Land Surface Temperature Retrieval from Sentinel-3A Sea and Land Surface Temperature Radiometer, Using a Split-Window Algorithm
Yitong Zheng,Huazhong Ren,Jinxin Guo,Darren Ghent,Kevin Tansey,Xingbang Hu,Jing Nie,Shanshan Chen +7 more
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TLDR
A split-window algorithm to estimate land surface temperature (LST) from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation is developed and can theoretically estimate LST with an error lower than 1 K on average.Abstract:
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.read more
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Journal ArticleDOI
Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data
TL;DR: The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data with an explicit use of land surface emissivity.
Journal ArticleDOI
BESS-STAIR: a framework to estimate daily, 30 m, and all-weather crop evapotranspiration using multi-source satellite data for the US Corn Belt
TL;DR: In this paper, a new high spatiotemporal resolution evapotranspiration (ET) mapping framework, BESS-STAIR, is presented, which integrates a satellite-driven water-carbon-energy coupled biophysical model, Bess (Breathing Earth System Simulator), with a generic and fully automated fusion algorithm, STAIR(SaTallite dAta IntegRation), providing daily 30'm multispectral surface reflectance by fusing Landsat and MODIS satellite data.
Journal ArticleDOI
Estimation of All-Weather 1 km MODIS Land Surface Temperature for Humid Summer Days
TL;DR: The strategy proposed in this study can improve the applicability of LSTs in a variety of research and practical fields, particularly for areas that are very frequently covered with clouds.
Journal ArticleDOI
Evaluation of Land Surface Temperature Retrieval from Landsat 8/TIRS Images before and after Stray Light Correction Using the SURFRAD Dataset
TL;DR: Evaluation of land surface temperature (LST) retrieval from Landsat 8 before and after the correction using ground-measured LST from five surface radiation budget network (SURFRAD) sites indicated that the correction increased the band radiance at the top of the atmosphere for low temperature but decreased such radiance for high temperature.
Journal ArticleDOI
Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models
TL;DR: Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.
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