Near real-time retrieval of lake surface water temperature using Himawari-8 satellite imagery and machine learning techniques: a case study in the Yangtze River Basin

Abstract

Lake Surface Water Temperature (LSWT) is essential for understanding and regulating various processes in lake ecosystems. Remote sensing for large-scale aquatic monitoring offers valuable insights, but its limitations call for a dynamic LSWT monitoring model. This study developed multiple machine learning models for LSWT retrieval of four representative freshwater lakes in the Yangtze River Basin using Himawari-8 (H8) remote sensing imagery and in-situ data. Based on the in situ monitoring dataset in Lake Chaohu, the dynamic LSWT retrieval models were effectively configured and validated to perform H8-based remote sensing inversion. The test results showed that six models provided satisfactory LSWT retrievals, with the Back Propagation (BP) neural network model achieving the highest accuracy with an R-squared (R2) value of 0.907, a Root Mean Square Error (RMSE) of 2.52°C, and a Mean Absolute Error (MAE) of 1.68°C. Furthermore, this model exhibited universality, performing well in other lakes within the Yangtze River Basin, including Taihu, Datonghu and Dongtinghu. The ability to derive robust LSWT estimates confirms the feasibility of real-time LSWT retrieval using synchronous satellites, offering a more efficient and accurate approach for LSWT monitoring in the Yangtze River Basin. Thus, this proposed model would serve as a valuable tool to support the implementation of more informed policies for aquatic environmental conservation and sustainable water resource management, addressing challenges such as climate change, water pollution, and ecosystem restoration.