Land surface temperature (LST) is a critical indicator of the interactions between the Earth’s surface and atmosphere and has long been available from satellite observations in the thermal infrared (TIR) region. Recognized as a primary way to evaluate the accuracy of LSTs, in situ validation is still a challenging task because of uncertainties in ground measurements, spatial scale mismatch between ground and satellite-based measurements, the heterogeneity of natural land surfaces, etc., leading to a lack of consistency among sets of validation results; therefore, to improve robustness against uncertainties, an optimized approach for LST validation via the Kalman filter is presented, and prediction of comprehensive validation estimate (CVE) which is close to “true” value, and more precise than those based on a single measurement alone is obtained. After the uncertainties involved in the validation process are constrained, this method is applied to FengYun-3D (FY-3D)/Medium Resolution Spectral Imager II (MERSI-II) LSTs with ground measurements from four sites in China. The results indicate that the CVE is 1.11 K, with an uncertainty of 0.07 K. Additionally, a comparison is performed with the weighted average method, and the efficacy of the Kalman filter approach in enhancing the validation accuracy is confirmed.

Surface temperature (ST) is a vital physical parameter influencing surface-atmosphere interactions. This study presents an uncertainty-based validation approach applied to Sentinel-3/SLSTR land surface temperature (LST) and sea surface temperature (SST) products.In situmeasurements were obtained from various sites in China, namely, the Dunhuang Gobi site (DHGS), Huailai Guanting Reservoir site (HGRS), Wuliangsuhai Lake site (WLSLS) and Yantai Ocean site (YTOS). The spatial representativeness ofin situmeasurements at each site was assessed using available clear-sky and high-quality ASTER LST products from April 2000 to June 2023. The four sites exhibited high spatial homogeneity, demonstrating suitability for validating STs. Therefore,in situmeasurements from these homogeneous sites were used to validate the Sentinel-3/SLSTR ST products during the daytime and nighttime using a temperature-based method. The results showed that the root mean square error (RMSE) values are lower than 1.6 K, except for those at DHGS. Furthermore, since ground-based ST validation is affected by the coupled effects of surface and atmospheric characteristics, the validation results are different under different atmospheric and surface conditions. Consequently, assessing the consistency among multiple validation results becomes challenging. To address this issue, by assuming the independence of the validation samples, we propose a method for obtaining the key comparison reference value (KCRV) from multiple validation results based on Sentinel-3/SLSTR ST products. The KCRV is close to the ‘true’ value, indicating the high quality of the validation results. For the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR LST products, the KCRVs are 1.91 K and 1.71 K, respectively, with corresponding uncertainties of 0.08 K and 0.08 K, respectively. Similarly, for the Sentinel-3A/SLSTR and Sentinel-3B/SLSTR SST products, the KCRVs are 0.78 K and 0.71 K, respectively, with uncertainties of 0.08 K and 0.07 K, respectively.