Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates
文献类型：期刊论文
| 作者 | Zhang, Mo1,4; Ge, Yong1,2,3,4; Wang, Jianghao1,4 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 16期号:21页码:3939 |
| 关键词 | spatial prediction machine learning random forest variable importance digital soil mapping |
| DOI | 10.3390/rs16213939 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurate high-resolution soil moisture maps are crucial for a better understanding of hydrological processes and energy cycles. Mapping strategies such as downscaling and interpolation have been developed to obtain high-resolution soil moisture maps from multi-source inputs. However, research on the optimization performance of integrating downscaling and interpolation, especially through the use of mutual covariates, remains unclear. In this study, we compared four methods-two standalone methods based on downscaling and interpolation strategies and two combined methods that utilize soil moisture maps as mutual covariates within each strategy-in a case study of daily soil moisture mapping at a 1 km resolution in the Tibetan Plateau. We assessed mapping performance in terms of prediction accuracy and differences in spatial coverage. The results indicated that introducing interpolated soil moisture maps into the downscaling strategy significantly improved prediction accuracy (RMSE: -5.94%, correlation coefficient: +14.02%) but was limited to localized spatial coverage (6.9% of grid cells) near in situ sites. Conversely, integrating downscaled soil moisture maps into the interpolation strategy resulted in only modest gains in prediction accuracy (RMSE: -1.07%, correlation coefficient: +1.04%), yet facilitated broader spatial coverage (40.4% of grid cells). This study highlights the critical differences between downscaling and interpolation strategies in terms of accuracy improvement and spatial coverage, providing a reference for optimizing soil moisture mapping over large areas. |
| WOS关键词 | WIRELESS SENSOR NETWORK ; TEMPORAL VARIABILITY ; WATER ; DATASET ; SMAP ; COVER ; LAND ; SIZE |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001351985100001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/209521]  |
| 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 | Ge, Yong |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100101, Peoples R China 2.Key Lab Intelligent Monitoring & Comprehens Manage, Nanchang 330022, Peoples R China 3.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang 330022, Peoples R China 4.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 | Zhang, Mo,Ge, Yong,Wang, Jianghao. Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates[J]. REMOTE SENSING,2024,16(21):3939. |
| APA | Zhang, Mo,Ge, Yong,&Wang, Jianghao.(2024).Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates.REMOTE SENSING,16(21),3939. |
| MLA | Zhang, Mo,et al."Optimized Soil Moisture Mapping Strategies on the Tibetan Plateau Using Downscaled and Interpolated Maps as Mutual Covariates".REMOTE SENSING 16.21(2024):3939. |
入库方式： OAI收割
来源：地理科学与资源研究所
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