NRT Product Available since Jan 2015
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.
This Operational product 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:
EDLST was provided by the EUMETSAT Satellite Application Facility on Land Surface Analysis (LSA SAF; Trigo et al., 2011)
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 EDLST (EPS Daily Land Surface Temperature) provides a composite of day-time and nigh-time retrievals of LST based on clear-sky measurements from the Advanced Very High Resolution Radiometer (AVHRR) on-board EUMETSAT polar system satellites, the Metop series.
The Generalised Split-Window (GSW) algorithm (Wan and Dozier, 1996) was chosen to retrieve LST. The GSW performs corrections for atmospheric effects based on the differential absorption in adjacent IR bands and requires EM as input data; a look-up table of optimal coefficients is previously determined at individual classes of satellite viewing angles, and covering different ranges of water vapour and near-surface air temperature. The retrieval of EM is based on the Vegetation Cover Method (VCM; Caselles et al., 1997) that relies on the use of a geometrical model to compute an effective emissivity based on the knowledge of the Fractional Vegetation Cover (FVC), also retrieved by the LSA SAF.
The EDLST product is available on a daily basis in a 1 km global sinusoidal grid centred at (0°N, 0°W). The day and night-time composites are performed using all Product Distribution Units (PDUs) available in a day. At any given grid point of the day or night dataset, if two or more PDUs overlap only the PDU with observation time closest to the Equatorial Crossing Time (ECT; 9.30 AM/PM) is used. Data is disseminated in HDF5 format; the relevant information concerning the data fields is included in the HDF5 attributes.
The quality of the LST product depends on sensor performance (stability of the spectral response function, signal-to-noise ratio, radiometric resolution and calibration accuracy), accuracy of cloudy pixels identification, accuracy of atmospheric corrections, and spectral variation in emissivities of different land-surface elements. The land surface heterogeneity and three-dimensional structure can produce significant variation in LST measurements as a function of view zenith angle. An automatic Quality Control (QC) is performed on LST data, and the quality information is provided on a pixel-by-pixel basis. The LST confidence level was defined based on the following parameters: viewing angle; atmospheric characteristics (i.e. surface temperature and column water vapour); EM confidence level. The three considered levels of confidence (above nominal, nominal and below nominal) correspond to estimated uncertainties of LST values (respectively less than 1K, between 1 and 2K and above 2K).
Wan. Z., J. Dozier, 1996. A generalised split-window algorithm for retrieving land-surface temperature from space, IEEE Trans. Geosci. Remote Sens., vol. 34 no. 34, pp. 892-905.