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: 2.93). 08/2005; DOI: 10.1109/TGRS.2005.848416
Source: IEEE Xplore

ABSTRACT 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.

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    ABSTRACT: Uncertainties in L-band (1.4 GHz) microwave radiative transfer modeling (RTM) affect the simulation of brightness temperatures (Tb) over land and the inversion of satellite-observed Tb into soil moisture retrievals. In particular, accurate estimates of the microwave soil roughness, vegetation optical depth and scattering albedo for large-scale applications are difficult to obtain from field studies and often lack an estimate of uncertainty. Here, a Markov Chain Monte Carlo (MCMC) simulation method is used to determine satellite-scale estimates of RTM parameters and their posterior uncertainty by minimizing the misfit between long-term averages and standard deviations of simulated and observed Tb at multiple incidence angles, at horizontal and vertical polarizations, and for morning and evening overpasses. Tb simulations are generated with the land model component of the Goddard Earth Observing System (version 5) and confronted with Tb observations from the Soil Moisture Ocean Salinity satellite mission. The maximum a posteriori density (MAP) parameter values reduce the root-mean-square differences between observed and simulated long-term Tb averages and standard deviations to 3.4 K and 2.3 K, respectively. The relative uncertainty of the posterior RTM parameter estimates is typically less than 25% of the MAP parameter value, whereas it exceeds 100% for literature-based prior parameter estimates. It is also shown that the parameter values estimated through Particle Swarm Optimization are in close agreement with those obtained from MCMC simulation. The MCMC results for the RTM parameter values and the uncertainties presented herein are directly relevant to the need for accurate Tb modeling in global land data assimilation systems.
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