A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning
Drought is a frequent, destructive, and complex natural hazard, and seriously threatens eco-environment, socio-economy, and the health of human. Previous studies suggested that integrated multi-source remote sensing drought indices have the potential to comprehensively monitor drought conditions, ho...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377425000174 |
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| author | Hao Chen Ni Yang Xuanhua Song Chunhua Lu Menglan Lu Tan Chen Shulin Deng |
| author_facet | Hao Chen Ni Yang Xuanhua Song Chunhua Lu Menglan Lu Tan Chen Shulin Deng |
| author_sort | Hao Chen |
| collection | DOAJ |
| description | Drought is a frequent, destructive, and complex natural hazard, and seriously threatens eco-environment, socio-economy, and the health of human. Previous studies suggested that integrated multi-source remote sensing drought indices have the potential to comprehensively monitor drought conditions, however most existing integrated drought indices still have several limitations. Here, we used solar-induced chlorophyll fluorescence, water balance, soil moisture, and land surface temperature to develop a new integrated remote sensing drought index, namely interpretable machine learning drought index (IMLDI), based on the Bayesian optimized tree-based Light Gradient Boosting Machine and SHapley Additive exPlainations. The different land cover types were further considered, and the categories of drought severity were objectively determined by the iterative optimized method. The drought monitoring performance of IMLDI was validated in the eastern parts of China, and three integrated drought indies composited by PCA, multiple linear regression, and gradient boosting method were also included for comparison. The results show that IMLDI has a higher spatial and temporal consistency with standardized precipitation evapotranspiration index, can better reflect the real-world observed drought-affected cropland areas and gross primary production, and can also well describe the evolutions of 2009/2010 and 2019 drought events in the eastern parts of China, indicating higher drought monitoring performance of IMLDI. Besides, IMLDI-based agricultural drought risk analysis shows that the Huang-Hai Region and Yunnan, Guizhou, and Guangxi Provinces have a high risk to suffer from severe agricultural droughts. Overall, IMLDI has a great potential to use as a new integrated remote sensing drought index for agricultural drought monitoring. |
| format | Article |
| id | doaj-art-2a4d720ee82346a9b42dda67b26779b3 |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-2a4d720ee82346a9b42dda67b26779b32025-01-25T04:10:54ZengElsevierAgricultural Water Management1873-22832025-03-01308109303A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learningHao Chen0Ni Yang1Xuanhua Song2Chunhua Lu3Menglan Lu4Tan Chen5Shulin Deng6Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Nanning 530001, China; School of Geography and Planning, Nanning Normal University, Nanning 530001, ChinaSchool of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China; School of Management Science and Engineering, Guangxi University of Finance and Economics, Nanning 530003, ChinaKey Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Nanning 530001, China; School of Geography and Planning, Nanning Normal University, Nanning 530001, ChinaKey Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Nanning 530001, China; School of Geography and Planning, Nanning Normal University, Nanning 530001, ChinaKey Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Nanning 530001, China; School of Geography and Planning, Nanning Normal University, Nanning 530001, ChinaKey Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, ChinaKey Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Nanning 530001, China; School of Geography and Planning, Nanning Normal University, Nanning 530001, China; Corresponding author at: Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education, Nanning 530001, China.Drought is a frequent, destructive, and complex natural hazard, and seriously threatens eco-environment, socio-economy, and the health of human. Previous studies suggested that integrated multi-source remote sensing drought indices have the potential to comprehensively monitor drought conditions, however most existing integrated drought indices still have several limitations. Here, we used solar-induced chlorophyll fluorescence, water balance, soil moisture, and land surface temperature to develop a new integrated remote sensing drought index, namely interpretable machine learning drought index (IMLDI), based on the Bayesian optimized tree-based Light Gradient Boosting Machine and SHapley Additive exPlainations. The different land cover types were further considered, and the categories of drought severity were objectively determined by the iterative optimized method. The drought monitoring performance of IMLDI was validated in the eastern parts of China, and three integrated drought indies composited by PCA, multiple linear regression, and gradient boosting method were also included for comparison. The results show that IMLDI has a higher spatial and temporal consistency with standardized precipitation evapotranspiration index, can better reflect the real-world observed drought-affected cropland areas and gross primary production, and can also well describe the evolutions of 2009/2010 and 2019 drought events in the eastern parts of China, indicating higher drought monitoring performance of IMLDI. Besides, IMLDI-based agricultural drought risk analysis shows that the Huang-Hai Region and Yunnan, Guizhou, and Guangxi Provinces have a high risk to suffer from severe agricultural droughts. Overall, IMLDI has a great potential to use as a new integrated remote sensing drought index for agricultural drought monitoring.http://www.sciencedirect.com/science/article/pii/S0378377425000174Interpretable machine learning drought index (IMLDI)Agricultural drought monitoringSolar-induced chlorophyll fluorescenceThe eastern parts of China |
| spellingShingle | Hao Chen Ni Yang Xuanhua Song Chunhua Lu Menglan Lu Tan Chen Shulin Deng A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning Agricultural Water Management Interpretable machine learning drought index (IMLDI) Agricultural drought monitoring Solar-induced chlorophyll fluorescence The eastern parts of China |
| title | A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning |
| title_full | A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning |
| title_fullStr | A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning |
| title_full_unstemmed | A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning |
| title_short | A novel agricultural drought index based on multi-source remote sensing data and interpretable machine learning |
| title_sort | novel agricultural drought index based on multi source remote sensing data and interpretable machine learning |
| topic | Interpretable machine learning drought index (IMLDI) Agricultural drought monitoring Solar-induced chlorophyll fluorescence The eastern parts of China |
| url | http://www.sciencedirect.com/science/article/pii/S0378377425000174 |
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