Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors?
Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the...
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2025-01-01
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/237 |
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| author | Liping Wang Huanjun Liu Xiang Wang Xiaofeng Xu Liyuan He Chong Luo Yong Li Xinle Zhang Deqiang Zang Shufeng Zheng Xiaodan Mei |
| author_facet | Liping Wang Huanjun Liu Xiang Wang Xiaofeng Xu Liyuan He Chong Luo Yong Li Xinle Zhang Deqiang Zang Shufeng Zheng Xiaodan Mei |
| author_sort | Liping Wang |
| collection | DOAJ |
| description | Environmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R<sup>2</sup> > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions. |
| format | Article |
| id | doaj-art-f4f15802faff4baca19f9e6c703ece1d |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-f4f15802faff4baca19f9e6c703ece1d2025-01-24T13:47:49ZengMDPI AGRemote Sensing2072-42922025-01-0117223710.3390/rs17020237Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors?Liping Wang0Huanjun Liu1Xiang Wang2Xiaofeng Xu3Liyuan He4Chong Luo5Yong Li6Xinle Zhang7Deqiang Zang8Shufeng Zheng9Xiaodan Mei10School of Hydraulic and Electric-Power, Heilongjiang University, Harbin 150080, ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, ChinaCollege of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, ChinaBiology Department, San Diego State University, San Diego, CA 92182, USABiology Department, San Diego State University, San Diego, CA 92182, USAState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences, Changchun 130102, ChinaSchool of Hydraulic and Electric-Power, Heilongjiang University, Harbin 150080, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaSchool of Public Administration and Law, Northeast Agricultural University, Harbin 150030, ChinaSchool of Hydraulic and Electric-Power, Heilongjiang University, Harbin 150080, ChinaSchool of Surveying and Mapping Engineering, Heilongjiang Institue of Technogly, Harbin 150050, ChinaEnvironmental variables have a substantial effect on the reliability of soil organic carbon (SOC) mapping. However, it is still challenging to identify which environmental variables are effective in cropland SOC prediction in sandy, saline, and black soil regions. To address this issue, we used the principal component analysis (PCA) method for the feature selection of bands, spectral indexes, and terrain factors for each region. Based on the selection feature, we used global RF and local RF for SOC prediction for these three regions. Our results indicated that (1) climate factors, particularly mean annual precipitation and mean annual temperature, were the most effective predictors in SOC mapping across sandy, saline, and black soil regions, as indicated by their significant contribution to RF model performance (R<sup>2</sup> > 0.63); (2) followed by climate factors, the Transformed Vegetation Index (TVI) was consistently identified as the most influential variable for SOC prediction among spectral indexes in all three regions; (3) a local regression method based on RF models showed good performance compared to a global model; (4) desertification and salinization were the main reasons for the spatial differences in AH and DM&LD, respectively. The SOC of HL in black soil regions was consistent with the climate change trend because of the latitude difference. This study provides valuable information for constructing a more precise soil prediction strategy for cultivated land in sandy, saline, and black soil regions.https://www.mdpi.com/2072-4292/17/2/237Sentinel-2digital soil mappingsoil classesenvironmental factorssustainability |
| spellingShingle | Liping Wang Huanjun Liu Xiang Wang Xiaofeng Xu Liyuan He Chong Luo Yong Li Xinle Zhang Deqiang Zang Shufeng Zheng Xiaodan Mei Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? Remote Sensing Sentinel-2 digital soil mapping soil classes environmental factors sustainability |
| title | Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? |
| title_full | Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? |
| title_fullStr | Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? |
| title_full_unstemmed | Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? |
| title_short | Identifying Optimal Variables to Predict Soil Organic Carbon in Sandy, Saline, and Black Soil Regions: Remote Sensing, Terrain, or Climate Factors? |
| title_sort | identifying optimal variables to predict soil organic carbon in sandy saline and black soil regions remote sensing terrain or climate factors |
| topic | Sentinel-2 digital soil mapping soil classes environmental factors sustainability |
| url | https://www.mdpi.com/2072-4292/17/2/237 |
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