Multi-scale analysis of soil property variability in Northeast China's black soils using advanced geospatial models
文献类型：期刊论文
| 作者 | Yu, Yong6; Geng, Jing5,6; Li, Guoxu4,6; Fang, Huajun1,2; Cheng, Shulan3 |
| 刊名 | SOIL & TILLAGE RESEARCH
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 254页码:106762 |
| 关键词 | Black soils Geospatial modeling Scale-dependent drivers Soil property variability Sustainable land management |
| ISSN号 | 0167-1987 |
| DOI | 10.1016/j.still.2025.106762 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Black soils are critical to global agriculture but are increasingly threatened by fertility decline due to intensive land use, particularly in Northeast China. Accurately mapping and understanding the spatial variability of soil properties in these spatially heterogeneous landscapes is vital for sustainable soil management. However, existing models often fail to capture the intricate multi-scale environmental drivers that influence soil dynamics. This study aimed to assess whether geographically weighted artificial neural networks (GWANN) as a localized nonlinear model can more effectively capture the spatial variability of soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) than global models such as artificial neural networks (ANN) and random forest (RF). Additionally, two-dimensional empirical mode decomposition (2D-EMD) and semivariogram analysis were applied to identify scale-dependent variation patterns, alongside variation partitioning analysis to quantify the contributions of climatic, topographic, and biological soil-forming factors. Results showed that GWANN outperformed RF and ANN, achieving reductions in RMSE by 0.063 g/kg and 0.362 g/kg for SOC, 0.013 g/kg and 0.028 g/kg for TN, and 0.005 g/kg and 0.006 g/kg for TP, providing more accurate predictions across all three soil properties. The 2D-EMD analysis revealed that meteorological factors predominantly drive large-scale variability (374-483 km) across all three soil properties. At medium (62-118 km) and small (14-28 km) scales, biological soil factors emerged as the main contributors for SOC and TN, while TP was influenced by meteorological factors at medium scale and by biological soil factors at small scale. Although topographic factors did not dominate at any particular scale, their relative contribution increased at medium and large scales compared to the small scale. This study provides valuable insights for optimizing soil fertility management and promoting sustainable land use practices in black soil regions. |
| URL标识 | 查看原文 |
| WOS关键词 | REGRESSION ; INDEX |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:001573608300001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216077]  |
| 专题 | 千烟洲站森林生态系统研究中心_外文论文 |
| 通讯作者 | Geng, Jing |
| 作者单位 | 1.Zhongke Jian Inst Ecoenvironm Sci, Jian 343000, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China; 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100094, Peoples R China; 5.Sun Yat sen Univ, Key Lab Comprehens Observat Polar Environm, Minist Educ, Zhuhai 519082, Peoples R China; 6.Sun Yat sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Yu, Yong,Geng, Jing,Li, Guoxu,et al. Multi-scale analysis of soil property variability in Northeast China's black soils using advanced geospatial models[J]. SOIL & TILLAGE RESEARCH,2025,254:106762. |
| APA | Yu, Yong,Geng, Jing,Li, Guoxu,Fang, Huajun,&Cheng, Shulan.(2025).Multi-scale analysis of soil property variability in Northeast China's black soils using advanced geospatial models.SOIL & TILLAGE RESEARCH,254,106762. |
| MLA | Yu, Yong,et al."Multi-scale analysis of soil property variability in Northeast China's black soils using advanced geospatial models".SOIL & TILLAGE RESEARCH 254(2025):106762. |
入库方式： OAI收割
来源：地理科学与资源研究所
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