Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China
文献类型：期刊论文
| 作者 | Zhou, Wei1,2; Li, Haoran1,3; Wen, Shiya1; Xie, Lijuan3; Wang, Ting1; Tian, Yongzhong1; Yu, Wenping1 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 14期号:6页码:20 |
| 关键词 | soil organic carbon visible-near infrared spectroscopy characteristic band extreme gradient boosting Tibetan plateau |
| DOI | 10.3390/rs14061521 |
| 通讯作者 | Li, Haoran(lhr97@mails.cqjtu.edu.cn) |
| 英文摘要 | Soil organic carbon (SOC) changes affect the land carbon cycle and are also closely related to climate change. Visible-near infrared spectroscopy (Vis-NIRS) has proven to be an effective tool in predicting soil properties. Spectral transformations are necessary to reduce noise and ensemble learning methods can improve the estimation accuracy of SOC. Yet, it is still unclear which is the optimal ensemble learning method exploiting the results of spectral transformations to accurately simulate SOC content changes in the Three-Rivers Source Region of China. In this study, 272 soil samples were collected and used to build the Vis-NIRS simulation models for SOC content. The ensemble learning was conducted by the building of stack models. Sixteen combinations were produced by eight spectral transformations (S-G, LR, MSC, CR, FD, LRFD, MSCFD and CRFD) and two machine learning models of RF and XGBoost. Then, the prediction results of these 16 combinations were used to build the first-step stack models (Stack1, Stack2, Stack3). The next-step stack models (Stack4, Stack5, Stack6) were then made after the input variables were optimized based on the threshold of the feature importance of the first-step stack models (importance > 0.05). The results in this study showed that the stack models method obtained higher accuracy than the single model and transformations method. Among the six stack models, Stack 6 (5 selected combinations + XGBoost) showed the best simulation performance (RMSE = 7.3511, R-2 = 0.8963, and RPD = 3.0139, RPIQ = 3.339), and obtained higher accuracy than Stack3 (16 combinations + XGBoost). Overall, our results suggested that the ensemble learning of spectral transformations and simulation models can improve the estimation accuracy of the SOC content. This study can provide useful suggestions for the high-precision estimation of SOC in the alpine ecosystem. |
| WOS关键词 | REMOTELY-SENSED DATA ; GRASSLAND DEGRADATION ; PREDICTION ; MATTER ; FRACTIONS ; NITROGEN ; LEGACY ; MODEL |
| 资助项目 | Project of Chongqing Science and Technology Bureau[cstc2021jcyj-msxmX0384] ; Project of Chongqing Science and Technology Bureau[cstc2019jscx-fxydX0036] ; Postdoctoral Start-Up Project of Southwest University[SWU020015] ; National Natural Science Foundation of China[41930647] ; National Natural Science Foundation of China[41501575] ; National Natural Science Foundation of China[41977337] ; Innovation Project of LREIS[O88RA600YA] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:000774654600001 |
| 出版者 | MDPI |
| 资助机构 | Project of Chongqing Science and Technology Bureau ; Postdoctoral Start-Up Project of Southwest University ; National Natural Science Foundation of China ; Innovation Project of LREIS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/173173]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Li, Haoran |
| 作者单位 | 1.Southwest Univ, Sch Geog Sci, Chongqing Jinfo Mt Karst Ecosyst Natl Observat &, Chongqing 400715, 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.Chongqing Jiaotong Univ, Dept Geog & Land & Resources, Chongqing 400074, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Wei,Li, Haoran,Wen, Shiya,et al. Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China[J]. REMOTE SENSING,2022,14(6):20. |
| APA | Zhou, Wei.,Li, Haoran.,Wen, Shiya.,Xie, Lijuan.,Wang, Ting.,...&Yu, Wenping.(2022).Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China.REMOTE SENSING,14(6),20. |
| MLA | Zhou, Wei,et al."Simulation of Soil Organic Carbon Content Based on Laboratory Spectrum in the Three-Rivers Source Region of China".REMOTE SENSING 14.6(2022):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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