Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches
文献类型：期刊论文
| 作者 | Yu, Wenping3,4; Zhou, Wei2,3; Wang, Ting3; Xiao, Jieyun3; Peng, Yao3; Li, Haoran1; Li, Yuechen3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 16期号:4页码:18 |
| 关键词 | soil organic carbon clustering algorithm machine learning digital soil mapping |
| DOI | 10.3390/rs16040688 |
| 通讯作者 | Zhou, Wei(zw20201109@swu.edu.cn) |
| 英文摘要 | Soil organic carbon (SOC) is generally thought to act as a carbon sink; however, in areas with high spatial heterogeneity, using a single model to estimate the SOC of the whole study area will greatly reduce the simulation accuracy. The earth surface unit division is important to consider in building different models. Here, we divided the research area into different habitat patches using partitioning around a medoids clustering (PAM) algorithm; then, we built an SOC simulation model using machine learning algorithms. The results showed that three habitat patches were created. The simulation accuracy for Habitat Patch 1 (R2 = 0.55; RMSE = 2.89) and Habitat Patch 3 (R2 = 0.47; RMSE = 3.94) using the XGBoost model was higher than that for the whole study area (R2 = 0.44; RMSE = 4.35); although the R2 increased by 25% and 6.8%, the RMSE decreased by 33.6% and 9.4%, and the field sample points significantly declined by 70% and 74%. The R2 of Habitat Patch 2 using the RF model increased by 17.1%, and the RMSE also decreased by 10.5%; however, the sample points significantly declined by 58%. Therefore, using different models for corresponding patches will significantly increase the SOC simulation accuracy over using one model for the whole study area. This will provide scientific guidance for SOC or soil property monitoring with low field survey costs and high simulation accuracy. |
| WOS关键词 | SUPPORT VECTOR MACHINE ; CLIMATE-CHANGE ; RANDOM FOREST ; STOCKS ; CLASSIFICATION ; MODELS ; SEQUESTRATION ; REGRESSION ; VEGETATION ; PREDICTION |
| 资助项目 | National Key Research and Development Program of China |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001172713500001 |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203082]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhou, Wei |
| 作者单位 | 1.Minist Nat Resources, Topog Survey Team 6, Chengdu 610500, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data App, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China 4.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yu, Wenping,Zhou, Wei,Wang, Ting,et al. Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches[J]. REMOTE SENSING,2024,16(4):18. |
| APA | Yu, Wenping.,Zhou, Wei.,Wang, Ting.,Xiao, Jieyun.,Peng, Yao.,...&Li, Yuechen.(2024).Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches.REMOTE SENSING,16(4),18. |
| MLA | Yu, Wenping,et al."Significant Improvement in Soil Organic Carbon Estimation Using Data-Driven Machine Learning Based on Habitat Patches".REMOTE SENSING 16.4(2024):18. |
入库方式： OAI收割
来源：地理科学与资源研究所
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