  We develop a deep learning based convolutional-regression model that
estimates the volumetric soil moisture content in the top ~5 cm of soil. Input
predictors include Sentinel-1 (active radar), Sentinel-2 (optical imagery), and
SMAP (passive radar) as well as geophysical variables from SoilGrids and
modelled soil moisture fields from GLDAS. The model was trained and evaluated
on data from ~1300 in-situ sensors globally over the period 2015 - 2021 and
obtained an average per-sensor correlation of 0.727 and ubRMSE of 0.054, and
can be used to produce a soil moisture map at a nominal 320m resolution. These
results are benchmarked against 13 other soil moisture works at different
locations, and an ablation study was used to identify important predictors.
