Estimating sub-daily resolution soil moisture using Fengyun satellite data and machine learning
文献类型：期刊论文
| 作者 | Wang, Jiao3; Zhang, Yongqiang; Song, Peilin2; Tian, Jing |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2024-03-01 |
| 卷号 | 632页码:130814 |
| 关键词 | Soil Moisture Sub-daily resolution Passive Microwave Artificial Neural Network Fengyun-3 |
| DOI | 10.1016/j.jhydrol.2024.130814 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Soil moisture (SM) is a critical parameter influencing hydrological cycles, evaporation, and plant transpiration, connecting land surface and atmospheric interactions. However, traditional SM inversion methods mainly offer daily resolution data, potentially overlooking diurnal fluctuations due to factors such as precipitation and human activities. This study addresses this limitation by shifting to sub-daily (four times per day) SM data, utilizing artificial neural networks (ANN) with microwave brightness temperature data obtained from Fengyun-3C and Fengyun-3D (FY-3C and FY-3D) satellites, alongside the microwave vegetation index (MVI) to correct for vegetation effects. The ANN was trained from July 2018 to December 2019 (FY-3C) and January 2019 to December 2022 (FY-3D) using the International Soil Moisture Network as the training target. The ANN method demonstrates favorable global performance, as indicated by r = 0.751-0.805, NSE = 0.56-0.64, RMSE = 0.069-0.077 m3/m3, ubRMSE = 0.066-0.071 m3/m3, and mean Bias = 0.002-0.007 m3/m3 under the crossvalidation mode. It can capture significant diurnal variations in SM, especially in regions like central Asia, western Australia, and South America. This research presents the feasibility of producing sub-daily high-temporal-resolution SM products with potential applications in large-scale agricultural drought and flood disaster monitoring, thereby enhancing national disaster management and mitigation strategies. |
| WOS关键词 | PASSIVE MICROWAVE MEASUREMENTS ; VEGETATION ; RETRIEVAL ; NETWORK ; TEMPERATURE ; PERFORMANCE ; CATCHMENT ; MODEL ; SMOS |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:001188612200001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203324]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 作者单位 | 1.Xi An Jiao Tong Univ, Fac Elect & Informat Engn, Key Lab Phys Elect & Devices, Minist Educ, Xian 710049, Shaanxi, Peoples R China 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Jiao,Zhang, Yongqiang,Song, Peilin,et al. Estimating sub-daily resolution soil moisture using Fengyun satellite data and machine learning[J]. JOURNAL OF HYDROLOGY,2024,632:130814. |
| APA | Wang, Jiao,Zhang, Yongqiang,Song, Peilin,&Tian, Jing.(2024).Estimating sub-daily resolution soil moisture using Fengyun satellite data and machine learning.JOURNAL OF HYDROLOGY,632,130814. |
| MLA | Wang, Jiao,et al."Estimating sub-daily resolution soil moisture using Fengyun satellite data and machine learning".JOURNAL OF HYDROLOGY 632(2024):130814. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。