Sentinel-2 adds colors to the 2017 super bloom in California

Texas had the super bowl in 2017 but California got the super bloom!

 

After its worst drought in 1,200 years, California is now experiencing one its wettest year. The Sierra Nevada snowpack is currently one of the biggest ever recorded. In February, severe winter rainfalls caused the evacuation of many California residents under the threat of the Oroville Dam failure.

 

Now, this unusual level of rainfall triggered the blossoming of flowers that were in a dormant state. The Washington Post collected some photographs and noted that it was so so massive that you can see it from space (images were provided by Planet, a California-based remote sensing company).

 

How does it look like in Sentinel-2 imagery? Below is the comparison between two Sentinel-2 images acquired in spring 2016 vs. spring 2017 near the Carrizo Plain National Monument (see by yourself in the sentinel-playground).

 


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Last Sunday's snow cover map from Sentinel-2

We know that fast distribution of satellite products is critical for many applications, including the planning of your next weekend.

 

Olivier just announced that Theia is now delivering Sentinel-2 Level 2A data in near real time. This is great because today I was able to make the snow cover map of the Pyrenees on Sunday at 20-m resolution.

 

The L2A image was actually published on Tuesday afternoon so I could have posted this snow map two days ago.

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MACCS, renamed MAJA, now distributed as binary for non-commercial use

I have already mentioned that CNES teams working on MACCS cloud screening and atmospheric correction software and DLR teams working on ATCOR, decided to join their efforts to build a common software, named MAJA which stands for MACCS-ATCOR Joint Algorithm. And thankfully, a funding from ESA helps a lot this initiative.

 

The MAJA code relies on MACCS architecture which is designed to use multi-temporal criteria, but the successive MAJA versions will progressively include several features coming from ATCOR which did not exist in MACCS or were less elaborated and accurate.

 

We just released MAJA V1.0 which includes a few improvements (an enhanced mono-temporal mask coming from ATCOR has been included, as well as a modification of the estimation of water vapour). MAJA V1_0 is in fact just a little evolution compared to MACCS latest versions. MAJA V2.0 will include a directional correction (coming from CESBIO team), and a correction of cirrus clouds, that comes from DLR.  V1_0 version is now being integrated to Muscate, and it will soon run on our platform, while V2_0 should be delivered to CNES this week.

 

But the main news related to that is the fact that MAJA is now distributed as a binary code for linux Platforms RedHat and Cent OS versions 6 and 7 only. MAJA is distributed on CNES free software platform, and you will have to accept a licence and provide your email address. The licence is granted for non commercial use only. If you would like to get an extension of this licence for commercial use, please ask CNES.

MAJA tutorial page on github

MAJA was developed to process time series, if you only want to process a few images, please use SEN2COR. Given the size of Sentinel-2 images, MAJA is not meant to run on a laptop, it needs really serious computer resources, fast drives and processors, and several Gigabytes of Memory. I also have to stress that MAJA is not easy to use, and that solid knowledge in computers, linux, python might be necessary. MAJA also needs several stages of data preparation, including specific DTM generation.

Tutorial pages

A few beta-users are now testing the installation procedures. The first feedback is that the users often do not read the documentation carefully ;) . I strongly urge you to read the document provided in the package ! I have written a tutorial page on Github, and I hope you will manage to use it. In case of any problem, please do not comment here or send me an email, use the support address : maja-support@cnes.fr or add an issue to the github tutorial platform.

 

 

 

10000 L2A products available at Theia for Sentinel-2

The number of L2A products from Sentinel-2 available at Theia, just passed above 10 000 ! Data from France, Belgium, North of Spain, Morocco, Tunisia, Senegal, the Burkina-Mali transect, or La Reunion Island are now processed in real time : our requirement is 60 hours, after the data is available in Copernicus hub. Next site to be added is Madagascar.

