  The Soil Moisture Active Passive (SMAP) mission has delivered valuable
sensing of surface soil moisture since 2015. However, it has a short time span
and irregular revisit schedule. Utilizing a state-of-the-art time-series deep
learning neural network, Long Short-Term Memory (LSTM), we created a system
that predicts SMAP level-3 soil moisture data with atmospheric forcing,
model-simulated moisture, and static physiographic attributes as inputs. The
system removes most of the bias with model simulations and improves predicted
moisture climatology, achieving small test root-mean-squared error (<0.035) and
high correlation coefficient >0.87 for over 75\% of Continental United States,
including the forested Southeast. As the first application of LSTM in
hydrology, we show the proposed network avoids overfitting and is robust for
both temporal and spatial extrapolation tests. LSTM generalizes well across
regions with distinct climates and physiography. With high fidelity to SMAP,
LSTM shows great potential for hindcasting, data assimilation, and weather
forecasting.
