MAJA now corrects thin cirrus clouds on Landsat-8 and Sentinel-2 images

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Thanks to our collaboration with  DLR,, who developed the method [1], and with an ESA funding, we added within MAJA a correction for thin cirrus clouds that works for LANDSAT 8 and Sentinel-2. This correction uses the cirrus band  at 1.38 µm, which allows to estimate cirrus reflectance, which is then subtracted from the other bands with an empiric factor derived from the images.

 

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Correction des cirrus sur les images de Sentinel-2 et Landsat 8

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Grâce à notre collaboration avec le DLR, qui a mis au point la méthode [1], nous avons mis en place dans MAJA une correction des cirrus fins pour LANDSAT 8 et Sentinel-2. Cette correction utilise la bande cirrus située à 1.38 µm, qui permet d'estimer la réflectance des cirrus qui est ensuite soustraite des autres bandes avec un facteur de proportionnalité calculé empiriquement.

 

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[MUSCATE news] MAJA upgraded to version 2

Yesterday, the MUSCATE ground segment started delivering Sentinel-2A L2A products using the MAJA V2 processor, while MAJA V1 was used until now. MAJA V2 corrects for a few bugs (such as the pixels flagged as cloud or cloud shadow on the edge of the images) and adds three new features :

  • We implemented a correction for directional effects for a better estimate of Aerosol optical thickness. As you probably know, MAJA uses multi-temporal criteria to detect clouds and estimate aerosol content. These criteria suppose that surface reflectance does not change much from one date to the next one. Over a given tile, we can combine data coming from two adjacent orbits, with slightly different viewing angles, and the directional effect can induce some variations in the surface reflectance of successive acquisitions, which would be interpreted as atmospheric effects. We are now using a simple directional correction to reduce this noise source (D.Roy et al). As this directional correction is not perfectly accurate, it is not applied to the surface reflectance we deliver at the end of MAJA processing.

Elsa Bourgeois, from Cap Gemini, compared the performances of processing with or without directional correction. In both cases (S2A, top, S2B, bottom), there is only a very slight improvement of performances.

  • We implemented a cirrus correction, that comes from DLR works. This correction is not in production yet, as it needs more validation on a large data set. We simply parametrise MAJA with "cirrus_correction= false". A coming post will address this subject.
  • We have started using the new high cloud threshold that was refined in this post. It will result in more cloud cover, with a large difference over mountains.

 

All these small improvements are now implemented in version 1.6. They do not justify a reprocessing of the Sentinel-2 archive, although the accumulation of successive slight improvement could justify it. We intend to start a reprocessing when MAJA V3 is put in production.

Speed-up downloads from PEPS S2 mirror site with peps_download.py

The French Sentinel mirror site, PEPS, has a very clever data management facility. All the products are stored on tapes, with a capacity of several PB, and there is some sort of cache made of disks. The products accessed recently are on disks, while the other products stay on tapes. The storage costs and also power consumption are therefore largely optimized.

 

The drawback is that before accessing a file on tape, some time is needed to get the tape, and read the file on tapes. This can take something like 2 to 10 minutes. My little tool, peps_download.py was designed when most of the products were on disks, and it was quite slow to download products on tapes. As I am not a patient person, I have tried to speed it up, and it works well, thanks to good advise from CNES peps  colleagues (Christophe Taillan and Erwann Poupart).

 

The previous version was working like that :

Make catalog request

For all product in the request result :

- while product is not downloaded

 - try to download the product

 - if still on tape, wait for 2 minutes

As a result, for each product on tape, it was necessary to wait for 2 to 10 minutes.

 
Now, it works like that

Make catalog request

For all products on tape in the request result

- ask to read it on disks

While (still some products to download):

- Redo catalog request

- Download products on disk

- If some products are not on disk yet

 - wait for 2 minutes

 
On my computer, it used to take more that 12 hours to download 2 years of Sentinel-2 data for a given tile. It has now been reduced to less that 3 hours (but my computer is on CNES network). I hope you will have similar results !

Systematic validation of Sentinel-2 THEIA L2A products

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Very recently, Camille Desjardins (from CNES), who is handling the validation of the L2A products generated by THEIA, has set up a systematic validation of the products delivered by MAJA, with the help of an operational service from CNES (OT/PE) (Bruno Besson, and Nicolas Guilleminot from Thales Services, using tools developed by Aurélie Courtois, also from Thales)

 

Systematically, a comparison of AOT and water vapour is made for every Sentinel-2 L2A product from THEIA which observes one of the sites of the Aeronet network.

 

Both plots below show the results obtained during the month of February, for the Aerosol Optical Thickness (left), and for the water vapour content (right). Blue dots correspond to validations in ideal conditions (low cloud amount, no gap filling, and quality assured Aeronet data (Level 2.0). The red dots allow degraded conditions, and most of them correspond to the unavailability, yet, of version 2.0 Aeronet data. As data are processed in near real time, and level 2.0 data are made available a few months later, these plots rely mainly on Level 1.5 data, which are more prone to errors (such as a calibration drift... or the presence of a spider in the instrument tubes).

