Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China
文献类型：期刊论文
| 作者 | Wang, Ting4; Zhou, Wei4,5; Xiao, Jieyun4; Li, Haoran4; Yao, Li4; Xie, Lijuan1; Wang, Keming3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 15期号:8页码:2118 |
| 关键词 | soil organic carbon complex surface remote retrieval machine learning karst trough valley area |
| DOI | 10.3390/rs15082118 |
| 文献子类 | Article |
| 英文摘要 | Climate change is closely linked to changes in soil organic carbon (SOC) content, which affects the terrestrial carbon cycle. Consequently, it is essential for carbon accounting and sustainable soil management to predict SOC content accurately. Although there has been an extensive utilization of optical remote sensing data and environmental factors to predict SOC content, few studies have explored their applicability in karst areas. Therefore, it remains unclear how SOC content can be accurately simulated in these areas. In this study, 160 soil samples, 8 environmental covariates and 14 optical remote sensing variables were used to build SOC content prediction models. Three machine learning models, i.e., support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost), were applied for each of three land use classes, including the entire study area, as well as farmland and forest areas. The variables with the greatest influence were the optical remote sensing bands, derived indices, as well as precipitation and temperature for forest areas, and optical remote sensing band11 and Pop-density for farmland. The results from this study suggest that RF and XGBoost are superior to SVM in prediction accuracy. Additionally, the simulation accuracy of the RF model for the forest areas (R-2 = 0.32, RMSE = 6.81, MAE = 5.63) and of the XGBoost model for farmland areas (R-2 = 0.28, RMSE = 4.03, MAE = 3.27) was the greatest. The prediction model based on different land use types could obtain a higher simulation accuracy than that based on the whole study area. These findings provide new insights for the estimation of SOC content with high precision in karst areas. |
| 学科主题 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS关键词 | CLIMATE-CHANGE ; TOTAL NITROGEN ; STOCKS ; VEGETATION ; MATTER ; SEQUESTRATION ; VARIABILITY ; LANDSCAPE ; PROVINCE ; INDEXES |
| 语种 | 英语 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/193502]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Chongqing Municipal Publ Secur Bur, Special Weap & Tact Police Aviat Management Off, Chongqing 401147, Peoples R China 3.Beijing Piesat Informat Technol Co, Beijing 100195, Peoples R China 4.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat & R, Chongqing 400715, Peoples R China 5.Southwest Univ, Chongqing Engn Res Ctr Remote Sensing Big Data App, Sch Geog Sci, Chongqing 400715, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Ting,Zhou, Wei,Xiao, Jieyun,et al. Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China[J]. REMOTE SENSING,2023,15(8):2118. |
| APA | Wang, Ting.,Zhou, Wei.,Xiao, Jieyun.,Li, Haoran.,Yao, Li.,...&Wang, Keming.(2023).Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China.REMOTE SENSING,15(8),2118. |
| MLA | Wang, Ting,et al."Soil Organic Carbon Prediction Using Sentinel-2 Data and Environmental Variables in a Karst Trough Valley Area of Southwest China".REMOTE SENSING 15.8(2023):2118. |
入库方式： OAI收割
来源：地理科学与资源研究所
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