PYTHON UNPACKING AND PREPROCESSING OF REMOTE SENSING IMAGES IN HDF FORMAT ON A SAMPLE OF TERRA ASTER DATA

Abstract

Remote sensing images are often used in Earth science as the source of information on landscapes, rocks and conditions of atmo-, hydro- and biosphere. Preparation of field works for geological mapping and prospecting of mineral deposits requires preliminary assessment of the area. Efficacy of field works depends on quality and relevance of remote sensing data. User handling of zero and first level processing data is often time and computational power consuming task. Moreover, desktop geographic information systems (GIS) may not possess enough capabilities for solving of that task. In general, available data of zero and first levels of processing express values of radiation on spectroradiometer sensor, which were subjected to band-specific atmospheric scattering. Deriving of top atmospheric reflectance and surface temperature requires channel-wise correction. Websites of companies, which provide access to satellite data and their specifications, also offer information for atmospheric correction. Also, multiband data could be provided in specific formats, which are not supported by user GIS. Paper considers algorithms of data extraction (unpacking) of ASTER data from hierarchical data format (HDF) including atmospheric correction, computing of surface temperature (for night temperature bands) and saving output into popular GeoTiff format using Python script bases on GDAL library. Script could be adapted for application on other satellite data, moreover, described software could be used for teaching Python programming, work with GDAL and basics of geoinformatics to Earth science students.