Analyzing Satellite Image Data Using PyTroll

Summary

Every day there are satellites collecting sensor readings and imagery of our Earth. To help make sense of that information, developers at the meteorological institutes of Sweden and Denmark worked together to build a collection of Python packages that simplify the work of downloading and processing satellite image data. In this episode one of the core developers of PyTroll explains how the project got started, how that data is being used by the scientific community, and how citizen scientists like you are getting involved.

Preface

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• Your host as usual is Tobias Macey and today I’m interviewing Martin Raspaud about PyTroll, a suite of projects for processing earth observing satellite data

Interview

• Introductions
• How did you get introduced to Python?
• Can you start by explaining what PyTroll is and how the overall project got started?
• What is the story behind the name?
• What are the main use cases for PyTroll? (e.g. types of analysis, research domains, etc.)
• What are the primary types of data that would be processed and analayzed with PyTroll? (e.g. images, sensor readings, etc.)
• When retrieving the data, are you communicating directly with the satellites, or are there facilities that fetch the information periodically which you can then interface with?
• How do you locate and select which satellites you wish to retrieve data from?
• What are the main components of PyTroll and how do they fit together?
• For someone processing satellite data with PyTroll, can you describe the workflow?
• What are some of the main data formats that are used by satellites?
• What tradeoffs are made between data density/expressiveness and bandwidth optimization?
• What are some of the common issues with data cleanliness or data integration challenges?
• Once the data has been retrieved, what are some of the types of analysis that would be performed with PyTroll?
• Are there other tools that would commonly be used in conjunction with PyTroll?
• What are some of the unique challenges posed by working with satellite observation data?
• How has the design and capability of the various PyTroll packages evolved since you first began working on it?
• What are some of the most interesting or unusual ways that you have seen PyTroll used?
• What are some of the lessons that you have learned while building PyTroll that you have found to be most useful or unexpected?
• What do you have planned for the future of PyTroll?

Keep In Touch

• Martin
  • mraspaud on GitHub
  • @MartinRaspaud on Twitter



• Pytroll
  • Website
  • Slack
  • Mailing List
  • @PyTroll on Twitter

Picks

• Tobias
• Tool
• A Perfect Circle
• Martin
• Vulfpeck

Links

• PyTroll
• Swedish Meteorological and Hydrological Institute
• Common Lisp
• Danish Meteorological Institute
• Trolls in Scandinavian Lore
• NumPy
• KISS (Keep It Simple Stupid)
• Spectroscopy
• Radiance
• Polar Orbiting Satellite
• Geostationary Satellite
• EUMETSAT
• SatPy
• PyResample
• Cartographic Projection
• Proj4
• GOES16
• [GOES17](https://en.wikipedia.org/wiki/GOES-17?utm_source=rss&utm_medium=rss
• Dask
• Data Engineering Podcast Episode
• NetCDF
• HDF5
• PySpectral
• PyCoast
• SupervisorD
• TrollCast
• European Space Agency

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA