Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China
文献类型：期刊论文
| 作者 | Tan, Qiuyuan5; Geng, Jing4,5; Fang, Huajun2,3; Li, Yuna1; Guo, Yifan3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-10-01 |
| 卷号 | 14期号:20页码:21 |
| 关键词 | soil organic carbon digital soil mapping Sentinel covariates selection model comparison resolution |
| DOI | 10.3390/rs14205151 |
| 通讯作者 | Geng, Jing(gengj9@mail.sysu.edu.cn) |
| 英文摘要 | Rapid and accurate mapping of soil organic carbon (SOC) is of great significance to understanding the spatial patterns of soil fertility and conducting soil carbon cycle research. Previous studies have dedicated considerable efforts to the spatial prediction of SOC content, but few have systematically quantified the effects of environmental covariates selection, the spatial scales and the model types on SOC prediction accuracy. Here, we spatially predicted SOC content through digital soil mapping (DSM) based on 186 topsoil (0-20 cm) samples in a typical hilly red soil region of southern China. Specifically, we first determined an optimal covariate set from different combinations of multiple environmental variables, including multi-sensor remote sensing images (Sentinel-1 and Sentinel-2), climate variables and DEM derivatives. Furthermore, we evaluated the impacts of spatial resolution (10 m, 30 m, 90 m, 250 m and 1000 m) of covariates and the model types (three linear and three non-linear machine learning techniques) on the SOC prediction. The results of the performance analysis showed that a combination of Sentinel-1/2-derived variables, climate and topographic predictors generated the best predictive performance. Among all variables, remote sensing covariates, especially Sentinel-2-derived predictors, were identified as the most important explanatory variables controlling the variability of SOC content. Moreover, the prediction accuracy declined significantly with the increased spatial scales and achieved the highest using the XGBoost model at 10 m resolution. Notably, non-linear machine learners yielded superior predictive capability in contrast with linear models in predicting SOC. Overall, our findings revealed that the optimal combination of predictor variables, spatial resolution and modeling techniques could considerably improve the prediction accuracy of the SOC content. Particularly, freely accessible Sentinel series satellites showed great potential in high-resolution digital mapping of soil properties. |
| WOS关键词 | TERRAIN ATTRIBUTES ; TOPSOIL CARBON ; RANDOM FORESTS ; STOCKS ; MATTER ; REGRESSION ; RESOLUTION ; SELECTION ; DYNAMICS |
| 资助项目 | National Natural Science Foundation of China[32101301] ; National Natural Science Foundation of China[41977041] ; Guangdong Basic and Applied Basic Research Foundation[2020A1515110172] ; Jiangxi Provincial Science and Technology Special Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone[ZJIEES-2021-01] ; Jiangxi Provincial Science and Technology Special Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone[ZJIEES-2022-02] ; Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone[202151] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000875229900001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Guangdong Basic and Applied Basic Research Foundation ; Jiangxi Provincial Science and Technology Special Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone ; Science and Technology Project of Jinggangshan Agricultural High-tech Industrial Demonstration Zone |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/186112]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Geng, Jing |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Zhongke Jian Inst Ecoenvironm Sci, Jian 343000, Jiangxi, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 4.Minist Nat Resources, Key Lab Nat Resources Monitoring Trop & Subtrop A, Zhuhai 519082, Peoples R China 5.Sun Yat Sen Univ, Sch Geospatial Engn & Sci, Zhuhai 519082, Peoples R China |
| 推荐引用方式 GB/T 7714 | Tan, Qiuyuan,Geng, Jing,Fang, Huajun,et al. Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China[J]. REMOTE SENSING,2022,14(20):21. |
| APA | Tan, Qiuyuan,Geng, Jing,Fang, Huajun,Li, Yuna,&Guo, Yifan.(2022).Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China.REMOTE SENSING,14(20),21. |
| MLA | Tan, Qiuyuan,et al."Exploring the Impacts of Data Source, Model Types and Spatial Scales on the Soil Organic Carbon Prediction: A Case Study in the Red Soil Hilly Region of Southern China".REMOTE SENSING 14.20(2022):21. |
入库方式： OAI收割
来源：地理科学与资源研究所
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