frédéric; Ramillien, Guillaume; Darrozes, José; Baup, Frédéric; Bustillo, Vincent

2014-05-01

As multipaths still represent a major problem for reaching precise GNSS positioning, the mitigation of their influence has been widely investigated. However, previous studies have lately proposed to use these interferences of GNSS electromagnetic waves to estimate parameters related to the reflecting surface (e.g., antenna heights, rugosity,…). Variations of the nature of the surface is likely to modify the properties of the reflected waves, and consequently lead to variations of amplitude / phase of the signal-to-noise ratio (SNR), e.g. recorded at 1 Hz by a GNSS L1 and L2 receiver. By analyzing the time variations of SNR measurements linked to the dielectric constant of the surrounding soil, we use a method to recover the local fluctuations of the soil moisture content. It is simply based on the obvious linear correlation between SNR amplitude / phase time series and measurements of humidity probe at 5 and 10 cm depths. This method of combination is applied to determine soil moisture in a corn and soya field at Lamasquère, France, for ten successive days. Possible improvements are currently investigated, in particular the possibility of cumulating SNR data from several GNSS satellites of different constellations (GPS, GLONASS, Galileo) to obtain denser and more accurate estimates of soil moisture.

Initializing numerical weather prediction models with satellite-derived surface soil moisture: Data assimilation experiments with ECMWF's Integrated Forecast System and the TMI soil moisture data set

NASA Astrophysics Data System (ADS)

Drusch, M.

2007-02-01

Satellite-derived surface soil moisture data sets are readily available and have been used successfully in hydrological applications. In many operational numerical weather prediction systems the initial soil moisture conditions are analyzed from the modeled background and 2 m temperature and relative humidity. This approach has proven its efficiency to improve surface latent and sensible heat fluxes and consequently the forecast on large geographical domains. However, since soil moisture is not always related to screen level variables, model errors and uncertainties in the forcing data can accumulate in root zone soil moisture. Remotely sensed surface soil moisture is directly linked to the model's uppermost soil layer and therefore is a stronger constraint for the soil moisture analysis. For this study, three data assimilation experiments with the Integrated Forecast System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have been performed for the 2-month period of June and July 2002: a control run based on the operational soil moisture analysis, an open loop run with freely evolving soil moisture, and an experimental run incorporating TMI (TRMM Microwave Imager) derived soil moisture over the southern United States. In this experimental run the satellite-derived soil moisture product is introduced through a nudging scheme using 6-hourly increments. Apart from the soil moisture analysis, the system setup reflects the operational forecast configuration including the atmospheric 4D-Var analysis. Soil moisture analyzed in the nudging experiment is the most accurate estimate when compared against in situ observations from the Oklahoma Mesonet. The corresponding forecast for 2 m temperature and relative humidity is almost as accurate as in the control experiment. Furthermore, it is shown that the soil moisture analysis influences local weather parameters including the planetary boundary layer height and cloud coverage.

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.

A practical approach for deriving all-weather soil moisture content using combined satellite and meteorological data

NASA Astrophysics Data System (ADS)

Leng, Pei; Li, Zhao-Liang; Duan, Si-Bo; Gao, Mao-Fang; Huo, Hong-Yuan

2017-09-01

Soil moisture has long been recognized as one of the essential variables in the water cycle and energy budget between Earth's surface and atmosphere. The present study develops a practical approach for deriving all-weather soil moisture using combined satellite images and gridded meteorological products. In this approach, soil moisture over the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky pixels are estimated from the Vegetation Index/Temperature (VIT) trapezoid scheme in which theoretical dry and wet edges were determined pixel to pixel by China Meteorological Administration Land Data Assimilation System (CLDAS) meteorological products, including air temperature, solar radiation, wind speed and specific humidity. For cloudy pixels, soil moisture values are derived by the calculation of surface and aerodynamic resistances from wind speed. The approach is capable of filling the soil moisture gaps over remaining cloudy pixels by traditional optical/thermal infrared methods, allowing for a spatially complete soil moisture map over large areas. Evaluation over agricultural fields indicates that the proposed approach can produce an overall generally reasonable distribution of all-weather soil moisture. An acceptable accuracy between the estimated all-weather soil moisture and in-situ measurements at different depths could be found with an Root Mean Square Error (RMSE) varying from 0.067 m3/m3 to 0.079 m3/m3 and a slight bias ranging from 0.004 m3/m3 to -0.011 m3/m3. The proposed approach reveals significant potential to derive all-weather soil moisture using currently available satellite images and meteorological products at a regional or global scale in future developments.

