Research on Weighted Fusion Method for Multi-Source Sea Surface Temperature Based on Cloud Conditions

The sea surface temperature (SST) is an important parameter reflecting the energy exchange between the ocean and the atmosphere, which has a key impact on climate change, marine ecology and fisheries. However, most of the existing SST fusion methods suffer from poor portability and a lack of consideration of cloudy conditions, which can affect the data accuracy and reliability. To address these problems, this paper proposes an infrared and microwave SST fusion method based on cloudy conditions. The method categorizes the fusion process according to three scenarios—clear sky, completely cloudy, and partially cloudy—adjusting the fusion approach for each condition. In this paper, three representative global datasets from home and abroad are selected, while the South China Sea region, which suffers from extreme weather, is used as a typical study area for validation. By introducing the buoy observation data, the fusion results are evaluated using the metrics of bias, RMSE, URMSE, r and coverage. The experimental results show that the biases of the three fusion results of VIRR-RH, AVHRR-RH and MODIS-RH are −0.611 °C, 0.043 °C and 0.012 °C, respectively. In the South China Sea region under extreme weather conditions, the bias is −0.428 °C, the RMSE is 0.941 °C, the URMSE is 0.424 °C and the coverage rate reaches 25.55%. These results confirm that this method not only produces significant fusion effects but also exhibits strong generalization and adaptability, being unaffected by specific sensors or regions.