Improvements of MAJA cloud masks in production

We are continuously working on improving the Sentinel-2 L2A products delivered by Theia. Since the 2nd of February, a new parametrization was put in production in order to improve two points of the cloud masks. There is a page which summarizes the variations of Theia product versions. Because of some error within Muscate production centre, we are not able to change the product version when we change a parameter. You have to look at the production date to know which version was used. This inconvenience will be corrected soon.

Clouds above water

Although MAJA is optimized for cloud detection and atmospheric correction over land, it is important to detect clouds well over water. For instance to detect the shadows of these clouds that are cast over land. But until now, the clouds mask was missing quite a lot of clouds over water. This was not a surprise as we had passed only a few hours determining the thresholds to apply. We recently found some more time to obtain a better tuning of the detection thresholds above water.

Thresholds in the SWIR have been halved (from 0.08 to 0.04), in the absence of sunglint. When sunglint is likely, due to the geometry of acquisition, the threshold is still higher (0.016, but it was 0.25 before).This means that when the sunglint flag is raised, the accuracy of cloud detection is reduced.

Impact of new cloud detection threshold over water : left, old threshold, right, new threshold.

Cloud dilation

 

 

 

 

The plane contrail illustrates the difference in observation angles of the spectral bands of Sentinel-2. The cumulus clouds above has the same effect, although lower because the cloud is at a lower altitude.

As it may be seen on the image above, the cloud limits are fuzzy, and except for the big and nice cumulus clouds, the pixel next to the cloud mask is also affected by clouds. Even if the cloud has sharp limits, it also changes the illumination of neighbouring pixels, and, in the case of Sentinel-2, as all the bands do not observe exactly in the same direction, there is a "parallax effect" which results in different cloud positions depending on the spectral band. For all these reasons, we need to dilate the cloud mask (by the way, this is an identified drawback of Sen2cor, which does not dilates its clouds).

 

Our dilation buffer was originally 480m. Following complaints from some users working in very cloudy countries, we had reduced the buffer to 240m in may 2017. But we recently figured out that the reduced dilation was degrading our estimates of  aerosols, as undetected clouds are considered as aerosols.  Due to this, the new version which runs since the2nd of February has once again a dilation of 480m.

Left, aerosol validation results with a dilation of 240m, right with a dilation of 480m. The new result brings a significant improvement (Merci à Bastien Rouquié pour ce résultat)

 

Using NDVI with atmospherically corrected data

NDVI

 

NDVI is by far the most commonly used vegetation index. NDVI was developed in the early seventies (Rouse 1973, Tucker 1979), and widely used with remote sensing in the nineties until now. It is computed from the surface reflectance in the red and near infra-red channels on each side of the red-edge.

 

 NDVI=\frac{\rho(NIR)-\rho(RED)}{\rho(NIR)+\rho(RED)}

 

where \rho(NIR) and  \rho(RED) are reflectances in the NIR and RED. Although several users still use top-of atmosphere reflectances (TOA), surface reflectances should be used to reduce sensitivity to variations of aerosol atmospheric content.

 

A time profile of surface reflectance from Sentinel2 satellite for the blue, green, red and NIR spectral bands for a summer crop in South East France. The observation under constant viewing angles minimizes directional effects.One can also notice that reflectance variations related to vegetation status are greater in the near infra-red, while the noise is usually lower. As a result, a vegetation index should rely more on the NIR than on the red.

I think NDVI is mainly used for the following reasons (but feel free to comment and add your reasons) :

  • it has the large advantage of qualifying the vegetation status with only one dimension, instead of N dimensions if we consider the reflectances of each channel. Of course, by replacing N dimensions by only one, a lot of information is lost.
  • it  enables to reduce the temporal noise due to directional effects. But with the Landsat, Sentinel-2 or Venµs satellites, which observe under constant viewing angles, the directional effects have been considerably reduced.

 

I therefore tend to tell students that if NDVI is convenient, it is not the only way to monitor vegetation.

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Users of THEIA Sentinel2 products are welcome in Toulouse, 13-14th June 2018

This is Toulouse in mid-June last year. Isn't that a nice place and time to gather the users of Sentinel-2 products delivered by Theia ?

CNES and THEIA are very happy to invite you to provide your feedback on the Sentinel-2 products we have been delivering for more than one year, thanks to the MUSCATE ground segment and the MAJA processor.

