[MUSCATE News] Some bad and good news


The bad news

The MUSCATE ground segment that delivers Sentinel-2A products has had several issues these past two weeks. We have had some performance issues with the data base which tells MAJA processor which product must be processed next. These lower performances caused traffic jams and disruptions in the data production, and the system crashed quite frequently. Because of these crashes, our real time production of L2A and snow products has currently a delay above one week for most sites.

The teams are working hard to find the roots of these issues and correct them. We are very sorry for the inconvenience this may cause to your applications with good Level2A data or snow products.

Number of L2A products processed every day by MUSCATE facility, since end of 2016.


The good news : cirrus correction

The Sentinel-2 Level 2A data which are being produced now have a new version number: version 1.8. The novelty with these products is the cirrus clouds correction which was implemented within MAJA thanks to our collaboration with DLR (the German aerospace agency) and thanks to ESA funding.


The quality of the correction is generally good, but depends on the cirrus cloud thickness, and does not correct for the underlying shadow. As a result, we only apply the correction where a cirrus cloud has been detected, and we keep these pixels flagged as cloudy, even if the correction is good.  For now, we advise you to use these pixels only after having checked visually the correction. If you are processing data automatically without any visual control, our advise is to go on discarding these pixels, except if you do not need a high accuracy.





Vegetation recovery in Saint Barthélemy after Irma

Last year, in this post, I showed the comparison of two Sentinel-2 images of Saint Barthélemy in the Caribbean before and after the powerful Hurricane Irma.

A new feature in the EO Browser enables to plot the evolution of the mean NDVI within a polygon. I drew a rough polygon of Saint Barthélemy to check the evolution of the vegetation after Irma from Sentinel-2 data.

Time series of the average Normalized Difference Vegetation Index in Saint-Barthélemy extracted from Sentinel-2 observations

Here I used L1C data but it is also possible to use the L2A products from ESA, although these data are not always available. I manually adjusted the cloud fraction to remove the most obvious artifacts in the mean NDVI due to cloud contamination (clouds cause abrupt drops in the NDVI) [1]. This nice tool is sufficient to see that the vegetation quickly recovered after the hurricane, in about 1 month [2]. Catastrophic disturbances like hurricanes are actually known to contribute to maintain tree species diversity in tropical regions [3].
In the cities, according to Le Point, most of the damages have been repaired and the island is almost back to normal. This is good news for the people of St Barth!
Notes and references
[1] Under the hood, it's a "local area cloud detection algorithm based on the Braaten-Cohen-Yang method" Milcinski, G. Multi-year time series of multi-spectral data viewed and analyzed in Sentinel Hub. Medium, Apr 5, 2018.
[2] This is very similar to what has been observed in other tropical areas, e.g. "a sudden drop in NDVI values after Hurricane Maria’s landfall (decreased about 0.2) which returns to near normal vegetation after 1.5 months", Hu, T., & Smith, R. B. (2018). The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing. Remote Sensing, 10(6), 827.
[3] Vandermeer, J., de la Cerda, I. G., Boucher, D., Perfecto, I., & Ruiz, J. (2000). Hurricane disturbance and tropical tree species diversity. Science, 290(5492), 788-791.

Three snow seasons in the Pyrenees through the eyes of Sentinel-2 and Landsat-8

On June 23 we will celebrate the third anniversary of Sentinel-2A in orbit. With three years of data we can start looking at the inter-annual variability of biophysical variables, like.. (random example), the snow cover.


This is what I attempted to do for the Theia workshop. I downloaded all available snow cover products from Theia over the Central Pyrenees (tile 31TCH) and I generated additional snow maps from the Theia Landsat-8 level-2A products using let-it-snow processor. Landsat-8 images enable to increase the frequency of observations when only Sentinel-2A was operational between 2015 to 2017.


I resampled the Landsat-8 snow maps to the same reference grid as Sentinel-2 at 20 m resolution using the nearest neighbor method. I cropped all snow maps to the intersection of the Sentinel-2 tile (green polygon) and Landsat-8 tile (red polygon).

When there was a snow map from Sentinel-2 (S2) and Landsat-8 (L8) on the same day, I merged them into a composite using a simple pixel-based rule:
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La vectorisation du produit OSO, comment ça marche ?

Le produit vecteur d'OSO 2017 est enfin sorti ! Après plusieurs semaines de traitements, les vecteurs de chaque département sont disponibles ici. La production requiert la mobilisation d'une grande quantité de ressources de calcul et une stratégie de traitements un peu particulière. Nous voulions vous expliquer comment parvient-on à produire cette couche d'information.

Exemple du raster initial (10 m), régularisé (20m) et vectorisé

A priori, le plus simple serait de prendre la couche raster issue de la chaine de traitements iota² de l'intégrer dans notre logiciel SIG préféré et d'appuyer sur le bouton "Vectorisation" ! Mais les choses ne sont pas si simples, certaines contraintes et besoins nous obligent à quelques tours de passe-passe :

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