EGU General Assembly 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

​​Distributed Earth-Observation satellite data processing with Pytroll/Satpy

Salomon Eliasson, Martin Raspaud, and Adam Dybbroe
Salomon Eliasson et al.
  • Swedish Meteorological and Hydrological Institute, Atmospheric Remote Sensing Unit, Norrköping, Sweden (
As earth-observing (EO) satellite data volume is growing, servers struggle to keep up with the computational load needed to process even single segments of satellite data with reasonable performance. Pytroll is a suite of free and open-source python tools from which the Satpy package is made to easily and efficiently read, process, write EO satellite data. To obtain computational efficiency, Pytroll approaches the performance problem from multiple angles through optimized data processing, built-in support for out-of-memory computations (using the underlying Dask python library), and allowing distributed processing (using the Dask Distributed tools). In this work, we will show how large volumes of satellite data can be read, processed, resampled, and written swiftly and easily with the Pytroll/Satpy package, in a cluster environment. Specifically, examples of efficiently processing Sentinel-1, Sentinel-2, and Himawari/AHI data will be shown, along with performance figures.

How to cite: Eliasson, S., Raspaud, M., and Dybbroe, A.: ​​Distributed Earth-Observation satellite data processing with Pytroll/Satpy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13133,, 2020


Display file

Comments on the display

AC: Author Comment | CC: Community Comment | Report abuse

displays version 1 – uploaded on 06 May 2020