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.

 

 

 

[MUSCATE News] Unavailability of CNES HPC center on 4-5 December

Le centre de calcul (de haute performance) du CNES sera en maintenance les 4 et 5 décembre, dans le but d'accroitre sa robustesse et ses performances. En conséquence, MUSCATE ne sera pas en mesure de produire les données de Sentinel-2, Landsat et Venµs en temps réel. Pour une fois, nous ne serons donc pas en mesure de tenir nos engagements de production en moins de 2.5 jours, et nous espérons que cela ne vous dérangera pas trop dans vos travaux. Les données déjà produites resteront disponibles.

Pour la même raison, les traitements avec MAJA sur PEPS seront suspendus.

 

CNES High Performance Computing center will be on maintenance on the 4th and 5th of December, to improve its robustness and capacity. As a result, MUSCATE will not be able to produce Sentinel-2, Landsat and Venµs data during these days. Exceptionally, we will not meet our target to process these data in less than 2.5 days, and we hope it will not cause too much inconvenience. The already produced data will still be available.

For the same reason, MAJA processing on PEPS will be suspended.

Theia just selected a new production zone for Sentinel-2 L2A in Sahel

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The Level 2A data production with MAJA processor, by MUSCATE processing center is now fast and efficient. Some margins have been found to add new zones to our production. (The zones already processed are displayed here). French public institutes of labs that would need new zones can tell us their needs, we should still have a few tiles left in our quota.

 

A few well informed colleagues acted fast, and Santiago Pena Luque (from CNES), who works for the SWOT satellite project made a large new proposal. The Senegal and Niger basins which have been chosen as integrative test sites by SWOT. So the main target is to monitor the height and flow of the rivers, but this implies to study rainfall, evapo-transpiration and run off. The Sentinel-2 data will provide the vegetation status. Of course, the same zone will also allow a large variety of applications related to climate, desertification, food production monitoring, starting from land cover. Several laboratories are associated to the proposal: namely, GET, LEGOS and CESBIO in Toulouse, and TETIS in Montpellier.

 

Here is the zone that was accepted by Theia today :

 


Sahel zone just accepted by Theia. In green, the tiles already produced by theia, in blue, the new tiles.

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The Khumbu Icefall by Venµs

This is a time-lapse of all clear-sky images captured by Venµs over the Khumbu Icefall near Mount Everest since November 2017 (one year of data, 51 images at 5 m resolution).

Khumbu Icefall by Venµs

Here I used the Level-1C products (i.e. without atmospheric correction) because the Level-2A products are provided at a lower resolution (10 m). Anyway, the atmosphere is rather thin in this area..

To make this animation (without the date annotation to simplify):
1) download
python ./theia_download.py -l 'Nepal' -c VENUS -a config_theia.cfg -d 2017-11-01 -f 2018-12-01 --level 'LEVEL1C'
2) unzip
mkdir -p ../VENUS
parallel unzip -d ../VENUS ::: $(find . -name "VENUS*zip")

3) export as natural color pictures
cd ../VENUS
mkdir -p VIS
parallel gdal_translate -srcwin 4118 3132 1058 770 -of JPEG -b 7 -b 4 -b 3 -scale 0 800 0 255 -ot byte {} VIS/{/.}.jpg ::: $(find . -name VE*[0-9].DBL.TIF)

4) animate with imagemagick
convert -delay 10 VIS/*jpg anim.gif

Sentinel-2 Level3A time series (July, August, September 2018)

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 September over France. You may access to each monthly synthesis using the following links :

 
Or you may also use the viewer below to compare with the previous months and see how France became brown in September :
 

See it full screen

The monthly syntheses are produced using the WASP processor, which is described here.

