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Land Surface Temperature Retrieval from Sentinel-3A Sea and Land Surface Temperature Radiometer, Using a Split-Window Algorithm

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posted on 18.06.2019, 15:24 by Z Yitong, H Ren, J Guo, D Ghent, K Tansey, X Hu, J Nie, S Chen
Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.


This research was funded by National Natural Science Foundation of China (No. 41771369), and the UK government for supporting the Agri-Tech in China Newton Network+ (ATCNN) Small Project Award “Using Sentinel data for drought monitoring” (No. SM007), and National key research and development program (2017YFB0503905-05).



Remote Sensing, 2019, 11 (6), pp. 650-650 (17)

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/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment/GIS and Remote Sensing


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Remote Sensing





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The Sentinel-3A images were downloaded from Sentinel-3 Pre-Operations Data Hub (, and the SURFRAD data were obtained from National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory: Global Monitoring Division (