Lei Xu , Xihao Zhang , Xi Zhang , Tingtao Wu , Hongchu Yu , Wenying Du , Zeqiang Chen , Nengcheng Chen
{"title":"基于注意力自回归迁移学习和SMAP数据的亚季节根区土壤湿度准确预测","authors":"Lei Xu , Xihao Zhang , Xi Zhang , Tingtao Wu , Hongchu Yu , Wenying Du , Zeqiang Chen , Nengcheng Chen","doi":"10.1016/j.jag.2025.104532","DOIUrl":null,"url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":73423,"journal":{"name":"International journal of applied earth observation and geoinformation : ITC journal","volume":"139 ","pages":"Article 104532"},"PeriodicalIF":7.6000,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data\",\"authors\":\"Lei Xu , Xihao Zhang , Xi Zhang , Tingtao Wu , Hongchu Yu , Wenying Du , Zeqiang Chen , Nengcheng Chen\",\"doi\":\"10.1016/j.jag.2025.104532\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>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.</div></div>\",\"PeriodicalId\":73423,\"journal\":{\"name\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"volume\":\"139 \",\"pages\":\"Article 104532\"},\"PeriodicalIF\":7.6000,\"publicationDate\":\"2025-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of applied earth observation and geoinformation : ITC journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1569843225001797\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of applied earth observation and geoinformation : ITC journal","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1569843225001797","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"REMOTE SENSING","Score":null,"Total":0}
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 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.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.