 

All users of MACCS/MAJA procesor and of THEIA products are welcome to tell us their findings, suggestions, and share experiences on methods, applications, and results.  We will also provide an update  about the processing perspectives, validation results, description of new versions, and you will have the opportunity to influence us on the related choices.

 

Please register and send your abstract to the workshop site. Here are the important dates to remember :

  • abstract submission deadline : March the 8th.
  • registration deadline : June the 3rd (we only have 100 seats, so please register quickly)
  • workshop dates 13-14th June 2018

 

 

[MUSCATE News] Production still on, but distribution stalled again

Update January 31st, 10h00 : Distribution was just restarted, a few hundreds of images will be added today, and meanwhile, data are processed in Near Real Time.


This January was a nightmare for MUSCATE. Following database issues to send data to the distribution server, our production system was stopped during the Christmas break and unstable the weeks after.

 

After understanding the issues, the MUSCATE team stopped the automatic sending of products, resumed the production, and started to update the production server manually. We were nearly back on track last Thursday, when the distribution server refuse to accept any new product. The explanation was found, a directory in the High Performance Storage System (a robot that handles tapes and disks) had 65535 files and could not accept a new one.  We need a little reorganisation of the folder structure to overcome that, and meanwhile, the distribution is stalled again.

 

Still, more than 60 000 L2A products are now available, and we have started distributing the snow cover products, in NRTWD  ("Near real time with delay"). We hope to be soon really in NRT.

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 ;) !

 

 

[MUSCATE News] Production resumed, distribution stalled

 

Sorry for those of you waiting for our real time products, MUSCATE production is stalled these days. The teams are working hard to put it back in production.

Update: it seems that the issues happened when sending products to the distribution server. A data base request became infinitely long, and the system waited for it. While a solution is being tested, the production just resumed at full speed, but the distribution is still stalled and should be started again on Monday.

Désolé pour ceux d'entre vous qui attendent nos produits. La production de MUSCATE est arrêtée. Les équipes travaillent d'arrache pied pour la remettre en route.

Mise à jour: les problèmes rencontrés se produisent lors de l'envoi des données au serveur de distribution. Une requête en base prend un temps infini, et le système finit par s'arrêter. La production vient de redémarrer à pleine vitesse, mais l'envoi des données pour la distribution a été désactivé, en attendant que la solution en cours de test soit validée.

 

[MUSCATE News] A difficult start of 2018 for our production center

As you have probably noticed, our production rate has been very low these days and we are more than 10 days late in our delivery of L2A and snow cover products.

This seems to be due to an intervention on CNES cluster end of December to add new nodes and disk space. MUSCATE sometimes loses communication with the platform that handles the databases and crashes. As it also happened when CNES was closed for Christmas, we really lost a lot of time.  All the teams are on the deck to try to solve this issue and catch the delay up. We are very sorry for that inconvenience.

 

Revised spectral bands for Sentinel-2A

The Sentinel-2 mission status document, edited by ESA, is a very interesting reading. On its last edition of 2017, ESA announced very discretely that the spectral bands of Sentinel-2 had been revised, following a review of the pre-flight measurements. Very few details are provided on the nature of the error contained in the previous version, and on the validation of the new ones. But still, a new version of the spectral response function is available here,, since the 19th of December 2017. The site provides an excel file with the spectral response functions.

All the visible and near infrared bands have changed a little, even if only three bands have significant changes, B1, B2 and B8: B2 equivalent wavelength changes by 4 nm, B1 by 1 nm, and B8 by 2 nm. The SWIR bands did not change.

Old and new versions of five VNIR S2A spectral bands, together with that of S2B.

Most users should not use bands B1 and B2, as they are affected by atmospheric effects. So I do not think much of you will have to change the coefficients in your methods. But for us, who take charge of the atmospheric correction, and heavily rely on B1 and B2, it probably has an effect, and we are changing our look-up tables to account for that. Stay tuned for the results.

Best wishes for 2018 !

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May this new year bring you happiness, and not only related to image time series !

 

As usual, this beginning of year brings the opportunity to summarize 2017. Here is what I would record, in our field of interest :

  • the consecration of Copernicus program and of the Sentinel satellites. Since 2015, more than 110,000 people have registered to access the data since 2015! In my opinion, this success is due to the combination of several factors: the data are free and easy to access, the observations are repetitive, regular and frequent worldwide, and the data are of high quality. Congratulations to ESA and the EU, not to mention the contribution of CNES for the quality of Sentinel-2 images and the calibration of Sentinel-3. Continue reading

Tous nos voeux pour 2018 !

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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