Surface properties

Part I: Determination of continental surface emissivity and temperature from 1st and 2nd generation space borne infrared sounders observations.

Surface emission depends on surface emissivity and temperature. Emissivity of land surfaces substantially varies with vegetation, soil moisture, composition, and roughness (Nerry et al., 1988; Salisbury and D’Aria, 1992). As emissivity depends on wavelength, it is referred to as spectral emissivity; it also depends on the viewing angle.

Continental surface emissivity in the thermal infrared window is a key parameter for estimating the surface radiation budget. Spectrally integrated surface emissivity and the energy emitted from the surface are proportional. A 10% error (from 0.9 to 1.0, for example) on the emissivity approximately corresponds to a 10% error in the energy emitted from the surface (a portion of which may be compensated by the reflected incoming radiation) (Prabhakara and Dalu, 1976; Ogawa et al., 2003). Analyses of the sensitivity of simulated energy balance to changes in soil emissivity (Zhou et al, 2003) revealed that, on average over Northern Africa and the Arabian Peninsula, a decrease of the surface emissivity in the atmospheric window by 0.1 would increase ground and surface air temperature by about 1.1°C and 0.8°C, respectively, and decrease surface net and upward longwave radiation fluxes by about 6.6Wm-2 and 8.1Wm-2, respectively. Also, a constant emissivity is often used for land surfaces in energy balance studies and general circulation models (GCM), because of limited information on the spectral and spatial distributions and time variations of the land surface emissivity (Ogawa et al., 2003).
It has also been shown that accounting properly for the surface emissivity in the solution of the radiative transfer equation inverse problem substantially improves the meteorological profiles (temperature, moisture) and cloud (Plokhenko and Menzel, 2000) characteristics retrieved from infrared vertical sounders. Also, over continental surfaces, knowledge of the infrared emissivity spectrum allows correcting observed brightness temperatures from surface emissivity effect, authorizing an accurate determination of semi-transparent clouds and aerosols properties.
Therefore, from both observational and modelling point of views, an accurate knowledge of surface emissivity and its spectral, spatial and temporal variations, especially in the atmospheric thermal infrared window, is necessary.

A method has been developed at LMD with the aim of retrieving surface emissivity and temperature, simultaneously through a non linear regression inference scheme. It has been applied to NOAA-10 polar satellite observations over Northern Africa, mostly characterized by desert regions, but also by savanna and tropical forest at the southern edge. Time series of zonal means have brought into evidence first evidence of seasonal variations at global scale that offer interesting comparisons with time series of precipitations and Normalized Difference Vegetation Index (NDVI) (see Chédin et al. (2004)).

More recently, the largely enhanced capabilities of the second generation sounders, as AIRS or IASI, have led us to develop a new approach, the so-called Multi Spectral Method (MSM), aiming at determining the surface infrared emissivity spectrum from 3.7 to 14 μm at high spectral resolution (here, 0.05 μm) together with the surface temperature by inverting analytically the radiative transfer equation. The method follows four main steps: (i) an estimation of the atmospheric temperature and water vapor profiles is first obtained through a proximity recognition within the Thermodynamic Initial Guess Retrieval ( TIGR) climatological library of about 2300 representative clear sky atmospheric situations. With this a priori information, all terms of the radiative transfer equation are calculated by using the Automatized Atmospheric Absorption Atlas ( 4A) fast line-by line radiative transfer model Scott and Chédin, (1981); (ii) surface temperature is retrieved from observations using a single (AIRS) or several (IASI) window channels located around 12 µm and selected for their almost constant emissivity with respect to soil type; (iii) emissivity is then calculated for a set of 40 (AIRS) or 101 (IASI) atmospheric windows (transmittance greater than 0.5); (iv) the complete infrared emissivity spectrum at 0.05 µm resolution is finally derived from a combination of the high spectral resolution laboratory spectra of selected materials (MODIS/UCSB and ASTER/JPL emissivity libraries) recognized as the closest to the set of retrieved emissivity values.