 

Aerosol optical thickness validation of Sentinel-2 L2A for all Aeronet match-ups gathered in February 2018 Water vapour validation of Sentinel-2 L2A for all Aeronet match-ups gathered in February 2018 (in g/cm2)

 

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Sentinel-2 goes global

Great news ! As announced in Sentinel-2 Mission status, laser links to geostationary relay satellites are now working, both for S2B (since last October) and S2B (since a few days ago). Sentinel-2 5 days repetitivity is now nominal above all lands, and that's cool ! A big thank you to ESA, Copernicus and all the engineers who strived to achieve that !

 

Map of S2A and S2B acquisition segments on the 28th of February. Almost all segments over continents were acquires, and are available on https://peps.cnes.fr

Une grand nouvelle ! Comme annoncé dans le "Sentinel-2 Mission status", les liaisons par laser vers un satellite relais géo-stationnaire fonctionnent, à la fois pour S2B (depuis Octobre 2017) et S2A (depuis quelques jours). Les deux Sentinel-2 observent les 5 continents avec la répétitivité nominale de 5 jours, et c'est chouette !  Un grand merci à l'ESA, Copernicus et tous les ingénieurs qui ont permis cette réussite !

[MUSCATE News] Reprocessing of Sentinel-2B data aquired from July to October 2017

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MUSCATE is in a good shape these days thznkd to the continuous efforts of the development team (CNES and CAP GEMINI) who solved several issues. The counter of Sentinel-2 Level 2A products reached 70 000 products this night, just one month after reaching 60000. If we sum all the products delivered by MUSCATE, we are reaching 99 000 images. MUSCATE also distributes Sentinel-2 Snow masks over mountains, and Spot World Heritage data (old SPOT data reprocessed after ortho-rectification.and made available for free).

 

This good shape allows us to increase our production rhythm. We have started processing the Sentinel-2B data acquired between July and October 2017, as we had started processing S2B in November 2017 only. But since MAJA is a multi-temporal processor, we are in fact starting a complete reprocessing of the data, including S2A and S2B. The quality of S2A products should therefore also benefit from the improved repetitivity of observations.

 

This reprocessing will last several weeks. We are starting with data from France and will go on with our neighbouring European countries, then sites in Maghreb, the remaining sites of Africa, and finally, the rest of the sites in the world.

 

In case you have an urgent need for some tiles, please ask ! (of course, it is only applicable to the tiles already in our list)

 

 

 

Amélioration de la détection des nuages hauts dans MAJA pour les images de Sentinel-2

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La recherche pour l'amélioration des performances des méthodes de MAJA repose actuellement sur une toute petite équipe au CESBIO, composée de deux personnes, Bastien Rouquié, qui travaille sur l'amélioration des estimations d'aérosols, et moi, qui passe presque tout mon temps en réunions, ou a écrire des articles de blog (et parfois les deux en même temps). C'est pour cette raison que de nombreux points que nous aimerions améliorer sur MAJA ne s'améliorent que lentement.

 

Absorption atmosphérique. En bleu, la réflectance de surface pour un pixel couvert de végétation, en fonction de la longueur d'onde, en rouge la réflectance au sommet de l'atmosphère pour ce même pixel. A 1.38 µm, la vapeur d'eau absorbe totalement la lumière provenant de la surface au niveau de la mer.

J'ai pu récemment consacrer un peu de temps à l'amélioration de la détection des nuages, notamment au dessus de l'eau, et plus récemment, à améliorer la détection des nuages hauts, en utilisant la  bande "cirrus" située à 1.38 µm, qui est disponible sur Sentinel-2 et Landsat 8. Comme précisé dans ce post, la bande "cirrus" est située dans une forte bande d'absorption de la vapeur d'eau, telle qu'au niveau de la mer, les photons ont très peu de chances de faire le parcours du soleil au satellite en passant par la surface de la terre sans être absorbés. Comme la majorité de la vapeur d'eau est située dans les basses couches de l'atmosphère, les nuages situés plus haut dans l'atmosphère ont davantage de chances de renvoyer de la lumière jusqu'au satellite. C'est donc un moyen de détecter les nuages hauts.

 

La méthode de détection parait donc très simple, il suffit de faire un seuillage sur la réflectance dans la bande 1.38 µm.  Malheureusement, c'est plus compliqué que ça.

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Theia produit au niveau 2A toute la bande côtière du Maghreb

Les tuiles en bleu viennent d'être ajoutées aux zones où Theia fournit des produits de niveau 2A.

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Depuis la découverte des récents bugs et la mise en place de moyens de les contourner, le rythme de  production de MUSCATE s'est accru, et nous avons donc pu étendre un peu les zones où nous fournissons des données Sentinel-2 (A&B) corrigées des effets atmosphériques, avec un bon masque de nuages: il s'agit de produits de niveau 2A fournis par la chaîne MAJA.

 

Nous venons de mettre en ligne toutes les données acquises sur la zone côtière du Maghreb, du Maroc à la frontière entre Algérie et Tunisie. Quelques tuiles manquantes ont également été rajoutées au sud du Maroc, et sur le Cap Bon, en Tunisie. Avec cet ajout de 50 tuiles, toutes les terres qui bordent la méditerranée occidentale sont donc suivies pat les produits de Theia (enfin, il manque les Baléares).

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