Understanding Reflection and Spectral Signatures
We can do a lot of wild stuff with all this data. We can even do math between rasters of different bands. First though, we need to talk a little about reflection.
Surfaces reflect light differently; intuitively, we both know that. If you look at a mirror, you’ll notice that it reflects much more than whatever surface you are standing on at the time. But have you ever noticed that sometimes you look a little ‘off’ when you look in a mirror you are less familiar with? Two mirrors reflect differently because they have literal physical differences, their surfaces reflect different wavelengths based on their molecular structure and composition. So what about that color difference you see?
Surfaces appear different from one another and have a perceptible color because of their structural and material properties. When light hits a surface, some wavelengths are absorbed while others are reflected. The wavelengths that are reflected determine the color we perceive.
The green leaf of a plant isn’t appearing green simply because it is green, we see it as green because the leaf is absorbing all the rest of the spectrum of light that we as humans can see, and reflecting green wavelengths of light.
One way to think about it is to change the positive to a negative. Instead of calling the plant’s leaf green, we should say that the plant’s leaf is not green, on the basis that it is accepting of all light excepting for green.
Why 13 Bands?
Our experience that differing materials reflect and absorb light differently is wildly useful when you have 13 differing wavelengths to choose from on a satellite. Our unapologetically coarse 13 bands from the free Sentinel data contain a TROVE of information.
By looking beyond the visible spectrum (Red, Green, Blue) and into the Near-Infrared (NIR) and Short-Wave Infrared (SWIR), we can see things that are completely invisible to the human eye. For example, healthy vegetation violently reflects near-infrared light, while water absorbs it. Concrete and rubble have entirely different thermal and reflective properties in the SWIR bands.
This is how we can differentiate between a muddy field, a dried-out agricultural zone, and a pile of pulverized concrete, even if they all look like the same shade of grey in a standard photograph.
What’s Next?
Understanding the spectrum is only half the battle. Because Sentinel-2 relies on capturing reflected sunlight, it is completely at the mercy of the atmosphere. In the next section, we will tackle the biggest obstacle in optical remote sensing: clouds, shadows, and smoke.
(Next: The Noise: Clouds, Shadows, & AI)