Pedosphere (5): 846--857, 2025
ISSN 1002-0160/CN 32-1315/P
©2025 Soil Science Society of China
Published by Elsevier B.V. and Science Press
| Spatial-temporal simulation and prediction of root zone soil moisture based on Hydrus-1D and CNN-LSTM-attention models in Yutian Oasis, southern Xinjiang, China |
Xiaobo LÜ1,2, Ilyas NURMEMET1,2 , Sentian XIAO1,2, Jing ZHAO1,2, Xinru YU1,2, Yilizhati AILI1,2, Shiqin LI1,2 |
1 College of Geography and Remote Sensing Sciences, Xinjiang University, Ürümqi 830046 (China)
2 Xinjiang Key Laboratory of Oasis Ecology, Xinjiang University, Ürümqi 830046 (China) |
| ABSTRACT |
| Root zone soil moisture (RZSM) plays a critical role in land-atmosphere hydrological cycles and serves as the primary water source for vegetation growth. However, the correlations between RZSM and its associated variables, including surface soil moisture (SSM), often exhibit nonlinearities that are challenging to identify and quantify using conventional statistical techniques. Therefore, this study presents a hybrid convolutional neural network (CNN)-long short-term memory neural network (LSTM)-attention (CLA) model for predicting RZSM. Owing to the scarcity of soil moisture (SM) observation data, the physical model Hydrus-1D was employed to simulate a comprehensive dataset of spatial-temporal SM. Meteorological data and moderate resolution imaging spectroradiometer vegetation characterization parameters were used as predictor variables for the training and validation of the CLA model. The results of the CLA model for SM prediction in the root zone were significantly enhanced compared with those of the traditional LSTM and CNN-LSTM models. This was particularly notable at the depth of 80-100 cm, where the fitness (R2) reached nearly 0.929 8. Moreover, the root mean square error of the CLA model was reduced by 49% and 57% compared with those of the LSTM and CNN-LSTM models, respectively. This study demonstrates that the integration of physical modeling and deep learning methods provides a more comprehensive and accurate understanding of spatial-temporal SM variations in the root zone. |
| Key Words: arid region,convolutional neural network,deep learning method,hybrid prediction model,leaf area index,long short-term memory neural network,normalized difference vegetation index,physical model,surface soil moisture |
| Citation: Lü X B, Nurmemet I, Xiao S T, Zhao J, Yu X R, Aili Y L Z T, Li S Q. 2025. Spatial-temporal simulation and prediction of root zone soil moisture based on Hydrus-1D and CNN-LSTM-attention models in Yutian Oasis, southern Xinjiang, China. Pedosphere. 35(5):846-857. |
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