Peyman Daneshkar Arasteh; Masoud Tajrishy; Bahram Saghafian
Abstract
Earth skin temperature including soil, water, snow, and vegetation surface temperatureis one of the main variables in geo-science studies. Generally, spatial distribution ofsurface temperature is needed in such studies. Spatial monitoring of surface temperatureis possible using remote sensing data, and ...
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Earth skin temperature including soil, water, snow, and vegetation surface temperatureis one of the main variables in geo-science studies. Generally, spatial distribution ofsurface temperature is needed in such studies. Spatial monitoring of surface temperatureis possible using remote sensing data, and the time series of images provide acontinuous spatio-temporal framework required in modeling energy balance of regionalevaporation, optimization of energy demands or dispersion of atmospheric pollutions. Inthis paper, some common methods of surface temperature estimation using satelliteimagery were introduced and calibrations of several forms of split window equationwere addressed for Sistan area, Iran. Split window method is based on the fact that theatmospheric transmittance varies with wave length and uses a combination of thermalinfra-red brightness temperature and emissivity. To calibrate the split window equation,ground observations and 22 NOAA/AVHRR images during 1992 to 2002 were used todevelop regression models. Statistical tests were performed to evaluate the equationsand coefficients. Ten AVHRR images were used to verify the developed equations. Theresults indicated that three different forms of split window equations successfullypassed the χ2 statistical test. Both, F and Kruskal-Wallis tests showed that they are notstatistically different at 95 percent significant level. Therefore, the simplest form ofequation was used to derive surface temperature maps in the Sistan region, and isrecommended as the most applicable one.
