Soil Moisture Retrieval in North America with Passive Microwave and Auxiliary Data Based on Variable Spatial Optimization

Soil moisture content (SMC) is critical in hydrological, agricultural, and meteorological research. There is an urgent need for spatiotemporal information on accurate SMC distribution on a large scale. Passive microwave remote sensing data are among the most commonly used sources for soil moisture retrieval. However, due to the high spatial heterogeneity of SMC and the low spatial resolution of passive microwave data, the SMC condition in the pixel of passive microwave data is rather complex. We propose a method incorporating spatially optimized auxiliary data related to land cover and normalized difference vegetation index (NDVI) to represent the SMC spatial heterogeneity. New variables, “percentages of typical land cover classes” and “average NDVIs corresponding to typical land cover classes”, were introduced. Random forest was adopted to construct an SMC retrieving model. The results of testing samples showed that after “percentages of typical land cover classes” were added into the input parameters, the maximum rise of correlation coefficient (r) was 0.114, and the ultimate decline of unbiased root mean square error (ubRMSE) was 0.0239 cm3cm−3. Similarly, substituting NDVI with “average NDVIs corresponding to typical land cover classes” increasesd r by 0.023, and ubRMSE declined by 0.0042 cm3cm−3 at most. For the optimal situation, where both groups of new variables were applied, the highest rise of r is 0.127, and the maximum decrease of ubRMSE is 0.0277 cm3cm−3.