Improving model performance in mapping black-soil resource with machine learning methods and multispectral features
Abstract Accurate information on the distribution of regional black-soil resource is one of the important elements for the sustainable management of soils. And its results can provide decision makers with robust data that can be translated into better decision making. This study utilized all Sentine...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82399-3 |
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| author | Jianfang Hu Yulei Tang Jiapan Yan Jiahong Zhang Yuxin Zhao Zhansheng Chen |
| author_facet | Jianfang Hu Yulei Tang Jiapan Yan Jiahong Zhang Yuxin Zhao Zhansheng Chen |
| author_sort | Jianfang Hu |
| collection | DOAJ |
| description | Abstract Accurate information on the distribution of regional black-soil resource is one of the important elements for the sustainable management of soils. And its results can provide decision makers with robust data that can be translated into better decision making. This study utilized all Sentinel-2 images covering the study area from April to July in 2022. After masking clouds, all images were synthesized monthly. Based on the revised random forest classification algorithm, model performance using different feature combination programs were evaluated to search for an efficient, high-precision method for mapping black-soil resource. The impact on model performance of adding data from temperature, precipitation and slope geographic covariates was analyzed. And the robustness of the model was verified using Landsat-8 data with lower spatial resolution. The results showed that (1) the model based on multi-temporal ensemble features for mapping black-soil resource shows the best performance, with an OA of 94.6%; (2) adding temperature covariate can effectively improve the accuracy of black-soil resource mapping; (3) compared to the sentinel data, the performance of the model based on Landsat-8 data is reduced but still plausible, verifying the robustness of the model. This study provides a robust method to improve model performance for rapid mapping of black-soil resource. |
| format | Article |
| id | doaj-art-36a1daec56454b67af865c29e0c88082 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-36a1daec56454b67af865c29e0c880822025-01-12T12:20:25ZengNature PortfolioScientific Reports2045-23222025-01-0115111510.1038/s41598-024-82399-3Improving model performance in mapping black-soil resource with machine learning methods and multispectral featuresJianfang Hu0Yulei Tang1Jiapan Yan2Jiahong Zhang3Yuxin Zhao4Zhansheng Chen5Center for Geophysical Survey, China Geological SurveyCenter for Geophysical Survey, China Geological SurveyCenter for Geophysical Survey, China Geological SurveyChina Aero Geophysical Survey and Remote Sensing Center for Natural ResourcesCollege of Resources, Environment and Tourism, Capital Normal UniversityCenter for Geophysical Survey, China Geological SurveyAbstract Accurate information on the distribution of regional black-soil resource is one of the important elements for the sustainable management of soils. And its results can provide decision makers with robust data that can be translated into better decision making. This study utilized all Sentinel-2 images covering the study area from April to July in 2022. After masking clouds, all images were synthesized monthly. Based on the revised random forest classification algorithm, model performance using different feature combination programs were evaluated to search for an efficient, high-precision method for mapping black-soil resource. The impact on model performance of adding data from temperature, precipitation and slope geographic covariates was analyzed. And the robustness of the model was verified using Landsat-8 data with lower spatial resolution. The results showed that (1) the model based on multi-temporal ensemble features for mapping black-soil resource shows the best performance, with an OA of 94.6%; (2) adding temperature covariate can effectively improve the accuracy of black-soil resource mapping; (3) compared to the sentinel data, the performance of the model based on Landsat-8 data is reduced but still plausible, verifying the robustness of the model. This study provides a robust method to improve model performance for rapid mapping of black-soil resource.https://doi.org/10.1038/s41598-024-82399-3Black-soil resourceSoil mappingModel performanceMachine learning |
| spellingShingle | Jianfang Hu Yulei Tang Jiapan Yan Jiahong Zhang Yuxin Zhao Zhansheng Chen Improving model performance in mapping black-soil resource with machine learning methods and multispectral features Scientific Reports Black-soil resource Soil mapping Model performance Machine learning |
| title | Improving model performance in mapping black-soil resource with machine learning methods and multispectral features |
| title_full | Improving model performance in mapping black-soil resource with machine learning methods and multispectral features |
| title_fullStr | Improving model performance in mapping black-soil resource with machine learning methods and multispectral features |
| title_full_unstemmed | Improving model performance in mapping black-soil resource with machine learning methods and multispectral features |
| title_short | Improving model performance in mapping black-soil resource with machine learning methods and multispectral features |
| title_sort | improving model performance in mapping black soil resource with machine learning methods and multispectral features |
| topic | Black-soil resource Soil mapping Model performance Machine learning |
| url | https://doi.org/10.1038/s41598-024-82399-3 |
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