A Markov chain Monte Carlo algorithm for upscaled soil-vegetation-atmosphere-transfer modeling to evaluate satellite-based soil moisture measurements

A Markov chain Monte Carlo (MCMC) based algorithm was developed to derive upscaled land surface parameters for a soil-vegetation-atmosphere-transfer (SVAT) model using time series data of satellite-measured atmospheric forcings (e.g., precipitation), and land surface states (e.g., soil moisture and vegetation). This study focuses especially on the evaluation of soil moisture measurements of the Aqua satellite based Advanced Microwave Scanning Radiometer (AMSR-E) instrument using the new MCMC-based scaling algorithm. Soil moisture evolution was modeled at a spatial scale comparable to the AMSR-E soil moisture product, with the hypothesis that the characterization of soil microwave emissions and their variations with space and time on soil surface within the AMSR-E footprint can be represented by an ensemble of upscaled soil hydraulic parameters. We demonstrated the features of the MCMC-based parameter upscaling algorithm (from field to satellite footprint scale) within a SVAT model framework to evaluate the satellite-based brightness temperature/soil moisture measurements for different hydroclimatic regions, and identified the temporal effects of vegetation (leaf area index) and other environmental factors on AMSR-E based remotely sensed soil moisture data. The SVAT modeling applied for different hydroclimatic regions revealed the limitation of AMSR-E measurements in high-vegetation regions. The study also suggests that inclusion of soil moisture evolution from the proposed upscaled SVAT model with AMSR-E measurements in data assimilation routine will improve the quality of soil moisture assessment in a footprint scale. The technique also has the potential to derive upscaled parameters of other geophysical properties used in remote sensing of land surface states. The developed MCMC algorithm with SVAT model can be very useful for land-atmosphere interaction studies and further understanding of the physical controls responsible for soil moisture dynamics at different scales.