The evaluation of stack metal emissions from hazardous waste incinerators: assessing human exposure through noninhalation pathways.

PubMed Central

Sedman, R M; Polisini, J M; Esparza, J R

1994-01-01

Potential public health effects associated with exposure to metal emissions from hazardous waste incinerators through noninhalation pathways were evaluated. Instead of relying on modeling the movement of toxicants through various environmental media, an approach based on estimating changes from baseline levels of exposure was employed. Changes in soil and water As, Cd, Hg, Pb, Cr, and Be concentrations that result from incinerator emissions were first determined. Estimates of changes in human exposure due to direct contact with shallow soil or the ingestion of surface water were then ascertained. Projected changes in dietary intakes of metals due to incinerator emissions were estimated based on changes from baseline dietary intakes that are monitored in U.S. Food and Drug Administration total diet studies. Changes from baseline intake were deemed to be proportional to the projected changes in soil or surface water metal concentrations. Human exposure to metals emitted from nine hazardous waste incinerators were then evaluated. Metal emissions from certain facilities resulted in tangible human exposure through noninhalation pathways. However, the analysis indicated that the deposition of metals from ambient air would result in substantially greater human exposure through noninhalation pathways than the emissions from most of the facilities. PMID:7925180

Estimating sources of Valley Fever pathogen propagation in southern Arizona: A remote sensing approach

NASA Astrophysics Data System (ADS)

Pianalto, Frederick S.

Coccidioidomycosis (Valley Fever) is an environmentally-mediated respiratory disease caused by the inhalation of airborne spores from the fungi Coccidioides spp. The fungi reside in arid and semi-arid soils of the Americas. The disease has increased epidemically in Arizona and other areas within the last two decades. Despite this increase, the ecology of the fungi remains obscure, and environmental antecedents of the disease are largely unstudied. Two sources of soil disturbance, hypothesized to affect soil ecology and initiate spore dissemination, are investigated. Nocturnal desert rodents interact substantially with the soil substrate. Rodents are hypothesized to act as a reservoir of coccidioidomycosis, a mediator of soil properties, and a disseminator of fungal spores. Rodent distributions are poorly mapped for the study area. We build automated multi-linear regression models and decision tree models for ten rodent species using rodent trapping data from the Organ Pipe Cactus National Monument (ORPI) in southwest Arizona with a combination of surface temperature, a vegetation index and its texture, and a suite of topographic rasters. Surface temperature, derived from Landsat TM thermal images, is the most widely selected predictive variable in both automated methods. Construction-related soil disturbance (e.g. road construction, trenching, land stripping, and earthmoving) is a significant source of fugitive dust, which decreases air quality and may carry soil pathogens. Annual differencing of Landsat Thematic Mapper (TM) mid-infrared images is used to create change images, and thresholded change areas are associated with coordinates of local dust inspections. The output metric identifies source areas of soil disturbance, and it estimates the annual amount of dust-producing surface area for eastern Pima County spanning 1994 through 2009. Spatially explicit construction-related soil disturbance and rodent abundance data are compared with coccidioidomycosis incidence data using rank order correlation and regression methods. Construction-related soil disturbance correlates strongly with annual county-wide incidence. It also correlates with Tucson periphery incidence aggregated to zip codes. Abundance values for the desert pocket mouse (Chaetodipus penicillatus), derived from a soil-adjusted vegetation index, aspect (northing) and thermal radiance, correlate with total study period incidence aggregated to zip code.

Estimates of potential childhood lead exposure from contaminated soil using the US EPA IEUBK Model in Sydney, Australia.

