[MUSCATE news] Two new sites added to Sentinel-2 L2A production : Lebanon and Telangana (India)

After a very difficult period, and thanks to the installation of new improvements MUSCATE ground segment is back in shape, and margins have been found to add new sites.

Number of L2A products produced every day.

 

We have added two new zones where Sentinel-2 data are processed to Level 2A, which provide surface reflectance after atmospheric correction and a good quality cloud mask, tanks to MAJA processor. These two sites are Lebanon and Telangana region in India. The data are processed in near real time, since May 2018, and we will later on add the acquired before that date.
As usual, the data are available for download from https://theia.cnes.fr

Snow conditions in southern Africa ski resorts

When I present the potential of Sentinel-2 for snow science, I often tell that the spatial resolution of Sentinel-2 is sufficient to detect snow at the scale of the ski runs. Because a picture is worth a thousand words, here is the Sentinel-2 view of the only two ski resorts in southern Africa on July 11.

Sentinel-2 true color composites on 11 July 2018

The snow on these ski slopes is artificial but this region can get quite a lot of snow!

[Venµs news] Distribution of Level2A has started

You might have noticed the apparition of the first Venµs L2A products on Theia web site within the real time production, since last Friday.  A first global processing will start this summer, to provide you with the data acquired from November until now. There will be probably further reprocessings to benefit from the fine tuning of all the parameters and to propagate the further evolution of Level 1 improvement.

Even if it took us a few months to check the software and set the parameters up, what took us very long... was waiting for the level 1 validation and calibration phase. As you know, our colleagues from CNES did a great work to rescue the Venµs raw data which were full of surprises. They started to provide us with calibrated products in April only, and that's when we started the validation.

 

We were quite happy with the first results, as our processor MAJA did not show any bug, and the first images looked good.  But the first validation results were quite poor, with undetected thin clouds, with biases in the estimates of atmospheric properties (Aerosol, water vapour), as well as biases in reflectances (with a lot of negative values). We then started iterating tests on the parameters, and after several iterations we corrected several errors in the parameters (Venµs band numbers are different from those of Sentinel-2, and in a couple of cases, I forgot to change them:( ), and we tuned better all the thresholds. Among those, we had to change the calibration of band 910 band by 6% (this band is hard to calibrate in flight due to the presence of water vapour and is also affected by some newly discovered stray light).

 

 

The following table compares the results we had initially, on the left, and the results obtained after tuning the parameters, on the right. Of course, what we distribute is on the right ! We will of course need to increase the number of validation points, and we expect that the low level stray light in band 910 that was discovered during the commissioning phase and is not yet corrected will introduce some site related bias in the water vapour estimates. We will therefore need a reprocessing after this defect has been fixed, if the Level 1 team finds a way to fix it. And finally, we have still some issues to solve with the shadows mask which can often be quite poor.

 

 

Before tuning After Tuning

RGB Quicklook with cloud mask contour

RGB Quicklook with cloud mask contour

Water vapour in g/cm2 compared with Aeronet

Water vapour in g/cm2 compared with Aeronet

Aerosol Optical Thickness compared with Aeronet (sorry for the scale different from that on the right)

Aerosol Optical Thickness compared with Aeronet (sorry for the scale different from that on the left)

A mining company doing land art?

On March 9th, 2018, a tailings dam has failed at Cadia, a large open pit gold mine in Australia. Dave Petley for the AGU landslide blog collected aerial and satellite (Planet) images of the dam before and after the event. The Newcrest mining company also published a report on the failure.

Cadia mine embankment slump dimensions (source: Newcrest, Cadia Northern Tailings Facility Presentation and Webcast, 15 March 2018)

Cadia mine embankment slump dimensions (source: Newcrest, Cadia Northern Tailings Facility Presentation and Webcast, 15 March 2018)


 

The embankment slump is also clearly visible in Sentinel-2 imagery. Here I used a near-infrared compositing because it enhances the contrast between the vegetation (bright in the near-infrared) and the water (dark in the near-infrared).
 
When I did this animation, I was not expecting to see this progressive color change in the northern tailings -- as if someone was methodically painting the landscape?
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Slides and conclusions of the MAJA/Theia worskhop

The Theia workshop for Sentinel-2 L2A MAJA products was held in Toulouse on the 13th and 14th of June 2018.

