High resolution soil moisture mapping in 3D space and time using machine learning and depth functions
文献类型：期刊论文
| 作者 | Zhang, Mo1,7; Ge, Yong1,5,6,7; Heuvelink, Gerard B. M.3,4; Ma, Yuxin2 |
| 刊名 | GEODERMA
 |
| 出版日期 | 2024-12-01 |
| 卷号 | 452页码:117117 |
| 关键词 | Spatial interpolation Downscaling method Random forest Equal-area spline depth function Knowledge-driven approach |
| DOI | 10.1016/j.geoderma.2024.117117 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Soil moisture is a key factor in hydrological, biological, and chemical processes, and plays a critical role in maintaining ecosystem balance. To generate high-resolution soil moisture maps at regional scales, researchers primarily employed in-situ observation-based spatial interpolation and remote sensing-based downscaling methods. However, direct comparisons between these methods are scarce. Additionally, remote sensing techniques are limited to the topsoil layer, and in-situ observations often have large depth intervals, thereby constraining the vertical resolution of subsurface soil moisture mapping. To address these challenges, we utilized an equal-area spline depth function combined with machine learning to map high spatial-vertical-resolution daily soil moisture across the Qinghai-Tibet Plateau. The performance of spatial interpolation and downscaling methods in mapping surface soil moisture at 0-5 cm depth were also compared. The results revealed that both spatial interpolation and downscaling methods produced unbiased predictions. However, prediction accuracy was lower in the peripheral subareas of the study area which had lower sampling density. Maps generated through the spatial interpolation method better captured detailed environmental covariates, whereas those obtained with downscaling methods were smoother. The fitting of depth functions introduced only small errors, but caution is still needed when predicting at unobserved depths. For subsurface soil moisture mapping using depth functions combined with spatial interpolation, validation results at two depth intervals showed improvements over surface predictions, with a root mean squared error (RMSE) reduced by 6.45 % to 17.2 % and unbiased RMSE by 5.95 % to 19.04 %. Furthermore, the analysis of variable importance highlighted the critical role of time-varying covariates. Future research should focus on optimizing depth functions and combining datadriven with knowledge-driven approaches. This study serves as a reference for mapping soil moisture with fine spatial-vertical-resolution in large-scale study areas. |
| WOS关键词 | ROOT-ZONE ; ORGANIC-CARBON ; DYNAMICS ; CLIMATE ; SMAP ; INTERPOLATION ; ASSIMILATION ; REGRESSION ; VEGETATION ; PROFILE |
| WOS研究方向 | Agriculture |
| WOS记录号 | WOS:001369334900001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/210449]  |
| 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 | Zhang, Mo; Ge, Yong |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China 2.New South Wales Dept Climate Change Energy Environ, Parramatta, NSW 2150, Australia 3.ISR World Soil Informat, POB 353, NL-6700 AJ Wageningen, Netherlands 4.Wageningen Univ, Soil Geog & Landscape Grp, POB 47, NL-6700 AA Wageningen, Netherlands 5.Key Lab Intelligent Monitoring & Comprehens Manage, Nanchang, Jiangxi, Peoples R China 6.Jiangxi Normal Univ, Key Lab Poyang Lake Wetland & Watershed Res, Minist Educ, Nanchang, Peoples R China 7.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Mo,Ge, Yong,Heuvelink, Gerard B. M.,et al. High resolution soil moisture mapping in 3D space and time using machine learning and depth functions[J]. GEODERMA,2024,452:117117. |
| APA | Zhang, Mo,Ge, Yong,Heuvelink, Gerard B. M.,&Ma, Yuxin.(2024).High resolution soil moisture mapping in 3D space and time using machine learning and depth functions.GEODERMA,452,117117. |
| MLA | Zhang, Mo,et al."High resolution soil moisture mapping in 3D space and time using machine learning and depth functions".GEODERMA 452(2024):117117. |
入库方式： OAI收割
来源：地理科学与资源研究所
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