Sensible (MH) & Latent (MLE) Heat Fluxes (MH, LSA-304; MLE, LSA-305)

NRT Product available since March 2019

Sensible (H) and Latent heat flux (LE) account for the fluxes of energy associated to the exchange of heat by convection (for H) and to the exchange of water vapour (for LE) at the Earth-atmosphere interface (soil + vegetation + water bodies). Accurate measurements of surface heat fluxes at large spatial scales are central to understand land and atmosphere interactions in the context of global warming. They are of great importance in disciplines like weather forecasting, water management, agriculture, hydrology, ecology and global climate monitoring.


Product Documentation

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:
ET and DMET were 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

Sensible (H) and Latent heat flux (LE) account for the fluxes of energy associated to the exchange of heat by convection (for H) and to the exchange of water vapour (for LE) at the Earth-atmosphere interface (soil + vegetation + water bodies). Latent heat flux is the heat flux associated to evapotranspiration. Like evapotranspiration, latent heat flux plays a crucial role in the recycling of precipitation, in soil water availability and consequently on food production. Both surface heat fluxes are dependent mainly on the solar energy available at the surface and in water content in the soil layers. Because of the dependence on solar energy, H and LE vary with latitude, season of year, time of day, and cloud cover. 


The surface heat fluxes generated in the framework of the LSA SAF consortium consists of two outputs: the instantaneous LE product (MLE) with a time interval of 30 minutes (in W.m-2) and the instantaneous H product (MH) with a time interval of 30 minutes (in W.m-2). Both products are generated over the full MSG disc domain, covering Europe, Africa and most of South America at SEVIRI spatial resolution (3 km at sub satellite point). Together with the H and LE estimates, additional information about the quality of the estimation is provided. The quality flag is calculated based on the quality of input variables, the algorithm performances and pre/post processing manipulations on the estimates.


Algorithm Description

The methodology adopted for the LE and H product combines the advantages of geostationary satellite remote sensing (high repetition rate, wide area coverage, high spatial resolution) with the ability of a Soil Vegetation Atmosphere Transfer (SVAT) model to describe physical and physiological process occurring in vegetation canopy. In this approach, radiation components at the surface derived from Meteosat geostationary satellites together with recent land-cover information (from ECOCLIMAP land cover database). In addition biophysical products, soil moisture status information (from land data assimilation system and land surface temperature) and ancillary meteorological data (from ECMWF forecasts) are used to drive a physical model of energy exchange between the soil-vegetation-atmosphere systems. The scope of the method is limited to evaporation and surface heat fluxes emitted from terrestrial surfaces and inland water rather than from ocean surfaces. The LE and H products have been validated on different climatic and environmental conditions, providing evidence that the algorithm is able to produce estimates with accuracy equivalent to the accuracy of measurements, ie 20-25% for instantaneous retrieval of LE, and 30 % for H (see validation report -VR-). The approach adopts the tile approach in which each model entity (pixel), is composed of a mix of homogeneous plant functional types (tiles), representing main land-coverage types (bare soil, grassland, crops, forests). The surface energy balance is solved for each tile separately, and the resulting pixel value is obtained as the weighted contribution of all tiles in the pixel.


Data Characteristics

The LE and H products output and accompanying quality flag, are stored each on single files on HDF5 (Hierarchical Data Format, version 5) format. HDF5 is a machine independent standard for storing/sharing scientific data. In this format, each file contains also the necessary information for manipulating the data.  For more information on this data format see


Product Uncertainties

The main sources of uncertainties on the LE and H products come from: a) the physical formalism of the algorithm itself, b) from the errors associated with each input of the algorithm, in particular from other LSA-SAF components and c) from surface heterogeneity and land cover classification used in the algorithm. From a global point of view, most uncertainties cumulated on the H and LE results from sensors performance, accuracy of cloudy pixels identification, accuracy of atmospheric corrections which propagates into algorithms using this information, and providing input for the surface fluxes algorithm.

Arboleda, A., Ghilain N., Gellens-Meulenberghs, F.: Continuous monitoring of evapotranspiration (ET): Overview of LSA-SAF evapotranspiration products” Proc. SPIE 10421, Remote Sensing for Agriculture, Ecosystems, and Hydrology XIX, 104210E; 11 – 14 September 2017 Warsaw, Poland; doi: 10.1117/12.2278249;, 2017

Arboleda, A., Ghilain N., Trigo, I., Coelho, S., Martins, J. P., Gellens-Meulenberghs, F.: LSA-SAF evapotranspiration and surface heat flux products 2.0, poster. EUMETSAT Satellite conference 2017. 02-05 October 2017, Rome, Italy, 2017.

Ghilain, N.: Continental scale monitoring of subdaily and daily evapotranspiration enhanced by the assimilation of surface soil moisture derived from thermal infrared geostationary data, Chapter 16, p. 309-332, in Satellite Soil Moisture Retrieval Techniques and Applications, P.K. Srivastava, G. Petropoulos & Y.H. Kerr, edts, Elsevier Publisher, 2016.

Ghilain, N., Arboleda, A., and Gellens-Meulenberghs, F: Evapotranspiration modelling at large scale using near-real time MSG SEVIRI derived data, Hydrol. Earth Syst. Sci., 15, 771-786, doi:, 2011.

Ghilain, N., Arboleda, A., Barrios, J., M. and Gellens-Meulenberghs, F.: LSA-SAF ET&SF – version 2: monitoring evapotranspiration & surface heat fluxes over entire continents at kilometer scale in near-real time thanks to satellite data. Abstract, EGU 2018, 8-13 April, Vienna, Austria, 2018.

Ghilain, N., Arboleda, A., Batelaan, O., Ardö, J., Trigo, I., Barrios, J. M. and Gellens-Meulenberghs, F., 2019: A new retrieval algorithm for soil moisture index from thermal infrared sensor on-board geostationary satellites over Europe and Africa and its validation. Remote Sensing, 11(17), 1968;

Ghilain, N., Arboleda, A., Barrios, J. M. and Gellens-Meulenberghs, F., 2020: Water interception by canopies for remote sensing based evapotranspiration models. International Journal of Remote Sensing, 14(8), 2934-2945, DOI:

Martins, J. P. A., Trigo, I., Ghilain, N., Jimenez, C., Goettsche, F.-M., Ermida, S., Olesen, F., Gellens-Meulenberghs, F., Arboleda, A., 2019: An All-Weather Land Surface Temperature Product based on MSG/SEVIRI observations. Remote Sensing, 11, 3044, 28 pp.

Petropoulos G., Ireland, G., Lamine S., Griffiths H., Ghilain N., Anagnostopoulos V., North M., Srivastava P.K., Georgopoulou H.: Operational evapotranspiration estimates from SEVIRI in support of sustainable water management, Int J. Applied EO Geoinf, 30, 190-202, 2016.

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