Deng Mingming, Ma Ronghua, Wang Lixin, Hu Minqi, Xue Kun, Cao Zhigang, Xiong Junfeng, Yu Zhengyang
Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China.
University of Chinese Academy of Sciences, Beijing, 100049, China.
Sci Data. 2025 Jul 30;12(1):1324. doi: 10.1038/s41597-025-05686-2.
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.
水体盐度表征了天然水体的物理化学性质，是评估湖泊水质的一个重要参数。然而，由于盐度是一个非光学活性参数，水体盐度遥感反演的效率受到限制，导致缺乏像素级的湖泊盐度数据集。基于盐度示踪剂或单个湖泊的传统函数模型区域适用性较低，而机器学习算法能够有效捕捉辐射率与盐度之间的非线性关系，提供了大规模反演的机会。我们的研究构建了一个极端梯度提升（XGB）盐度模型，该模型利用哨兵2号遥感反射率生成了内蒙古湖泊盐度（IMSAL）数据集。IMSAL数据集包含2016年至2024年八个湖泊的928个10米空间分辨率的栅格场景。通过与实测盐度和已发表文献记录盐度进行交叉验证和独立验证，证实了该数据集具有良好的一致性和可靠性。该数据集提供了关于湖泊盐度空间格局和长期变化的宝贵信息，有助于防止湖泊盐碱化，并促进湖泊管理以实现生态系统可持续发展。
