Near-real time analysis of the 2018-2019 snow season in the Pyrenees and the Alps from satellite data

Here in southwest France ski lovers did not really enjoy the beginning of the snow season... But how does it compare to the previous years? Using Sentinel-2 and Landsat-8 data, we computed the snow cover duration since September 01 until January 20 for the past three snow seasons in the Alps and Pyrenees.

Snow cover duration (in days) from 01 September of year N-1 to 20 January of year N

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Anak Krakatau before and after the December 2018 eruption

Anak Krakatau (Indonesia) erupted on 22 December 2018. During the eruption the collapse of the volcano summit triggered a tsunami in Sunda Strait causing a death toll of 437. The first post-event clear-sky image was finally acquired by Sentinel-2 today on 13 Jan 2019 (after 10 cloudy acquisitions). Here is an image comparison of the Krakatau Island before and after the eruption.

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Improvement of water vapour retrieval in MAJA

Similarly to the aerosol retrieval, the retrieval of water vapour in MAJA atmospheric correction has also been improved, thanks to the work of Elsa Bourgeois (Cap Gemini) and Camille Desjardins (CNES). An accurate estimation of water vapour is not necessary to perform an accurate atmospheric correction, because water vapour absorption in most of Sentinel-2 bands is much lower than 5%. But the Sentinel-2 water vapour product could also prove useful, and when we plot validation results, showing a large bias for high water vapour contents is not nice.




Here is the kind of results we have been having with MAJA from the beginning, with a large bias when water vapour content is high :

Our very simple method uses the ratio between Sentinel-2 B9 and B8a bands to estimate the water vapour. B9 is located within a water vapour absorption band at 940 nm, while B8a serves as reference and is only moderately affected by water vapour. The ratio is converted thanks to the use of a Look-up table, which is obtained using radiative transfer calculations. Our method assumes that the water vapour is above the scattering layer, which is obviously not true. The errors due to this assumption increase with the amount of water vapour.


Elsa and Camille just empirically computed a new water vapour LUT to cancel this bias, and it works! As you can see, the RMS errors have been divided by a factor 2, from 0.2 g/cm2 to 0.1 g/cm2.

We will put this new parameter set in production in January within Theia, and make it available to the users of MAJA processor.





MAJA 3.1.2 with CAMS option finally validated

We had announced quite a long time ago the coming availability of MAJA 3.1 to correct for the atmospheric effects on Sentinel-2, Landsat 8 or Venµs satellites. This version brings a significant improvement in the estimation of Aerosol Optical Thickness, thanks to the use of Copernicus Atmosphere Monitoring Service (CAMS) data to constrain the aerosol type. The details of the methodscan be found here. Bastien Rouquié obtained them on our python prototype of MAJA.


We then implemented them in the operational and fast version of MAJA. If the validation tests of MAJA 3.1 were correct on the two test products we had defined, a large scale validation using 10 sites over two year time series showed that instead of improving, using the CAMS option was degrading the results. We had to search for the cause (a bad interpolation of CAMS data in space and time), and correct the errors and perform again a large validation.


This time, the validation results are improving a lot, as it may be seen on the figures below.

Without CAMS option With CAMS option

On the left column, we provide the results without activating CAMS option, while on the right, it is activated. The top row corresponds to the comparison between Aeronet AOT used as reference, and MAJA AOT, for eight sites in diverse landscapes. The bottom row provide an example on the well known validation site in Mongu, Zambia.The blue dots correspond to good quality aerosol measurements (no clouds, level 2.0 aeronet values), while red dots correspond to degraded conditions (with either clouds or not quality assured aeronet data (level 1.5 data)


Using CAMS to constrain the aerosol type improves the results by 25%, compared to the use of a continental aerosol model everywhere. Errors for the quality assured validation pixels decrease from 0.085 to 0.065 on the 8 sites, and from 0.143 to 0.094 on Mongu site in Zambia. This site has various types of aerosols depending on the season, including dust, biomass burning and continental aerosols. The results are still far from perfect, and we have work for the next 5 years, but it is still good to have them improved !


MAJA 3.1.2 is available starting from this link on github, as an executable program for linux. To be allowed to use it, you will have to sign the licence first, from this site.  If you want to use it for commercial applications, you should ask for a special licence (still for free), sending me an email. In January, I will provide the parameters to allow activate the CAMS options.


