Abstract
Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.
Highlights
As a crucial parameter in the global energy balance and environmental monitoring [1,2,3,4], land surface temperature (LST) plays an essential role in evapotranspiration estimation [5,6,7,8], hazard monitoring [9], fire detection [10,11], and global climate change studies [12]
The atmospheric water vapor content obtained from the National Centers for Environmental Prediction (NCEP) reanalysis profiles was used to compensate atmospheric effects
Surface emissivity was estimated by means of the ASTER GEDv3 product and fractional vegetation cover
Summary
As a crucial parameter in the global energy balance and environmental monitoring [1,2,3,4], land surface temperature (LST) plays an essential role in evapotranspiration estimation [5,6,7,8], hazard monitoring [9], fire detection [10,11], and global climate change studies [12]. Various methods and algorithms have been proposed for retrieving LST from thermal infrared (TIR) satellite data, including single-channel (SC) algorithms [15,16,17], split-window (SW) algorithms [18,19,20], a temperature and emissivity separation (TES) algorithm [21], and a physics-based day and night (D/N) algorithm [22] There are some existing studies on improving the accuracy of surface emissivity for VIIRS and Landsat 8 satellite data [30,31] In those studies, the ASTER Global Emissivity Database Version 3 (ASTER GEDv3) product was used to derive soil emissivity combined with fractional vegetation cover and the ASTER spectral library. We made some modifications and applied the method to estimate surface emissivity for Sentinel-3A SLSTR data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
