Full text: Mesures physiques et signatures en télédétection

2.1 NOAA and Meteosat 
A first approach uses brightness temperatures derived from thermal infrared channels erf 
Meteosat (cf. Hiltbrunner, 1991) and NOAA-AVHRR (Kidwell et al., 1986) as an estimation of the physical 
temperature of the land surface. Fig. la shows the correlation between the geolocated infrared brightness 
temperatures derived from NOAA channel 4 and ground-measurements of grass-temperature provided by the 
Swiss Operational Meterological Network (ANETZ) for May 3,4:30 UTC. The infrared brightness temperature 
scenes where visually classified as cloud-free but not subject to any atmospheric correction or cloud separation. 
Differences between estimated and measured temperatures up to 14°C occur. In the case of Meteosat (Fig lb), 
even differences of 18°C are observed. This may be attributed to thin cirrus clouds within certain Meteosat 
pixels or to the fact that the Meteosat pixel with a size of about 6 x 8 km cover a wide variety of different 
altitude levels and therefore different temperature values. A transformation of the temperature to potential 
temperatures showed no significant improvement 
ANETZ Tgroee [C] 
ANETZ Tgroee [C] 
Fig. 1 (a, left): Comparison between grass-temperature measurements from 70 ANETZ stations and NOAA 
Channel 4 brightness temperatures for May 3, 1989, 13:00 UTC. (b, right): Same for Meteosat thermal 
infrared channel, 4:30 UTC. Temperatures are in degrees Celsius. 
For our purposes, the observed differences between measured and estimated temperatures are 
too high. While Cooper & Asrar (1989) reported that only the McClain et al. (1983) split window model gave 
satisfying atmospheric corrections of the NOAA temperatures, Sugita and Brutsaert (1993) concluded that an 
atmospheric corrections of the NOAA thermal infrared data using split window or linearized profile method 
did not result in a significant improvement of the temperature estimation. Therefore, no atmospheric correction 
was applied. 
22 Interpolation of in-situ measurements 
A second approach consists in the spatial interpolation of ground-based measurements of 
physical air-temperatures provided by ANETZ using the so-called Kriging interpolation. Kriging is a statistical 
interpolation method which takes the anisotropic autocorrelation of the spatial distribution of a geophysical 
phenomenon like the surface temperature into account (Ishida & Kawashima, 1993; Lam, 1983). We applied 
this method to 25 ANETZ-stations located at an altitude between 316 and 779 m.a.s. distributed over the 
Central Plains, which are located between the Jura mountains and the Swiss Alps (Fig. 2). The resulting 
temperature field is strongly dependent on the local temperature values of the meteorological stations used as 
tie-points. This method may be appropriate for large-scaled studies of the temperature-field, where the 
variations of the temperature within some kilometers are less important For our purposes, the interpolated 
temperature does not sufficiently reflect the true variability of the different surface types.

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