PubMed

Laidlaw, Mark A S; Mohmmad, Shaike M; Gulson, Brian L; Taylor, Mark P; Kristensen, Louise J; Birch, Gavin

2017-07-01

Surface soils in portions of the Sydney (New South Wales, Australia) urban area are contaminated with lead (Pb) primarily from past use of Pb in gasoline, the deterioration of exterior lead-based paints, and industrial activities. Surface soil samples (n=341) were collected from a depth of 0-2.5cm at a density of approximately one sample per square kilometre within the Sydney estuary catchment and analysed for lead. The bioaccessibility of soil Pb was analysed in 18 samples. The blood lead level (BLL) of a hypothetical 24 month old child was predicted at soil sampling sites in residential and open land use using the United States Environmental Protection Agency (US EPA) Integrated Exposure Uptake and Biokinetic (IEUBK) model. Other environmental exposures used the Australian National Environmental Protection Measure (NEPM) default values. The IEUBK model predicted a geometric mean BLL of 2.0±2.1µg/dL using measured soil lead bioavailability measurements (bioavailability =34%) and 2.4±2.8µg/dL using the Australian NEPM default assumption (bioavailability =50%). Assuming children were present and residing at the sampling locations, the IEUBK model incorporating soil Pb bioavailability predicted that 5.6% of the children at the sampling locations could potentially have BLLs exceeding 5µg/dL and 2.1% potentially could have BLLs exceeding 10µg/dL. These estimations are consistent with BLLs previously measured in children in Sydney. Copyright © 2017 Elsevier Inc. All rights reserved.

Estimating soil temperature using neighboring station data via multi-nonlinear regression and artificial neural network models.

PubMed

Bilgili, Mehmet; Sahin, Besir; Sangun, Levent

2013-01-01

The aim of this study is to estimate the soil temperatures of a target station using only the soil temperatures of neighboring stations without any consideration of the other variables or parameters related to soil properties. For this aim, the soil temperatures were measured at depths of 5, 10, 20, 50, and 100 cm below the earth surface at eight measuring stations in Turkey. Firstly, the multiple nonlinear regression analysis was performed with the "Enter" method to determine the relationship between the values of target station and neighboring stations. Then, the stepwise regression analysis was applied to determine the best independent variables. Finally, an artificial neural network (ANN) model was developed to estimate the soil temperature of a target station. According to the derived results for the training data set, the mean absolute percentage error and correlation coefficient ranged from 1.45% to 3.11% and from 0.9979 to 0.9986, respectively, while corresponding ranges of 1.685-3.65% and 0.9988-0.9991, respectively, were obtained based on the testing data set. The obtained results show that the developed ANN model provides a simple and accurate prediction to determine the soil temperature. In addition, the missing data at the target station could be determined within a high degree of accuracy.

Spatial distribution and vertical migration of (137)Cs in soils of Belgrade (Serbia) 25 years after the Chernobyl accident.

PubMed

Petrović, Jelena; Ćujić, Mirjana; Đorđević, Milan; Dragović, Ranko; Gajić, Boško; Miljanić, Šćepan; Dragović, Snežana

2013-06-01

In this study, the specific activity of (137)Cs was determined by gamma-ray spectrometry in 72 surface soil samples and 11 soil profiles collected from the territory of Belgrade 25 years after the Chernobyl accident. Based on the data obtained the external effective gamma dose rates due to (137)Cs were assessed and geographically mapped. The influence of pedogenic factors (pH, specific electrical conductivity, cation exchange capacity, organic matter content, soil particle size and carbonate content) on the spatial and vertical distribution of (137)Cs in soil was estimated through Pearson correlations. The specific activity of (137)Cs in surface soil samples ranged from 1.00 to 180 Bq kg(-1), with a mean value of 29.9 Bq kg(-1), while in soil profiles they ranged from 0.90 to 58.0 Bq kg(-1), with a mean value of 15.3 Bq kg(-1). The mean external effective gamma dose at 1 m above the ground due to (137)Cs in the soil was calculated to be 1.96 nSv h(-1). Geographic mapping of the external effective gamma dose rates originating from (137)Cs revealed much higher dose rates in southern parts of Belgrade city and around the confluence of the Sava and Danube. Negative Pearson correlation coefficients were found between pH, cation exchange capacity and (137)Cs specific activity in surface soil. There were positive correlations between organic matter and (137)Cs specific activity in surface soil; and between specific electrical conductivity, organic matter, silt content and (137)Cs specific activity in soil profiles.

