Spatially adaptive estimation of multi-layer soil temperature at a daily time-step across China during 2010-2020
文献类型：期刊论文
| 作者 | Wang, Xuetong4,5; He, Liang3; Li, Peng4,5; Ma, Jiageng2; Shi, Yu1; Tian, Qi4,5; Zhao, Gang4,9; He, Jianqiang8; Feng, Hao4; Shi, Hao6,7 |
| 刊名 | EARTH SYSTEM SCIENCE DATA
 |
| 出版日期 | 2026-01-06 |
| 卷号 | 18期号:1页码:97-116 |
| ISSN号 | 1866-3508 |
| DOI | 10.5194/essd-18-97-2026 |
| 产权排序 | 4 |
| 文献子类 | Article ; Data Paper |
| 英文摘要 | Soil temperature (Ts) is critical in regulating agricultural production, ecosystem functions, hydrological cycling and climate dynamics. However, the inherent spatial and temporal heterogeneity of soil thermal regimes constitutes a persistent challenge in obtaining high-resolution, continuous gridded Ts datasets along vertical profiles. To address this issue, we propose a spatially adaptive layer-cascading Extreme Gradient Boosting (XGBoost) algorithm to generate daily multi-layer Ts data (0, 5, 10, 15, 20, and 40 cm) at a spatial resolution of 1 km in China from 2010 to 2020. The methodology dynamically partitions non-uniformly distributed measuring sites (2093 sites across the country) to quadtrees and incorporates thermal coupling effects propagated between neighbor soil layers. Multi-source data, including satellite retrievals of land surface temperature and vegetation index, and ERA5 reanalysis climate variables were used as inputs. Validation using both spatially independent test sets and flux-tower observations demonstrated the robustness and accuracy of the product. It is noted the model's performance was lower in summers and winters than in springs and autumns. Compared to existing global or regional Ts products, the dataset developed here is characterized by its fine spatio-temporal patterns and high reliability, enabling it to provide supports for precision agriculture, ecosystem modeling and understanding climate-land feedback. Free access to the dataset can be found at 10.11888/Terre.tpdc.302333 (Wang et al., 2025b). |
| URL标识 | 查看原文 |
| WOS关键词 | LAND-SURFACE TEMPERATURES ; THERMAL-PROPERTIES ; SERIES DATA ; SNOW ; VARIABILITY ; SENSITIVITY ; PERFORMANCE ; GENERATION ; ALGORITHM ; MOISTURE |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001654368100001 |
| 出版者 | COPERNICUS GESELLSCHAFT MBH |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219636]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | He, Liang; Shi, Hao; Yu, Qiang |
| 作者单位 | 1.Peking Univ, Inst Carbon Neutral, Sino French Inst Earth Syst Sci, Coll Urban & Environm Sci, Beijing 100871, Peoples R China; 2.Chinese Acad Sci, Key Lab Ecosyst Network Observat & Modeling, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 3.Natl Meteorol Ctr, Beijing 100081, Peoples R China; 4.Northwest A&F Univ, State Key Lab Soil & Water Conservat & Desertifica, Yangling 712100, Peoples R China; 5.Northwest A&F Univ, Coll Nat Resources & Environm, Yangling 712100, Peoples R China; 6.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 7.Chinese Acad Sci, Res Ctr Ecoenvironm Sci, State Key Lab Ecol Secur Reg & Cities, Beijing 100085, Peoples R China; 8.Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid Area, Minist Educ, Yangling 712100, Peoples R China; 9.Northwest A&F Univ, Coll Soil & Water Conservat Sci & Engn, Yangling 712100, Shaanxi, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, Xuetong,He, Liang,Li, Peng,et al. Spatially adaptive estimation of multi-layer soil temperature at a daily time-step across China during 2010-2020[J]. EARTH SYSTEM SCIENCE DATA,2026,18(1):97-116. |
| APA | Wang, Xuetong.,He, Liang.,Li, Peng.,Ma, Jiageng.,Shi, Yu.,...&Yu, Qiang.(2026).Spatially adaptive estimation of multi-layer soil temperature at a daily time-step across China during 2010-2020.EARTH SYSTEM SCIENCE DATA,18(1),97-116. |
| MLA | Wang, Xuetong,et al."Spatially adaptive estimation of multi-layer soil temperature at a daily time-step across China during 2010-2020".EARTH SYSTEM SCIENCE DATA 18.1(2026):97-116. |
入库方式： OAI收割
来源：地理科学与资源研究所
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