High cloud detection using the cirrus band of LANDSAT 8 or Sentinel-2

Atmospheric absorption : in blue, the surface reflectance of a vegetation pixel, as a function of wavelength. In red, the reflectance of the same pixel at the top of atmosphere.  For a wavelength of 1.38 µm, water vapour totally absorbs the light that comes from the earth surface at sea level.

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The LANDSAT-8 and SENTINEL-2 satellites have a spectral band centered on the 1.38µm wavelength, which is designed to allow the detection of high altitude clouds. This spectral band corresponds to a strong absorption band of water vapour. its absorption is so strong that a photon emitted by the sun in this wavelength has nearly no chance to reach the earth surface, and even less to reach the satellite after that without being absorbed. The consequence is therefore that the surface is usually not visible on the images taken for the 1.38 µm channel.

 

 

However, as water vapour is concentrated in the lower layers of the atmosphere, the photons reflected by high clouds have much less chances to be absorbed. The 1.38 µm images display the higher parts of the atmosphere, and can be used to screen the high clouds, as it may be seen on the image below, on which a very large number of plane contrails may be observed (I counted 35, what about you ?)

 

This spectral band is therefore useful to detect these thin cirrus clouds which, without this band, were usually difficult to spot and used to degrade our reflectances time series.

 

LANDSAT 8 image taken over Paris in April 2013. On the left, the RGB color composite, and on the right the 1.38µm channel. The plane contrails can be easily detected, and given their number, one can see that we might have to choose whether to fly or to observe...

 

 

It is just sad that a simple threshold cannot do the detection with a 100% accuracy (but if it was the case, cloud detection would be easy for everyone, and we would not be useful anymore !)

First of all, the low clouds and the fog are very close to the surface and are not visible in that band. One has to use other criteria to detect them. Moreover, some mountains may emerge from the absorbing layers, all the more when the atmosphere is dry. A thresholding to detect high clouds must take into account the surface altitude, and for a better accuuracy, should take into account the waper vapour quantity and vertical repartition, which may be predicted using weather analyses.

 

Landsat 8 image taken above the center of Madagascar, in September 2013. On the left, the RGB color composite, and on the rght, the 1.38 µm channel. There is nearly no cloud on this image, but the surface reflectance is much greater than zero, because part or the region has an altitude above 1500m, and because the atmosphere was particularly dry on that day..


Finally, the 1.38µm channel is efficient to detect high clouds and especially thin cirrus, but has to be used with some precautions to avoid that all mountains be classified as high clouds. This is how we proceed in the MACCS processor.

 

 

 

 

 

 

La détection des nuages hauts avec la bande Cirrus de Landsat et Sentinel-2

Absorption atmosphérique. En bleu, la réflectance de surface pour un pixel couvert de végétation, en fonction de la longueur d'onde, en rouge la réflectance au sommet de l'atmosphère pour ce même pixel. A 1.38 µm, la vapeur d'eau absorbe totalement la lumière provenant de la surface au niveau de la mer.

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Les satellites LANDSAT-8 et SENTINEL-2 possèdent une bande spectrale centrée sur la longueur d'onde 1.38 µm, destinée à la détection des nuages hauts. Cette bande spectrale correspond à une forte bande d'absorption de la vapeur d'eau. Cette absorption est tellement forte qu'il est très peu probable qu'un photon émis par le soleil arrive à la surface terrestre, et si celà arrive, il est encore moins probable qu'il parvienne ensuite jusqu'au satellite sans être absorbé. Résultat, sur les images de cette bande, la surface n'est en général pas visible.

 

Par contre, comme la vapeur d'eau est en général concentrée dans les basses couches de l'atmosphère, les photons réfléchis par les nuages hauts ont beaucoup moins de chances d'être absorbés. Les images que l'on observe dans cette bande permettent donc d'observer la partie haute de l'atmosphère, et donc de détecter les nuages hauts, comme on le voit sur l'image ci dessous, sur laquelle de très nombreuses traces d'avions sont visibles (j'en compte 35, et vous ?).

 

Cette bande permet donc enfin de détecter, par un simple seuillage, ces fameux cirrus fins qui jusqu'ici étaient assez mal détectés, étaient parfois pris pour des aérosols, et en général perturbaient nos mesures.