 

Le compteur de produits de niveau 2A de Sentinel-2, disponibles à Theia, vient de passer la barre des 10 000 produits. Les données acquises par Sentinel-2 sur la France, la Belgique, le Nord de l'Espagne, la Tunisie, le Sénégal, le transect Burkina Mali, et l'île de la Réunion sont maintenant traitées en temps quasi réel : notre spécification est de les mettre à disposition en moins de 60 heures après leur apparition sur les serveurs du hub de Copernicus. Le prochain site couvrira une bonne partie de Madagascar.

THEIA/MUSCATE nears real time for Sentinel-2 L2A

THEIA MUSCATE production of Sentinel-2 L2A products nears real time, and L2A products acquired until March 2017 over France and Reunion Island have started to appear on our distribution server :

https://theia.cnes.fr

The production will progressively be extended over the whole France and over the other sites (Spain, Morocco, Belgium Tunisia, Senegal, Burkina, Mali...) and then follow the real time acquisitions with the shortest delay possible.

 

La production par MUSCATE des données de niveau 2A de Sentinel-2 s'approche du temps réel, et les données acquises jusqu'à mars 2017 sur la France et l’île de la Réunion ont commencé à apparaître sur le serveur de distribution.

https://theia.cnes.fr

 

La production va être progressivement étendue à toute la France et aux autres sites (Espagne, Maroc, Belgique Tunisie, Sénégal, Burkina Faso, Mali...), et suivra ensuite les acquisitions en temps réel avec le plus court délai possible


Quantitative comparison of cloud masks from MACCS/MAJA, Sen2Cor and GEOSYS (hand made)

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As already explained in a previous post, we obtained some Sentinel-2 hand made cloud masks from GEOSYS company. We used those to validate the cloud masks from MACCS/MAJA. But we wanted to use them further to make a quantitative comparison with Sen2Cor cloud masks.
But this comparison required solving a little issue : GEOSYS cloud masks are generously dilated to avoid any risk to let a cloud pass through the operational processing. Those of MACCS:MAJA are also dilated while those of Sentinel-2 are not at all. In the following paragraphs, we'll explain how we solved that issue. Sen2cor (v2.3.0) has also three levels of cloud mask (High Medium and Low probability). We used here the Medium Probability mask. But let's start with the final result comparing the performances of Sen2Cor and MACCS:MAJA.

 

Overall accuracies for MACCS/MAJA, in red and Sen2cor, in blue for 11 images compared to GEOSYS cloud masks.

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2016/17 : un record d’écobuages dans les Pyrénées ?

Image du 10 décembre 2016Image SENTINEL2 du 30 novembre 2016, "SWIR"
Mais que se passe-t-il ce 10 décembre 2016 à midi, au-dessus du village de Villelongue (à 15 kilomètres au sud de Lourdes) ? Un des plus beaux écobuages des Hautes-Pyrénées de cet hiver 2016/17 ! Une très large bande de feu actif se dirige vers le sud. Ce feu a démarré la veille d’après les contacts terrain. En 2 jours, une grande zone a donc déjà été brulée. La répétitivité de SENTINEL2 permet d'observer la même scène quelques jours avant et après ce feu.

Un nouvel algorithme de détection des nuages pour la Bretagne

Ce nouvel algorithme baptisé MACCROW (Multitemporal Atmospheric Correction and Cloud Retrieval Of Western-France) a été développé par l'équipe MACCS pour résoudre le cas spécifique des régions à forte nébulosité. Comme nous l'a expliqué Olivier Hagolle, chercheur au Cesbio, "l'algorithme se base sur le fait que la reflectance au sommet de l'atmosphère chute brutalement lorsque le ciel s'éclaircit. Ces évènements aberrants peuvent être détectés par un test de Fisher puisque leur distribution statistique suit généralement une loi de Poisson. Les premières validations ont donné des résultats satisfaisants (kappa=0.76). Il reste que certaines trouées passent encore à travers les mailles du filet."

La série de Plouguemeau traitée par MACCROW. Les zones claires sont délimitées en rouge.

NB) l'image du 07-aout-2016 a été volontairement exclue car elle était trop claire et donc faisait planter l'algorithme.