Sentinel-2 Level 3A products : syntheses or composites ?

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For the past few months, Copernicus has been distributing Level 3A products for the Sentinel-2 mission as part of the S2GM (Sentinel-2 Global Mosaics) .

 

This ambitious project aims to provide monthly, quarterly or annual Level 3A products worldwide. The user defines his zone and period of interest and orders the product. The web site seems to be hosted by SInergise, and as everything done by this company, is easy and very straightforward to use.

 

The call for tender, with two or three million euros, was launched by the Copernicus program of the European Union two years ago. It required the use of ESA Level 2A official products obtained with Sen2Cor. For this reason, we decided not to participate because our Level 3A method, which calculates a weighted average of non-cloudy observations, requires very good cloud masks, which is not quite the case of Sen2cor products.

 

The tender was won by a consortium of Brockman Consult, Geoville and SInergise companies. To compensate for the poor quality of cloud detection, the authors of the S2GM product had to use a BAP method: "Best Available Pixel". This method chooses for each pixel the best date according to certain criteria (no cloud or shadow detected, minimum reflectance in the blue, maximum NDVI ...). This method minimizes cloud disturbances when clouds are not detected correctly, but also has the disadvantage of suddenly changing the date from one pixel to another, which causes artifacts and noise. Outputs are therefore composite products , which assemble pieces of images acquired for the different dates available over the period.

 

Theia Level 3 products are not composites, but syntheses, which use all cloudless observations of a single pixel over the entire monthly observation period to find the value that best represents the surface reflectance we would have had at the central date of the product. Theia's syntheses use the WASP (Weighted Average Synthesis Processor) chain, which calculates a weighted average of surface reflectances over a month, after atmospheric correction and detection of clouds obtained from Level 2A products generated by our MAJA channel , of course. If the clouds are badly detected, they enter into the synthesis and disturb it.

Comparison of a synthesis obtained with WASP + MAJA, with a composite product from S2GM + Sen2cor, on the Toulouse region, in October 2018. (Click on image to enlarge)

The animation provided above shows a full resolution comparison over Toulouse region, of a synthesis of WASP and of the corresponding composite of S2GM obtained on the same date in October 2018. We see very quickly that the composite of S2GM is very noisy, much more than the synthesis from WASP. It is quite often possible to locate the areas where the synthesis tool has chosen to change the date in its composite. You will also notice the appearance of many white dots, which are in fact pixels without clouds, but quite bright that Sen2Cor systematically classifies as clouds.

 

In short, provided you have a good level 2A product, syntheses can provide much better results than composites.

 

 

New paper ! An active learning cloud detection tool to generate reference cloud masks for Sentinel-2. Application to the validation of MAJA, Sen2cor and FMask cloud masks

Example of reference cloud mask generated by ALCD, and comparison with the cloud masks generated by three operational processors (Sen2cor, FMask and MAJA). True positive invalid pixels appear in blue, true negative in green, false negative in red and false positive in purple..

It is not that frequent when the work of a trainee ends up as a peer reviewed publication, but Louis Baetens was a brilliant trainee. In a six months training period at CESBIO, funded by CNES, here is what Louis Baetens did:

  • developed an active learning method to generate reference cloud masks for Sentinel-2, using multi-temporal data as input
  • validated the quality of the produced masks (around 99% overall accuracy)
  • generated cloud and shadow masks covering 32 entire Sentinel-2 images
  • produced these same scenes with Sen2cor 2.5.5, FMask 4.0 and MAJA 3.3
  • evaluated the results using ALCD masks
  • wrote a report and a user manual for ALCD
  • released the masks and tools on open access platforms
  • And wrote (with Camille and myself) a scientific publication

 

The publication was just released by remote sensing :

Baetens, L.; Desjardins, C.; Hagolle, O. Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens. 2019, 11, 433.

 

The remaining of the post provides a plain language summary (but it's better to read the paper !)

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Apport des images radar et optiques pour la cartographie des surfaces irriguées

(English version below)

Dans le cadre du projet Simult’eau (partenaires : Arvalis, CACG, Chambres d’Agriculture du Tarn et des Hautes-Pyrénées, financement CASDAR) nous avons testé l’apport d’une utilisation combinée des images radar et optiques pour la cartographie des surfaces irriguées (maïs et soja) dans le Sud-Ouest de la France. Les résultats publiés dans Remote Sensing (https://www.mdpi.com/2072-4292/11/2/118) ont révélé que l’utilisation d’images radar Sentinel-1 combinées aux images optiques (Landsat-8) permettait de détecter les surfaces irriguées plus précocément qu’avec les images optiques seules. En effet ces dernières sont souvent perturbées par la présence de nuages qui rendent la détection impossible à certaines périodes de l'année. Ce résultat, qui doit être confirmé par des études complémentaires (autres lieux et autres dates), est très encourageant. Il ouvre de nouvelles perspectives pour une gestion "optimisée" des ressources en eau notamment pour des organismes tels que la CACG (Compagnie d'Aménagement des Coteaux de Gascogne) ou les Organismes de Gestion Collective de l’eau (OUGC). Les cartes produites sont en libre accès sur le site Theia: http://www.theia-land.fr/fr/ces-surfaces-irriguees.


Ces recherches se poursuivent actuellement dans le cadre de la thèse de
Yann Pageot financée par le CNES, l'Agence de l'Eau Adour-Garonne et la CACG.
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S1-Tiling, on demand ortho-rectification of Sentinel-1 images on Sentinel-2 grid

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Sentinel-1 is currently the only system to provide SAR images regularly on all lands on the planet. Access to these time series of images opens an extraordinary range of applications.
In order to meet the needs of a large number of users, including our needs, we have created an automatic processing chain to generate "Analysis Ready" time series for a very large number of applications. Sentinel-1 data is ortho-rectified on the Sentinel-2 grid to promote joint use of both missions.

 

 

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Sentinel-2 + OpenStreetMap = ♡

You may have heard about the tailings dam collapse at Feijão mine in Brumadinho, Brazil. The disaster occurred two days ago on Jan 25 and at least 58 people were found dead, while 300 are still missing. A Sentinel-2 acquisition was planned for today, therefore tonight I checked the EO Browser to see if the mud flow was visible.

Before/after images of Brumadinho mudflow from Sentinel2 imagery (false color composite using the near-infrared band)

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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.