MSG Land Surface Temperature (MLST)

[LSA-001/LSA-004]

NRT Product Available since May 2005

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Land Surface Temperature (LST) 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.

In the most recent version of the dataset, information on the expected deviation of LST estimates from SEVIRI/MSG with respect to a reference view – here considered to be nadir view – has been added to the original product (LSA-001) as an extra data layer (LSA-004).

 

NEW: NRT Demonstration Product available for Indian Ocean region since Jun 2017

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Product Documentation

This operational product is documented in the following documents:

Please see Product Peer-Review publications in References.

Here you will find LST averaged over the 2004-2019 period, per month and hourly time slot.

This dataset was derived by joining the LST CDR (MLST-R, LSA-050) for 2004-2015 and the operational NRT product (MLST, LSA-001) for 2016-2019.

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Example:   

NETCDF4_LSASAF_CLIMA-12-2004-2019_MSG_LST_MSG-Disk_200412012000.nc

This NetCDF file contains the mean LST for all days of "December" for slot 20:00UTC, averaged over the 2004-2019 period. The datasets are available on a regular 0.05º grid and the files' format is fully CF-compliant.

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 retrieval of LST is based on clear-sky measurements from MSG system in the thermal infrared window (MSG/SEVIRI channels IR10.8 and IR12.0). Theoretically, LST values can be determined 96 times per day from MSG but in practice less observations are available due to cloud cover. The identification of cloudy pixels is based on the cloud mask generated by the Nowcasting and Very Short Range Forecasting Satellite Application Facility (NWC SAF) software.

Algorithm Description

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 estimation of the LST angular effects makes use of a parametric model developed by Ermida et al. (2018a), referred to as kernel-hotspot model. The kernel-hotspot model is calibrated taking into account the surface characteristics (vegetation cover and structure, topography) and provides estimates of LST dependence on viewing a illumination geometries (Ermida et al., 2018b).

Data Characteristics

The LST MSG product is computed within the area covered by the MSG disk, every 15 minutes. For each time-slot, the LST field and respective uncertainty, Quality Control (QC) and directionality data are disseminated in HDF5 format; the relevant information concerning the data fields is included in the HDF5 attributes.

Product Uncertainties

The quality of LST MSG 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).

Ermida, S. L., I. F. Trigo, C. C. DaCamara, F. M. Goettsche, F. S. Olesen, G. Hulley, 2014: Validation of remotely sensed surface temperature over an oakwood landscape – The problem of viewing and illumination geometries. Remote Sens. Env., 148, 16-27, doi: 10.1016/j.rse.2014.03.016

Ermida, S.L., Trigo, I.F., DaCamara, C.C., Roujean, J.-L., 2018a. Assessing the potential of parametric models to correct directional effects on local to global remotely sensed LST. Remote Sens. Environ. 209. https://doi.org/10.1016/j.rse.2018.02.066

Ermida, S.L., Trigo, I.F., DaCamara, C.C., Pires, A.C., 2018b. A Methodology to Simulate LST Directional Effects Based on Parametric Models and Landscape Properties. Remote Sens. 10, 1114. https://doi.org/10.3390/rs10071114

Freitas, S. C., Trigo, I. F., Bioucas-Dias, J. M., Goettsche, F.-M., 2010: Quantifying the Uncertainty of Land Surface Temperature Retrievals From SEVIRI/Meteosat, IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2009.2027697

Goettsche, F.-M., F.-S. Olesen, I. F. Trigo, A. Bork-Unkelbach, and M. A. Martin, 2016: Long term validation of land surface temperature retrieved from MSG/SEVIRI with continuous in-situ measurements in Africa. Remote Sensing, 8, 410, doi:10.3390/rs8050410

Trigo, I. F., L. F. Peres, C. C. DaCamara, and S. C. Freitas, 2008: Thermal Land Surface Emissivity retrieved from SEVIRI/Meteosat. IEEE Trans. Geosci. Remote Sens., doi: 10.1109/TGRS.2007.905197

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.