Parameter sensitivity of soil moisture retrievals from airborne L-band radiometer measurements in SMEX02

Global Hydrology & Climate Center, Universities Space Res. Assoc., Huntsville, AL, USA
IEEE Transactions on Geoscience and Remote Sensing (Impact Factor: 3.51). 08/2005; 43(7):1517 - 1528. DOI: 10.1109/TGRS.2005.848416
Source: IEEE Xplore


Over the past two decades, successful estimation of soil moisture has been accomplished using L-band microwave radiometer data. However, remaining uncertainties related to surface roughness and the absorption, scattering, and emission by vegetation must be resolved before soil moisture retrieval algorithms can be applied with known and acceptable accuracy using satellite observations. Surface characteristics are highly variable in space and time, and there has been little effort made to determine the parameter estimation accuracies required to meet a given soil moisture retrieval accuracy specification. This study quantifies the sensitivities of soil moisture retrieved using an L-band single-polarization algorithm to three land surface parameters for corn and soybean sites in Iowa, United States. Model sensitivity to the input parameters was found to be much greater when soil moisture is high. For even moderately wet soils, extremely high sensitivity of retrieved soil moisture to some model parameters for corn and soybeans caused the retrievals to be unstable. Parameter accuracies required for consistent estimation of soil moisture in mixed agricultural areas within retrieval algorithm specifications are estimated. Given the spatial and temporal variability of vegetation and soil conditions for agricultural regions it seems unlikely that, for the single-frequency, single-polarization retrieval algorithm used in this analysis, the parameter accuracy requirements can be met with current satellite-based land surface products. We conclude that for regions with substantial vegetation, particularly where the vegetation is changing rapidly, any soil moisture retrieval algorithm that is based on the physics and parameterizations used in this study will require multiple frequencies, polarizations, or look angles to produce stable, reliable soil moisture estimates.


Available from: Charles A. Laymon, Dec 30, 2013
  • Source
    • "HE remotely sensed microwave backscattering coefficient is one of the primary data quantities used to measure surface soil moisture [1], [2]. However, uncertainties related to the sensitivities of surface parameters in retrieval algorithms must be identified and understood [3], [4]. To identify the sources of these uncertainties and to quantify the uncertainties, investigations should be performed from two perspectives simultaneously . "
    [Show abstract] [Hide abstract]
    ABSTRACT: A profound and comprehensive understanding of the sensitivity of soil parameters related to backscattering coefficient is significant for the use of active microwave algorithms for soil moisture inversion. This paper presents a global sensitivity analysis (SA) based on the Advanced Integral Equation Model for soil moisture retrieval. The analysis involves diverse parameter ranges, sensor frequencies, incidence angles, surface correlation functions, and polarizations across various experiments. The primary objectives are to quantitatively and systematically evaluate the parameter sensitivities and their variations under various conditions, resulting in an improved understanding of microwave scattering and suggesting potential approaches to the improvement of soil moisture retrieval. The performance of this SA leads to the parameter sensitivities being quantified. Sensitive and insensitive parameters are distinguished. The existence of the former informs the direction of model calibration, implying that these parameters can be inverted with high confidence. Setting the latter as constants would be a step toward model simplification. Various conditions are observed to influence the parameter sensitivities, suggesting that it is possible to perform soil moisture or roughness inversions under the most sensitive conditions for the parameters. Finally, an SA of various combinations of dual-polarization, dual-frequency, and dual-incidence-angle backscatter is conducted. The results suggest that certain combinations enhance the sensitivities of certain parameters and allow for better estimation of their values. Ultimately, the presented global SA highlights the quantitative and systematic evaluation of parameter sensitivities, particularly their interactions, leading to a more profound understanding of scattering and an improvement in soil moisture estimation.
    IEEE Transactions on Geoscience and Remote Sensing 07/2015; 53(10). DOI:10.1109/TGRS.2015.2426194 · 3.51 Impact Factor
  • Source
    • "Information about surface soil moisture over large areas is obtained using remote sensing techniques. Remote sensing methods include different kind of sensors operating in microwave bands that are not influenced by solar radiation and cloud cover (Crosson et al., 2005; Western et al., 2004; Blumberg et al., 2006; and Sugiura et al., 2007). For bare soil surfaces and at high resolution (<20 m), synthetic aperture radars are characterized as one of the best sensing methods to obtain soil moisture data (Baghdadi et al., 2008). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Scaling relationships are needed as measurements and desired predictions are often not available at concurrent spatial support volumes or temporal discretizations. Surface soil moisture values of interest to hydrologic studies are estimated using ground based measurement techniques or utilizing remote sensing platforms. Remote sensing based techniques estimate field-scale surface soil moisture values, but are unable to provide the local-scale soil moisture information that is obtained from local measurements. Further, obtaining field-scale surface moisture values using ground-based measurements is exhaustive and time consuming. To bridge this scale mismatch, we develop analytical expressions for surface soil moisture based on sharp-front approximation of the Richards equation and assumed log-normal distribution of the spatial surface saturated hydraulic conductivity field. Analytical expressions for field-scale evolution of surface soil moisture to rainfall events are utilized to obtain aggregated and disaggregated response of surface soil moisture evolution with knowledge of the saturated hydraulic conductivity. The utility of the analytical model is demonstrated through numerical experiments involving 3-D simulations of soil moisture and Monte-Carlo simulations for 1-D renderings—with soil moisture dynamics being represented by the Richards equation in each instance. Results show that the analytical expressions developed here show promise for a principled way of scaling surface soil moisture.
    Chaos An Interdisciplinary Journal of Nonlinear Science 06/2015; 25(7). DOI:10.1063/1.4913235 · 1.95 Impact Factor
  • Source
    • " moisture algorithms , especially over the U . S . ( e . g . , Njoku et al . , 2005 ) . SCA has been extensively described in the literature , validated with in situ data , and com - pared with alternative microwave soil moisture products under a wide range of ground conditions and climate regimes ( Jackson , 1993 ; Jackson et al . , 2002 , 2010 ; Crosson et al . , 2005 ; Bindlish et al . , 2008 ) ; therefore , we will only briefly introduce the main algorithm components here . The model is based on an inverse solution of the radiative transfer model , with the following basic assumptions that are made in most algorithms : a . Soil effective temperature is considered equal to the canopy effective temper"
    [Show abstract] [Hide abstract]
    ABSTRACT: A comparison between two algorithms for estimating soil moisture with microwave satellite data was carried out by using the datasets collected on the four Agricultural Research Service (ARS) watershed sites in the US from 2002 to 2009. These sites collectively represent a wide range of ground conditions and precipitation regimes (from natural to agricultural surfaces and from desert to humid regions) and provide long-term in-situ data. One of the algorithms is the artificial neural network-based algorithm developed by the Institute of Applied Physics of the National Research Council (IFAC-CNR) (HydroAlgo) and the second one is the Single Channel Algorithm (SCA) developed by USDA-ARS (US Department of Agriculture-Agricultural Research Service). Both algorithms are based on the same radiative transfer equations but are implemented very differently. Both made use of datasets provided by the Japanese Aerospace Exploration Agency (JAXA), within the framework of Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Global Change Observation Mission-Water GCOM/AMSR-2 programs. Results demonstrated that both algorithms perform better than the mission specified accuracy, with Root Mean Square Error (RMSE) ≤0.06 m3/m3 and Bias
    04/2015; 3. DOI:10.3389/feart.2015.00016
Show more