For a fun piece to chew on during the Thanksgiving holiday, National Geographic asked Descartes Labs if we could figure out a way to map every cranberry bog in the United States. We had less than a week to complete the project end-to-end, but we were fortunate to have some pre-existing algorithmic work and data products to consult, which gave us a huge head start.

In the end, the project proved to be a fun and unique problem to solve from a remote sensing perspective. Here, we’ll unpack how we actually did it!

Why was this project hard?

While it sounds simple, it turns out that identifying cranberry bogs from satellite imagery is actually quite tricky. Depending on the time of year, they might look like ponds, bare earth, or lush, green agriculture (like corn)!

The life cycle of a cranberry bog is best observed over time. In the four images above, which depict bogs in Massachusetts, we see snow, healthy green vegetation, flooded bogs, and even flashes of red during harvest!

Unless they’re dry harvested, most cranberry bogs in North America are manually flooded before harvest. Unfortunately, the timing of the flooding has enough variability from region to region that we couldn’t get away with simply selecting a single moment in time that would allow us to identify the unique optical imagery signature that indicates a flooded bog.

However, what we could do was take advantage of the manual, pre-harvest floods that inundate these bogs by analyzing their signature in synthetic aperture radar (SAR) imagery, which is particularly sensitive to water content.

The difference between vegetated and flooded bogs (top to bottom) is evident in optical imagery, shown on the left. To the right, this difference is also evident in synthetic aperture radar imagery, with a sharp decline in backscatter once the bogs are flooded.

After analyzing the flood signature in radar imagery, we leveraged techniques we’d used in a previous Descartes Labs project that involved using radar data to map rice paddies — which are also periodically flooded — in Southeast Asia, and combined those with information available to us through the Descartes Labs platform that provides an annual composite of Sentinel-1 radar data, which — critically — includes annual statistics computed from raw amplitude backscatter data. These changes in backscatter represent multiple states in the lifecycle of a cranberry bog. Without this groundwork in place, we most likely would not have been able to accommodate the very short turn-around time for this project.

How did we do it?

Since temporally aggregated radar statistics are a somewhat abstract way to represent bogs, we applied machine learning to these data sets to find a decision surface that accurately separates the positive (cranberry bog) and negative (not a cranberry bog) class. For the positive class, we were able to locate several active cranberry bogs by scraping data from the GIS departments of Wisconsin, New Jersey and Massachusetts, which, as the top producers of cranberries in the U.S., thankfully make this information freely available.