By comparing the various syntheses, you will see the evolution of the landscape, generally much brownler in September, but this representation will also help you spot the composite artefacts. These are not very numerous, but you will see them :

  • on some web browsers (firefox V58), geometrical differences appear even at a low resolution. Other browsers and versions do not have this defect. It is really not due to Sentinel-2 or Theia products
  • above water and snow (we must work on this defect)
  • where clouds have covered a place during the whole month of July or August. These pixels are flagged as invalid in the products (but not on the mosaic).
  • where clouds or shadows were not properly detected by MAJA
  • at the edges of Sentinel-2 swath. For the first time, in october, a swath edge is clearly visible near Cambrai. The area must have been quite cloudy, and we observe here a greener part on the right, observed later in October, that the browner part on the left. The only way to correct this kind of atefact while keeping a physical meaning to the reflectances, would be to improve Sentinel-2 revisit time
  • some tile edges in July, due to the fact that Level 3A products were not all generated for the 15th of July, but for dates between the 8th and the 26th. This has been corrected for the next months

 

Sentinel-HR, a metric resolution optical mission with global systematic and frequent observation, and free and open data

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Every four years, CNES brings together its scientific advising committees (and I have never been invited :( )  for its scientific prospective seminar. This seminar is where the scientific priorities for CNES's next space missions are defined. A few months before, a call for ideas for new missions is issued, and it was the opportunity to submit the idea of a mission called SENTINEL-HR (following Gérard Dedieu who had already proposed it two years ago).

 

Sentinel-HR mission

This mission would make it possible to observe all the lands at a metric resolution once a season. Taking into account the clouds, it would require a systematic revisit frequency of 20 to 30 days. As the mission name suggests, access to data would be free and open. The minimum configuration would have four bands (blue, green, red, near-infra-red), with optional bands in the short wave infra-red and the possibility of making stereoscopic observations.

2018 Image cover of France at a high resolution (6m), acquired by SPOT-6/7, for Theia/GeoSud project (1). Data were acquired between the 6 of april and the 11th of October .

 

As I already presented numerous tiles and for many years,  the benefit of Sentinel-2 data, compared to the SPOT missions of the past, is the ability to make frequent and systematic acquisitions, coupled with the free and open availability of data. This clearly explains the success of the SENTINEL-2 mission, which brought the number of users each year from a few thousands for SPOT to tens of thousands for SENTINEL-2.

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Snow cover duration from 01 Sep 2017 to 31 Aug 2018 in the Alps and Pyrenees

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We are thinking to distribute a new product that would provide the snow cover duration in the Alps and the Pyrenees over a hydrological year (snow persistence map). Here is a preview of a "beta" version that is only 100 m resolution for now.
 

Snow cover duration from 01 Sep 2017 to 31 Aug 2018. Pixels with less than 60 days were masked out.


 
A few words on the method (see also here): this map is generated after a linear interpolation at the daily time step of every available Theia snow product. Here we have selected 26 Sentinel-2 tiles, which represents 3189 snow maps for the study period, that is 96 billion pixels to process.
 
With the accumulation of Sentinel-2 data it becomes possible to look at interannual variability. Here is for example a visual comparison of the snow cover duration calculated from January to May for three years in the Pyrenees (31TCH):

 Snow cover duration 31TCH

Snow Duration from Jan 01 to May 31 in the Pyrenees (tile 31TCH)

 
We can see longer snow durations for the year 2018, which was an exceptional year!
 

[MUSCATE News] A very productive summer

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Updated on October 4th

 

This summer, while most of us were getting sunburnt near the coast, or getting sore legs after hiking in mountains, or having long naps after hectic nights, the lucky MUSCATE teams stayed at work, with a good and efficient air conditioning, convenient armchairs, fast computers and accessed to the best canteen in the world at CNES. So it's no surprise they progressed a lot in all aspects, but even if they benefited from perfect conditions, we should thank them a lot.

 

Improved production performances

Theia's L2A counter reached 100 000 images on August 12th

 

First of all, the colleagues solved the issue that delayed a lot MUSCATE production in late spring. It was a memory issue, with a heavy swap consumption that slowed then crashed the MUSCATE ground segment. Doubling the available memory on the catalogue server solved the issue.

As a result, since beginning of July, MUSCATE had only two interruptions of production, due to maintenance interventions on the CNES platform. As it may be seen below, the monthly average plot in orange shows that the production rate is at its best in two years.

 

Number of L2A produced each day by MUSCATE (after removing the products with more than 90% of clouds)

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