 

Image LANDSAT 8 acquise sur Paris le 14/04/2013. A gauche, composition colorée RGB, à droite, image de la bande 1.38µm. A voir le nombre de traces d'avions, on se dit qu'il va falloir choisir entre voler ou observer la terre...

 

Malheureusement, un simple seuillage pour détecter les nuages n'est pas infaillible. (mais si c'était le cas, la détection de nuages serait à la portée de tous et nous ne servirions plus à rien...).

 

D'abord, les nuages bas et les brouillards sont souvent proches de la surface et donc ne sont pas visibles dans cette bande, il faut utiliser d'autres critères pour les détecter. De plus, les montagnes peuvent émerger de la couche absorbante, et ce d'autant plus que l'atmosphère est sèche. Le seuillage pour détecter les nuages hauts doit donc prendre en compte l'altitude de la surface et, pour bien faire, devrait aussi dépendre de la quantité de vapeur d'eau, qui peut être prédite par les modèles météorologiques.

 

Image LANDSAT 8 acquise au centre de Madagascar le 13/09/2013. A gauche, composition colorée RGB, à droite, image de la bande 1.38µm. Il n'y a quasiment aucun nuage sur cette image, mais la réflectance de surface ne s’annule pas en raison de l'altitude supérieure à 1500m sur la partie visible de l'image et de l'atmosphère particulièrement sèche ce jour là.


Bref, cette bande 1.38 µm est efficace pour détecter les nuages hauts et notamment les cirrus fins,  mais doit être employée avec quelques précautions afin d'éviter que toutes les montagnes soient systématiquement déclarées nuageuses. C'est ce que nous faisons dans la chaîne MACCS.

 

 

 

 

 

Take 5 : a happy end for SPOT5

In a classical Hollywood thriller, in which the hero nearly dies several times, a last sequence of the film is usually dedicated to the happy ending with a swinging music. The same happens with the SPOT5 (Take5) story : after several periods with very little hopes of success, the SPOT5 (TAKE5) experiment is finally close to be decided (at least with a 90% likelihood...)

 

Despite an intensive campaign to show that repeating the experiment would still be useful, with very motivated users writing that the experiment would be useful (thanks to all !),  it quickly turned out that CNES could not afford the full cost of the experiment a second time alone. Therefore the support of ESA was sought, as the experiment is considered to fall in the core mandate of ESA's Third Party Mission Earthnet Program. ESA's decision to takeover CNES external costs still has to be approved by its Member States in autumn, however funding is already set aside. And finally, CNES as agreed to hold the experiment thanks to ESA's decision to take charge of CNES external costs : CNES has contracted several companies to operate its satellites and ground segments and these expenses for the experiment will be covered by ESA, while CNES will fund its internal costs, and provide the satellite.

 

The experiment will start in April 2015 and stop at the end of August, the exact start date still needs to be determined and will depend on the finally selected orbit. Sentinel-2 launch is expected between the end of April 2015 and the end of June 2015, but the "ramp-up phase" will take a couple of months to start the routine acquisitions, probably too late to monitor the main part of 2015 growing season in the Northern Hemisphere routinely. For a number of selected sites, the SPOT5 (TAKE5) experiment will be useful to enable several teams to start building their applications for 2015 growing season, without having to wait for an additional year. Early Sentinel-2 images might also be taken on these sites.

 

This time, the selection of the sites will be lead by ESA, who will issue a call for sites proposal this autumn, in a very short time frame, as the final site list needs to be determined before the end of the year. If you are interested by proposing a site, get ready to answer and stay tuned on this blog  !

 

(Thanks to Sylvia Sylvander (CNES) and Bianca Hoersch  (ESA) for their contributions to this post)

Directional effect correction for Sentinel-2 composites.

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Sentinel-2 orbits

Swaths observed by Sentinel-2A, for day 1 (green), 4 (Blue), 7 (grey), 8 (Pink). For Sentinel-2B, we will have to shift that by 5 days. Distance between swaths was computed so that a little overlap is available at the equator.

The Sentinel-2 orbit was set so that the swaths observed by the satellite have a little overlap at the equator. The width of the overlap increases quickly at higher latitudes. For instance, at France latitude (45 degrees), about half of the surface will be observed twice per satellite cycle, from two adjacent swaths.

It is not very fair, since it will always be the same places that will be observed twice and the rest of the world will only be observed once (The Cesbio site is well located !)

