an ESA DUE initiative


International LST and Emissivity Group

The International Land Surface Temperature and Emissivity Working Group (ILSTE-WG) aims to provide advice and recommendations to the wider scientific and user communities on the best practices for retrieval, validation and exploitation of Land Surface Temperature (LST), Ice Surface Temperature (IST), Lake Surface Water Temperature (LSWT), and Land Surface Emissivity (LSE).

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Authors: S. Ermida, C. Jimenez, C. Prigent, I.F. Trigo and C. daCamara


A comparison of land surface temperature (Ts) derived from the Advanced Microwave Scanning Radiometer - Earth observation system (AMSR-E) with infrared Ts is presented. The infrared Tsinclude clear-sky estimates from the MODerate resolution Imaging Spectroradiometer (MODIS), the Spinning Enhanced Visible and Infrared Imager (SEVIRI), the Geostationary Operational Environmental Satellite (GOES) Imager, and the Japanese Meteorological Imager (JAMI). The higher discrepancies between AMSR-E and MODIS are observed over deserts and snow covered areas. The former seems to be associated with Ts underestimation by MODIS, whereas the latter are mostly related to uncertainties in microwave emissivity over snow/ice. Ts differences between AMSR-E and MODIS are significantly reduced after masking out snow and deserts, with a bias change from 2.6/4.6 K to 3.0/1.4 K for day/nighttime, and a standard deviation (STD) decrease from 7.3/7.9 K to 5.1/3.9 K. When comparing with all infrared sensors, the STD of the differences between microwave and infrared Ts is generally higher than between IR retrievals. However, the biases between microwave and infrared Ts are, in some cases, of the same order as the ones observed between infrared products. This is the case for GOES, with daytime biases with respect to AMSR-E and MODIS of 0.45 K and 0.60 K, respectively. While the infrared Ts are clear-sky estimates, AMSR-E also provides Ts under cloudy conditions. For frequently cloudy regions, this results in a large increase of available Ts estimates (>250%), making the microwave Ts a very powerful complement of the infrared estimates.

Authors: C. Jimenez, C. Prigent, S. Ermida and J.-L. Moncet


Inversions of the Earth observation satellite (EOS) Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to derive the land surface temperature (Ts) are presented based on building a global transfer function by neural networks trained with AMSR-E Tbs and retrieved microwave Ts. The only required inputs are the Tbs and monthly climatological emissivities, minimizing the dependence on ancillary data. The inversions are accompanied by a coarse estimation of retrieval uncertainty, an estimate of the quality of the retrieval, and a series of flags to signal difficult inversion situations. For 75% of the land surface the Root Mean Square Difference (RMSD) between the training target Ts and the neural network retrieved Ts is below 2.8 K. The RMSD when comparing with the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky Ts is below 3.9 K for the same conditions. Over 10 ground stations, AMSR-E and MODIS Ts were compared with the in situ data. Overall, MODIS agrees better with the station Ts than AMSR-E (all-station mean RMSD of 2.4 K for MODIS and 4.0 for AMSR-E), but AMSR-E provides a larger number of Ts estimates by being able to measure under cloudy conditions, with an approximated ratio of 3 to 1 over the analyzed stations. At many stations the RMSD of the AMSR-E clear and cloudy-sky are comparable, highlighting the ability of the microwave inversions to provide Ts under most atmospheric conditions. Closest agreement with the in situTs happens for stations with dense vegetation, where AMSR-E emissivity is less varying.

Authors: C. Jimenez (a,b), D. Michel (c), M. Hirschi (c), S. Ermida (d), C. Prigent (b,a)

(a) Estellus, Paris, France
(b) LERMA, Paris Observatory, Paris, France
(c) Institute for Atmospheric and Climate Science, ETH Z¨urich, Z¨urich, Switzerland
(d) Instituto Dom Luiz, University of Lisbon, Portugal


Land heat fluxes are essential components of the water and energy cycle and important variables in the management of agronomy and forestry resources. The estimation of the heat fluxes can be done with a number of methodologies, with some of them having the land surface temperature (Ts) as one of their key inputs to derive the fluxes. Here the production of Ts-driven surface heat fluxes over a grassland site in Switzerland is demonstrated by running a specific heat flux methodology (SEBS) fed by a number of satellite Ts estimates (from the instru- ments AATSR, MODIS, SEVIRI, AMSR-E, and SSMIS).

Authors: C. Prigent, C. Jimenez, and F. Aires

(a) Estellus, Paris, France


The land surface temperature can be estimated from satellite passive microwave observations, with limited contamination from the clouds as compared to the infrared satellite retrievals. With ∼60% cloud cover in average over the globe, there is a need for “all weather,” long record, and real-time estimates of land surface temperature (Ts) from microwaves. A simple yet accurate methodology is developed to derive the land surface temperature from microwave conical scanner observations, with the help of precalculated land surface microwave emissivities.