I would like to conduct a supervised classification of land cover types in a region that features fairly small “objects” relative to Sentinel-2 pixel size (e.g. riverine vegetation). Importantly, I am interested in using multi-temporal S-2 data to achieve this. Now I noticed some positional misalignment between images taken e.g. April and July 2018 in the range of at least 0.5 pixels. So I must keep a safe buffer distance when delineating areas to train the classifier. This in turn reduces the potentially available training areas significantly, and also limits the validity of the classified map in “border regions”. Not good.
In October 2018 Charis Lanaras et al. reported that the current (empirical) geolocational accuracy of S-2 data (cf. in flat terrain) was about 11 m, but would be reduced to <0.3 pixels between passes at 95% confidence. (https://doi.org/10.1016/j.isprsjprs.2018.09.018). This appears to have happened (Lin Yan et al. 2018 (https://doi.org/10.1016/j.rse.2018.04.021) - I can see the same magnitude of misaligement in the current S2 data - and these authors developed a method to reduce misregistration to 0.15 pixels. But has their method (or equivalent) found its way into ESA’s default processing chain yet?
According to Olivier Hagolle from CESBIO (https://labo.obs-mip.fr/multitemp/a-bit-of-bad-news-regarding-sentinel-2-multi-temporal-registration/), ESA has started working on this in 2019 and plans to reprocess the entire archive accordingly. But so far this appears to not have happened (for my test data across Germany for the year 2018).
Do you guys have any new information on these developments? Is there a good workaround? I am curious to hear from you. Thank you!