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Mesures physiques et signatures en télédétection

meteorological forecasts. It produces a hourly diagnostic of air temperature, wind, humidity, cloudiness,
rainfall, snowfall, and surface radiations
w grains morphology,
zrains. This parameter
irs of about 30 to 50
:tive snow grains size
i convex diameter and
r these measurements,
mal boxes to the cold
itical parameters were
finite assumption. The
i pm.
with a DEM (Digital
CROCUS results, we
All the images Nvere
ground control points,
h cartesian projection,
graphic and the image
ations on a 30 m x 30
e difficulty is to locate
ite are computed from
xlel (Simulation of the
rent ground reflectance
er reflectance on lakes
still are processing the
i in laboratory but also
eorological conditions,
h different layer. They
umerical model, called
». It derives a complete
age and stratigraphy of
orological observations
'¡cal objective analysis
y input data. It aims to
it points of the Alpine
s as well as numerical
The bidirectional reflectance of the snow was computed with a model based on the radiative transfer
(Stamnes et al„ 1988). It was found indeed that, if the snow is assumed to be iambertian, the measured
reflectance was much too low. The phase function is computed from the Mie theory, i.e. assuming that the
snow grains are spherical. All the calculations were done for pure snow without any pollution and for a
uniform grain size.
Two examples are given hereafter about the 24 April 1992 Landsat TM data. One example is about the
large scale comparison which can be done between the satellite data and the CROCUS output on the snow
surface temperature. The other one is a study of the dépendance between ratios of reflectances at different
wavelenghts on the snow grain size: the measurements (satellite and in situ data) are compared to the
theoretical curves.
6.1. Snow surface temperature
The thermal infrared channel 6 of Landsat TM is used for the surface snow temperature determination.
After calibration of the data, the Planck function is inverted, assuming a snow emissivity e-1, to get the
apparent temperature of the snow at the top of the atmosphere. Instead of using a model for atmospheric
corrections, which would be difficult in this Alpine context, we assume a linear relationship between the
apparent and the ground temperature. In situ measurements are used to get the coefficients of the linear
regression (Fig. 1). A water surface temperature on a lake is also used because the range of in situ
measured snow temperatures is small. Some ground measurements were rejected because the large pixel
size in channel 6 (120 m) enhances the environment effects.
Therefore a map of surface snow temperature can be derived and, with the DEM, the variation of
temperature against elevation and slope orientation. Comparisons will then be made between this set of data
and CROCUS output which also gives the variation of snow surface temperature against elevation and slope
orientation. Preliminary results were made on a few sites and are satisfactory.
F 'g- I: Linear regression between TM6 apparent temperatures and measured surface temperatures