This is part 2 of a 5 part series. Be sure to read the earlier parts first if you haven’t already.
- [Getting the images on your computerhttps://catamounthardware.com/blog/23-2-3-satellite-imaging-for-ice-scouting-part-1/)
- Visualizing the data (this article)
- Examples: Lake Winnipesaukee; Lake Sunapee
- Getting the images while on the ice
- Additional information
2. Visualizing the data
Now that you have something to look at in the EO Browser, what do you look for?
A short primer within the primer:
How satellite sensor data becomes images on your screen
Each satellite captures different bands of information, using different sensors. Those bands of information are turned into the red, green, and blue of images that our eyes can process. So the simplest case are the sensors that capture visible light — ie red, green, and blue — because those are just displayed as received, and show us what the satellite saw.
Slightly more complex is using non-visible light sensors to create false color images. For example, the common infrared satellite images (“False color” in the EO Browser “Visualize” tab) are created by replacing the normal visible light blue color with what the near-infrared sensor sees (and leaving the visible light green and red data alone).
More complex, and more useful, yet is using multiple different bands of information and doing calculations with them. I have a lot more to say about this, but I’ll save it for another day.
With all that in mind, let’s take a look at the images. If you select a Sentinel-2 data source, you should see this list of available visualizations.
If you select an HLS data source, you’ll see this one instead.
Luckily, the visualizations that matter to us for scouting ice are available from both satellite data sources.
“True color” is just a standard satellite image (red, green, and blue visible light). This is the Leavitt Beach plate we’ve been skating this past week seen in the Sentinel-2 data.
Both Sentinel-2 and HLS are good enough, but the HLS data is slightly pixelated. No big deal for our purposes (though it can be a problem for very small bodies of water). From here on I’m going to use the Sentinel-2 data, since it’s slightly clearer. Everything should apply to the HLS data as well.
The next visualization option in the list is “False color”, which is visible red and green light plus near infrared light in place of visible blue light. I’ll quote Jamie’s recent description of how to interpret this:
Bluish gray and bluish white areas are the most likely to be skateable. These areas have remained frozen long enough to have been snowed on, and then the surface was subjected to a thaw-and-refreeze cycle. Bluish white indicates older and thicker ice than bluish gray.
Grayish black areas are slightly less likely. They consist of black ice that’s old enough to have cracks in it - the cracks give it the grayish appearance - but it might not be skateable yet. To make an assessment, you’d need to look at the date the satellite image was taken, and how cold it’s been since then.
Areas that appear solid black or solid white are probably either open water or snow-covered ice. Unless you have information to the contrary (like an eyewitness report) I would steer clear of them.
The “Moisture index” (NDMI) visualization sounds promising, but it’s meant for vegetation. Sentinel Hub recommends using the “NDWI” (water index) visualization for water bodies instead.
Each pixel in an NDWI image is on a spectrum from green to white to blue (as illustrated in the screenshot), where green means vegetation, blue means water, and white means not water or vegetation. How do we interpret this? More on that in the part 3 examples.
“NDSI” (snow index) is very simple: bright blue is snow (or rather “snow”, which very much includes ice in this context), everything else is not.
“SWIR” (short-wave infrared) is another false color visualization, but this one uses short-wave infrared, narrow infrared, and red. It looks much the same as the “False color” infrared visualization (well, except different colors), but it tells us a bit more. Again, more on this in the part 3 examples.
Now that you have a sense of how to manipulate the satellite images to reveal different kinds of information, continue on to part 3, examples.
— Christopher Boone, some rights reserved (CC BY-NC-SA 4.0)