Same as above, with a zoom over France. Here, half of the land will be observed twice per cycle (red segments), and the other half (yellow segments) once per cycle.

 

 

 

Directional correction for composites.

 

Well, the issue is that each point within the overlap zone will be observed twice, but under two different viewing angles, and therefore will have different reflectances in each swath, due to the directional effects. The users of our data often ask for monthly syntheses as cloud free as possible, that merge the data observed from different orbits.To obtain such products, a directional correction is therefore necessary.

Monthly syntheses in Toulouse region, without directional correction on the left,  with a directional correction on the right.

N.B.. The scattered green points you may see are invalid points due to saturated pixels (saturations are often observed with SPOT? which will not be the case for Sentinel-2).

 

To do that, directional models have been developed, such as the ones of Roujean or Ross-Li, that model the directional variations as a function of viewing angles and solar angles, with a rather good accuracy for most types of surfaces. Here is how they look like :

 \rho= \rho_0 (1 + K_1. F_1(angles), + K_2. F_2 (angles))

 

 \rho is the reflectance for the actual viewing and solar angles  \rho_0 is the reflectance for a given angular condition chosen to standardise the data (for instance viewing at nadir and solar angle at 45 degrees), F1 and F2 are the directional functions that depend on the angles, and  K_1 and K_2 are the coefficients of the directional model, that depend on the observe pixel type of surface.

Fortunately, in the case of S2, the angle differences are low, no more than 20 degrees. We have tried, as a first test; to find mean coefficient that could work more or less for all surfaces. Tu compute these coefficients, we used the SPOT4 (Take5) sites which have been observed under two viewing directions. These are Maricopa (In the USA), and Midi-Pyrénées, Bretagne and Provence in France. They show very different landscapes, with desert and irrigated crops in Maricopa, a very diverse agricultural landscape in Bretagne and Midi-Pyrénées, and Mediterranean forests and vineyards in Provence. We have used all the available couples of clear images separated by less than 5 days and we searched for the coefficients K_1 and K_2 that allow to minimise differences.

 

Finally, these coefficients were used to correct the data and produce composites. The monthly syntheses are finally obtained by computing  a weighted mean value of the reflectance of cloud free pixels obtained during a period of 42 days. The images above or below show the results obtained by M. Kadiri on the French sites (Maricopa is still running), with on the left the synthesis without directional correction, and on the right the one with directional correction. The shading observed from right to left on the image without correction almost disappears on the images with correction. It is the same for all 3 sites and the chosen images are the ones which show the highest differences. Knowing that the angle difference is greater for SPOT4 (Take5) than for Sentinel-2, we have good hopes that this simple method could work for Sentinel-2.

However, our sampling of 4 sites is not sufficient, we will have to prove that theses results still hold for other types of surfaces. We could do that with SPOT5 (Take5) or with the first Sentinel-2 data (which should come soon !).

 

Same as above, for Provence-Languedoc.

 

Same as above, for Bretagne

Fauchée d'un instrument : c'est la surface observée par un satellite au cours d'un passage.

Correction des effets directionnels pour les synthèses mensuelles de Sentinel-2

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Les orbites de Sentinel-2

Fauchées observées par Sentinel-2A, le jour 1 du cycle (vert), le 4 (Bleu), le 7 (gris), le 8 (rose). Pour Sentinel-2B, il faut décaler le tout de 5 jours. L'espacement entre les fauchées est déterminée par l'orbite, qui est calculé pour permettre un léger chevauchement des fauchées adjacentes à l'équateur.

L'orbite de Sentinel-2 a été calculée pour que les fauchées observées par le satellite aient une petite intersection à l'équateur. La largeur de cette intersection augmente rapidement lorsqu'on s'éloigne de l'équateur et qu'on se rapproche des pôles. A la latitude de la France (45 degrés), c'est quasiment la moitié des surfaces qui pourront être observées à deux reprises, à partir de deux fauchées adjacentes.

C'est d'ailleurs un peu injuste car ce seront toujours les mêmes endroits qui seront observés deux fois tous les 5 jours, alors que d'autres endroits ne seront observés qu'une fois, mais à la verticale. Le site Sudmipy du CESBIO semble faire partie des endroits observés deux fois, mais, je ne suis pas sûr de disposer des orbites définitives de Sentinel-2).

