Data_share.zip

Soil moisture plays a critical role in land-atmosphere interactions. Prediction of its dynamics is still a grand challenge. While in-situ measurements using sensors offer highly temporally resolved and accurate information compare to satellite observations, existing sensor networks are sparse and scarce. Here we propose a deep learning model for bridging the gap between infrequent satellite observations and sparse in-situ sensor network to improve near-term soil moisture predictions. The Long Short-Term Memory (LSTM)-based deep learning model was used to forecast soil moisture dynamics using soil parameters and climatic variables (e.g. air temperature, relative humidity, pressure, wind speed, turbulent fluxes, solar and terrestrial waves) collected from a dense network of sensors in a field located in Germany in an area of about 20 hectares. The dynamic time-lagged cross-correlation between soil moisture and other co-located soil and climatic features was calculated and a set of optimal predictors for training the LSTM model was selected. To efficiently learn the long-term dependency of soil moisture on its historical trends and to improve the prediction capability of the model, we optimized the LSTM structure, hyperparameters, and the size of the sliding window based on the goodness of fit ($R^2$ score) of the model. We also examined the feasibility of employing the model developed using the temporal data from one location for the prediction of soil moisture at other locations across the landscape. The results illustrate the robustness and efficiency of the proposed model for the spatio-temporal prediction of soil moisture.