A Simplified Sea Surface Emissivity Model for Retrieving Sea Surface Temperature From Sentinel-3A SLSTR Data

Sea surface temperature (SST) is an important parameter for assessing sea-atmosphere energy interaction and understanding climate change. One of the primary approaches for obtaining global-scale SST is retrieving from satellite thermal infrared (TIR) remote sensing data. However, it is challenging to accurately retrieve large-scale SST due to the complexity of retrieving the key intermediate parameter, i.e., sea surface emissivity (SSE), using the standard theoretical model. In this study, we proposed a simplified SSE estimation model based on the satellite view zenith angle (VZA) and wind speed and compared it with three commonly used SSE estimation models. Then, the retrieved SSTs based on those SSEs were validated against in situ SSTs. The results show that the SSE from the proposed estimation model shows the highest consistency and the lowest biases with the theoretical values compared to the other three estimation models, especially in large VZAs. In situ observation-based SST validation results show that the SST retrieved using the proposed SSE estimation model also achieves the highest accuracy compared to the other three SSTs, with a mean bias error of 0.08 K, and a root-mean-square error (RMSE) of 0.30 K, which is close to the official SST products. In conclusion, the proposed SSE estimation model shows good performance both in SSE estimating and SST retrieving. Furthermore, the proposed model has the potential to estimate SSE on large scales that can serve as a reference for obtaining SST from other similar sensors to promote the development of marine remote sensing.