Multiple Satellites, One Optical Image

ClearSky Vision, Sentinel-2, and Landsat on the same day (Denmark, March 2022)

Optical Data Fusion:

The easiest data to merge is by far two identical optical satellites from the same constellation since they will have identical or very similar spectral bands, revisit times, and spatial resolution — which should come as no surprise. Imagine having two images that are clouded in different places and merging them together for a cloud-free image. Well, it’s never that easy. A situation we encounter more often is the imagery having thin clouds and thus the job becomes extraction and merging. These examples are also the easiest to understand and, to some degree, being able to visually confirm their validity and accuracy. My eyes can’t measure the actual error between the two images but they can quickly spot differences in intra-field variance and color. Not unlike the game “Spot the Difference” where you need to find differences between two images. Our human pattern recognition is really strong even in cases where an uneven veil of mist and clouds makes it all more difficult. That’s an easy statement to write so here are a couple of examples to prove my point (or disprove, you decide!).

Optical Data Fusion with Multiple Constellations:

The next best is two optical satellite constellations with similar spectral bands and revisit times — as is the case with Sentinel-2 and Landsat 8/9. Well, actually the Landsat constellation has a worse spatial resolution, slower revisit time, and slightly different composition of spectral bands than Sentinel-2. However, that also means its orbit is offset from Sentinel-2, which gives data on days with previously no optical data, and it introduces thermal measurements — something Sentinel-2 does not have. So while these two constellations are harder to merge together, in practice we see a strong synergy due to the differences in specifications and instruments.

Different combinations of Sentinel-2 and Landsat 8/9 (March/April 2022)
A lot of Sentinel-2 data but most images are still affected (2022)
A lot of Sentinel-2 data but most images are still affected (2022)

SAR and Optical Data Fusion:

This is where the fun starts, where human intuition breaks down, and where the results become a bit more abstract. It’s almost impossible for us to prove the exact improvement by adding SAR data to the estimation. However, removing this SAR data from our estimation significantly changes the prediction. The data from Sentinel-1a (a sad goodbye to Sentinel-1b) is very sparse and it’s not easy to make good cloud-free biomass predictions using SAR data alone. Impossible when it’s multi-spectral data. We use SAR data from Sentinel-1 to adjust our optical data which can be seen in the example below. On the third last image (a day with only SAR data) we see our cloudless imagery becoming more saturated with vegetation and the day after with partly clouded Sentinel-2 imagery it continues the same growth pattern.

Six days of continuous monitoring last week (Holzkirchen Germany, April 2022)
Nine days of continuous monitoring (Lolland Denmark, April 2022)
Intra-field changes on days with no optical data (Denmark)
Rapeseed field blooming between 9th of May and 8th of June (Denmark)
Shortened Time-Series (Top Row: Sentinel-2, Bottom Row: ClearSky Vision)

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Morten Fjord Pedersen

Morten Fjord Pedersen

Co-founder of ClearSky Vision | Cloudless Sentinel-2 Data | Data Fusion with Deep Learning | I’m always out of free disk space.