 

Attendance on the 13th of June

 

About 80 people participated either on the 13th or 14th, and nearly 70 participants attended each day of  this workshop,  whose object was to collect feedback and share experiences on the quality, use and applications of the L2A surface reflectance products delivered by Theia from Sentinel-2 data.

Slides

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NDVI time series in 2018 World Cup stadiums

The figure below shows the evolution of the Normalized Difference Vegetation Index (NDVI) in the pitches of all the 2018 World Cup stadiums.

NDVI in the piches of the 2018 FIFA World Cup stadiums. Data from Copernicus Sentinel-2.

NDVI in the 2018 FIFA World Cup stadiums

I retrieved these data from Sentinel-2 observations using the new time series plotter in the EO Browser. I just drew a polygon in each of the 12 stadiums to get the average NDVI values on every available cloud-free date.

Sentinel-2 NDVI on June 27 in Kaliningrad Stadium (Arena Baltika)

Arena Baltika

Time series of the NDVI in Arena Baltika from Sentinel-2 observations in the EO Browser

Because the NDVI is a proxy of the vegetation health (here the grass on the pitch), these charts allow us to identify which stadiums were built for the 2018 World Cup (Volgograd Arena, Cosmos Arena). On the other hand, the Fisht stadium in Sotchi looks well maintained since 2016. It "served as the venue for the opening and closing ceremonies of the 2014 Winter Olympics (...) was originally built as an enclosed facility; it was re-opened in 2016" (wikipedia). Also it should be noted that the Krestovsky Stadium in Saint Petersburg is a retractable roof stadium. "As of May 2017, the stadium was 518% late and 548% over budget (...) At a cost of $1.1 billion at current exchange rates, it is considered one of the most expensive stadiums ever built." (wikipedia). Hopefully the grass will remain green in the next months, unlike some stadiums in Brazil after the Olympics.
 
In the meantime, as you can see by yourself, the grass is blue in the Kazan Arena!

Color composite of the Kazan Arena on June 27, three days before the first encounters of the Round of 16 (France vs. Argentina)

Vegetation recovery in Saint Barthélemy after Irma

Last year, in this post, I showed the comparison of two Sentinel-2 images of Saint Barthélemy in the Caribbean before and after the powerful Hurricane Irma.
 


 
A new feature in the EO Browser enables to plot the evolution of the mean NDVI within a polygon. I drew a rough polygon of Saint Barthélemy to check the evolution of the vegetation after Irma from Sentinel-2 data.
 

Time series of the average Normalized Difference Vegetation Index in Saint-Barthélemy extracted from Sentinel-2 observations


 
Here I used L1C data but it is also possible to use the L2A products from ESA, although these data are not always available. I manually adjusted the cloud fraction to remove the most obvious artifacts in the mean NDVI due to cloud contamination (clouds cause abrupt drops in the NDVI) [1]. This nice tool is sufficient to see that the vegetation quickly recovered after the hurricane, in about 1 month [2]. Catastrophic disturbances like hurricanes are actually known to contribute to maintain tree species diversity in tropical regions [3].
 
In the cities, according to Le Point, most of the damages have been repaired and the island is almost back to normal. This is good news for the people of St Barth!
 
Notes and references
[1] Under the hood, it's a "local area cloud detection algorithm based on the Braaten-Cohen-Yang method" Milcinski, G. Multi-year time series of multi-spectral data viewed and analyzed in Sentinel Hub. Medium, Apr 5, 2018.
[2] This is very similar to what has been observed in other tropical areas, e.g. "a sudden drop in NDVI values after Hurricane Maria’s landfall (decreased about 0.2) which returns to near normal vegetation after 1.5 months", Hu, T., & Smith, R. B. (2018). The Impact of Hurricane Maria on the Vegetation of Dominica and Puerto Rico Using Multispectral Remote Sensing. Remote Sensing, 10(6), 827.
[3] Vandermeer, J., de la Cerda, I. G., Boucher, D., Perfecto, I., & Ruiz, J. (2000). Hurricane disturbance and tropical tree species diversity. Science, 290(5492), 788-791.

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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