Regarding the production of Theia, our ground segment has been adapted to use MAJA version 3.1.2, and will soon be able to fetch the CAMS products from Copernicus Atmosphere. Then we will have an operational qualification phase, to check that we can download CAMS products in time for real time production. We should be able to start using in in February or March.  And after a few months, if the results are good, yoohoo, we will reprocess everything !


Many thanks to Bastien Rouquié, CESBIO, who did the scientific work, to Camille Desjardins w ho helpled with the validation, to Aurelien Bricier and Benjamin Esquis, at CS-SI for coding the operational version, and Peter Kettig (CNES) and Bruno Angeniol (Cap Gemini), and Bastien, for checking the consistency between prototype and operational versions.




Two billion pixels to check your next ski destination

More exactly: two maps of 934'343'100 pixels!

We [1] have processed 6205 Sentinel-2 images and 593 Landsat-8 images to compute the annual snow cover duration in the Alps and the Pyrenees at 20 m resolution for hydrological years 2016-2017 and 2017-2018. The snow cover duration (or snow persistence) is defined as the total number of days with snow on the ground over a hydrological year (from 01 September to 31 August). We also added the ski runs from the great OpenSnowMap project.


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[MUSCATE News] Unavailability of CNES HPC center on 4-5 December

Le centre de calcul (de haute performance) du CNES sera en maintenance les 4 et 5 décembre, dans le but d'accroitre sa robustesse et ses performances. En conséquence, MUSCATE ne sera pas en mesure de produire les données de Sentinel-2, Landsat et Venµs en temps réel. Pour une fois, nous ne serons donc pas en mesure de tenir nos engagements de production en moins de 2.5 jours, et nous espérons que cela ne vous dérangera pas trop dans vos travaux. Les données déjà produites resteront disponibles.

Pour la même raison, les traitements avec MAJA sur PEPS seront suspendus.


CNES High Performance Computing center will be on maintenance on the 4th and 5th of December, to improve its robustness and capacity. As a result, MUSCATE will not be able to produce Sentinel-2, Landsat and Venµs data during these days. Exceptionally, we will not meet our target to process these data in less than 2.5 days, and we hope it will not cause too much inconvenience. The already produced data will still be available.

For the same reason, MAJA processing on PEPS will be suspended.

Theia just selected a new production zone for Sentinel-2 L2A in Sahel


The Level 2A data production with MAJA processor, by MUSCATE processing center is now fast and efficient. Some margins have been found to add new zones to our production. (The zones already processed are displayed here). French public institutes of labs that would need new zones can tell us their needs, we should still have a few tiles left in our quota.


A few well informed colleagues acted fast, and Santiago Pena Luque (from CNES), who works for the SWOT satellite project made a large new proposal. The Senegal and Niger basins which have been chosen as integrative test sites by SWOT. So the main target is to monitor the height and flow of the rivers, but this implies to study rainfall, evapo-transpiration and run off. The Sentinel-2 data will provide the vegetation status. Of course, the same zone will also allow a large variety of applications related to climate, desertification, food production monitoring, starting from land cover. Several laboratories are associated to the proposal: namely, GET, LEGOS and CESBIO in Toulouse, and TETIS in Montpellier.


Here is the zone that was accepted by Theia today :


Sahel zone just accepted by Theia. In green, the tiles already produced by theia, in blue, the new tiles.

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THEIA : Une nouvelle zone de production des données Sentinel-2 L2A au Sahel


La production des données Sentinel-2 au niveau 2A, avec la chaîne MAJA, par le centre MUSCATE de Theia est maintenant rapide et efficace, nous disposons donc de quelques marges pour ajouter de nouvelles zones (les zones actuelles sont visibles ici). Les organismes français intéressés par l'ajout de nouvelles zones peuvent d'ailleurs me contacter, il devrait nous rester une petite cinquantaine de tuiles à choisir, compte tenu des demandes déjà reçues.


