Exploitation combinée de VENµS, Sentinel-2 and Landsat-8 : les bandes spectrales


L’utilisation combinée des données de VENµS, Sentinel-2 et Landsat-8 peut permettre d’augmenter la probabilité d’obtenir des images sans nuage ou de suivre de manière détaillée des phénomènes à évolution rapide.

Afin de faciliter cette combinaison, le tableau ci-dessous présente de manière résumée les correspondances entre les bandes spectrales des instruments. VENµS ne comporte pas de bande spectrale dans le moyen infrarouge.

La figure ci-dessous présente les bandes spectrales de VENµS et Sentinel-2 dans le domaine 400 à 1000 nm. Les bandes SWIR de Sentinel-2 ne sont incluses.Le tableau ci-dessous présente les combinaisons de bandes usuelles

La figure ci-après permet d'apprécier le degré de similarité des réponses spectrales de ces bandes usuelles.

Les réponses spectrales détaillées de chaque instrument sont disponibles via les pages web suivantes :











MAJA 3.3 is available, with a LOT of improvements

What's new ?

Pfew ! It has been quite long, but MAJA 3.3 is available, and it improves a LOT of things !

  • Some bugs have been fixed, like the one which caused detection of cloud or cloud shadows on the edges of the images
  • It seems we have finally solved the bugs that plagued the CAMS option since we released MAJA V3.0. Since V3.0, this option uses the Copernicus Atmosphere aerosol forecasts to set the aerosol type before retrieving the aerosol optical thickness (AOT) from Sentinel-2 data
  • We now also use CAMS AOT as a default value, when it is not possible to estimate AOT using the images, for instance above a snow covered landscape or for small gaps in a large cloud cover. Before that, we used 0.1 everywhere as a default value. The default value is used in the cost function with a very low weight, it has no impact when conditions for AOT estimates are good, but a large impact in bad conditions.
  • The cirrus correction module was over correcting the impact of thick cirrus clouds, providing images with dark clouds. We have limited the correction in order to get more realistic values
  • We have improved the cloud detection, with a better compromise between false positives and false negatives. We also handle better the variation against altitude of the cirrus cloud detection with band 10 (1.38 µm). MAJA 3.3 is the version with which we obtained the results of our recently published article. This paper shows that MAJA has slightly better performances than FMask 4.0, and much better performances than Sen2Cor.

Moreover, when we validated the results, we figured out that one of the parameters in our settings had a wrong value (10 instead of 1). It is easy to make such errors, because there are about 150 parameters in MAJA, and it's easy to make an error. We have set up a version management of MAJA settings since 2017, but the erroneous value was already there before that. And this value has a big impact ! The standard deviation of errors in AOT estimates is reduced by 30 to 40% !!

The W_dark parameter controls the weight of the dark pixel method in the AOT estimation. This method is just supposed to be used as a safeguard in case the multi-temporal or multi-spectral methods provide wrong results. It should therefore have a low weight, but with a weight of 10, it was in fact the method which had the highest weight in our estimates. As this method provides a maximum value of the AOT, it tended to reduce the provide too low AOTs. This improvement is therefore a great piece of news, but it comes with some shame not to have found this error before.


AOT validation against AERONET for 10 sites with the wrong W_dark value AOT validation against AERONET for 10 sites with the correct W_dark value

The blue dots correspond to validation obtained in good conditions, while red dots correspond to less reliable validation points.

These are the results of comparison of version 3.3 with CAMS activated, changing only the W_dark parameter, but if we compare with the results of version 3.1, the improvement is even more impressive :

AOT validation against AERONET for 10 sites, version 3.1/td> AOT validation against AERONET for 10 sites version 3.3, with the correct W_dark value

How to access MAJA ?

Here is how to access MAJA 3.3 :

  • MAJA 3.3 is distributed as a free software for non commercial purposes from CNES free software site (select the 3.3 TM version in the download tab). If you need it for commercial purposes, you just have to ask me for a different licence, but it still will be free of charge.
  • The best way to use MAJA is to run it with Start_MAJA, which is a simple python code that runs MAJA for a whole time series for a given Sentinel-2 tile. The Strart MAJA readme also explains how to get the good settings, with the good W_dark value, how to prepare the DEM or how to get CAMS data.
  • PEPS on-demand processing facility will be updated soon, but it is still working with MAJA 3.2 so far
  • THEIA is also running MAJA 3.2. We will update first the wdark parameter, and then MAJA 3.3 and then start production with CAMS, hopefully before summer. If everything goes well, we will then start a reprocessing of all our data set. So, stay tuned on this information channel.

Sentinel-2 Level-3A time series


April, despite a certain amount of cloudiness, once again offered us a beautiful, cloudless synthesis. In fact, Theia's Level 3A products use 46 days periods centered on the 15th of the month, and the good weather of the last days of March or the first days of May compensates the cloudy April. As every month, Peter Kettig from CNES produced the Level 3A syntheses from the previous month's Sentinel-2 data. As you can see, a column of tiles is degraded in western France. This is due to a problem during the download of Sentinel data by PEPS. We're going to reprocess it.

Translated with www.DeepL.com/Translator

The full resolution data, and the corresponding data quality masks, can be downloaded from Theia's distribution server at CNES.
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 each month since July over France. Each monthly synthesis is accessible using the following links :

Or you may also use the nice viewer below (merci Michel Lepage !) to compare with the previous months.

See it full screen
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Spot the odd one out


There is an odd image in this time series of L2A products of the 31TCJ Sentinel-2 tile (Toulouse region). Can you guess which one ?


Yes it is the last one, acquired on the 26th of February. But what's odd with it ?

  • The black South East corner ? No, just a different orbit
  • The date ? Close enough...
  • Two images separated by one day (25 th, 26 th of January) ? You're boiling !

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Trouvez l'intrus


Parmi cette série temporelle d'images de niveau 2A de la tuile 31TCJ (Toulouse), il y a une intruse. Saurez vous déviner laquelle ?


Oui, c'est la dernière,, acquise le 26 Février. Mais qu'a t'elle de spécial ?

  • Le coin noir au sud-est ? Oui, mais c'est juste le résultat d'une orbite diffférente. Je ne vais quand même pas écrire tout un article là dessus.
  • La date ? Oui, vous chauffez.
  • Deux images séparées par un seul jour (les 25 et 26 février ?) ? Ah, là, vous brulez !

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Synthèses de la durée de l'enneigement au 1er avril dans les Pyrénées

Le 1er avril est la date souvent utilisée par les hydrologues pour caractériser le stock de neige disponible avant la saison de fonte. A partir des produits neige Theia, j'ai calculé la durée d'enneigement par pixel de 20 m dans les Pyrénées depuis le début de l'année hydrologique (le 1er septembre 2018) jusqu'au 1er avril 2019.

Durée de l'enneigement entre le 01 Septembre 2018 et le 01 Avril 2019

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"Claude Shannon’s nightmare"

How did I miss the blog animated by Remi Cresson, from "la maison de la télédétection" in Montpellier (House of Remote Sensing) ? It is well written, very funny  and above all it shows impressive results from our favourite optical satellites, exactly what I intended to do in multitemp blog when I had time to take time to write my posts.

My colleagues know I am quite sceptic regarding the deep learning buzz we have had these last years, but this one really impressed me.

Extrapolation to 1.5m resolution (right) of a 10m resolution Sentinel-2 image.

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