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.

 

 

 

Les séries temporelles de niveau 3A de Sentinel-2, de Juillet à Novembre

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Comme chaque mois, Peter Kettig du CNES a produit les synthèses de niveau 3A à partir de Sentinel-2 du mois précédent (Merci Peter !). Les données à pleine résolution, avec leurs masques de qualité, peuvent être téléchargées depuis le serveur de distribution Theia au CNES.
 
Si vous n'avez pas peur d'y passer trop de temps, alors que de nombreuses urgences vous attendent, vous pouvez jeter un oeil aux mosaïques de ces produits disponibles sur la France depuis Juillet. Chaque mosaique est accessible à partir des liens ci-dessous :

 
Une chouette interface de visualisation (merci à Michel Lepage !), est aussi disponible ci-dessous, pour comparer la synthèse d'octobre avec celle des mois précédents.
 
En novembre , en France, nous avons eu une météo française de novembre, et plusieurs régions sont restées couvertes à chaque passage de Sentinel-2, sur la période de synthèse de 45 jours, centrée sur le 15 Novembre. Dans ce cas, nous fournissons les réflectances de la date où la réflectance dans le bleu est minimale, et nous indiquons dans les produits, pas sur la mosaïque, que le pixel est nuageux. Donc, la synthèse du mois de novembre n'est pas aussi belle que les précédentes. Comme en octobre (voir ci-dessous), des bords d'orbite deviennent visibles.
 
Ceci dit, les résultats restent corrects sur de nombreuses régions, et on peut observer les sols plus humides et plus sombres, le démarrage des cultures d'hiver, la chute des feuilles dans les forêts de feuillus, et les sommets enneigés.
 
En octobre, pour la première fois, un artefact de bord d'orbite apparaît clairement du côté de Cambrai. Même si c'est un endroit où l'on peut faire des bêtises, cet artefact est dû au changement de date. la partie Ouest est brune, et la partie Est est verte. A cause de la couverture nuageuse importante, la date moyenne de la partie Est se situe bien après celle de la partie Ouest. Le seul moyen d'éviter ce genre d'artefacts sera d'a jouter un ou deux satellites Sentinel-2 de plus, pour faire des synthèses sur des données moins longues (ici nous utilisons 45 jours).

 

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Sentinel-2 Level3A time series (July to November 2018)

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As every month, Peter Kettig from CNES processed the Sentinel-2 L3A composites of France from the Month before. 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.

In November, in France, we had a... French November weather, and several zones stayed overcast for all Sentinel-2 overpasses during the synthesis period of 45 days. In that case, we try to provide a value, which is the minimum reflectance in the blue band. Of course this value is flagged as invalid. So the November synthesis is not as nice as the previous ones, due to the presence of remaining clouds. As in October (see below), we now also see artefacts at the edges of the swath.

Anyway, in many regions, the results are rather correct and they allow us to see the changes. Forests are now brown, soils are wetter and darker, winter crops have started, and the highest mountains are turning white.

In October, we had the first the opportunity to observe a neat swath edge effect in four months, near Cambrai, North of France. The Western part of the artefact is browner than the Eastern part. Because of the cloud cover, the average date used in the eastern part is several days before the average date of the western part. , due to the observation at very different dates on each side of the swath. So the only way to improve that with the current method would be to add a third or even a fourth Sentinel satellite.

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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.


Link: http://osr-cesbio.ups-tlse.fr/echangeswww/majadata/simon/snowMaps.html

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