Organochlorines in surface soil at electronic-waste wire burning sites and metal contribution evaluated using quantitative X-ray speciation

NASA Astrophysics Data System (ADS)

Fujimori, Takashi; Takigami, Hidetaka; Takaoka, Masaki

2013-04-01

Heavy metals and toxic chlorinated aromatic compounds (aromatic-Cls) such as dioxins and polychlorinated biphenyls (PCBs) are found at high concentrations and persist in surface soil at wire burning sites (WBSs) in developing countries in which various wire cables are recycled to yield pure metals. Chlorine K-edge near-edge X-ray absorption fine structure (NEXAFS) is used to detect the specific chemical form of Cl and estimate its amount using a spectrum jump in the solid phase. Quantitative X-ray speciation of Cl was applied to study the mechanisms of aromatic-Cls formation in surface soil at WBSs in Southeast Asia. Relationships between aromatic-Cls and chlorides of heavy metals were evaluated because heavy metals are promoters of the thermochemical solid-phase formation of aromatic-Cls.

Rainfall simulation and Structure-from-Motion photogrammetry for the analysis of soil water erosion in Mediterranean vineyards.

PubMed

Prosdocimi, Massimo; Burguet, Maria; Di Prima, Simone; Sofia, Giulia; Terol, Enric; Rodrigo Comino, Jesús; Cerdà, Artemi; Tarolli, Paolo

2017-01-01

Soil water erosion is a serious problem, especially in agricultural lands. Among these, vineyards deserve attention, because they constitute for the Mediterranean areas a type of land use affected by high soil losses. A significant problem related to the study of soil water erosion in these areas consists in the lack of a standardized procedure of collecting data and reporting results, mainly due to a variability among the measurement methods applied. Given this issue and the seriousness of soil water erosion in Mediterranean vineyards, this works aims to quantify the soil losses caused by simulated rainstorms, and compare them with each other depending on two different methodologies: (i) rainfall simulation and (ii) surface elevation change-based, relying on high-resolution Digital Elevation Models (DEMs) derived from a photogrammetric technique (Structure-from-Motion or SfM). The experiments were carried out in a typical Mediterranean vineyard, located in eastern Spain, at very fine scales. SfM data were obtained from one reflex camera and a smartphone built-in camera. An index of sediment connectivity was also applied to evaluate the potential effect of connectivity within the plots. DEMs derived from the smartphone and the reflex camera were comparable with each other in terms of accuracy and capability of estimating soil loss. Furthermore, soil loss estimated with the surface elevation change-based method resulted to be of the same order of magnitude of that one obtained with rainfall simulation, as long as the sediment connectivity within the plot was considered. High-resolution topography derived from SfM revealed to be essential in the sediment connectivity analysis and, therefore, in the estimation of eroded materials, when comparing them to those derived from the rainfall simulation methodology. The fact that smartphones built-in cameras could produce as much satisfying results as those derived from reflex cameras is a high value added for using SfM. Copyright © 2016 Elsevier B.V. All rights reserved.

The Soil Moisture Active and Passive Mission (SMAP): Science and Applications

NASA Technical Reports Server (NTRS)

Entekhabi, Dara; O'Neill, Peggy; Njoku, Eni

2009-01-01

The Soil Moisture Active and Passive mission (SMAP) will provide global maps of soil moisture content and surface freeze/thaw state. Global measurements of these variables are critical for terrestrial water and carbon cycle applications. The SMAP observatory consists of two multipolarization L-band sensors, a radar and radiometer, that share a deployable-mesh reflector antenna. The combined observations from the two sensors will allow accurate estimation of soil moisture at hydrometeorological (10 km) and hydroclimatological (40 km) spatial scales. The rotating antenna configuration provides conical scans of the Earth surface at a constant look angle. The wide-swath (1000 km) measurements will allow global mapping of soil moisture and its freeze/thaw state with 2-3 days revisit. Freeze/thaw in boreal latitudes will be mapped using the radar at 3 km resolution with 1-2 days revisit. The synergy of active and passive observations enables measurements of soil moisture and freeze/thaw state with unprecedented resolution, sensitivity, area coverage and revisit.

