On the Radiative Transfer Model for Soil Moisture across Space, Time and Hydro-Climates

Abstract

A framework is proposed for understanding the efficacy of the microwave radiative transfer model (RTM) of soil moisture with different support scales, seasonality (time), hydroclimates, and aggregation (scaling) methods. In this paper, the sensitivity of brightness temperature TB (H- and V-polarization) to physical variables (soil moisture, soil texture, surface roughness, surface temperature, and vegetation characteristics) is studied. Our results indicate that the sensitivity of brightness temperature (V- or H-polarization) is determined by the upscaling method and heterogeneity observed in the physical variables. Under higher heterogeneity, the TB sensitivity to vegetation and roughness followed a logarithmic function with an increasing support scale, while an exponential function is followed under lower heterogeneity. Surface temperature always followed an exponential function under all conditions. The sensitivity of TB at H- or V- polarization to soil and vegetation characteristics varied with the spatial scale (extent and support) and the amount of biomass observed. Thus, choosing an H- or V-polarization algorithm for soil moisture retrieval is a tradeoff between support scales, and land surface heterogeneity. For largely undisturbed natural environments such as SGP’97 and SMEX04, the sensitivity of TB to variables remains nearly uniform and is not influenced by extent, support scales, or an upscaling method. On the contrary, for anthropogenically-manipulated environments such as SMEX02 and SMAPVEX12, the sensitivity to variables is highly influenced by the distribution of land surface heterogeneity and upscaling methods.

Enhancing Spatial Resolution of SMAP Soil Moisture Products through Spatial Downscaling over a Large Watershed: A Case Study for the Susquehanna River Basin in the Northeastern United States

“…On the other hand, among the predictors used, the vertically polarized SMAP brightness temperature (TBv) was identified as the most important predictor in all the experiments. This was because the brightness temperature carried the integrated information of the factors responsible for the variation of SM, such as vegetation opacity, soil temperature, soil texture and surface roughness, among others (e.g., [37,49,76]). This corresponded with the results of Hu et al [25], who found the brightness temperature in VV polarization to be the most important among the predictors they used in the downscaling of SMAP SM over Inner Mongolia, northern China.…”

Spatial Heterogeneity and Driving Factors of Soil Moisture in Alpine Desert Using the Geographical Detector Method

“…It was reported that the change of soil moisture in sandy soils is affected not only by topography and vegetation but also by the spatiotemporal variation of precipitation with a strong dependency. Meanwhile, the previous studies have mainly focused on the effects of different land use patterns, vegetation types, spatiotemporal factors, and soil properties on soil hydrological changes in sandy soils [7][8][9][10][11][12][13].…”