Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates
The accurate prediction of soil organic matter (SOM) content is important for sustainable agriculture and effective soil management. This task is particularly challenging due to the variability in factors influencing SOM distribution across different cultivated land types, as well as the site-specif...
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MDPI AG
2025-02-01
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| author | Yu Zhang Chong Luo Wenqi Zhang Zexin Wu Deqiang Zang |
| author_facet | Yu Zhang Chong Luo Wenqi Zhang Zexin Wu Deqiang Zang |
| author_sort | Yu Zhang |
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| description | The accurate prediction of soil organic matter (SOM) content is important for sustainable agriculture and effective soil management. This task is particularly challenging due to the variability in factors influencing SOM distribution across different cultivated land types, as well as the site-specific responses of SOM to remote sensing data and environmental covariates, especially in the black soil region of northeastern China, where SOM exhibits significant spatial variability. This study evaluated the variations on the importance of different remote sensing imagery and environmental covariates in different cultivated land zones. A total of 180 soil samples (0–20 cm) were collected from Youyi County, Heilongjiang Province, China, and multi-year synthetic bare soil images from 2014 to 2022 (focusing on April and May) were acquired using Google Earth Engine. Combining three types of environmental covariates such as drainage, climate and topography, the study area was categorized into dry field and paddy field. Then, the SOM prediction model was constructed using random forest regression method and the accuracy of different strategies was evaluated by 10-fold cross-validation. The findings indicated that, (1) in the overall regression analysis, combining drainage and climate variables and multi-year synthetic remote sensing images of May could attain the highest prediction accuracy, and the importance of environmental covariates was ranked as follows: remote sensing (RS) > climate (CLI) > drainage (DN) > Topography (TP). (2) Zonal regression analysis was conducted with a high degree of precision, as evidenced by an R<sup>2</sup> of 0.72 and an impressively low RMSE of 0.73%. The time window for remote monitoring of SOM was different for dry field and paddy field. More specifically, the optimal time frames for SOM prediction in dryland were identified as April and May, while those for paddy fields were concentrated in May. (3) In addition, the importance of diverse environmental covariates was observed to vary with the cultivated land types. In regions characterized by intricate topography, such as dry fields, the contributions of remote sensing images and climate variables assumed a heightened importance. Conversely, in paddy fields featuring flat terrain, the roles of climate and drainage variables played a more substantial role in influencing the outcomes. These findings underscore the importance of selecting appropriate environmental inputs for improving SOM prediction accuracy. |
| format | Article |
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| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-02-01 |
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| series | Agriculture |
| spelling | doaj-art-e2dcd6088e284824919ea2e31cca2cab2025-08-20T02:48:06ZengMDPI AGAgriculture2077-04722025-02-0115333910.3390/agriculture15030339Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental CovariatesYu Zhang0Chong Luo1Wenqi Zhang2Zexin Wu3Deqiang Zang4School of Economics and Management, Jilin Agricultural University, Changchun 130118, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaSchool of Economics and Management, Jilin Agricultural University, Changchun 130118, ChinaNortheast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, ChinaSchool of Public Administration and Law, Northeast Agricultural University, Harbin 150030, ChinaThe accurate prediction of soil organic matter (SOM) content is important for sustainable agriculture and effective soil management. This task is particularly challenging due to the variability in factors influencing SOM distribution across different cultivated land types, as well as the site-specific responses of SOM to remote sensing data and environmental covariates, especially in the black soil region of northeastern China, where SOM exhibits significant spatial variability. This study evaluated the variations on the importance of different remote sensing imagery and environmental covariates in different cultivated land zones. A total of 180 soil samples (0–20 cm) were collected from Youyi County, Heilongjiang Province, China, and multi-year synthetic bare soil images from 2014 to 2022 (focusing on April and May) were acquired using Google Earth Engine. Combining three types of environmental covariates such as drainage, climate and topography, the study area was categorized into dry field and paddy field. Then, the SOM prediction model was constructed using random forest regression method and the accuracy of different strategies was evaluated by 10-fold cross-validation. The findings indicated that, (1) in the overall regression analysis, combining drainage and climate variables and multi-year synthetic remote sensing images of May could attain the highest prediction accuracy, and the importance of environmental covariates was ranked as follows: remote sensing (RS) > climate (CLI) > drainage (DN) > Topography (TP). (2) Zonal regression analysis was conducted with a high degree of precision, as evidenced by an R<sup>2</sup> of 0.72 and an impressively low RMSE of 0.73%. The time window for remote monitoring of SOM was different for dry field and paddy field. More specifically, the optimal time frames for SOM prediction in dryland were identified as April and May, while those for paddy fields were concentrated in May. (3) In addition, the importance of diverse environmental covariates was observed to vary with the cultivated land types. In regions characterized by intricate topography, such as dry fields, the contributions of remote sensing images and climate variables assumed a heightened importance. Conversely, in paddy fields featuring flat terrain, the roles of climate and drainage variables played a more substantial role in influencing the outcomes. These findings underscore the importance of selecting appropriate environmental inputs for improving SOM prediction accuracy.https://www.mdpi.com/2077-0472/15/3/339cultivated land typessoil organic matterenvironmental covariateszonal regressiondigital soil mapping |
| spellingShingle | Yu Zhang Chong Luo Wenqi Zhang Zexin Wu Deqiang Zang Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates Agriculture cultivated land types soil organic matter environmental covariates zonal regression digital soil mapping |
| title | Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates |
| title_full | Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates |
| title_fullStr | Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates |
| title_full_unstemmed | Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates |
| title_short | Mapping Soil Organic Matter in Black Soil Cropland Areas Using Remote Sensing and Environmental Covariates |
| title_sort | mapping soil organic matter in black soil cropland areas using remote sensing and environmental covariates |
| topic | cultivated land types soil organic matter environmental covariates zonal regression digital soil mapping |
| url | https://www.mdpi.com/2077-0472/15/3/339 |
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