A Deep Learning Data Fusion Model Using Sentinel-1/2, SoilGrids, SMAP, and GLDAS for Soil Moisture Retrieval

We develop a deep learning-based convolutional-regression model that estimates the volumetric soil mois-ture content in the top -5 cm of soil. Input predictors include Sentinel-1 (active radar) and Sentinel-2 (multispectral imag-ery), as well as geophysical variables from SoilGrids and modeled soil moisture fields from SMAP and GLDAS. The model was trained and evaluated on data from -1000 in situ sensors globally over the period 2015-21 and obtained an average per-sensor correlation of 0.707 and ubRMSE of 0.055 m3 m23, and it can be used to produce a soil moisture map at a nominal 320-m resolution. These results are benchmarked against 14 other soil moisture evaluation research works at different locations, and an ablation study was used to identify important predictors.