Land Surface Temperature (MLST-R, LSA-050)

DOI for scientific and technical data:

https://dx.doi.org/10.15770/EUM_SAF_LSA_0001

GET DATA HERE

Land Surface Temperature (LST) is the radiative skin temperature over land. LST plays an important role in the physics of land surface as it is involved in the processes of energy and water exchange with the atmosphere. LST is useful for the scientific community, namely for those dealing with meteorological and climate models. Accurate values of LST are also of special interest in a wide range of areas related to land surface processes, including meteorology, hydrology, agrometeorology, climatology and environmental studies. Land Surface Emissivity (EM), a crucial parameter for LST retrieval from space, is independently estimated as a function of (satellite derived) Fraction of Vegetation Cover (FVC) and land cover classification.

 

Product Documentation

This released Data Record is documented in the following documents:

Please see Product Peer-Review publications in References.

The use of LSA SAF products in publications is kindly requested to be duly acknowledged:
LST was provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)
http://lsa-saf.eumetsat.int

Trigo, I. F., C. C. DaCamara, P. Viterbo, J.-L. Roujean, F. Olesen, C. Barroso, F. Camacho-de Coca, D. Carrer, S. C. Freitas, J. García-Haro, B. Geiger, F. Gellens-Meulenberghs, N. Ghilain, J. Meliá, L. Pessanha, N. Siljamo, and A. Arboleda, 2011: The Satellite Application Facility on Land Surface Analysis. Int. J. Remote Sens., 32, 2725-2744, doi: 10.1080/01431161003743199

The MSG mission (in operations since 2004) already provides a relatively long time series of VIS and IR observations over the full Earth Disk centred at 0º. The full archive of MSG/SEVIRI data was reprocessed to provide the user comunity a consistent, homogeneous and continuous Data Record of the 15-min Land Surface Temperature (LST) for the period 2004-2015. 

This Data Record was obtained with the best version of its equivalent NRT product (MLST) which can also complement the time series from 2016 onwards.