An attention-aware LSTM model for soil moisture and soil temperature prediction

• Published in: Geoderma 2022-03, Vol.409, p.115651, Article 115651
• Main Authors: Li, Qingliang, Zhu, Yuheng, Shangguan, Wei, Wang, Xuezhi, Li, Lu, Yu, Fanhua
• Format: Article
• Language: English
• Subjects: Attention mechanism; Deep learning; Interpretability; Soil moisture; Soil temperature
• ISSN: 0016-7061; 1872-6259
• DOI: 10.1016/j.geoderma.2021.115651

•A novel attention-aware LSTM model (ILSTM_Soil) is developed for soil prediction.•The temporal and predictor’s importances are widely investigated.•The interpretation of attention weights coincides well with physical knowledge.•ILSTM_Soil model outperforms other existing predictive models in most cases.•Attention mechanism is advocated for better performance and interoperability. Accurate prediction of soil moisture (SM) and soil temperature (ST) plays an important role in Earth system science, helping to forecast and understand ecosystem changes. They present great challenges because land-atmospheric interactions are complex and diverse in space and time. Although deep learning methods have excellent performance for land surface variables’ prediction such as SM and ST, they are often questioned due to their over-parameterized black-box nature and neglect of physical knowledge and interpretability. From this, we propose an attention-aware LSTM Model (ILSTM_Soil) by taking multi-feature attention, predictor attention and temporal attention into account. We first used LSTM to generate multi-feature vectors of all predictors, and then the three attention mechanisms were designed to summarize these feature vectors for SM and ST prediction. Experiment results for SM and ST prediction at the lead time of 1 and 7 days on ten FLUXNET sites suggest that the proposed ILSTM_Soil model outperforms Random Forest (RF), Support Vector Regression (SVR), Elastic-Net (ENET), original Long Short-Term Memory (LSTM), and attention LSTM (A-LSTM) models in most cases. The interpretation of attention weights verifies that the proposed model can capture physical knowledge over SM and ST. The code of ILSTM_Soil is made publicly available and we hope it can encourage researchers to develop effective DL models in land surface variables’ prediction conveniently.