Soil Moisture Estimation by Linear Regression from Smap Polarimetric Radar Data with Aquarius Derived Coefficients

Algorithms for soil moisture estimation from radars conventionally use substantial amounts of ancillary data to parametrize complex electromagnetic models. In contrast, we describe radar data of a vegetated scene as a linear function of soil moisture. This eliminates the dependence on ancillary data while providing reasonable global soil moisture estimates. We derive two polarization dependent coefficients of a linear model on the basis of spatial and temporal similarity at a global scale from nearly 4 years of L-band Aquarius radar and radiometer derived soil moisture data. These global coefficients are then used to derive soil moisture from 2.5 months of L-band SMAP radar data. The resulting soil moisture estimates are evaluated with the SMAP Level 2 radiometer-only soil moisture product.