The MCM approach has first been applied to 3 years of AQUA/AIRS observations (April 2003 - March 2006) of the tropical zone (30°S-30°N): see Péquignot et al. (2008). In a second step, the MCM approach has been applied to two years of IASI observations (January 2008 - December 2009): see Capelle et al. (2012). Comparisons made with Modis-retrieved emissivities from Seemann et al. (2008) and temperatures (from: highlight the much higher spectral resolution of IASI (emissivity) and show a good agreement for skin surface temperature (bias of 0.7 K, mostly due to the difference in the time passes of the two instruments, and a standard deviation of about 2 K).


  • Péquignot É., Chédin A., Scott N.A.
    Infrared continental surface emissivity spectra retrieved from AIRS hyperspectral sensor.
    J. Appl. Meteor. Climatol., 47, 1619-1633 (2008)
  • Capelle V., Chédin A., Péquignot E., Schluessel P., Newman S.M. and Scott N.A.
    Infrared continental surface emissivity spectra and skin temperature retrieved from IASI observations over the tropics.
    J. Appl. Meteor. Climatol., 51, 1164–1179 (2012)

Interactive plot

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Emissivity: see time series from a zone Select a wavelength between 3.7μm =< λ[μm] <= 14μm

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Estimation of the land skin surface temperature
Figure 1 : Estimation of the land skin surface temperature TS : comparison with MODIS (monthly averaged MODIS 041 MYD11 level 3 operational land surface emissivity product) for June (left) and December (right) 2008. Part of the bias observed is likely due to the 1 hour time shift between IASI and TERRA (21h30 vs 22h30).
(click the figure to enlarge it or click here)
Continuous spectra for June 2008
Figure 2 : Continuous spectra for June 2008 and four soil types. Comparisons between MSM IASI (red) and MODIS emissivity: continuous spectrum obtained from the baseline fit method (Seemann, et al, 2007) (black); datapoints from MODIS 041 MYD11(blue).
(click the figure to enlarge it or click here)
Map of emissivity at 4.05 microns and 8.55 microns for June 2008
Figure 3 : Map of emissivity at 4.05 microns and 8.55 microns for June 2008
(click the figure to enlarge it or click here)
  • Seemann S.W., Borbas E. E., Knuteson R. O. , Stephenson G. R., Huang H.-L.
    Development of a Global Infrared Land Surface Emissivity Database for Application to Clear Sky Sounding Retrievals from Multi-spectral Satellite Radiance Measurements.
    J. Appl. Meteor. Climatol., 47, 108-123 (2007)

Part II: Classification of Surface types

Physical properties of the land surface determine the energy, water and carbon exchanges between the biosphere and the atmosphere. We use satellite measurements to characterize surface properties and classify them according to their specific vegetation-climate regime. Measurements from the passive microwave instrument SSM/I (Special Sensor Microwave / Imager), onboard the DMSP (Defense Meteorological Satellite Program) polar satellite, are associated with a priori information from various sources (ISCCP, NCEP, AVHRR/NDVI, and ERS satellite data) to remove the effects of the atmosphere, clouds and rain to obtain surface microwave emissivities [Prigent et al., 1997] and all-weather skin temperatures Aires et al. (2001); Prigent et al. (2002) We also use the information of vegetation index NDVI (Normalized Difference Vegetation Index) from the AVHRR (Advanced Very High Resolution Radiometer) instrument and satellite radar measurements from the ERS (European Remote Sensing) satellite. The parallel use of all these sources of information (visible, near infrared, passive and active microwave) allows for a more comprehensive description of surface properties: wetlands, ice types, vegetation index. Prigent et al., (2001a, 2001b). For that purpose, a Kohonen clustering algorithm is used. This technique allows extraction of clusters organized in an index structure. Each cluster is a surface prototype found in the multi-instrument dataset. We have defined indices of vegetation, ice, wetland, desert over the whole globe, whereas other studies have been based so far only on one variable, using a unique instrument over specific regions.

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Last update : 2012/11/15

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