Mapping soil organic carbon in fragmented agricultural landscapes: The efficacy and interpretability of multi-category remote sensing variables
文献类型：期刊论文
| 作者 | Wei, Yujiao8; Chen, Yiyun7,8; Wang, Jiaxue8; Yu, Peiheng1; Xu, Lu6; Zhang, Chi8; Shen, Huanfeng8; Li, Yaolin5,7,8; Zhang, Ganlin2,3,4 |
| 刊名 | JOURNAL OF INTEGRATIVE AGRICULTURE
 |
| 出版日期 | 2025-11-01 |
| 卷号 | 24期号:11页码:4395-4414 |
| 关键词 | soil organic carbon remote sensing-derived variables Shapley additive explanations efficacy and interpretability fragmented agricultural landscapes |
| ISSN号 | 2095-3119 |
| DOI | 10.1016/j.jia.2025.02.049 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Accurately mapping the spatial distribution of soil organic carbon (SOC) is crucial for guiding agricultural management and improving soil carbon sequestration, especially in fragmented agricultural landscapes. Although remote sensing provides spatially continuous environmental information about heterogeneous agricultural landscapes, its relationship with SOC remains unclear. In this study, we hypothesized that multi-category remote sensing-derived variables can enhance our understanding of SOC variation within complex landscape conditions. Taking the Qilu Lake watershed in Yunnan, China, as a case study area and based on 216 topsoil samples collected from irrigation areas, we applied the extreme gradient boosting (XGBoost) model to investigate the contributions of vegetation indices (VI), brightness indices (BI), moisture indices (MI), and spectral transformations (ST, principal component analysis and tasseled cap transformation) to SOC mapping. The results showed that ST contributed the most to SOC prediction accuracy, followed by MI, VI, and BI, with improvements in R2 of 29.27, 26.83, 19.51, and 14.43%, respectively. The dominance of ST can be attributed to the fact that it contains richer remote sensing spectral information. The optimal SOC prediction model integrated soil properties, topographic factors, location factors, and landscape metrics, as well as remote sensing-derived variables, and achieved RMSE and MAE of 15.05 and 11.42 g kg-1, and R2 and CCC of 0.57 and 0.72, respectively. The Shapley additive explanations deciphered the nonlinear and threshold effects that exist between soil moisture, vegetation status, soil brightness and SOC. Compared with traditional linear regression models, interpretable machine learning has advantages in prediction accuracy and revealing the influences of variables that reflect landscape characteristics on SOC. Overall, this study not only reveals how remote sensing-derived variables contribute to our understanding of SOC distribution in fragmented agricultural landscapes but also clarifies their efficacy. Through interpretable machine learning, we can further elucidate the causes of SOC variation, which is important for sustainable soil management and agricultural practices. |
| URL标识 | 查看原文 |
| WOS关键词 | IMPACTS ; MACHINE ; INDEXES ; IMAGERY ; STOCKS |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:001621522800001 |
| 出版者 | KEAI PUBLISHING LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217675]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Wei, Yujiao; Chen, Yiyun |
| 作者单位 | 1.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 4.Chinese Acad Sci, Inst Soil Sci, State Key Lab Soil & Sustainable Agr, Nanjing 210008, Peoples R China; 5.Duke Kunshan Univ, Kunshan 215316, Peoples R China; 6.Tianjin Chengjian Univ, Sch Geol & Geomat, Tianjin 300384, Peoples R China; 7.Minist Nat Resources, Key Lab Digital Cartog & Land Informat Applicat En, Wuhan 430079, Peoples R China; 8.Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wei, Yujiao,Chen, Yiyun,Wang, Jiaxue,et al. Mapping soil organic carbon in fragmented agricultural landscapes: The efficacy and interpretability of multi-category remote sensing variables[J]. JOURNAL OF INTEGRATIVE AGRICULTURE,2025,24(11):4395-4414. |
| APA | Wei, Yujiao.,Chen, Yiyun.,Wang, Jiaxue.,Yu, Peiheng.,Xu, Lu.,...&Zhang, Ganlin.(2025).Mapping soil organic carbon in fragmented agricultural landscapes: The efficacy and interpretability of multi-category remote sensing variables.JOURNAL OF INTEGRATIVE AGRICULTURE,24(11),4395-4414. |
| MLA | Wei, Yujiao,et al."Mapping soil organic carbon in fragmented agricultural landscapes: The efficacy and interpretability of multi-category remote sensing variables".JOURNAL OF INTEGRATIVE AGRICULTURE 24.11(2025):4395-4414. |
入库方式： OAI收割
来源：地理科学与资源研究所
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