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Journal Articles Remote Sensing Year : 2019

An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations

João P. A. Martins
  • Function : Author
Isabel F. Trigo
  • Function : Author
Nicolas Ghilain
  • Function : Author
Frank-M. Göttsche
Sofia L. Ermida
  • Function : Author
Folke-S. Olesen
  • Function : Author
Alirio Arboleda
  • Function : Author
João Martins
  • Function : Author
Isabel Trigo
Sofia Ermida


A new all-weather land surface temperature (LST) product derived at the Satellite Application Facility on Land Surface Analysis (LSA-SAF) is presented. It is the first all-weather LST product based on visible and infrared observations combining clear-sky LST retrieved from the Spinning Enhanced Visible and Infrared Imager on Meteosat Second Generation (MSG/SEVIRI) infrared (IR) measurements with LST estimated with a land surface energy balance (EB) model to fill gaps caused by clouds. The EB model solves the surface energy balance mostly using products derived at LSA-SAF. The new product is compared with in situ observations made at 3 dedicated validation stations, and with a microwave (MW)-based LST product derived from Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) measurements. The validation against in-situ LST indicates an accuracy of the new product between -0.8 K and 1.1 K and a precision between 1.0 K and 1.4 K, generally showing a better performance than the MW product. The EB model shows some limitations concerning the representation of the LST diurnal cycle. Comparisons with MW LST generally show higher LST of the new product over desert areas, and lower LST over tropical regions. Several other imagers provide suitable measurements for implementing the proposed methodology, which offers the potential to obtain a global, nearly gap-free LST product.
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obspm-03988366 , version 1 (21-02-2023)



João P. A. Martins, Isabel F. Trigo, Nicolas Ghilain, Carlos Jimenez, Frank-M. Göttsche, et al.. An All-Weather Land Surface Temperature Product Based on MSG/SEVIRI Observations. Remote Sensing, 2019, 11 (24), pp.3044. ⟨10.3390/rs11243044⟩. ⟨obspm-03988366⟩
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