Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data

Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAB, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAB were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAB were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAB had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAB, with the lowest RMSE ranges of 1.57-1.62 K and 0.49 - 0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAB usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAB identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data.