High-Frequency Monitoring and Short-Term Forecasting of Surface Water Temperature Using a Novel Hyperspectral Proximal Sensing System

Abstract

Highlights: What are the main findings? A high-precision lake surface water temperature (LSWT) inversion model was developed using a novel hyperspectral proximal sensing system (HPSs) and a DNN algorithm, achieving an R2 = 0.99, an RMSE = 0.92 °C, and an MAE = 0.64 °C. A short-term LSWT forecasting model based on the LSTM algorithm and HPSs data was established, providing accurate 1–3-day predictions (R2 > 0.985). What is the implication of the main finding? The approach enables the real-time, ultra-high-frequency monitoring of lake thermal dynamics, enhancing the detection of rapid temperature fluctuations and extreme events. This study provides a practical early warning and management tool to mitigate harmful algal blooms and safeguard drinking water security under climate change. The lake surface water temperature (LSWT) is one of the key indicators for monitoring and predicting changes in lake ecosystems, as it regulates numerous physical and biogeochemical processes. However, current LSWT measurements mainly rely on infrared thermometry and traditional in situ sensors, and lack effective short-term LSWT forecasting and early warning capabilities. To overcome these limitations, we established a high-frequency, real-time, and accurate monitoring and forecasting method for the LSWT based on a novel hyperspectral proximal sensing system (HPSs). An LSWT inversion method was constructed based on a deep neural network (DNN) algorithm with a satisfactory accuracy of R2 = 0.99, RMSE = 0.92 °C, MAE = 0.64 °C. An analysis of data collected from October 2021 to December 2023 revealed distinct seasonal fluctuations in the LSWT in the northern region of Lake Taihu, with the LSWT ranging from 2.61 °C to 38.52 °C. The hourly LSWT for the next three days was forecasted based on a long short-term memory (LSTM) model, with the accuracy having an R2 = 0.99, an RMSE = 1.01 °C, and an MAE = 0.87 °C. This study complements lake water quality monitoring and early warning systems and supports a deeper understanding of dynamic processes within lake physical systems.