6533b85cfe1ef96bd12bd644

RESEARCH PRODUCT

Automatic Generation of Land Surface Emissivity Maps

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description

The remote sensing measurement of the land surface temperature (LST) from satellites provides an overview of this magnitude on a continuous and regular basis. The study of its evolution in time and space is a critical factor in many scientific fields such as weather forecasting, detection of forest fires, climate change, etc. The main problem of making this measurement from satellite data is the need to correct the effects of the atmosphere and the land surface emissivity (LSE). Nowadays, these corrections are usually made using a split-window algorithm, which has an explicit dependence on land surface emissivity. Therefore, the aim of our work was to define an enhanced vegetation cover method and develop a computer system that used it, in order to calculate and generate, automatically, maps of land surface emissivity from images of the AATSR (Advanced Along Track Scanning Radiometer) onboard the ENVISAT satellite. The most innovative part of our method is that we provide it with the resources and the capability to calculate the most accurate coefficients according to the specific characteristics of each area (vegetation cover fraction, vegetation type, season, etc.). This allows the method to be applied to generate large-scale maps of this magnitude (Caselles et al., 2009). On the other hand, the current procedure (global, fully operational and supported by ESA) for obtaining the emissivity from an AATSR pixel (Noyes et al. 2007) causes systematic errors when calculating the temperature of 2 to 5 K (Coll et al. 2005), showing that the current classification and the vegetation cover maps made with a resolution of 0.5o x 0.5o could be highly improved and provided with the same spatial resolution of the AATSR images (1km x 1km). This is the main reason of this paper. In this chapter, this new method is presented, with its algorithm, and it is applied to several different types of vegetation in AATSR images of Europe, making all the calculations automatically with the developed software. Eventually, an on field validation of the method was carried out by comparing the data of the generated emissivity maps (as the one in figure 6) with the values obtained in previous campaigns (Coll et al. 2005) carried out in the area of rice fields of Valencia, Spain (Caselles et al., 2009). An error of less than ±0.01 in the land surface emissivity assessment was successfully obtained. Its validation was made by comparing the obtained results and the values measured in previous field campaigns carried out in the area of rice fields of Valencia, Spain.