Learning about SWOT data products using Shiny

This post describes a Shiny application that I built to show the structure and content of data products for an upcoming satellite mission. You can try it out for yourself here.

I recently returned from a trip to Bordeaux, France for the 2019 SWOT mission Science Team meeting. If you’ve never heard of the SWOT mission, it’s an upcoming NASA/CNES satellite mission that seeks (among other things) to measure every major river worldwide, and it’s occupied my work for the better part of the last 3 years.

My contribution to the meeting was an interactive demonstration of what SWOT river data products will look like, and how one might interact with them. In my own work I’ve been privileged to have had access to hard-gotten simulations of SWOT data, and really understanding the details of their structure has at times been an uphill battle. My goal was to ease the learning process for newcomers to SWOT data and hopefully shorten the distance between data curiosity and scientific contribution.

To do this, I created a Shiny app that takes users through a set of simulated datasets from the Sacramento River in California. As this app was designed to be an instructional tool rather than any deep-dive analysis, I opted for simplicity and narrative linearity over richness of features–although I couldn’t resist adding some of my favorite visuals (including pixel-cloud classification in range-azimuth coordinates).

Please have a look for yourself (here’s the link again). Even if you are new to SWOT or ambivalent about river hydrology, I hope you have fun playing around with the interactive visuals, and hopefully learn something about what kind of information the SWOT mission will provide.

Under the hood, the app uses a package that I’ve been developing for working with SWOT-like data. I plan to have a post about that in the near future.

Mark Hagemann
Post-Doctoral Researcher

I use statistics to learn about rivers from space.