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.
Highlights
Water plays a fundamental role in terrestrial ecosystems and human society
The surface soil moisture (SSM) estimates from SMAP had an overall similar performance for the same validation dataset with an ME of −0.01 m3 m−3, RMSE of 0.08 m3 m−3, and R2 of 0.508
In terms of the moderate performance of the empirical model and the SMAP model in RSMN, this may be attributed to the complex land surface characteristics within the mountainous regions of the Romania Soil Moisture Network (Haggard et al, 2010) that affect the relationships between SSM with backscatter and brightness temperature collected from Sentinel-1 and SMAP satellites
Summary
Water plays a fundamental role in terrestrial ecosystems and human society. Soil moisture is a critical factor for many terrestrial biochemical, climate, and atmospheric processes and is the source of water for most of the crops (Vereecken et al, 2014). Successful monitoring and forecasting soil moisture dynamics at high spatio-temporal resolutions globally are hampered by many factors, including heterogeneity of soil hydraulic properties in space (Robinson et al, 2008), complex interactions between water, environment, and human activities (Vereecken et al, 2014), and computational challenges (Chaney et al, 2018). Remote sensing satellite missions have been launched to monitor coarse-resolution soil water dynamics and high-resolution land surface parameters (e.g., vegetation, terrain, and soil properties) have become available (Reuter et al, 2007; Friedl et al, 2010; Hengl et al, 2017; Fisher et al, 2020), which characterize the heterogeneity of land cover, soil, and terrain features at the field scale. It may be feasible to combine these remote sensing and land surface datasets for improved delineation of soil moisture variability at the field scale across the globe
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