Zoom sur la France de l'image ci-contre. On constate qu'à la latitude de 45 degrés, la moitié des terres (trait jaune) est observée une fois par cycle, et l'autre moitié (trait rouge) deux fois par cycle (donc deux fois tous les 5 jours avec les deux satellites).

 

 

 

La correction directionnelle.

 

Bref, le problème, c'est qu'un point à l'intersection de deux fauchées adjacentes sera observé sous deux angles différents et n'aura pas les mêmes réflectances sur les deux images, en raison des effets directionnels. Or les utilisateurs de nos données (oui, vous) nous demandent souvent des images de synthèses mensuelles (si possible sans nuages), et assemblant les données acquises depuis plusieurs orbites, de préférence sans que les coutures entre orbites soient visibles. Pour obtenir de tels produits, il faut donc pratiquer une correction des effets directionnels.

Synthèses mensuelles calculées avec la méthode de la moyenne pondérée, sans correction directionnelle à gauche, avec correction directionnelle à droite.

N.B.. Les points verts que l'on voit par-ci par là sont des pixels invalides, car tout le temps nuageux ou saturés (sur SPOT, les saturations sont fréquentes, ce qui ne sera pas le cas sur Sentinel-2)

 

Pour cela, il existe des modèles directionnels, comme celui de Roujean, ou ceux de Ross-Li, qui permettent de modéliser l'évolution des réflectances en fonction des angles de prise de vue et des angles solaires, avec une précision correcte pour la plupart des surfaces. Ils se présentent sous la forme suivante :

 \rho= \rho_0 (1 + K_1. F_1(angles), + K_2. F_2 (angles))

 

 \rho est la réflectance dans les conditions de la prise de vue,  \rho_0 est la réflectance pour une direction donnée (par exemple, observation à la verticale et élévation solaire à 45 degrés), F1 et F2 sont des fonctions directionnelles qui dépendent des angles de prise de vue et des angles solaires, et  K_1 et K_2 sont les coefficients du modèle directionnel, qui vont en général dépendre de la nature du pixel observé.

 

Dans le cas de Sentinel-2, nous avons la chance que les différences d'angles de prise de vue entre deux orbites adjacentes soient faibles, tout au plus 20 degrés. Nous avons donc tenté de trouver des coefficients moyens qui fonctionneraient à peu près pour tous les paysages. Pour trouver ces coefficients, nous avons utilisé les sites de l'expérience SPOT4 (Take5) qui ont été observés sous deux angles différents. Il s'agit de Maricopa (aux USA), Midi-Pyrénées, Bretagne et Provence en France. Il s'agit de paysages très différents, avec du désert et de l'agriculture irriguée à Maricopa, un paysage agricole varié en Bretagne et en Midi-Pyrénées, et un paysage de forêts méditerranéennes et de vignes en Provence. Nous avons utilisé tous les couples d'images claires séparées par moins de 5 jours et cherché les coefficients  K_1 et K_2 qui permettent de minimiser les différences.

 

Enfin, ces coefficients ont été utilisés pour corriger les données et produire les composites. Les produits de synthèses mensuelles, sont finalement obtenus en calculant la moyenne des pixels non nuageux pendant une période de 42 jours. Les images ci-dessus présentent les derniers résultats obtenus par Mohamed Kadiri sur le site de CESBIO près de Toulouse. à gauche, sans correction directionnelle, à droite avec correction directionnelle. Le dégradé de couleurs qui apparaît sur la partie droite de l'image de gauche, sans correction, disparaît presque complètement sur l'image de droite (avec correction). Il en va de même pour toutes les dates et pour les 3 autres sites, et j'ai choisi ici l'image qui comportait les effets les plus prononcés. Sachant que la différence angulaire entre les images SPOT acquises depuis des orbites adjacentes est plus grande que pour Sentinel-2, je pense qu'on peut espérer obtenir de bons résultats avec Sentinel-2 avec cette méthode simple.

Ceci dit, notre échantillon statistique, composé de 4 sites est largement insuffisant, ces résultats devront donc être confirmés, par exemple avec SPOT5 (Take5), ou avec les premières données de Sentinel-2 (c'est bientôt !)

 

Même figure que ci-dessus pour le site Provence-Languedoc.

 

Même figure que ci-dessus pour le site Bretagne

Fauchée d'un instrument : c'est la surface observée par un satellite au cours d'un passage.

