A non-optically active lake salinity dataset by satellite remote sensing
Abstract Water salinity characterizes the physicochemical properties of natural water, serving as an essential parameter for assessing lake water quality. However, the efficiency of remote sensing inversion of water salinity is limited as salinity is a non-optically active parameter, leading to the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05686-2 |
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| author | Mingming Deng Ronghua Ma Lixin Wang Minqi Hu Kun Xue Zhigang Cao Junfeng Xiong Zhengyang Yu |
| author_facet | Mingming Deng Ronghua Ma Lixin Wang Minqi Hu Kun Xue Zhigang Cao Junfeng Xiong Zhengyang Yu |
| author_sort | Mingming Deng |
| collection | DOAJ |
| description | Abstract Water salinity characterizes the physicochemical properties of natural water, serving as an essential parameter for assessing lake water quality. However, the efficiency of remote sensing inversion of water salinity is limited as salinity is a non-optically active parameter, leading to the lack of a pixel-scale lake salinity dataset. Conventional function models based on salinity tracers or single lakes have low regional applicability, while machine learning algorithms can effectively capture the nonlinear relationship between radiance and salinity, providing large-scale inversion opportunities. Our study constructed an extreme gradient boosting (XGB) salinity model, which was used to generate the Inner Mongolia lake salinity (IMSAL) dataset with Sentinel-2 remote sensing reflectance. The IMSAL dataset contains 928 raster scenes with 10-meter spatial resolution for eight lakes from 2016 to 2024. Cross-validation and independent validation with measured and published literature-recorded salinities confirmed the good consistency and reliability. This dataset provides invaluable information on spatial patterns and long-term variations in lake salinity useful to prevent lake salinization and facilitate the lake management for sustainable ecosystem development. |
| format | Article |
| id | doaj-art-1b1975afff9f4e2380b3ea1c5e2337f3 |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-1b1975afff9f4e2380b3ea1c5e2337f32025-08-20T03:04:10ZengNature PortfolioScientific Data2052-44632025-07-0112111210.1038/s41597-025-05686-2A non-optically active lake salinity dataset by satellite remote sensingMingming Deng0Ronghua Ma1Lixin Wang2Minqi Hu3Kun Xue4Zhigang Cao5Junfeng Xiong6Zhengyang Yu7Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesSchool of Ecology and Environment, Inner Mongolia UniversityKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesKey Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of SciencesAbstract Water salinity characterizes the physicochemical properties of natural water, serving as an essential parameter for assessing lake water quality. However, the efficiency of remote sensing inversion of water salinity is limited as salinity is a non-optically active parameter, leading to the lack of a pixel-scale lake salinity dataset. Conventional function models based on salinity tracers or single lakes have low regional applicability, while machine learning algorithms can effectively capture the nonlinear relationship between radiance and salinity, providing large-scale inversion opportunities. Our study constructed an extreme gradient boosting (XGB) salinity model, which was used to generate the Inner Mongolia lake salinity (IMSAL) dataset with Sentinel-2 remote sensing reflectance. The IMSAL dataset contains 928 raster scenes with 10-meter spatial resolution for eight lakes from 2016 to 2024. Cross-validation and independent validation with measured and published literature-recorded salinities confirmed the good consistency and reliability. This dataset provides invaluable information on spatial patterns and long-term variations in lake salinity useful to prevent lake salinization and facilitate the lake management for sustainable ecosystem development.https://doi.org/10.1038/s41597-025-05686-2 |
| spellingShingle | Mingming Deng Ronghua Ma Lixin Wang Minqi Hu Kun Xue Zhigang Cao Junfeng Xiong Zhengyang Yu A non-optically active lake salinity dataset by satellite remote sensing Scientific Data |
| title | A non-optically active lake salinity dataset by satellite remote sensing |
| title_full | A non-optically active lake salinity dataset by satellite remote sensing |
| title_fullStr | A non-optically active lake salinity dataset by satellite remote sensing |
| title_full_unstemmed | A non-optically active lake salinity dataset by satellite remote sensing |
| title_short | A non-optically active lake salinity dataset by satellite remote sensing |
| title_sort | non optically active lake salinity dataset by satellite remote sensing |
| url | https://doi.org/10.1038/s41597-025-05686-2 |
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