Saltwater intrusion is a natural mixture process between watershed freshwater and seawater that frequently occurs in estuaries. Station-based monitoring of saltwater intrusion is time-consuming and labor-intensive. To enable quick monitoring of saltwater intrusion, this study developed new remote sensing algorithms for water surface salinity measurement using four decision tree-based machine learning models. These models were built based on simultaneously collected salinity data from a waterway in the Pearl River Delta and Unmanned Aerial Vehicle (UAV) hyperspectral images. A 10-fold cross-validation was applied to assess the performance of the models, with XGBoost outperforming the other three models (=0.93, RMSE = 0.88 psu). Then the developed model was employed for Sentinel-2 multispectral satellite images to invert the estuarine salinity distribution at a larger spatial scale. Results displayed the high performance of the machine learning models proposed in this study for mapping the salinity distribution in river channels, making it an efficient and practical technique for monitoring saltwater intrusion in river channels at a regional scale.

中文翻译：

---

基于无人机和卫星图像以及机器学习模型监测海水入侵河口

咸水入侵是流域淡水和海水之间的自然混合过程，经常发生在河口。基于站点的盐水入侵监测既耗时又费力。为了能够快速监测盐水入侵，本研究使用四种基于决策树的机器学习模型开发了用于水面盐度测量的新遥感算法。这些模型是根据同时收集的珠江三角洲水道盐度数据和无人机（UAV）高光谱图像构建的。应用 10 倍交叉验证来评估模型的性能，XGBoost 优于其他三个模型（= 0.93，RMSE = 0.88 psu）。然后将开发的模型应用于Sentinel-2多光谱卫星图像，以在更大的空间尺度上反演河口盐度分布。结果表明，本研究提出的机器学习模型在绘制河道盐度分布方面具有高性能，使其成为监测区域尺度河道咸水入侵的有效且实用的技术。