Using the eo-learn framework, I came across some issues when sampling Sentinel-2 L1C in eopatches.
Image 1 in overlap area - 2018-11-21 07:22:24:
image 2 in overlap area - 2018-11-21 07:22:39:
As you can see above, it picks up two images on the same day, with a few seconds acquisition time difference. That is not surprising as it is an area where the two Data Takes overlap. Scihub illustrates the overlap:
So far it is understood. I am expecting these gaps present at certain timestamps to be removed when performing interpolation because I am interpolating 36 timestamp to 10 interpolated output values.
However, it turns out differently and two issues turn up:
- First and last dates of the interpolated dates contain no values.
- The second to last date still contains no data values in the horizontal strips at the bottom of the image.
Gif illustrating this point:
My take on this is that when performing WCS requests in those overlap areas and storing them into eopatches, there should be an automatic procedure which stitches these two together into a single timestamp to avoid the occurrence of these gaps, as they are from the same swath (i.e. redundant because they are showing the exact same data).
It would avoid any downstream errors when interpolating, because two almost identical timestamp will bias the interpolator.
In my particular use case of land cover classification, it is creating classification inconsistencies in output because of the absence of key time steps in the time series:
The background land cover is interpreted to be water just in that specific strip, and that’s the only location such a systematic bias happens in the entire extraction extent (Qatar in this case):
Let me know if you’ve come across such an issue. I’d be keen on knowing how we can get this fixed!
P.S: Why are we limited on links and images we can put… A point needs illustrating! Anyway, I put the links in there anyway so you can take a look.