Hindcast of Soil Moisture Using SMAP, Land Surface Model Output Data, and Regression Methods

Abstract

This work addresses the problem of artificially extending satellite-derived soil moisture data using soil moisture estimates generated by a land surface model. We calibrate regression algorithms in a set of spatially and temporally coincident surface soil moisture estimates derived from the coarse Soil Moisture Active Passive (SMAP) radiometer and soil moisture simulated by the Global Land Data Assimilation System (GLDAS) Noah model, which assimilates atmospheric forcing and ancillary data. Once calibrated, we apply the regression model to the GLDAS-Noah soil moisture, and ancillary data, to estimate the soil moisture that would have been observed if SMAP had been available on a given past date. We explore the feasibility of the approach in a study area of size $12\\times 12$ degrees located in Southern Brazil. The Random Forests and XGBoost regression algorithms show reasonable reconstruction skills over a 642-day hindcast period $(r^{2}=0.84,\\text{RMSE}=0.051\\mathrm{m}^{3}/\\mathrm{m}^{3})$. These results suggest the approach is worth further investigation.