Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

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.

Retrieving Soil and Vegetation Temperatures From Dual-Angle and Multipixel Satellite Observations

“…where , ′ and , ′ represent the generated BT and FVC, respectively; and represent the mean and standard deviation, respectively; and , −1 represents an inverse process of the Planck function from thermal radiance to BT. According to the uncertainty analysis in [30,45], the uncertainties associated with the angular variations in LST retrieval were considered by setting to 0.25 K and 0.75 K with = 0. Based on [46], angular errors in FVC were included by setting to 0 and to 0.03 and 0.09.…”

“…where 0 − 7 are the SW coefficients; 8 and 9 represent the top-of-atmosphere BTs in SLSTR channels 8 (10.85 μm) and 9 (12.0 μm), respectively; and ̅ and are the mean and the differences of emissivity 8, and 9, in SLSTR channels 8 and 9, respectively. In-depth details on the SW algorithm are readily found in the published literature [28,30,45,48]. The SW coefficients were determined by fitting (11) to a synthetic dataset obtained with MODTRAN 5.2 [49] for data from the ASTER spectral library [50] and Seebor V5.2 atmosphere profile library [51]: we used 81 emissivity spectra, including for natural samples of vegetation, water, ice, snow, rock, sand and soil, and 4948 daytime atmospheric profiles to obtain a globally representative data collection.…”

Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

“…Several validations have shown that this emissivity product has high accuracy over bare soil surface in arid and semi-arid lands [44], [45]. Therefore, to characterize the variation of bare soil emissivity relatively accurately, this dataset has been used in emissivity estimation for many satellites sensors [46]- [49].…”

An Atmospheric Correction Using High Resolution Numerical Weather Prediction Models for Satellite-Borne Single-Channel Mid-Wavelength and Thermal Infrared Imaging Sensors

“…Currently, most land surface temperature and sea surface temperature (LST and SST, respectively) products are based on multichannel methods. The split-window (SW) algorithm is widely used for TIR sensors, as it is easy to apply and can be used without extensive computation of radiative transfer models (RTMs) or explicit information of atmospheric conditions [1,[6][7][8][9]. However, these multi-channel methods have limited application in other types of single-channel-based IR sensors, such as LANDSAT-7 [10].…”