S1-Tiling, on demand ortho-rectification of Sentinel-1 images on Sentinel-2 grid


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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4 thèses en cours à Toulouse pour étudier les forêts tempérées par télédétection

Le pôle toulousain de recherche publique en télédétection est surtout connu sur la thématique forestière grâce à la mission BIOMASS, qui sera lancée vers 2021 et qui est portée par le CESBio, mais qui concerne surtout les forêts tropicales. L’objectif de cet article est de présenter les travaux en cours sur les forêts tempérées, dans le contexte de la France métropolitaine, qui sont portées en synergie par l’UMR Dynafor (collègues INRA, Ensat et EI Purpan) et par l’UMR CESBio. En effet, 4 thèses sont actuellement en cours dont 2 qui seront soutenues fin 2019. Le point commun à ces 4 thèses comme aux travaux qui les ont précédées est l’utilisation de séries temporelles, d’abord basse résolution (Modis), puis, depuis 2015, en haute résolution spatiale avec Sentinel 1 et 2 (‘S1’ et ‘S2’).


Différence de phenologie entre chênes

Figure 1. Différences de phénologie entre espèces de chênes.

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S1Tiling : ortho-rectification à la demande des données Sentinel-1 sur la grille Sentinel-2


​Sentinel-1 est actuellement le seul système à fournir des images SAR régulièrement sur toutes les terres de la planète. L'accès à ces séries temporelles d'images ouvre un champ d'application hors du commun.

Afin de répondre aux besoins d'un grand nombre d'utilisateurs, dont les nôtres, nous avons créé une chaîne de traitement automatique permettant de générer des séries temporelles "prêtes à l'emploi" pour un très grand nombre d'applications. Les données Sentinel-1 sont ortho-rectifiées sur la grille Sentinel-2 pour favoriser l'usage conjoint des deux missions.

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.





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.

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

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