Certains collègues bien informés ont devancé cet appel, et parmi eux, Santiago Pena Luque,  du CNES, qui travaille pour le projet SWOT. Dans le cadre de la préparation du projet, deux grands bassin fluviaux africains ont été choisis pour concentrer les premières expérimentations. Il s'agit des bassins du Niger et du Sénégal. Les applications concerneront le suivi des cours d'eau bien sûr, mais aussi de l'occupation des sols et de la dynamique de la végétation. Plusieurs laboratoires y seront impliqués, dont le GET, le CESBIO, le LEGOS à Toulouse, le laboratoire TETIS à Montpellier.

Voici donc la zone acceptée par Theia :


Nouvelle zone de traitement Sentinel-2 sur le Sahel . En vert les tuiles déjà traitées, en bleu, les tuiles que nous allons ajouter.

La zone a été établie à partir des contraintes suivantes :
- ne pas dépasser 300 tuiles
- proposer une zone contiguë
- éviter les zones les plus nuageuses, comme les côtes du golfe de Guinée
- couvrir la quasi totalité des bassins du Sénégal et du Niger (à l'exception des zones systématiquement nuageuses ou complètement désertiques)
- compléter éventuellement des zones administratives connexes: nous avons pu par exemple couvrir la totalité du Sénégal, de la Gambie, du Burkina Faso, tout l'Ouest et le Sud du Mali, le Nord des guinées, de la Côte d'Ivoire, du Bénin et du Nigeria, le Sud du Niger et l'Ouest du Tchad.


La production va démarrer très prochainement, avec les données acquises en décembre 2016. Il nous faudra probablement quelques mois pour rejoindre le traitement en temps réel. Celà va donner beaucoup de travail à nos équipes de production, mais c'est pour la bonne cause.


The Khumbu Icefall by Venµs

This is a time-lapse of all clear-sky images captured by Venµs over the Khumbu Icefall near Mount Everest since November 2017 (one year of data, 51 images at 5 m resolution).

Khumbu Icefall by Venµs

Here I used the Level-1C products (i.e. without atmospheric correction) because the Level-2A products are provided at a lower resolution (10 m). Anyway, the atmosphere is rather thin in this area..

To make this animation (without the date annotation to simplify):
1) download
python ./ -l 'Nepal' -c VENUS -a config_theia.cfg -d 2017-11-01 -f 2018-12-01 --level 'LEVEL1C'
2) unzip
mkdir -p ../VENUS
parallel unzip -d ../VENUS ::: $(find . -name "VENUS*zip")

3) export as natural color pictures
cd ../VENUS
mkdir -p VIS
parallel gdal_translate -srcwin 4118 3132 1058 770 -of JPEG -b 7 -b 4 -b 3 -scale 0 800 0 255 -ot byte {} VIS/{/.}.jpg ::: $(find . -name VE*[0-9].DBL.TIF)

4) animate with imagemagick
convert -delay 10 VIS/*jpg anim.gif

Sentinel-2 Level3A time series (July, August, September 2018)

If you are not afraid to spend too much time while you have urgent things to do, you may have a look to the mosaic of Sentinel-2 monthly syntheses for September over France. You may access to each monthly synthesis using the following links :

Or you may also use the viewer below to compare with the previous months and see how France became brown in September :

See it full screen

The monthly syntheses are produced using the WASP processor, which is described here.

By comparing the various syntheses, you will see the evolution of the landscape, generally much brownler in September, but this representation will also help you spot the composite artefacts. These are not very numerous, but you will see them :

  • on some web browsers (firefox V58), geometrical differences appear even at a low resolution. Other browsers and versions do not have this defect. It is really not due to Sentinel-2 or Theia products
  • above water and snow (we must work on this defect)
  • where clouds have covered a place during the whole month of July or August. These pixels are flagged as invalid in the products (but not on the mosaic).
  • where clouds or shadows were not properly detected by MAJA
  • at the edges of Sentinel-2 swath. For the first time, in october, a swath edge is clearly visible near Cambrai. The area must have been quite cloudy, and we observe here a greener part on the right, observed later in October, that the browner part on the left. The only way to correct this kind of atefact while keeping a physical meaning to the reflectances, would be to improve Sentinel-2 revisit time
  • some tile edges in July, due to the fact that Level 3A products were not all generated for the 15th of July, but for dates between the 8th and the 26th. This has been corrected for the next months