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:
Si vous avez vu le JT du 26 mars dernier, vous savez que le lac de Serre-Ponçon est à un niveau exceptionnellement bas en ce moment .
Extrait du journal 12/13 de France 3 du 26/03/2018
Did you know that the St. Moritz Casino is the highest in Switzerland? If you like gambling, I have a little game for you: what are the odds to find snow near St. Moritz?
Fortunately, I just finished the processing of 218 Sentinel-2 dates from 2015-Dec-04 to 2018-Apr-10 of tile 32TNS with our let-it-snow processor. I did this off-line production for a colleague because, as of today, Theia only distributes the snow products after July 2017 in this region of Switzerland (see the available products here).
A quick way to check the output is to compute a snow cover probability map: that is, for each pixel, the number of times that snow was observed divided by the number of times that the snow could be observed.
To compute this map we just need to know that the Theia snow products (LIS_SEB.TIF raster files) are coded as follows:
205: Cloud including cloud shadow
254: No data
Here is a piece of script to do this:
# initialize snow.tif with zeros
# store in Byte because we have less than 255 dates
f0=$(find . -name LIS_SEB.TIF | head -1)
gdal_calc.py --overwrite -A $f0 --type=Byte --calc=A*0 --outfile=snow.tif
# accumulate snow pixels in snow.tif
for f in $(find . -name LIS_SEB.TIF)
# snow is coded with 100
gdal_calc.py --overwrite -A $f -B snow.tif --type=Byte --calc="B+(A==100)" --outfile=snow.tif
# now do the same for clear.tif
gdal_calc.py --overwrite -A $f0 --type=Byte --calc=A*0 --outfile=clear.tif
# accumulate clear pixels in clear.tif
for f in $(find . -name LIS_SEB.TIF)
# only snow and no snow are coded with values lower than 101
gdal_calc.py --overwrite -A $f -B clear.tif --type=Byte --calc="B+(A<101)" --outfile=clear.tif
# Finally compute the snow probability in % (100.0* makes the calculation in float)
gdal_calc.py -A snow.tif -B clear.tif --type=Byte --calc="(100.0*A)/B" --outfile=snowProba.tif
This is the output:
The images are scaled from 0 (black) to 100 (white). The units are number of days for snow and clear, percentage for snowProba.
From which you can map the odds to find snow near St. Moritz (click on the image to animate)!
Theia just published the first Venµs images today, including a beautiful view of the Pyrenees. Once you have dezipped/untared/unzipped the files you can make a true color composite using the command:
gdal_translate -b 7 -b 4 -b 3 -scale 0 300 0 255 -ot byte VE_VM01_VSC_PDTIMG_L1VALD_ES_LTERA_20180419.DBL.TIF myColorCompo.tif
I tend to focus on the snow so I stretched the colors between reflectances 0-1000 instead of 0-300:
gdal_translate -b 7 -b 4 -b 3 -scale 0 1000 0 255 -ot byte VE_VM01_VSC_PDTIMG_L1VALD_ES_LTERA_20180419.DBL.TIF mySnowColorCompo.tif
First, I was a bit puzzled by the orange shade in the northern part of the image. We inspected carefully the image with Olivier because at this stage radiometric calibration issues are still possible..
Le 1er avril est le moment privilégié par les hydrologues pour caractériser le potentiel hydrologique du manteau neigeux. Dans le cadre de l'OPCC  nous avons compilé différents indicateurs  :
- L'équivalent en eau du manteau neigeux dans les sous-bassins pyrénéens du bassin de l'Ebre est calculé par la Confederación Hidrográfica del Ebro (agence de bassin) à partir d'observations MODIS, des données météorologiques, et un modèle de type "degré-jour" (la fonte est proportionnelle à la température de l'air).
The ESA website featured this Sentinel-2 image of a large and thick plume of dust off Libya (click here to view in the Sentinel-Hub)
Contains modified Copernicus Sentinel data (2018), processed by ESA, CC BY-SA 3.0 IGO
Connaissiez-vous ce dicton coréen "On n'est jamais trop prudent" ?
Voici une série d'images Sentinel-2 près du site olympique de Pyeongchang. On voit de la neige artificielle apparaître dès le mois de novembre !
Les images suivantes montrent qu'au cours du mois de janvier de la neige naturelle a finalement recouvert le site...
Great news, we can announce that the operational production of the Theia snow collection has started well. It means that maps of the snow cover area are now constantly added to the Theia portal. These maps are automatically generated from Sentinel-2 observations and have a spatial resolution of 20 m. The Snow collection will progressively cover most mountain regions in west Europe, but also the Atlas in Morocco, eastern Canada... The Snow collection can be freely downloaded from http://theia.cnes.fr by any registered user.
Today's front page of the Theia website featured this nice example in Sierra de Ancares (western end of the Cantabrian Mountains, Spain). In the southeast, snow was also detected on the Montes Aquilanos, including the small ski resort El Morredero. The image was captured yesterday! It illustrates well the value of multispectral imagery to discriminate the snow cover from the clouds. There is a cloud which looks alike snow but it is actually a valley fog confined by local topography.
Theia Sentinel-2 level 2A and snow product in the region de los Ancares, Spain. Image captured by Sentinel-2A on 30 Jan 2018.
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 !
For Christmas Sentinel-2 offered us this nice picture of the Bezymianny volcano ash trail. Bezymianny is located in the central part of the Klyuchevskaya cluster of volcanoes in the Kamachatka peninsula. The image was acquired on Christmas eve but the ash plume was observed on 20 December.
Bezymianny volcano on 24-Dec-2017 by Sentinel-2