Geospatial heterogeneity-informed machine learning for mapping soil hydraulic properties across China's drylands
文献类型：期刊论文
| 作者 | Niu, Liantao2,3; Jia, Xiaoxu2,3; Dai, Xiaoliang1,3; Gao, Lei6; Huang, Laiming2,3; Wei, Xiaorong4; Yang, Xiaofan5; Shao, Ming'an2,3,4 |
| 刊名 | GEODERMA
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 464页码:117622 |
| 关键词 | Soil hydraulic parameters Pedo-transfer functions Machine learning Geospatial heterogeneity Ensemble model |
| ISSN号 | 0016-7061 |
| DOI | 10.1016/j.geoderma.2025.117622 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Soil hydraulic properties (SHPs) are essential for hydrological modeling, yet their large-scale measurement remains challenging. Pedotransfer functions (PTFs) provide an alternative for estimating SHPs at broader scales. Integrating geospatial data into machine learning (ML) frameworks can significantly enhance the predictive performance and generalizability of PTFs, highlighting their potential for large-scale SHP estimation. In this study, we developed spatially explicit PTFs for China's drylands using data from 4,382 sites (12,306 soil samples) combined with more than ten ML algorithms. Results demonstrate that ML models accounting for geospatial heterogeneity outperformed simpler ML algorithms substantially. By integrating soil, vegetation, climate, and topographic factors, the models improved the prediction accuracy of various SHPs, including saturated soil hydraulic conductivity (Ks) and van Genuchten parameters (8s, 8r, a, n), by 31 %-79 % compared to existing PTFs, exhibiting strong robustness for applications in China's drylands. The optimal PTFs were applied to a 500 m x 500 m regional map of soil and environmental variables, generating maps of five SHPs (Ks, 8s, 8r, a, n) across six soil depths (0-5, 5-10, 25-30, 55-60, 95-100, and 195-200 cm) in the study region. From these maps, four additional SHPs, i.e., field capacity (8fc), wilting point (8wp), plant available water (8pa), and soil macroporosity ((bm), were derived at the same depths and resolution. The SHP dataset reveals distinct spatial distribution patterns and vertical heterogeneity of SHPs across China's drylands. This high-resolution, deep-profile dataset provides a robust foundation for large-scale hydrological and land surface modeling in China's drylands. |
| URL标识 | 查看原文 |
| WOS关键词 | PEDOTRANSFER FUNCTIONS ; WATER-RETENTION ; BULK-DENSITY ; CONDUCTIVITY ; VARIABILITY ; PARAMETERS ; ROSETTA ; DATASET ; PREDICT ; MODEL |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:001631627800002 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219478]  |
| 专题 | 黄河三角洲现代农业工程实验室_外文论文 |
| 通讯作者 | Jia, Xiaoxu |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Modern Agr Engn Lab, Beijing 100101, Peoples R China; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100190, Peoples R China; 4.Northwest A&F Univ, State Key Lab Soil & Water Conservat & Desertifica, Yangling 712100, Shaanxi, Peoples R China; 5.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China 6.Chinese Acad Sci, Inst Soil Sci, Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Niu, Liantao,Jia, Xiaoxu,Dai, Xiaoliang,et al. Geospatial heterogeneity-informed machine learning for mapping soil hydraulic properties across China's drylands[J]. GEODERMA,2025,464:117622. |
| APA | Niu, Liantao.,Jia, Xiaoxu.,Dai, Xiaoliang.,Gao, Lei.,Huang, Laiming.,...&Shao, Ming'an.(2025).Geospatial heterogeneity-informed machine learning for mapping soil hydraulic properties across China's drylands.GEODERMA,464,117622. |
| MLA | Niu, Liantao,et al."Geospatial heterogeneity-informed machine learning for mapping soil hydraulic properties across China's drylands".GEODERMA 464(2025):117622. |
入库方式： OAI收割
来源：地理科学与资源研究所
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