SPOT5 (Take5) : a step forward / un pas en avant

CNES just gave a green light to go on with the studies to repeat the Take5 experiment with SPOT5. These studies will determine the exact cost of the experiment and see if it fits with what could be available, accounting for ESA's help. If everything goes well, the experiment would start around April 2015, until the end of August 2015. It will provide the opportunity, for several sites, to anticipate the launch of Sentinel-2. I do not know yet how the sites would be chosen, but I will keep you posted.

Le CNES vient de donner son feu vert pour poursuivre les études de faisabilité d'une nouvelle expérience Take5 réalisée avec SPOT5. Ces études auront notamment pour but de déterminer le coût exact de l'expérience et de voir s'il correspond aux budgets qui peuvent être dégagés, avec l'aide de l'ESA. Si tout se passe bien, l'expérience démarrerait en Avril 2015 et durerait jusqu'à fin Août, elle permettrait donc d'anticiper le lancement de Sentinel-2 pour quelques sites. Je ne sais pas encore comment les sites seraient choisis, mais je vous tiens bien sûr au courant.

SPOT4 (Take5) communications at the Sentinel-2 Symposium

The second "Sentinel-2 for science" symposium , organised by ESA, took place in italy late may 2014. More than 400 future Sentinel-2 users participated, which is a record for a conference organised by ESA at ESA premises. Compared to the first Sentinel-2 users workshop, it turns out that most of the talks were based on time series of images, while this proportion was less than a third for the first users symposium (other talks were about spectral indexes, mono date model inversions, which is good science but is not specifically tailored for Sentinel-2). This shows that the Sentinel-2 users community state of preparation did a lot of progress during the two last years.

 

To this respect, it seems that the SPOT4(Take5) experiment has helped a lot, as at least 15 of the 55 talks (and a lot of posters) of the symposium were largely based on the data set. That was exactly the purpose of the experiment and I am quite please to see it succeeded. The data are still available there, and there are still a lot of things to do.

 

Here are the links to the 15 talks that use SPOT4 (Take5) data (I may have forgotten one of two, if so please tell me ! I have not found the links to the posters, if someone found them, please tell me !).  You may also access the whole program here (some talks, although not based on SPOT4 (Take5), were also very stimulating ;-) )

 

Ground Segment


MUSCATE : An Operational Tool for Atmospheric Corrections And Monthly Composites Sentinel-2

Marc Leroy1, Olivier Hagolle2, Mireille Huc2, Mohammed Kadiri2, Gérard Dedieu2, Joëlle Donadieu1, Philippe Pacholczyk1, Céline L'Helguen1, Selma Cherchali1

1: CNES, France; 2: CESBIO


Pre-processing


Lessons learned from the SPOT4 (Take5) experiment : simulations of Sentinel-2 time series on 45 large sites

Olivier Hagolle1,3, Mireille Huc1,2, Mohamed Kadiri1,2, Dominique Clesse4, Sylvia Sylvander3, Marc Leroy3, Martin Claverie5, Gérard Dedieu1,3

1: CESBIO Umr 5126 CNRS-CNES-IRD-UPS, Toulouse, France; 2: CNRS,France; 3: CNES, France; 4: CAP GEMINI, France; 5: NASA/GSFC, USA

Eric Vermote1, Martin Claverie1,2, Jeffrey Masek3, Inbal Becker-Reshef2, Chris Justice2

1: NASA/GSFC Code 619; 2: University of Maryland, Dept of Geographical Sciences; 3: NASA/GSFC Code 618


Restoration of Missing Data due to Clouds on Optical Satellite Imagery Using Neural Networks

Nataliia Kussul, Sergii Skakun, Ruslan Basarab

Space Research Institute NASU-SSAU, Ukraine


Agriculture


Agronomy and hydrology with Sentinel-2 type time series: Towards spatial characterization of crop productivity and its impacts on water and nutrient cycle at the catchment scale

Sylvain Ferrant1,2, Simon Gascoin2,3, Amanda Veloso2, Martin Claverie4, Gérard Dedieu1,2, Valerie Demarez2,5, Eric Ceschia2,5, Patrick Durand6, Jean-luc Probst3,7, Vincent Bustillo2,5

1: CNES, France; 2: CESBIO, France; 3: CNRS, France; 4: University of Maryland; 5: University of Toulouse; 6: INRA, France; 7: ECOLAB, France

Based on Formosat-2 rather than SPOT4 (Take5), but these data are similar and produced with the same methods.