SAR-aided method for rural soil evaluation

NASA Astrophysics Data System (ADS)

Lay-Ekuakille, Aime; Dellisanti, Carmelo; Pelillo, Vincenza; Tralli, Francesco

2003-03-01

The principal land characteristics that can be estimated by means of airphoto interpretation are bedrock type, landform, soil texture, site drainage conditions, susceptibility to flooding, and depth of unconsolidated materials over bedrock. In addition, the slope of the land surface can be estimated by airphoto interpretation and measured by phptpgrammetric methods. The aim of this paper is to show an experimental use of satellite images in determining soil quality affected by anthropic activities as rock crushing, or scarifying. Scarifying activities began, in Murgia area, Apulia Region, Italy), as land improvement for agriculture uses. Scarifying is defined as loosening (the surface of soil) by using an agricultural tool or a machine with prongs. This kind of activity is facilitated by the availability, on the market, of scarifying machines and the objective is to get a stratum of agriculture-useful loose material on the soil surface. Apulia Region Government has permitted calcareous stone scarifying with Regional Law n.54 (August 31, 1981) according to National Law n.984 (Dicember 27,1977), that provides for encouraging to transform grazing in sown land in order to create new possibility of forage production to increase zootecnical facilities. We have used ERS-2/SAR images as contribution in the process of soil characterization.The area we have considered is in Puglia Region and is subject to soil transformation due to rocks crushed on land for agricultural facilities. European Union, through the same Apulia Region Government, has renewed funds for the improvement of meadow and grazing for an overall surface of 2000 hectares. In this way it is clear to understand the importance of qualitative and quantitative evaluation of rock crushing or scarifying by using airphoto interpretation. We have evaluated the soil quality by introducing a multicriteria, analysis by using a qualitative and quantitative methodology, so that it will be possible to prevent damages on soil, sub-soil and hydrology. Decision analysis in Impact assessment is a set of procedures for analyzing complex decision problems. The strategy is to divide the decision problem into small, understandable parts; analyze each part; and integrate the parts in a logical manner to produce a meaningful solution. The terms multicriteria decision making (MCDM) and multicriteria decision analysis (MCDA) are used interchangeably.

Vegetation Coverage Mapping and Soil Effect Correction in Estimating Vegetation Water Content and Dry Biomass from Satellites

NASA Astrophysics Data System (ADS)

Huang, J.; Chen, D.

2005-12-01

Vegetation water content (VWC) attracts great research interests in hydrology research in recent years. As an important parameter describing the horizontal expansion of vegetation, vegetation coverage is essential to implement soil effect correction for partially vegetated fields to estimate VWC accurately. Ground measurements of corn and soybeans in SMEX02 resulted in an identical expolinear relationship between vegetation coverage and leaf area index (LAI), which is used for vegetation coverage mapping. Results illustrated two parts of LAI growth quantitatively: the horizontal expansion of leaf coverage and the vertical accumulation of leaf layers. It is believed that the former part contributes significantly to LAI growth at initial vegetation growth stage and the latter is more dominant after vegetation coverage reaches a certain level. The Normalized Difference Water Index (NDWI) using short-wave infrared bands is convinced for its late saturation at high LAI values, in contrast to the Normalized Difference Vegetation Index (NDVI). NDWI is then utilized to estimate LAI, via another expolinear relationship, which is evidenced having vegetation species independency in study of corn and soybeans in SMEX02 sites. It is believed that the surface reflectance measured at satellites spectral bands are the mixed results of signals reflected from vegetation and bare soil, especially at partially vegetated fields. A simple linear mixture model utilizing vegetation coverage information is proposed to correct soil effect in such cases. Surface reflectance fractions for -rpure- vegetation are derived from the model. Comparing with ground measurements, empirical models using soil effect corrected vegetation indices to estimate VWC and dry biomass (DB) are generated. The study enhanced the in-depth understanding of the mechanisms how vegetation growth takes effect on satellites spectral reflectance with and without soil effect, which are particularly useful for modeling in hydrology, agriculture, forestry and meteorology etc.

