A non-optically active lake salinity dataset by satellite remote sensing.

Journal: Scientific data
Published Date: Jul 30, 2025

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.

Authors

  • Mingming Deng
    Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
  • Ronghua Ma
    Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; University of Chinese Academy of Sciences, Nanjing, 211135, China. Electronic address: rhma@niglas.ac.cn.
  • Lixin Wang
    Department of Vascular Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
  • Minqi Hu
    Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China; Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
  • Kun Xue
    Institute of Materials Research and Engineering, A*STAR (Agency for Science, Technology and Research), 2 Fusionopolis Way, Innovis, #08-03, Singapore, 138634, Singapore.
  • Zhigang Cao
    Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China; State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China.
  • Junfeng Xiong
  • Zhengyang Yu
    Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.

Keywords

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