Temperature gradient method for extracting background temperature and calculating temperature rise area of nuclear power plants' thermal discharge

Remote sensing has become an essential tool for monitoring the discharge range and thermal discharge temperature rise classification of nuclear power plants. The core of remote sensing-based thermal discharge monitoring is the accurate extraction of background water temperature. Existing methods for extracting background temperature mainly involve two approaches: one is based on expert prior knowledge to define a temperature range, with the average temperature within this range used as the background temperature. However, this method is somewhat arbitrary and heavily influenced by human judgment. The second approach is based on deep learning, which can accurately extract background temperature but requires a large amount of training data and needs to be retrained for different datasets. To address these issues, we propose a background temperature extraction method based on the temperature gradient algorithm. To validate the applicability and accuracy of this method, we utilized 991 scenes of Landsat data from January 1, 2023, to June 30, 2024, and performed validation across 65 nuclear power plants worldwide. The results show that, compared to methods such as the average temperature correction and adjacent-zone substitution method, the temperature gradient method can automatically and accurately extract background temperature and temperature rise areas. Moreover, this method demonstrates strong general applicability, making it suitable for both coastal and lakeside nuclear power plants.