中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 烟台海岸带研究所
  3. 中国科学院烟台海岸带研究所
  4. 中科院海岸带环境过程与生态修复重点实验室
High-resolution soil organic carbon mapping in the Yellow River Delta utilizing Sentinel-2 composite and geographically weighted machine learning algorithms

文献类型：期刊论文

作者Li, Guoxu3,4; Song, Wanjuan4; Li, Yuan2; Li, Zishen1,5; Liu, Bingcheng1,5; Zhang, Xin4; Wang, Li4
刊名JOURNAL OF APPLIED REMOTE SENSING
出版日期2025
卷号19期号:1页码:17
关键词geographically weighted machine learning soil properties spatial estimation Sentinel-2 composite
DOI10.1117/1.JRS.19.014520
通讯作者Song, Wanjuan(songwj@aircas.ac.cn)
英文摘要Soil organic carbon is vital for climate change mitigation and soil fertility enhancement. We aim to achieve high-resolution mapping of soil organic carbon density (SOCD) in the Yellow River Delta using Sentinel-2 satellite imagery and machine learning algorithms. We evaluated the potential of exposed soil composite reflectance (ESCR) for SOCD mapping, incorporating four machine learning algorithms: random forest, artificial neural network, geographically weighted random forest, and geographically weighted artificial neural network (GWANN). The study compared the predictive performance of these algorithms, spotlighting the capability of GWANN methods in addressing spatial nonstationary. The GWANN model incorporating ESCR achieved the highest accuracy (R-2 = 0.50). The spatial distribution trends and uncertainties of SOCD mapped using random forest and GWANN were examined, revealing the robustness of GWANN in generating accurate SOCD maps and the superior spatial scalability and transferability of random forest.
WOS关键词LAND-USE ; PREDICTION ; MATTER ; TOPSOIL ; REGION
WOS研究方向Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001489645900039
资助机构National Key R&D Program of China and Shandong Province, China ; National Key R&D Program of China ; Shandong Province, China ; Science & Technology Fundamental Resources Investigation Program ; National Natural Science Foundation of China
源URL[http://ir.yic.ac.cn/handle/133337/41130]  
专题烟台海岸带研究所_中科院海岸带环境过程与生态修复重点实验室
通讯作者Song, Wanjuan
作者单位1.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing, Peoples R China
2.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yellow River Delta Ecol Res Stn Coastal Wetland, Yantai, Peoples R China
3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China
4.Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Remote Sensing & Digital Earth, Beijing, Peoples R China
5.Qilu Aerosp Informat Res Inst, Jinan, Peoples R China
推荐引用方式
GB/T 7714		 Li, Guoxu,Song, Wanjuan,Li, Yuan,et al. High-resolution soil organic carbon mapping in the Yellow River Delta utilizing Sentinel-2 composite and geographically weighted machine learning algorithms[J]. JOURNAL OF APPLIED REMOTE SENSING,2025,19(1):17.
APA Li, Guoxu.,Song, Wanjuan.,Li, Yuan.,Li, Zishen.,Liu, Bingcheng.,...&Wang, Li.(2025).High-resolution soil organic carbon mapping in the Yellow River Delta utilizing Sentinel-2 composite and geographically weighted machine learning algorithms.JOURNAL OF APPLIED REMOTE SENSING,19(1),17.
MLA Li, Guoxu,et al."High-resolution soil organic carbon mapping in the Yellow River Delta utilizing Sentinel-2 composite and geographically weighted machine learning algorithms".JOURNAL OF APPLIED REMOTE SENSING 19.1(2025):17.

入库方式： OAI收割

来源：烟台海岸带研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。