Great news for our little Venµs satellite project: Theia has started distributing Venµs L1C data in near real time. The data acquired yesterday are already available on https://theia.cnes.fr
These L1C products are tagged with 0.9 version, which means that their quality is not as good as what we think we will be able to deliver in a couple of months. The multi-spectral and multi-temporal registration can still be improved, even if it is not too far from our expectations yet in most cases. The image quality teams are finishing an error budget of the current situation, and also preparing the improvement with our Israeli partner who manage the satellite. We will tell you about that.
The L2A delivery will also start in a few weeks, but the L1C teams, who had much more work than expected with this satellite, handled us correct products very late, and we still need to tune a few parameters to provide good quality products.
On the Theia website, you will find several ways to download the tiles. My little download tool has also been updated. Once you have registered, and updated the config.cfg file, you will be able to download at once all the products in Australia with the following command line :
python theia_download.py -l 'Australia' -c VENUS -a config.cfg -d 2018-01-01
The data format is explained here. Its packaging with useless zips and tars is still provisional and will be simplified very soon.
Update from May 4th 2018 : ESA has started planning for a global reprocessing in 2019
The information gathered by Sentinel-2 system on Sentinel-2 orbit, attitude, date accuracy, and viewing directions of all detectors allows an excellent accuracy for the geolocation of all Sentinel-2 pixels. The overall geo-location accuracy is better than 11 or 12 meter, for about 97 % of the cases, which is about the size of one Sentinel-2 pixel. Such a performance is more or less equivalent to that obtained for Pleiades, but Pleiades has a resolution of 0.7m. It is therefore really an achievement, which is to be credited to ESA, to the satellite and instrument manufacturer, and to the image quality teams (including my CNES colleagues).
But even if it is excellent, it is not enough. The standard need for multi-temporal registration errors is 0.3 pixels, and the current performances show that for more than 50% of the cases, the performance does not meet that requirement.
From Sentinel-2 data quality report
For many users, I guess, these figures do not mean much, and it is not easy to figure out their impact on real life applications. The animation below (made by the twitter star Simon Gascoin) makes that much more concrete :
Time series of Sentinel-2 images of the construction of Nour solar power station near Ouarzazate in Morocco. (made by Simon Gascoin using Sentinelhub)
Thanks to our collaboration with DLR,, who developed the method , 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.
Grâce à notre collaboration avec le DLR, qui a mis au point la méthode , 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.
A first set of 35 images (L1C) is available for download on the following CNES web site:
Venµs is now systematically acquiring data on all selected sites since January 2018, although a few acquisitions were missed from time to time for various technical reasons, which should become progressively less frequent.
The commissioning phase of the Venµs mission is not yet completed regarding image quality (radiometry and geometry). This delay is due to several issues, the most important of which are detailed hereafter. However, since these issues do not prevent the use of the data, CNES decided to process and distribute a first set of 35 Level-1 (L1) images for which the radiometric and geometric qualities are acceptable. Level 1 corresponds to orthorectified Top of the Atmosphere reflectances. This preliminary data set is intended to allow the users to familiarize with the data and their format.
These 35 images are only a subset of the data that are systematically acquired over the Venµs sites since January 2018. We plan to start the distribution of these L1 Venµs time series by April 2018. We expect to start the distribution of the Level-2 products (surface reflectances) by late May or beginning of June, 2018.
- The absolute calibrations of the spectral bands B1 (415 nm) and B2 (440 nm), both located in the blue, are less accurate than the calibration of the other spectral bands. This issue prevents the use of B1 and B2 for retrieving the aerosol optical depth (AOD) or for water color applications.
- The accuracy of the satellite attitude restitution is for now lower than expected. Due to the fact that the different bands are not acquired simultaneously this has mainly impacts on the inter-band registration. For some sites with heavy cloud cover or uniform landscape, this issue also impacts the multi-temporal registration.
The characterization of radiometric and geometric performances is still ongoing. Efforts are also devoted to the improvement of the preprocessing algorithms.
As soon as significant progresses are made, the whole data set acquired since January 2018 will be reprocessed with the new parameters and algorithms.
The product format document is available here. A more complete version will soon be made available there.
The Venµs page on the Theia web site will keep you informed on the progress made.
Time series of Venµs images acquired over Oklahoma (USA) and processed at level 2A (with a first try of atmospheric effects correction applied)
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.
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)
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)
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.
Les tuiles en bleu viennent d'être ajoutées aux zones où Theia fournit des produits de niveau 2A.
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).