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Mesures physiques et signatures en télédétection

Centre Universitaire
10 - 12 Av. de l'Europe
78140 Vélizy, France
Different studies have shown that the estimation of the surface temperature from Split-Window algorithms can
be greatly improved by a rough knowledge of the total atmospheric water vapour. A new algorithm is proposed
to estimate this variable from infrared measurements in the 11pm region. This algorithm has been adjusted
with ATSR/ERS1 data, taking advantage of the simultaneous measurements of the microwave radiometer. The
first results show that the atmospheric water vapour column can be estimated with an accuracy better than 0.5
g/cm 2 which is widely sufficient for our purpose.
Further studies are necessary now to test the robustness of this algorithm whose simplicity of
application seems promising.
KEY WORDS : Précipitable Water, Thermal 1R , ATSR/ERS1, Surface Temperature
Surface temperature is a parameter which is important to map and monitor at large scale and over long time
periods. This parameter can be obtained from space from thermal infrared satellite data after elimination of the
atmospheric absorption which is principally caused by the water vapour in this spectral region.
Different methods may be applied to estimate this effect but the most easy to use is the Split-Window
method because it does not require any other ancillary data than the radiometric brightness temperatures.
As a matter of fact, the combination of two measurements in two different spectral bands, both
sensitive to water vapour only, allow to estimate the atmospheric absorption. The surface temperature is linked
to the two brightness temperatures by a linear relationship. But the coefficients of this relationship are not
constant and depend on several parameters like the surface emissivity, the mean air temperature and the
tropospheric water vapour, as shown for example by Becker (1987). Consequently, it appeared necessary to
take into account these parameters in the algorithms. Recently, different local Split-Window (SW) algorithms
have been proposed which require the knowledge of local surface and atmospheric conditions (Sobrino et al.,
1991,1994, Li andBecker, 1993, Prata, 1993, Ulivieri et al„ 1993).
Since it is not possible to obtain these parameters at the pixel scale easily, the local SW algorithms
can not be applied routinely for most applications. For this reason, in a previous study, Ottlé and Vidal-Madjar
(1992) proposed 3 different sets of coefficients depending only on the surface emissivity and on the satellite
viewing angle, calculated for 3 different classes of atmosphere namely tropical, temperate and polar, on the
TIGR dataset containing 1207 radiosoundings ( Scott and Chédin, 1981). But this classification was proved
not suitable because the latitude can not be in most cases a choice criteria and large errors ( greater than 4K for
high land surface temperatures) may appeared in the case where the atmospheric water vapour is far from the
mean of the atmospheric class considered, since it is mostly the total water vapour which differentiate these 3
classes. Therefore, we have proposed another set of coefficients for ATSR and AVHRR data for 8 classes of
water vapour, ranging between 0.26 and 5 g/cm 2 ( François and Ottlé, 1994). At the same rime, we have
investigated the ways to estimate the total atmospheric water vapour from infrared data. ATSR/ERS1 is a
good tool to test such methods because the infrared instrument is coupled with a microwave passive radiometer