This chapter demonstrates the Snappy Python module for the automatization of the ESA SNAP tool. Code examples will be shown for an automated processing chain for the preprocessing of Sentinel-1 SAR data including Calibration, Subsetting and Terrain Correction of GRD (Ground Range Detected data). A detailed installation tutorial for snappy can be found here: https://senbox.atlassian.net/wiki/display/SNAP/How+to+use+the+SNAP+API+from+Python First,… Read More


We are going to classify a multitemporal image stack of MODIS NDVI time series (MOD13Q1). The stack consists of 23 bands (16-day composites) with a spatial resolution of 231m in sinusoidal projection. We want to classify the different land use types, especially to discriminate different crop types. Install Python and required image processing and scientific… Read More

Here is a simple Python code to extract the central strip from Landsat 7 imagery (SLC-off),  that is not affected by the SLC failure. The algorithm shrinks the striping zones through a morphological filter (erosion) and creates a new shapefile AOI that extracts the desired raster extent without striping effects. The code is based on Python for ArcGIS… Read More


PostGIS is the spatial extension of the open source database management system PostgreSQL. It helps you to manage your data (vector and raster) within a coherent geodatabase through a variety of spatial functions. Having a spatial database, the times of data clutter and messiness are over, especially when you are dealing with big data. Initially PostGIS was created to… Read More