Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial Resolution
Under the background of global warming, the impacts of extreme climate events are becoming increasingly severe, with drought posing particularly significant threats to both human society and the natural environment. Drought is recognized as the second most devastating natural disaster globally. Char...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-07-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1173/2025/isprs-archives-XLVIII-G-2025-1173-2025.pdf |
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| _version_ | 1849706502727663616 |
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| author | Y. Peng J. Liu Z. Wang M. Li |
| author_facet | Y. Peng J. Liu Z. Wang M. Li |
| author_sort | Y. Peng |
| collection | DOAJ |
| description | Under the background of global warming, the impacts of extreme climate events are becoming increasingly severe, with drought posing particularly significant threats to both human society and the natural environment. Drought is recognized as the second most devastating natural disaster globally. Characterized by its complexity and variability, identifying, assessing, and predicting drought features remains challenging. The Standardized Precipitation Evapotranspiration Index (SPEI), known for its multi-temporal scale characteristics, can represent various drought types and better reflect changes in drought dynamics. It has been increasingly applied in climatological and hydrological studies. However, using SPEI data with a 0.5-degree resolution to assess drought conditions in localized regions of China yields relatively low accuracy, hindering precise evaluation and prediction of drought severity and trends. Therefore, enhancing the spatial resolution of SPEI data is critically important. This study proposes the High Spatial-Resolution SPEI Network (HSR-SPEINet), which integrates environmental factors and remote sensing reflectance data to generate a 1 km resolution SPEI dataset. Experimental results demonstrate its strong accuracy. |
| format | Article |
| id | doaj-art-12db75452ad44d2e93ae59dc0c8bc7ea |
| institution | DOAJ |
| issn | 1682-1750 2194-9034 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj-art-12db75452ad44d2e93ae59dc0c8bc7ea2025-08-20T03:16:11ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342025-07-01XLVIII-G-20251173117910.5194/isprs-archives-XLVIII-G-2025-1173-2025Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial ResolutionY. Peng0J. Liu1Z. Wang2M. Li3School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha 410208, ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha 410208, ChinaSchool of Informatics, Hunan University of Chinese Medicine, Changsha 410208, ChinaUnder the background of global warming, the impacts of extreme climate events are becoming increasingly severe, with drought posing particularly significant threats to both human society and the natural environment. Drought is recognized as the second most devastating natural disaster globally. Characterized by its complexity and variability, identifying, assessing, and predicting drought features remains challenging. The Standardized Precipitation Evapotranspiration Index (SPEI), known for its multi-temporal scale characteristics, can represent various drought types and better reflect changes in drought dynamics. It has been increasingly applied in climatological and hydrological studies. However, using SPEI data with a 0.5-degree resolution to assess drought conditions in localized regions of China yields relatively low accuracy, hindering precise evaluation and prediction of drought severity and trends. Therefore, enhancing the spatial resolution of SPEI data is critically important. This study proposes the High Spatial-Resolution SPEI Network (HSR-SPEINet), which integrates environmental factors and remote sensing reflectance data to generate a 1 km resolution SPEI dataset. Experimental results demonstrate its strong accuracy.https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1173/2025/isprs-archives-XLVIII-G-2025-1173-2025.pdf |
| spellingShingle | Y. Peng J. Liu Z. Wang M. Li Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial Resolution The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial Resolution |
| title_full | Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial Resolution |
| title_fullStr | Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial Resolution |
| title_full_unstemmed | Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial Resolution |
| title_short | Deep Learning-based Multi-scale Monitoring of Drought in China with High Spatial Resolution |
| title_sort | deep learning based multi scale monitoring of drought in china with high spatial resolution |
| url | https://isprs-archives.copernicus.org/articles/XLVIII-G-2025/1173/2025/isprs-archives-XLVIII-G-2025-1173-2025.pdf |
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