Retrieving Heterogeneous Surface Soil Moisture at 100 m Across the Globe via Fusion of Remote Sensing and Land Surface Parameters

Abstract

Successful monitoring of soil moisture dynamics at high spatio-temporal resolutions globally is hampered by the heterogeneity of soil hydraulic properties in space and complex interactions between water and the environmental variables that control it. Current soil moisture monitoring schemes via in situ station networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100 m and performed well under cultivated, herbaceous, forest, and shrub soils (overall R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different soil moisture networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was applied to map SSM dynamics at 30–100 m across a field-scale soil moisture network (TERENO-Wüstebach) and an 80-ha cultivated cropland in Wisconsin, USA. Without the use of local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was relatively insensitive to the resolution of Sentinel-1 data but was affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and assimilation with process-based models.