The Sen2Agri System, funded by ESA, is now on its pre-operational phase. It has just completed its first mass production and in this context, we just obtained our first Level 3A syntheses obtained with Sentinel-2A. Level 3A products are monthly syntheses of non cloudy pixels.
The monthly syntheses are produced by the Sen2Agri system. They use as input 2A level products processed by the MACCS processor, which provides surface reflectance along with cloud masks and shadow masks and snow and water masks. As their name suggests, the syntheses are produced once a month, but can be based on a bit longer time periods to increase chances to get cloud free observations.
Le système Sen2Agri, financé par l'ESA, est en phase de validation pré-opérationnelle. Il vient donc de réaliser ses premières productions de masse. Dans ce cadre, nous venons d'obtenir, avec Sentinel-2A, nos premières synthèses de Niveau 3A, c'est à dire des synthèses mensuelles des réflectances de surface des pixels non nuageux.
Les synthèses mensuelles sont produites par le système Sen2Agri. Elles utilisent en entrée des produits de niveau 2A traités par la chaîne MACCS, qui fournit des réflectances de surface accompagnées de masques de nuages et d'ombres et masques de neige et d'eau. Comme leur nom l'indique, les syntheses sont produites une fois par mois, mais peuvent se baser sur des périodes de temps un peu plus longues pour accroître les chances d'avoir des observations claires.
If I haven't posted for more than a week, it is because I have been participating to the Living Planet Symposium 2016 in Prague. With the preparation, travel, and participation to this crazy symposium. I say crazy because it is packed with about 3000 people, of which I know only about 300. When you want to go from a room to another, it takes as much as 20 minutes as you meet at least 3 or 4 of your colleagues and have a chat. But I do not need to tell you, as very likely, you were there too !
One of the 6 to 8 rooms, packed with people at the same time
The good news is that I have had access to a lot of information. I will start by some news about Sentinel-2 of course. Some of these news are not good.
- The launch of Sentinel-2B has been postponed by to 2017, probably between March and June, due to a delay with the Rockot launcher. This is very annoying as accounting with the time needed for the commissioning phase, it means we will only rely on a 10 days repetitivity during modt of 2017.
- The availability of Sentinel-2 ortho-rectified data with ground control points has also been postponed to the end of 2016, while initially it was planned in June. ESA says it is due to the fact that the global reference images (GRI) are not ready yet worldwide. We thus will have to cope with registration errors of about 1 pixel within the same orbit and 2 pixels when comparing data from 2 orbits. ESA had announced last year that they would introduce the reference data per continents starting by Europe, but it seems they changed their minds. Still I was told that the GRI for Europe and Australia are available or will be very soon, so why not starting a prodution of ortho-rectified data on those continents ?
- ESA is also going to change and shorten the very long names of their products and start to distribute data tile wise. Of course, this is good news as the choices made before were not convenient, and it is better correcting it now, as the data backlog is short, but it means everyone will have to change his software. This might delay several productions downstream ESA ground segment.
- ESA has published a new version of Sen2cor, which is said to seriously enhance the scene classification which was really bad in the previous version, especially for its cloud and cloud shadows mask. I will test it of course, as soon as I find some time.
This image background image is a monthly synthesis of Sentinel-2 images of august 2016, covering the whole Czech Republic. It was processed by MACCS to level 2A and then to level 3A by the synthesis method we developed at CESBIO and implemented within Sen2Agri package. The overlayed landcover map itself was generated by GISAT in the Czech Republic based mainly on Sentinel-1 data, as Sentinel-2 data last summer were still quite scarce. This poster was shown on the 10x10m advert on the congress centre, alternating with a nice bikini (sorry, I only have the Sentinel image !)
I have been very positively surprised by how our user community has started using the time series, instead of using images only. It is clear we have entered a new chapter of remote sensing history on the application side, with much more robust results. The case studies based on one image have completely disappeared on the presentations, even if they are still present on the posters. Sentinel-1 examples were impressive and joint uses of both Sentinels 1 & 2 are rising. As the recently launched Sentinel-3A seems to be working well, it is clear ESA has set up a great system Europe can be proud of. And on top of that, ESA really know how to organise a symposium !