Crop mapping in complex landscape by multi-source data mining and remote sensing for food security

Elodie Vintrou1, Valentine Lebourgeois2, Agnès Bégué2, Dino Ienco3, Maguelonne Teisseire3, Pierre Todoroff1, Fidiniaina Ramahandry Andriandrahona4

1: CIRAD UR AIDA, Station Ligne Paradis, 7 chemin de l’Irat, 97410 Saint Pierre, La Réunion; 2: CIRAD UMR TETIS, Maison de la Télédétection, 500 rue J.F. Breton, Montpellier, France; 3: IRSTEA UMR TETIS, Maison de la Télédétection, 500 rue J.F. Breton, Montpellier, France; 4: FOFIFA, Station Régionale de Recherche FOFIFA Tsivatrinikamo ANTSIRABE 110, Madagascar


Sentinel-2 Agriculture project: Preparing Sentinel-2 exploitation for agriculture monitoring

Defourny Pierre1, Bontemps Sophie1, Cara Cosmin4, Dedieu Gérard2, Guzzonato Eric3, Hagolle Olivier2, Inglada Jordi2, Rabaute Thierry3, Savinaud Mickael3, Sepulcre Guadalupe1, Valero Silvia2, Koetz Benjamin5

1: UCLouvain, Belgium; 2: CESBIO, France; 3: CS-Systèmes d’Information, France; 4: CS-Systèmes d’Information, Romania; 5: ESA, ESRIN, Italy


Crop Identification and acreage estimate using a combination of Spot4-Take5 & Landsat 8.  A preparatory study for Sentinel 2

N. Knox1,2, L.T. Tsoeleng1, C. Adjorlolo1,2, T. Newby3

1: South African National Space Agency (SANSA), South Africa; 2: University of KwaZulu-Natal (UKZN), South Africa; 3: National Earth Observation and Space Secretariat (NEOSS), c/o SIIU - CSIR, South Africa.


Multisource EO Data for the optimal agricultural drainage water management in semi-arid area of Doukkala (Western MOROCCO): Potential of Sentinel-2 Type Observation

Kamal Labbassi1, Nadia Akdim1, Silvia Maria Alfieri2,3, Massimo Menenti2

1: Chouaib Doukkaly University, Morocco; 2: Delft University of Technology, Netherlands; 3: Institute for Mediterranean Agricultural and Forest Systems, Italy


Forests


Potential of Sentinel 2 constellation to provide near real time forest disturbance mapping over cloudy areas in Gabon

Christophe Sannier, Louis-Vincent Fichet

SIRS, France


Assessing Forest Degradation from Selective Logging using Time Series of Fine Spatial Resolution Imagery in Republic of Congo

Astrid Verhegghen, Baudouin Desclée, Hugh Eva, Frédéric Achard

Joint Research Centre of the European Commission, Italy


Potential benefits that Sentinel-2 data could bring to characterise and monitor forestry, simulated through SPOT 4 Take5 data

Colette Meyer1, Hervé Yesou1, Stephen Clandillon1, Henri Giraud1, Jérôme Maxant1, Paul de Fraipont1, Arnaud Selle2

1: SERTIT, France; 2: CNES, France


 

Coastal and inland waters


Mapping estuarine turbidity using high and medium resolution time series imagery Virginie Lafon1, Arthur Robinet1, Tatiana Donnay2, David Doxaran2, Bertrand Lubac3, Eric Maneux1, Aldo Sottolichio3, Olivier Hagolle4, Alexandra Coynel3

1: GEO-Transfert, ADERA, Université de Bordeaux, France; 2: Laboratoire d'Océanographie de Villefranche, UMR 7093 - CNRS / UPMC, France; 3: UMR EPOC, Université de Bordeaux-CNRS, France; 4: CESBIO, CNRS,UPS, CNES, IRD, France


CoastColour Spot 4 Take 5

Carsten Brockmann1, Ruescas Ana1, Pinnock Simon2

1: Brockmann Consult GmbH, Germany; 2: ESA ESRIN, Italy


Sentinel-2 Time Series for GlobWetland II to map Threats in Wetlands

Kathrin Weise1, Marc Paganini2, Max Tobaschus1,3, Martin Faber1,3

1: Jena-Optronik GmbH, Germany; 2: European Space Agency, Italy; 3: Friedrich Schiller University Jena, Germnay

Une nouvelle application hydrologique pour les séries temporelles ?

