Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China
文献类型：期刊论文
| 作者 | Xiao, Jieyun1,2; Zhou, Wei1,2,3; Wang, Ting1,2; Peng, Yao1,2; Shi, Zhan1,2; Li, Saibo3; Li, Yang4; Yue, Tianxiang3 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18 |
| 关键词 | Soil Accuracy Estimation Biological system modeling Remote sensing Meteorology Habitats Graphical models Distribution functions Data models Digital soil mapping (DSM) feature selection (FS) habitat patches soil organic carbon (SOC) |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2024.3521034 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | High-precision digital soil mapping in complex terrain is challenging. This study proposed a new method using the partitioning around medoids clustering algorithm to partition the study area into distinct habitat patch types. Utilizing multisource data and three machine learning models, we estimated soil organic carbon (SOC) content in southwest China. Results showed higher SOC content (0-15 cm) in the southwestern mountains and the northwestern plateaus of Sichuan, while lower in the Sichuan Basin. The prediction uncertainty exhibited a similar pattern. Topographic and climatic variables played crucial roles in SOC estimation. Among the three machine learning models, RF and XGBoost demonstrated higher simulation accuracy than SVM (R-2 increased by 2.86%-82.35%). Using the RF feature selection (FS) method to select optimal factors as model input variables improved simulation accuracy compared with using all factors or selecting based on Pearson correlation analysis (R-2 increased by 1.75%-64.71%). The study found that a hybrid model based on different habitat patches achieved higher accuracy than the single model for the whole study area (for example, with RF FS method and modeling, R-2 increased by 2.17%-34.78%, and RMSE decreased by 2.19%-28.80%). These findings enhanced the accuracy and refinement of existing mapping in southwest China (compared to SoilGrids 1 km and SoilGrids 250 m products, R-2 increased by 20.45% and 317.73%, and RMSE decreased by 35.51% and 60.49%). Such improvements better characterized the spatial variability of SOC and provided important implications for future soil carbon stock accounts in complex terrain areas. |
| URL标识 | 查看原文 |
| WOS关键词 | PREDICTION ; FOREST ; RANGELANDS ; REGION ; DEPTH |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001396520800007 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211362]  |
| 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 | Zhou, Wei |
| 作者单位 | 1.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China; 2.Southwest Univ, Sch Geog Sci, Chongqing Engn Res Ctr Remote Sensing Big Data App, Chongqing 400715, Peoples R China; 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 4.Jilin Prov Intellectual Property Protect Ctr, Changchun 130022, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiao, Jieyun,Zhou, Wei,Wang, Ting,et al. Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18. |
| APA | Xiao, Jieyun.,Zhou, Wei.,Wang, Ting.,Peng, Yao.,Shi, Zhan.,...&Yue, Tianxiang.(2025).Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18. |
| MLA | Xiao, Jieyun,et al."Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025). |
入库方式： OAI收割
来源：地理科学与资源研究所
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