A Generalised Split-Window Algorithm for Land Surface Temperature Estimation from MSG-2/SEVIRI Data

  • Published: 2013-04-11
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A Generalised Split-Window Algorithm for Land Surface Temperature Estimation from MSG-2/SEVIRI Data
Author: Caixia Gao, Bohui Tang, Hua Wu, Xiaoguang Jiang,and Zhaoliang Li
This study aims to determine the land surface temperature (LST) using data from a spinning enhanced visible and infrared imager (SEVIRI) onboard MSG-2 using the generalised split-window (GSW) algorithm. Coefficients in the GSW algorithm are pre-determined for several overlapping sub-ranges of the LST, land surface emissivity (LSE) and atmospheric water vapour content (WVC) using the data simulated with the atmospheric radiative transfer model MODTRAN 4.0 under various surface and atmospheric conditions for 11 view zenith angles (VZAs) ranging from 0º to 67º. The results show that the root mean square error (RMSE) varies with VZA and atmospheric WVC and that the RMSEs are within 1.0 K for the sub-ranges in which the VZA is less than 30º and the atmospheric WVC is less than 4.25 g/cm2. A sensitivity analysis of LSE uncertainty, atmospheric WVC uncertainty and instrumental noise (NEΔT) is also performed, and the results demonstrate that LSE uncertainty can result in a larger LST error than other uncertainties and that the total error for the LST is approximately 1.21 K and 1.45 K for dry atmosphere, 0.86 K and2.91 K for wet atmosphere at VZA=0° and at VZA=67°, respectively, if the uncertainty of the LSE is 1% and that of the WVC is 20%.The GSW algorithm is then applied to the MSG-2/SEVIRI data with the LSE determined using the temperature-independent spectral indices methodand the WVC either determined using the measurements in two split-window channels or interpolated temporally and spatially using the ECMWF data. Finally, the SEVIRI LST (SEVIRI LST1) derived in this study is evaluated through thecomparisonswith SEVIRI LST (SEVIRI LST2) provided by the land surface analysis satellite applications facility and MODISLST product, respectively. The results show that more than 80% of the differences between the SEVIRI LST1 and SEVIRI LST2 are within 2 K, and approximately 70% of the differences between SEVIRI LST1 and MODIS LST are within 4 K. Furthermore, compared to the MODIS LST, for four specific areas with different land surfaces, our GSW algorithm overestimates the LST within 1 K for vegetated surfaces and at 1.3 K for bare soil.