This study demonstrated the use of GRASS GIS scripts for satellite image analysis. The aim is to perform topographic and environmental analysis in eastern China, Shandong Province, Qingdao region. Variables include land surface temperature evaluated by correlation between the Landsat ETM+ channels and colour composites. The methodology is based on scripting by GRASS GIS modules. Data include two satellite Landsat TM images with a 5 years span (2009 and 2004). The images were imported to GRASS GIS using GDAL library by 'r.in.gdal' module. The topography (DEM) was assessed by GRASS GIS 'd.rast' module with isolines plotted using 'r.contour' and 'd.vect'. The map elements were displayed by 'v.in.region', 'd.vect', 'v.info'. The slope and aspect maps based on DEM were plotted using module 'r.slope.aspect' and visualised by a consecutive combination of 'd.rast', 'r.colours', 'd.legend', 'd.text' and 'd.grid' modules. The scatterplot of pairwise correlation of the selected Landsat TM7 channels (10 and 20, 50 and 70) was done by 'd.correlate'. The applied code from the 'r.mapcalc' was used for image calibration from the DN to apparent radiance at sensor. Two maps showing land surface temperature for 2009 and 2004 were plotted by 'r.mapcalc'. The land surface temperature maps show lower values over the water and forests, while higher values in cities. Plotting land surface temperature map was made to analyse variations in climate settings in Shandong during 5 years based on the comparative analysis using 'r.mapcalc' module. The images were plotted by natural and false colour band composites using 'd.rgb' module and band combinations: 1) colour infrared with focus on vegetation (5-4-3); 2) natural colour composites (1-2-3); 3) contrasting slopes for topography (4-5-7); 4) vegetation analysis (2-3-4); 5) focus on land/water borders (5-6-7). The study has shown cartographic functionality of GRASS GIS scripts specifically for remote sensing data processing and topographic analysis. The environmental modelling has shown variations in land surface temperature, slope steepness and orientation (aspect). The GRASS GIS demonstrated to be an effective tool for remote sensing. Full codes are available on the GitHub repository of the author with provided link to scripts.