Sentinel-2 adds colors to the 2017 super bloom in California

Texas had the super bowl in 2017 but California got the super bloom!


After its worst drought in 1,200 years, California is now experiencing one its wettest year. The Sierra Nevada snowpack is currently one of the biggest ever recorded. In February, severe winter rainfalls caused the evacuation of many California residents under the threat of the Oroville Dam failure.


Now, this unusual level of rainfall triggered the blossoming of flowers that were in a dormant state. The Washington Post collected some photographs and noted that it was so so massive that you can see it from space (images were provided by Planet, a California-based remote sensing company).


How does it look like in Sentinel-2 imagery? Below is the comparison between two Sentinel-2 images acquired in spring 2016 vs. spring 2017 near the Carrizo Plain National Monument (see by yourself in the sentinel-playground).


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10000 L2A products available at Theia for Sentinel-2

The number of L2A products from Sentinel-2 available at Theia, just passed above 10 000 ! Data from France, Belgium, North of Spain, Morocco, Tunisia, Senegal, the Burkina-Mali transect, or La Reunion Island are now processed in real time : our requirement is 60 hours, after the data is available in Copernicus hub. Next site to be added is Madagascar.


Le compteur de produits de niveau 2A de Sentinel-2, disponibles à Theia, vient de passer la barre des 10 000 produits. Les données acquises par Sentinel-2 sur la France, la Belgique, le Nord de l'Espagne, la Tunisie, le Sénégal, le transect Burkina Mali, et l'île de la Réunion sont maintenant traitées en temps quasi réel : notre spécification est de les mettre à disposition en moins de 60 heures après leur apparition sur les serveurs du hub de Copernicus. Le prochain site couvrira une bonne partie de Madagascar.

THEIA/MUSCATE nears real time for Sentinel-2 L2A

THEIA MUSCATE production of Sentinel-2 L2A products nears real time, and L2A products acquired until March 2017 over France and Reunion Island have started to appear on our distribution server :

The production will progressively be extended over the whole France and over the other sites (Spain, Morocco, Belgium Tunisia, Senegal, Burkina, Mali...) and then follow the real time acquisitions with the shortest delay possible.


La production par MUSCATE des données de niveau 2A de Sentinel-2 s'approche du temps réel, et les données acquises jusqu'à mars 2017 sur la France et l’île de la Réunion ont commencé à apparaître sur le serveur de distribution.


La production va être progressivement étendue à toute la France et aux autres sites (Espagne, Maroc, Belgique Tunisie, Sénégal, Burkina Faso, Mali...), et suivra ensuite les acquisitions en temps réel avec le plus court délai possible

Quantitative comparison of cloud masks from MACCS/MAJA, Sen2Cor and GEOSYS (hand made)


As already explained in a previous post, we obtained some Sentinel-2 hand made cloud masks from GEOSYS company. We used those to validate the cloud masks from MACCS/MAJA. But we wanted to use them further to make a quantitative comparison with Sen2Cor cloud masks.
But this comparison required solving a little issue : GEOSYS cloud masks are generously dilated to avoid any risk to let a cloud pass through the operational processing. Those of MACCS:MAJA are also dilated while those of Sentinel-2 are not at all. In the following paragraphs, we'll explain how we solved that issue. Sen2cor (v2.3.0) has also three levels of cloud mask (High Medium and Low probability). We used here the Medium Probability mask. But let's start with the final result comparing the performances of Sen2Cor and MACCS:MAJA.


Overall accuracies for MACCS/MAJA, in red and Sen2cor, in blue for 11 images compared to GEOSYS cloud masks.

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The land cover classification for France in 2016 is available



just this once, we are ahead of time. Well, nearly. We had promised the 2016 land cover map or France before the end of first term of 2017. It exists and is available here. It's resolution is 10m, with the same 17 classes nomenclature that we used for Landsat landcover map of 2014..

The map is mainly based on the Sentinel-2 data acquired from end 2015 to end 2016, but we have also processed the LANDSAT 8 data.  We will provide some details below.

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Quantitative validation of MACCS cloud masks


At the annual review  of PEPS project (CNES Sentinel global mirror site), the GEOSYS company showed its operational activities centered on the use of Sentinel-2 data to provide advise on agriculture on many regions of the world. On this occasion, GEOSYS showed their cloud detection process for Sentinel-2 images. The Sen2cor solution was not considered reliable enough y GEOSYS, and the regions processed by MACCS within MUSCATE are far from covering all the regions of interest of the company. GEOSYS decided to rely on human operators to improve the cloud masking. For each processed Sentinel-2 image, a man made valid pixel mask is build ("valid" means without clouds and cloud shadows).


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Theia releases Sentinel-2A L2A data over Morocco


After France, Belgium, North of Spain, Tunisia, a transect from Burkina Faso to Mali and some tiles in Ethiopia, Theia just released the production of one year of Sentinel-2A data at Level 2A above a large region of Morocco, from Ouarzazate to Casablanca. Level 2A products provide surface reflectances corrected from atmospheric effects, with a high quality cloud mask.


The L2A data are, as always, available from Please refer to the help page to find the data format description. To download the data without a click, you may use our python download tool (after having registered on Theia's site).

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MUSCATE S2 product versions

V1_0 was a preliminary processing performed with early version of MACCS adapted to Sentinel-2. It was plagued with a bug in the cloud shadow mask : when more than 255 clouds were present in one image, shadow detection went completely wrong and shadows were detected anywhere. V1_0 was not produced in MUSCATE operationnal context, but in an earlier validation context. Production was available over France only.


V1_1 replaces V1_0 over France, Production started mid November, but was only released in February because of many difficulties encountered by MUSCATE. It corrects for the bug related to cloud shadows observed with V1_0, and  was fully processed by the operational MUSCATE center.


V1_2 was used in another context not related to MUSCATE, you will not find any product with this version number within MUSCATE server.


V1_3 was applied starting from February 2017, to data sets produced above Reunion, Burkina, Senegal, Tunisia, Morocco.The aerosol estimate is improved compared to V1_1. Together with Bastien Rouquié, from CESBIO, we worked on the tuning of the blue-red ratio which is used in the multi-spectral method to estimate aerosols (which is combined with a multi-temporal method). Initially, we used bands B2 (Blue) and B4 (Red), with a ratio of 0.45. We found out that better results were obtained with B1 (Blue) and B4 (Red), still with a ratio of 0.45. More accurate studies tend to recommend a higher value, closer to 0.5


As can be seen in the figure below, the estimates obtained with V1_3 are not biased anymore, and have a reduced standard deviation. As a consequence of getting a lower aerosol optical thickness, the surface reflectances of V1_3 products are 2% higher than those of the earlier versions (some user feedbacks from V1_0 said reflectances were sometimes too low).


Using B2/B4 ratio

Using B1/B4 ratio


V1_4 will be provided with a better shadows mask, as it has been found that the shadows masks are too severe and contain too many commission errors. It is in its final stages of validation.