A Transformer-based method to simulate multi-scale soil moisture
文献类型：期刊论文
| 作者 | Liu, Yangxiaoyue1,7; Xin, Ying4,5,6; Yin, Cong2,3 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2025-07-01 |
| 卷号 | 655页码:132900 |
| 关键词 | Soil moisture Transformer Simulation Reconstruction Downscaling |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2025.132900 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The Transformer model, as an emerging deep learning method, shows great potential in spatiotemporal sequence-related simulation tasks. However, there is very limited understanding of its performance in soil moisture (SM) simulation. Here, we present a Transformer-based SM simulation network (SMSNet) to improve the quality of the widely used Soil Moisture Active Passive (SMAP) SM product by reconstructing and downscaling the pixels, and obtain daily SM products with resolutions of 9 km and 1 km. The model employs spatiotemporal attention in separate Transformer structures to extract patterns of 10 dynamic (MODIS Bands 1-7, land cover, land surface temperature, and precipitation) and 8 static variables (soil bulk density, clay content, gravel content, sand content, silt content, digital elevation model, latitude, and longitude) to establish the relationship between variable pattern and SMAP SM distribution. The SM product reconstructed by SMSNet shows good agreement with in-situ measurements (SCAN network: R = 0.639, RMSE = 0.086 m3/m3. UCSRN network: R = 0.665, RMSE = 0.097 m3/m3) at the Continental United States. Moreover, it can effectively mitigate the overestimation degree of SMAP SM and improve the accuracy in forest. The seamless mapping of SMAP SM is achieved using reconstructed SM to fill the gap, and the gap-filling SM exhibits reasonable spatiotemporal pattern. The downscaled 1 km SM performs similar accuracy degrees to those of the reconstructed ones, proving the applicability of transferring 9 km scale established SMSNet to 1 km scale SM simulation. The downscaled dataset can provide detailed SM characteristics, which further enhances the merit of SMAP SM at regional analysis. Moreover, both reconstructed and downscaled SMs exhibit accuracy superiority compared to the corresponding results from Cubic Spline Interpolation, Random Forest, and Convolutional Neural Network. Overall, our study highlights the benefits and potential of SMSNet in generating SM products with favorable accuracy over diverse and vast regions. |
| URL标识 | 查看原文 |
| WOS关键词 | SURFACE-TEMPERATURE ; PRODUCT ; MODIS ; SATELLITE ; VEGETATION ; ENSEMBLE ; RADAR ; WATER |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001434706600001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/213360]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Xin, Ying |
| 作者单位 | 1.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China 2.Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY 10964 USA; 3.Univ Calif Merced, Sierra Nevada Res Inst, Merced, CA 95343 USA; 4.Minist Nat Resources, South China Sea Dev Res Inst, Technol Innovat Ctr South China Sea Remote Sensing, Guangzhou 510310, Peoples R China; 5.Minist Nat Resources, Key Lab Marine Environm Survey Technol & Applicat, Guangzhou 510310, Peoples R China; 6.State Ocean Adm, South China Sea Inst Planning & Environm Res, Guangzhou 510310, Peoples R China; 7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Liu, Yangxiaoyue,Xin, Ying,Yin, Cong. A Transformer-based method to simulate multi-scale soil moisture[J]. JOURNAL OF HYDROLOGY,2025,655:132900. |
| APA | Liu, Yangxiaoyue,Xin, Ying,&Yin, Cong.(2025).A Transformer-based method to simulate multi-scale soil moisture.JOURNAL OF HYDROLOGY,655,132900. |
| MLA | Liu, Yangxiaoyue,et al."A Transformer-based method to simulate multi-scale soil moisture".JOURNAL OF HYDROLOGY 655(2025):132900. |
入库方式： OAI收割
来源：地理科学与资源研究所
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