Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data
Root zone soil moisture (RZSM) is an important hydrological variable for agricultural planning and water resources management. The Soil Moisture Active Passive Level 4 (SMAP L4) data demonstrates great value in RZSM estimation. Accurate sub-seasonal RZSM prediction based on SMAP L4 holds great signi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225001797 |
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| author | Lei Xu Xihao Zhang Xi Zhang Tingtao Wu Hongchu Yu Wenying Du Zeqiang Chen Nengcheng Chen |
| author_facet | Lei Xu Xihao Zhang Xi Zhang Tingtao Wu Hongchu Yu Wenying Du Zeqiang Chen Nengcheng Chen |
| author_sort | Lei Xu |
| collection | DOAJ |
| description | Root zone soil moisture (RZSM) is an important hydrological variable for agricultural planning and water resources management. The Soil Moisture Active Passive Level 4 (SMAP L4) data demonstrates great value in RZSM estimation. Accurate sub-seasonal RZSM prediction based on SMAP L4 holds great significance for agricultural management and drought assessment. Current deep learning-based RZSM prediction models tend to accumulate error in long-term forecasting and the limited SMAP RZSM samples may result in insufficient model generalization. To address these issues, this study proposes a multi-head self-attention-based autoregressive transfer learning model based on long short-term memory (MAATL) model for sub-seasonal RZSM prediction. The proposed MAATL model is evaluated over the Continental United States (CONUS) for 1- to 60-day RZSM prediction and compared with some ablation and long short-term memory (LSTM) models. The results showed that compared with LSTM, the skills of the MAATL model were significantly improved, with an average correlation coefficient increase of 18.26% and a root mean square error (RMSE) reduction of 42.55%. Furthermore, 118 in-situ soil moisture stations are used for predictive validation and the proposed MAATL model demonstrates higher accuracy compared to the Global Forecast System (GFS) and the LSTM model, with an average correlation skill improvement of 16.02% and 15.08% for MAATL over GFS and LSTM, respectively. These findings indicate superior performance for the proposed MAATL model in sub-seasonal RZSM prediction, which has great potential for agricultural drought preparations. |
| format | Article |
| id | doaj-art-ca435fb0c5274ad29a02f76c381fa0df |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-ca435fb0c5274ad29a02f76c381fa0df2025-08-20T03:49:42ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-05-0113910453210.1016/j.jag.2025.104532Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP dataLei Xu0Xihao Zhang1Xi Zhang2Tingtao Wu3Hongchu Yu4Wenying Du5Zeqiang Chen6Nengcheng Chen7National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, ChinaNational Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, ChinaNational Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, ChinaNational Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430063, ChinaNational Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, ChinaNational Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China; Corresponding authors.National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, ChinaRoot zone soil moisture (RZSM) is an important hydrological variable for agricultural planning and water resources management. The Soil Moisture Active Passive Level 4 (SMAP L4) data demonstrates great value in RZSM estimation. Accurate sub-seasonal RZSM prediction based on SMAP L4 holds great significance for agricultural management and drought assessment. Current deep learning-based RZSM prediction models tend to accumulate error in long-term forecasting and the limited SMAP RZSM samples may result in insufficient model generalization. To address these issues, this study proposes a multi-head self-attention-based autoregressive transfer learning model based on long short-term memory (MAATL) model for sub-seasonal RZSM prediction. The proposed MAATL model is evaluated over the Continental United States (CONUS) for 1- to 60-day RZSM prediction and compared with some ablation and long short-term memory (LSTM) models. The results showed that compared with LSTM, the skills of the MAATL model were significantly improved, with an average correlation coefficient increase of 18.26% and a root mean square error (RMSE) reduction of 42.55%. Furthermore, 118 in-situ soil moisture stations are used for predictive validation and the proposed MAATL model demonstrates higher accuracy compared to the Global Forecast System (GFS) and the LSTM model, with an average correlation skill improvement of 16.02% and 15.08% for MAATL over GFS and LSTM, respectively. These findings indicate superior performance for the proposed MAATL model in sub-seasonal RZSM prediction, which has great potential for agricultural drought preparations.http://www.sciencedirect.com/science/article/pii/S1569843225001797RZSMSMAPMulti-head self-attentionAutoregressiveTransfer learning |
| spellingShingle | Lei Xu Xihao Zhang Xi Zhang Tingtao Wu Hongchu Yu Wenying Du Zeqiang Chen Nengcheng Chen Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data International Journal of Applied Earth Observations and Geoinformation RZSM SMAP Multi-head self-attention Autoregressive Transfer learning |
| title | Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data |
| title_full | Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data |
| title_fullStr | Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data |
| title_full_unstemmed | Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data |
| title_short | Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data |
| title_sort | accurate sub seasonal root zone soil moisture prediction using attention based autoregressive transfer learning and smap data |
| topic | RZSM SMAP Multi-head self-attention Autoregressive Transfer learning |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225001797 |
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