Exploring the Long-Term Soil Moisture Predictability with FLUXNET Site Data using Circulating Learning Model

The skillful long-term (from 3 days delay) prediction of soil moisture can provide more help than the short-term prediction of soil moisture for many practical applications including ecosystem management and precision agriculture. It presents great challenges because the far future variation of soil moisture has more uncertainties than the near future on soil moisture. Therefore, a novel circulating learning deep learning (DL) model based on Long Short-Term Memory (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 were used to enhance the long-term prediction ability by considering the intermediate time-series data between the input timestep and the predictive timestep. We applied this model by using FLUXNET2015 tie1 and tie2 subset data product over seven sites in different countries. The result shows that our model predicts soil moisture with better accuracy in average state and fluctuation pattern and amplitude when compared with other state-of-the-art DL methods, such as Multiple Linear Regression (MLR), Long Short-Term Memory (LSTM) and encoder-decoder LSTM models. Furthermore, the different-term (short-term, medium-term and long-term) predictability of soil moisture over various conditions (i.e., different hyper-parameters in our model, different predictive models, different climate regions and different sites) has been widely discussed in this paper. The code of our model is publicly available at https://github.com/ljz1228/CLM-LSTM-soil-moisture-prediction. We hope that this work will accelerate the research for long-term soil moisture prediction.