Using aerosol type from Copernicus Atmosphere in MAJA

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The main difficulty of the atmospheric correction comes from the determination of the aerosols optical properties: one has to know the optical properties of the aerosol type present in the atmosphere and determine their optical thickness. Using Sentinel-2 data to determine the aerosol type is very complicated, and our MAJA processor, used to generate Theia L2A products, only computes the aerosol optical thickness, while assuming a specific aerosol type.

 

The current operational version of the MAJA processor uses a constant aerosol type during the atmospheric correction, independently from the location and from the time of the year, thus affecting the quality of the atmospheric correction if the chosen aerosol type is not appropriate.

 

As an alternative, we tried to use the information from CAMS (Copernicus Atmosphere Monitoring Service), whichprovides forecasts of the Aerosol Optical Thickness (AOT, see figure below) of five different aerosol types: dust, black carbon, sea salt, sulfate and organic matter.

 

CAMS aerosol optical thickness (AOT) forecasts at 550 nm on 14 June 2016, 03:00 UTC: (top left) Dust, (top right) Sea Salt, (bottom left) Black Carbon, and (bottom right) Sulfate.

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Utilisation de Copernicus Atmosphere dans la correction atmosphérique de MAJA

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La principale difficulté de la correction atmosphérique réside dans la détermination des propriétés optiques des aérosols : il faut connaître les propriétés optiques du type d'aérosols présent dans l'atmosphère et déterminer leur quantité, symbolisée par l'épaisseur optique. Il est très difficile, à partir des données Sentinel-2, de déterminer le type d'aérosols, et notre chaîne MAJA, utilisée pour générer les produits L2A de Theia se contente de déterminer l'épaisseur optique des aérosols en supposant le type d'aérosols connu.
 
La version opérationnelle actuelle de MAJA utilise, durant la correction atmosphérique, un type d'aérosol constant spatialement et temporellement, ce qui affecte la qualité de la correction atmosphérique si le type d'aérosol choisi n'est pas le bon. L'alternative proposée ici est d'utiliser l'information venant de CAMS (Copernicus Atmosphere Monitoring Service), qui fournit des prévisions d'épaisseur optique (AOT pour Aerosol Optical Thickness, voir figure ci-dessous) pour cinq types d'aérosols différents : dust, black carbon, sea salt, sulfate et organic matter.

Cartes d'épaisseur optique des aérosols issues de CAMS (AOT) à la longueur d'onde 550 nm le 14 Juin 2016, 03:00 UTC: (haut gauche) pussières, (haut droit) sel de mert, (bas gauche) carbone noir, and (bas droit) sulfate.

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Using multi-temporal high-resolution remote sensing in surface modeling

Version française par ici.

 

The land surface models simulate the water and energy fluxes between soil, land cover and atmosphere. Their scopes of application spread from numerical weather prevision to soil water modeling.

 

Energy and water budgets of the soil-plant continuum

 

However, these models are initially conceived to be applied on wide areas. Thus, they use low resolution cover parameters (>1km) derived from mid-resolution satellite observations (MODIS, VEGETATION). These parameters are mainly the Leaf Area Index (LAI), the vegetation type or the surface albedo. Yet the agricultural landscapes of Western Europe are characterized by a patchwork of plots smaller than one square kilometer. These plots have very different vegetation cycles, i.e. winter and summer crops, which could only be described at high resolution. The crop management practices like crop rotation or irrigation are also generally not taken into account.

 

The products of the Sentinel-2 space mission, with their high spatial and temporal resolution, could bring elements to fill in this missing information.

 
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Les séries temporelles d'images à haute résolution pour la modélisation des échanges de surface

English version right here.

 

Les modèles de surface simulent les échanges d’eau et d’énergie entre le sol, le couvert (végétal ou non) et l’atmosphère. Leurs  applications vont de  la prévision numérique du temps à la modélisation de l’état hydrique des sols.

 

Bilans d’eau et d’énergie du continuum sol-plante

 

Cependant, ces modèles, initialement conçus pour simuler de grandes étendues, utilisent des paramètres du couvert à basse résolution (au-delà du kilomètre) qui sont issus des observations satellites à moyenne  résolution (MODIS, VEGETATION). Ces paramètres sont principalement l’indice foliaire, le type de végétation, l’albédo de la surface. Or, les paysages agricoles d’Europe de l’Ouest sont caractérisés par un patchwork de cultures avec des parcelles bien inférieures au kilomètre carré et des cycles de végétation très différentes (cultures d’été, cultures d’hiver, …) qui ne peuvent être décrits qu’à haute résolution. De plus, les pratiques culturales (dites anthropiques car liées à une action de l’homme) comme la rotation des cultures ou l’irrigation, ne sont également généralement pas prises en compte.

 

Les produits satellite à haute résolution spatiale et temporelle issus de la mission Sentinel-2 peuvent contribuer à palier à ces lacunes puisqu’ils ont une résolution inférieure à l’échelle de la parcelle.

 
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Building a global cropland mask, is not an easy task

Criticizing is easy, and doing is hard, especially when trying to create a global map of croplands. Some collegues from CESBIO have worked on that subject within the Sen2Agri project, and obtained good resuts, but only at the local or country scale. Finding a method that works everywhere must clearly be much harder.

