Abstract
The radiative transfer scheme implemented for the retrieval of soil moisture from passive microwaves is a function of scattering, polarization mixing and attenuation effects of soil and vegetation. Theses factors are usually represented by Vegetation Optical Depth (VOD), vegetation scattering albedo, and surface roughness parameter, along with soil moisture. The VOD is the degree to which vegetation attenuates the microwave radiation. It has generally the dominant effect from vegetation, whereas scattering is negligible and close to zero. The surface roughness (which varies in space but not much in time) is until recently, often assumed to be a global constant. In this work, we attempted to simultaneously retrieve the VOD, the surface roughness parameter, and the soil moisture at the global scale using the Level 3 daily 0.25° X-band brightness temperatures of the Advanced Microwave Scanning Radiometer – Earth Observing System (AMSR-E) sensor. The methodology, coined as the Simultaneous Parameter Retrieval Algorithm (SPRA), is based on the premise that the vegetation dynamics undergo slower temporal changes than the soil moisture - an assumption, which is successfully used in the past for microwave radiometric retrievals at lower frequencies. Results indicate that the SPRA produces the VOD retrievals with reduced high-frequency noise when compared to the baseline Land Parameter Retrieval Algorithm (LPRM) retrievals. This effect assisted in identifying the influence of precipitation and cropping patterns on the temporal dynamics of the VOD. Good agreement is observed between the mean SPRA VOD and canopy height data (global correlation = 0.75). The spatial patterns of surface roughness parameter agree well with the roughness product (HR map) developed using Soil Moisture Ocean Salinity (SMOS) sensor based data (global correlation = 0.57). Validation of SPRA and LPRM soil moisture products with in-situ observations over the Contiguous United States (CONUS) indicated an improvement in mean ubRMSE with SPRA product (SPRA-0.11 m3/m3 and LPRM-0.18 m3/m3) and comparable mean Pearson correlations between the two products (SPRA-0.36 and LPRM-0.38). Further, a precipitation based consistency evaluation of SPRA and LPRM soil moisture retrievals indicated better skill of the SPRA product over India.
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