Three snow seasons in the Pyrenees through the eyes of Sentinel-2 and Landsat-8

On June 23 we will celebrate the third anniversary of Sentinel-2A in orbit. With three years of data we can start looking at the inter-annual variability of biophysical variables, like.. (random example), the snow cover.


This is what I attempted to do for the Theia workshop. I downloaded all available snow cover products from Theia over the Central Pyrenees (tile 31TCH) and I generated additional snow maps from the Theia Landsat-8 level-2A products using let-it-snow processor. Landsat-8 images enable to increase the frequency of observations when only Sentinel-2A was operational between 2015 to 2017.


I resampled the Landsat-8 snow maps to the same reference grid as Sentinel-2 at 20 m resolution using the nearest neighbor method. I cropped all snow maps to the intersection of the Sentinel-2 tile (green polygon) and Landsat-8 tile (red polygon).

When there was a snow map from Sentinel-2 (S2) and Landsat-8 (L8) on the same day, I merged them into a composite using a simple pixel-based rule:
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MAJA V3.1 will be distributed this May

Example of cirrus cloud correction

We will start distributing MAJA V3.1 this May to replace MAJA V1 on CNES free software platform.


It is also in the pipeline of enhancements of Theia processing platform (MUSCATE), but this pipeline is quite full, so we will need to be patient (which requires a big effort for me, patience not being my best quality...)


MAJA V3 comes with a lot of enhancements compared to V1 :

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MAJA now corrects thin cirrus clouds on Landsat-8 and Sentinel-2 images

Thanks to our collaboration with  DLR,, who developed the method [1], and with an ESA funding, we added within MAJA a correction for thin cirrus clouds that works for LANDSAT 8 and Sentinel-2. This correction uses the cirrus band  at 1.38 µm, which allows to estimate cirrus reflectance, which is then subtracted from the other bands with an empiric factor derived from the images.


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From Multitemp blog to Nature Geoscience

You probably remember Simon Gascoin's story about the Aru glacier avalanches, which started from Simon's observations of the twin avalanches using the Sentinels. It was one of the big buzz pages of the blog in 2016. The first images were published here, then spread out in many scientific websites and the social networks.


The same mountain valley in Tibet is shown before and after part of a glacier sheared off on 17 July 2016. Credit: NASA/Joshua Stevens/USGS/ESA

It seems that the story finally made its way to Nature Geoscience, after a large work from many scientists lead by Andreas Kaab.  Congratulations to all the team !


So, dear CESBIO colleagues, or remote sensing time series users, it is time to submit your work to this blog as a first step to future publications in Nature ;) !



Tous nos voeux pour 2018 !


Que cette nouvelle année vous apporte joie et bonheur, et pas seulement dans l'utilisation de séries temporelles !


Sans aucune originalité, ce début d'année est l'occasion de faire un petit bilan de l'année 2017. Voici, dans notre domaine, quelques uns des faits que je retiendrai :

  • la consécration du programme Copernicus, et des satellites Sentinel. Plus de 110 000 personnes se sont inscrites pour accéder aux données depuis 2015 !  A mon avis, ce succès est dû à la combinaison de plusieurs facteurs : les données sont gratuites et faciles d’accès, les observations sont répétitives, régulières et fréquentes sur le monde entier, et les données sont de grande qualité. Un grand bravo à l'ESA et à l'UE, sans oublier la contribution du CNES pour la qualité des images de Sentinel-2 et l'étalonnage de Sentinel-3. Continue reading

Building a global cropland mask, is not an easy task

Criticizing is easy, and doing is hard, especially when trying to create a global map of croplands. Some collegues from CESBIO have worked on that subject within the Sen2Agri project, and obtained good resuts, but only at the local or country scale. Finding a method that works everywhere must clearly be much harder.

These days, I have received a lot of emails, tweets and posts about a new cropland global product at 30 m resolution, edited by USGS. I have no doubt it was a serious work from a serious team, done with appropriate terrain data and methods, validation, and of course a tremendous data processing.



But there it is, I checked it over a lot of places that I know very well, and it seems to me that the cropland mask, at least in South West France, is clearly overestimated. Is it the same in tour region ? Here are some examples :

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NASA selected CESBIO's land cover map of France as "image of the day"


It is a well deserved recognition for the land cover product developed at CESBIO : NASA's earth observatory blog dedicated its "image of the day" blog post for the 15th of August to this product.


This work was done in the framework of Theia, using iota2 free software also developed at CESBIO. Thanks to the information conveyed by time series,iota2 is a fully automatic processor.

Jordi Inglada's team first published in 2015 a land cover map based on LANDSAT 8 data acquired in 2014, with a 30m resolution.This production was followed, in 2017 by a new map, based on Sentinel-2 data, acquired in 2016, at 10m resolution.

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La NASA choisit le produit d'occupation des sols OSO du CESBIO comme "image du jour "


C'est une reconnaissance bien méritée pour le produit d'occupation des sols OSO développé et produit au CESBIO : le blog "earthobservatory" de la NASA a consacré son article "image du jour" du 15 août à ce produit.


Ce travail a été conduit dans le cadre de Theia, avec le logiciel libre iota2 développé au CESBIO. Grâce à l'utilisation de toute l'information contenue dans les séries temporelles  d'images, la chaîne iota2 arrive à fournir un résultat de grande qualité, tout en étant complètement automatique.


L'équipe de Jordi Inglada a publié une première carte en 2015, basée sur les données LANDSAT 8 acquises en 2015, avec une résolution de 30m. Cette production a été suivie, début 2017, d'une carte basée sur les données Sentinel-2, acquises en 2016, cette fois à une résolution de 10m.

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