30 CFR 917.15 - Approval of Kentucky regulatory program amendments.

Code of Federal Regulations, 2010 CFR

2010-07-01

... amendments. 917.15 Section 917.15 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE KENTUCKY... Production After Mining;” “Estimated Crop Yields on Prime Farmland Soils in Western Kentucky Coalfields...

30 CFR 917.15 - Approval of Kentucky regulatory program amendments.

Code of Federal Regulations, 2012 CFR

2012-07-01

... amendments. 917.15 Section 917.15 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE KENTUCKY... Production After Mining;” “Estimated Crop Yields on Prime Farmland Soils in Western Kentucky Coalfields...

30 CFR 917.15 - Approval of Kentucky regulatory program amendments.

Code of Federal Regulations, 2011 CFR

2011-07-01

... amendments. 917.15 Section 917.15 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE KENTUCKY... Production After Mining;” “Estimated Crop Yields on Prime Farmland Soils in Western Kentucky Coalfields...

30 CFR 917.15 - Approval of Kentucky regulatory program amendments.

Code of Federal Regulations, 2014 CFR

2014-07-01

... amendments. 917.15 Section 917.15 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE KENTUCKY... Production After Mining;” “Estimated Crop Yields on Prime Farmland Soils in Western Kentucky Coalfields...

30 CFR 917.15 - Approval of Kentucky regulatory program amendments.

Code of Federal Regulations, 2013 CFR

2013-07-01

... amendments. 917.15 Section 917.15 Mineral Resources OFFICE OF SURFACE MINING RECLAMATION AND ENFORCEMENT, DEPARTMENT OF THE INTERIOR PROGRAMS FOR THE CONDUCT OF SURFACE MINING OPERATIONS WITHIN EACH STATE KENTUCKY... Production After Mining;” “Estimated Crop Yields on Prime Farmland Soils in Western Kentucky Coalfields...

30 CFR 800.40 - Requirement to release performance bonds.

Code of Federal Regulations, 2010 CFR

2010-07-01

... pollution of surface and subsurface water is occurring, the probability of future occurrence of such pollution, and the estimated cost of abating such pollution. The surface owner, agent, or lessee shall be... subchapter K of this chapter or until soil productivity for prime farmlands has returned to the equivalent...

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

NASA Astrophysics Data System (ADS)

Citakoglu, Hatice

2017-10-01

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient ( R 2 )) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R 2 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R 2 0.93, 0.91) in estimating soil temperature in Turkey.

Lunar soil strength estimation based on Chang'E-3 images

NASA Astrophysics Data System (ADS)

Gao, Yang; Spiteri, Conrad; Li, Chun-Lai; Zheng, Yong-Chun

2016-11-01

Chang'E-3 (CE-3) was the third mission by China to explore the Moon which had landed two spacecraft, the CE-3 lander and Yutu rover on the lunar surface in late 2013. The paper presents analytical results of high-resolution terrain data taken by CE-3's onboard cameras. The image data processing aims to extract sinkage profiles of the wheel tracks during the rover traverse. Further analysis leads to derivation or estimation of lunar soil physical properties (in terms of strength and stiffness) based on the wheel sinkage, despite the fact Yutu does not possess in situ soil measurement instruments. Our findings indicate that the lunar soil at the CE-3 landing site has similar stiffness to what is measured at the Luna 17 landing site but has much less strength compared to the Apollo 15 landing site.

