Improving soil moisture prediction using a novel encoder-decoder model with residual learning

•Skillful prediction of soil moisture (SM) is useful but great challenges exist.•A novel encoder-decoder LSTM model with residual learning (EDR-LSTM) is developed.•EDR-LSTM improved about 20% in SM prediction at the lead time of 3, 5 and 10 days.•Both encoder-decoder and residual learning are useful in improving SM prediction.•The predictability of SM over various conditions was widely investigated. The skillful prediction of soil moisture can provide much help for many practical applications including ecosystem management and precision agriculture. It presents great challenges because the future variation of soil moisture has much uncertainty. Therefore, a novel encoder-decoder deep learning model with residual learning based on Long Short-Term Memory (EDT-LSTM) is developed in this study as an alternative data-intelligence tool. This model includes two layers: the encoder-decoder LSTM layer and LSTM with a fully connected layer, which is used to enhance the prediction ability by considering the intermediate time-series data between the input time step and the predictive time step. We tested EDT-LSTM for soil moisture prediction at the lead time of 1, 3, 5, 7 and 10 days by using data from FLUXNET sites. The result shows that the improvements brought by EDT-LSTM were about 7.95% (1 day), 10.10% (3 days), 12.68% (5 days), 15.49% (7 days) and 19.71% (10 days) in average according to the R2 taking LSTM as the baseline. Furthermore, the predictability of soil moisture over various conditions (i.e., different hyper-parameters in EDT-LSTM, different predictive models, different climate regions and different sites) has been widely discussed for the understanding of models’ behavior in this paper. The proposed EDT-LSTM offered a new tool to predict soil moisture better. The code of EDT-LSTM is publicly available at https://github.com/ljz1228/CLM-LSTM-soil-moisture-prediction.