Thuy le Toan, Thi-Hoa Phan and Alexandre Bouvet from CESBIO just released a nice result on ESA website obtained with time series of Sentinel- 1A images.
On this very cloudy region, the C band radar Sentinel-1A allows to monitor the growth stages of rice, and allow to detect a large reduction of the surface of rice crops at the end of 2015, 2015, in the Mekong delta region. This region suffered from a severe drought related to the El Niño. This news gives us the opportunity to recall that Sentinel familly will receive a new member, Sentinel-1B, to be launched on April the 22nd.
It may be also an opportunity to great new authors on this blog, for instance to explain us how this nice result was obtained ?
Thuy le Toan, Thi-Hoa Phan et Alexandre Bouvet du CESBIO viennent de publier sur le site de l'ESA un beau résultat obtenu avec des séries temporelles d'images Sentinel...1-A (pour une fois sur ce blog, ce n'est pas Sentinel-2)
Sur cette région très nuageuse, Sentinel-1A permet de bien suivre les stades de croissance du riz, et a permis de détecter une forte diminution des cultures de riz sur le delta du Mekong, sur la fin de l'année 2015 marquée par une grande sécheresse liée au phénomène El Niño. Ce communiqué est l'occasion de vous rappeler que la famille des Sentinel va encore s'agrandir avec le lancement du satellite Sentinel-1B, ce vendredi 22 avril.
C'est aussi peut-être l'occasion d’accueillir de nouveaux auteurs sur ce blog, par exemple pour nous expliquer comment ce résultat a été obtenu ?
In the framework of the THEIA land data center, we have developed a simple but robust method to map the snow cover from Sentinel-2-like level 2A products. This code was tested with SPOT-4 Take-5 and Landsat-8 series, but it remained to adapt it so that it can run on real Sentinel-2 images! This is now done thanks to Manuel Grizonnet, which allowed us to process the Sentinel-2A image acquired on 06-July-2015 in the Pyrenees as a first example. This image was produced at level 2A by Olivier Hagolle using the MACCS processor. The snow mask from Sentinel-2 images is calculated at 20 m resolution after resampling the green and red bands that are originally at 10 m resolution while the NIR band is at 20 m.
How to make sure everything went well? We can control the snow mask by superposing the mask boundaries on a false color composite:
The Sentinel-2A image of 06-July-2015 (level 2A, tile 30TYN) and its snow mask. The snow mask is in magenta and the background image is a color composite RGB NIR/Red/Green. We also show a zoom in the Vignemale area.
Dans le cadre du Centre d'expertise scientifique THEIA "surface enneigée" nous avons développé une méthode simple et robuste pour détecter la neige à haute-resolution à partir des produits de niveau 2A de type Sentinel-2. Ce code a été testé sur des séries SPOT-4 Take-5 et Landsat-8, mais il restait à l'adapter pour qu'il puisse tourner sur de vraies images Sentinel-2 ! C'est chose faite grâce à Manuel Grizonnet, ce qui nous a permis de traiter l'image Sentinel-2A du 06-juillet-2015 sur les Pyrénées. Cette image avait été produite au niveau 2A par Olivier Hagolle avec la chaine MACCS. Le masque de neige est calculé à 20 m de résolution après ré-échantillonnage des bandes vertes et rouges qui sont d'origine à 10 m de résolution alors que la bande MIR est à 20 m. Continuer à lire
Franck Roux told this sentence in his lecture "Should we be afraid of climate change?" given at the University Paul Sabatier on December 10, 2015 (I quote from memory):
"The human being is a very good weather sensor, but it is a poor climate sensor."
Since our memory can play tricks on us, satellite images are valuable data. As we have seen in a previous article, the snow cover area in the Pyrenees was rather small in January 2016. We can reconstruct the snow extent across the whole mountain range since 2000 with MODIS or even 1998 with SPOT-VGT. However if you want to zoom in on a specific region, the spatial resolution offered by these sensors quickly becomes insufficient so we must turn to the Landsat archive. Continue reading