Dominant controls of transpiration along a hillslope transect inferred from ecohydrological measurements and thermodynamic limits

NASA Astrophysics Data System (ADS)

Renner, Maik; Hassler, Sibylle K.; Blume, Theresa; Weiler, Markus; Hildebrandt, Anke; Guderle, Marcus; Schymanski, Stanislaus J.; Kleidon, Axel

2016-05-01

We combine ecohydrological observations of sap flow and soil moisture with thermodynamically constrained estimates of atmospheric evaporative demand to infer the dominant controls of forest transpiration in complex terrain. We hypothesize that daily variations in transpiration are dominated by variations in atmospheric demand, while site-specific controls, including limiting soil moisture, act on longer timescales. We test these hypotheses with data of a measurement setup consisting of five sites along a valley cross section in Luxembourg. Both hillslopes are covered by forest dominated by European beech (Fagus sylvatica L.). Two independent measurements are used to estimate stand transpiration: (i) sap flow and (ii) diurnal variations in soil moisture, which were used to estimate the daily root water uptake. Atmospheric evaporative demand is estimated through thermodynamically constrained evaporation, which only requires absorbed solar radiation and temperature as input data without any empirical parameters. Both transpiration estimates are strongly correlated to atmospheric demand at the daily timescale. We find that neither vapor pressure deficit nor wind speed add to the explained variance, supporting the idea that they are dependent variables on land-atmosphere exchange and the surface energy budget. Estimated stand transpiration was in a similar range at the north-facing and the south-facing hillslopes despite the different aspect and the largely different stand composition. We identified an inverse relationship between sap flux density and the site-average sapwood area per tree as estimated by the site forest inventories. This suggests that tree hydraulic adaptation can compensate for heterogeneous conditions. However, during dry summer periods differences in topographic factors and stand structure can cause spatially variable transpiration rates. We conclude that absorption of solar radiation at the surface forms a dominant control for turbulent heat and mass exchange and that vegetation across the hillslope adjusts to this constraint at the tree and stand level. These findings should help to improve the description of land-surface-atmosphere exchange at regional scales.

Transformation of peat horizon in swampy southern taiga forests under the impact of surface drainage

NASA Astrophysics Data System (ADS)

Vomperskii, S. E.; Vomperskaya, M. I.; Glukhova, T. V.; Valyaeva, N. A.

2017-10-01

The results of stationary studies of swampy southern taiga forests in Yaroslavl oblast are presented. Estimates of changes in the thickness of peat horizon in peat podzolic gley soils (Folic Albeluvisols) of forests subjected to clearcutting and further intensive forest management in the past 30 years are given. The mean annual precipitation in these three decades has been 116 mm higher than that during the preceding three decades, which has led to a progressive swamping of spruce stands on heavy loamy soils within virtually flat (with slopes up to 0.0035) surfaces and an increase in the organic matter storage in the peat soil horizon with the mean annual rate of 22-68 g/m2. On more pronounced slopes (0.0050), no swamping of spruce and pine stands growing on sandy soils has taken place. Surface drainage of swampy forests through the network of shallow ditches has led to an increase in the productivity of forests; in most cases, the pool of organic matter in the peat horizon has been decreasing with the mean annual rate of 32-46 g/m2. This attests to the reversible character of swamping in dependence on climatic fluctuations and forestry measures. Changes in the carbon pool of swampy soils during short (several years) excessively wet or excessively dry periods may be significantly higher than the average values for 30 years in different types of forests. This allows us to consider swampy forests as the source of significant errors in the estimates of the current contribution of biota to the carbon cycle, because their role (as well as the role of other forests) is assessed without taking into account considerable short-term fluctuations in the carbon pool of their soils.

A comparison of surface biophysical properties and remotely sensed variables from FIFE

NASA Technical Reports Server (NTRS)

Sellers, Piers; Heiser, Mark; Walthall, C. W.; Huemmrich, F.; Strebel, D. E.; Hall, F. G.

1990-01-01

A method for calculating surface energy balances is investigated which incorporates the vegetation index and/or other indicators of surface conductance at visible and near-IR channels. Data from the Konza Prairie are employed to confirm the hypothesized relationship between maximum canopy conductance and the observed simple-ratio vegetation index. The relationship is established, but more data regarding soil-surface contributions are required to estimate the total surface conductance to evapotranspiration.

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.