These days, I have received a lot of emails, tweets and posts about a new cropland global product at 30 m resolution, edited by USGS. I have no doubt it was a serious work from a serious team, done with appropriate terrain data and methods, validation, and of course a tremendous data processing.

 

 

But there it is, I checked it over a lot of places that I know very well, and it seems to me that the cropland mask, at least in South West France, is clearly overestimated. Is it the same in tour region ? Here are some examples :

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Yesterday's snow cover area in the Pyrenees

Olivier pointed to me that ESA's ground segment, PEPS and MUSCATE were all in really good shape today... And the sky was clear yesterday at the time of the Sentinel-2A acquisition!

So I could download the Level-2A product from theia.cnes.fr, run our let-it-snow processor, start QGIS and here it is: the map of yesterday's snow cover area at 20 m resolution. If you know the region, you might notice that there is currently a big contrast in the snow cover extent between the French and the Spanish Pyrenees. This is due to the blocking of the moist air masses coming from the north.

Snow cover area on 22 Nov 2017. blue: snow, grey: no snow, white: cloud.

Stay tuned! Theia should start to distribute these Sentinel-2 snow products in near real time very soon.

L'Antarctique de nouveau sous l'œil de Sentinel-2

Après un hiver pleins de rebondissements en Antarctique, j'avais écris "il est temps que la luminosité revienne pour que les acquisitions Sentinel-2 redémarrent !".

Ça y est ! Le paysage qui se découvre petit-à-petit sous l’œil de Sentinel-2 est toujours aussi somptueux et pleins de surprises. Par exemple, la première image claire de la langue flottante du glacier de l'Île du Pin montre l'iceberg géant qui s'est décroché en septembre. Nous avions suivi l'évolution de la fissure à l'origine de ce détachement l'été dernier avec Sentinel-2. En zoomant, on peut apercevoir le bleu éclatant de la glace comprimée d'un iceberg renversé. Ce mini iceberg couvre quand même une surface équivalente à 20 terrains de football.


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Le bulletin Theia est sorti !

De la part de Sophie Ayoubi, chargée de la communication de Theia

Le 8ème numéro du bulletin du pôle de données et de services surfaces continentales Theia vient de paraître.

A découvrir dans ce numéro :

  • Actualités
  • Nouveaux produits
  • Services et outils
  • Applications et thématiques
  • Développement instrumental
  • Accompagnement utilisateur

Téléchargez le bulletin de Theia en version impression (3.6 Mo) ou version allégée (932 Ko) ou feuilletez-le en ligne dans l'espace Calaméo de Theia.

Retrouvez l’ensemble des numéros dans la rubrique bulletins.

MUSCATE news : 50000 L2A products from Sentinel-2A and 2B

Since it became operationnal in December last year, MUSCATE has produced 50 000 level 2A products from Sentinel-2A. Let's recall what has been processed so far :
  • For 550 tiles, we have processed all Sentinel-2A data acquired since December 2015.
  • For 100 tiles, mainly in South America, and in Italy, we have processed all Sentinel-2A data from December 2016. We are currently catching up the backlog for Italy, and later on, for South American sites.
  • For all these 650 tiles, we are producing all Sentinel-2 data (Sentinel-2A and Sentinel-2B) in near real time. I think THEIA is the only place where you can download Sentinel-2B L2A data so far. ESA has not started that production yet (nah, nah, nah :) )
  • For all these 650 tiles, we have processed all Sentinel-2B data since beginning of October 2017. We will soon catch-up with the Sentinel-2B data acquired from July 2017.

 

 

See full screen

Map of the 650 tiles currently processed in near real time (in red). The blue tiles will be added beginning of next year.

 

All these products are available from https://theia.cnes.fr

 

Let's recall that MUSCATE uses the MAJA L2A processor, which uses multi-temporal criteria to perform a high quality cloud detection and atmospheric correction. Despite the recent installation of version 2.4, MUSCATE still regularly suffers from instability as soon as CNES High Performance Computer is overloaded. The problem does not lie in MAJA, but in the information exchanges between all the components of MUSCATE which need to respect an accurate timing (sorry, I am not able to explain better).

 

The exploitation team just installed a new version of MUSCATE (v 2.4.16.p2 (!)), which is expected to increase stability. But that's the theory, let's see if it works in the coming days and if we are able to increase our production rate.

 

 

Evolution of the snow cover area in the Pyrenees from MODIS

The Cesbio contributes to the Pyrenees Climate Change Observatory (OPCC) through the analysis of the snow cover evolution using satellite imagery. We are working on three remote sensing products in the framework of the CLIM'PY project:

    1. Daily cloud-free maps of the snow cover area in the Pyrenees at 500 m resolution since 2000 from MODIS [1];
    2. Maps of the snow cover area in the Pyrenees at 20 m and 30 m resolution since 2013 from Sentinel-2 and Landsat-8 [2];
    3. Maps of the annual peak snow depth in the Bassiès-Vicdessos region at 4 m resolution since 2015 (i.e., one map per year) from Pléiades stereo imagery [3].

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