Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing
文献类型：期刊论文
| 作者 | Bo, Yong5,9,10; Li, Xueke1; Liu, Kai5,10; Wang, Shudong5,10; Tang, Qiuhong2; Jiang, Yelin3; Li, Zhengqiang10; Lu, Shanlong4; Wang, Litao4; Feng, Chenglian6 |
| 刊名 | EARTHS FUTURE
 |
| 出版日期 | 2026-03-19 |
| 卷号 | 14期号:3页码:e2025EF006261 |
| DOI | 10.1029/2025EF006261 |
| 产权排序 | 5 |
| 文献子类 | Article |
| 英文摘要 | Understanding historical and future surface soil moisture (SSM) drying is pivotal due to its close links with droughts, heatwaves, and wildfires, yet debates regarding its evolution persist. In this study, we leverage advanced deep learning techniques to fill gaps of remote sensing-based SSM data during 1983-2020 and therefore use these gap-filled observations to constrain SSM estimates from 23 Earth System Models (ESMs) during 1901-2100. Our enhanced observations reveal that approximately half of Earth's landmass experienced SSM drying over the past four decades. However, in contrast to projections from current-generation ESMs, observation-constrained simulations indicate a less pronounced drying trend in dry-wet transitions and monsoon margins during 2021-2100 compared to 1901-1980. Current ESMs may overestimate SSM drying in these regions, likely due to their limited representation of soil moisture-atmosphere feedback. These findings highlight the need to integrate remote sensing and artificial intelligence into ESMs to improve projections of future droughts and their socio-economic consequences. |
| URL标识 | 查看原文 |
| WOS关键词 | CLIMATE-CHANGE ; DROUGHT ; MODEL ; LAND ; REGIONS ; TRENDS |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001720401600001 |
| 出版者 | AMER GEOPHYSICAL UNION |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221190]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Liu, Kai; Wang, Shudong |
| 作者单位 | 1.Univ Penn, Dept Earth & Environm Sci, Philadelphia, PA USA; 2.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; 3.Columbia Univ, Lamont Doherty Earth Observ, Div Ocean & Climate Phys, New York, NY USA; 4.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China; 5.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteorol, Nanjing, Peoples R China; 6.Chinese Res Inst Environm Sci, State Key Lab Environm Criteria & Risk Assessment, Beijing, Peoples R China; 7.Chinese Acad Sci, Inst Geol & Geophys, Key Lab Earth & Planetary Phys, Beijing, Peoples R China; 8.Chinese Acad Meteorol Sci, Beijing, Peoples R China 9.Univ Chinese Acad Sci, Beijing, Peoples R China; 10.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing & Digital Earth, Beijing, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Bo, Yong,Li, Xueke,Liu, Kai,et al. Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing[J]. EARTHS FUTURE,2026,14(3):e2025EF006261. |
| APA | Bo, Yong.,Li, Xueke.,Liu, Kai.,Wang, Shudong.,Tang, Qiuhong.,...&Zhou, Guangsheng.(2026).Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing.EARTHS FUTURE,14(3),e2025EF006261. |
| MLA | Bo, Yong,et al."Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing".EARTHS FUTURE 14.3(2026):e2025EF006261. |
入库方式： OAI收割
来源：地理科学与资源研究所
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