Le débit des rivières naturelles est une donnée de base en hydrologie qui reste pourtant difficile à obtenir dans de nombreuses régions pour des raisons pratiques, politiques, etc.. Dans la revue Proceeding of the National Academy of Sciences, Gleason et Smith (2014) présentent une nouvelle méthode qui permet de calculer les débits d'une rivière seulement à partir d'images satellites à haute-résolution (e.g. Landsat). Continuer à lire

SPOT4 (Take5) surface reflectance validation using CNES ROSAS station in la Crau.

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More than 10 years ago, on the Crau plain, in Provence, CNES set up an automatic calibration station to measure the atmospheric optical properties and the surface reflectances. This station, named ROSAS (RObotic Station for Atmosphere and Surface), is at the top of a 10 meter mast, and is equipped with a CIMEL instrument similar to the ones of the AERONET network that are used to characterize the atmospheric aerosols. But this one has been modified to observe also the ground. The initial objective of this station was to check the absolute calibration of optical remote sensing instruments with a high resolution (because the site uniformity is not sufficient for satellites with a kilometric resolution). But this station proves also useful to validate the surface reflectances from satellite level 2A products.

 

This work was done by some CNES colleagues, Vincent Lonjou, Sébastien Marcq et Aimé Meygret, using the level 2A products obtained from SPOT4 (Take5) experiment.

The ROSAS station needs 90 minutes to fully characterize the downward radiance and thus the atmosphere, and the upward radiance. The ratio of both measurements enable to compute the surface reflectance. However, the process is a little more complex than described here, as the surface around the mast is not perfectly uniform and the reflectances are affected by directional effects. A bidirectional model is therefore fitted to the measurements, and this model is then used to predict the reflectances measured by the satellite.

B2

The ROSAS instrument, during the SPOT4 (Take5) experiment, was equipped with 10 spectral bands described in the table below. The instrument is now being modified in view of Sentinel-2 and Venµs launches, to accommodate new spectral bands, in the near infra-red mainly, where the sampling of the spectrum was not sufficient.

Band λ (nm), detector
1 1020Si
2 1600 InGaAs
3 870 Si
4 670 Si
5 440 Si
6 550 Si
7 1020 InGaAs
8 937 Si
9 380 Si
10 740 Si

B3 (clear symbols for SPOT4, dark symbols for ROSAS)

 

B4 (clear symbols for SPOT4, datk symbols for ROSAS)

The agreement of ROSAS and SPOT4(Take5) surface reflectance measurement is excellent, in all band but near-infrared : better than 5% in the green (B1), red (B2) and SWIR (B4) channels, and 7-8% in the NIR (B3). The differences observed in the NIR are being investigated, but could be linked to the spectral interpolation, as SPOT4 B3 band is quite far from ROSAS spectral bands.

In the SWIR, the greater variations of surface reflectances with time may be noticed, with large reflectance drops after rains. The SWIR band is very sensitive to the soil moisture, at least when the vegetation cover is sparse, which is the case at La Crau. In the other bands, these variations are much less visible, and what should be noticed is the great stability of surface reflectances with time, thanks to the acquisitions with constant viewing angles and also to the quality of atmospheric correction...

 

 

A poster was shown by Aimé Meygret at the "Sentinel-2 for science" symposium in Frascati in may 2014.

 

 

 

Express your needs concerning agriculture monitoring using Sentinel-2 time series

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As you may know, we have been selected for ESA's project "Sentinel-2 Agriculture".  Among the tasks we must fulfill, we have to ask the users about their needs concerning the use of Sentinel-2 time series to monitor agriculture, and of course we need to write a synthesis.

 

ESA had already distributed a questionnaire at the S2 symposium in 2012, which was used as a basis to define the Sen2Agri project. My revered colleague (and boss) Gérard Dedieu, just cooked a new detailed survey form. If you are a potential user of remote sensed images for agriculture monitoring,  you are very welcome to fill this survey.

 

Although the baseline of SenAgri products was already defined in the call for tender, your answers will be very useful to detail the product requirements, and to forward your needs to ESA and other space agencies, and to define the next versions of our products.