Combined exploitation of VENμS, Sentinel-2 and Landsat-8: the spectral bands

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The combined use of VENμS, Sentinel-2 and Landsat-8 data can increase the likelihood of obtaining cloud-free images or may allow detailed tracking of rapidly evolving phenomena.

In order to facilitate this combination, the table below summarizes the correspondences between the spectral bands of the instruments. VENμS does not have a spectral band in the middle infrared.

The figure below shows the spectral bands of VENμS and Sentinel-2 in the 400 to 1000 nm range. The SWIR bands of Sentinel-2 are not included.The table below shows the usual band combinations


The figure below makes it possible to assess the degree of similarity of the spectral responses of these usual bands.

The detailed spectral responses of each instrument are available via the following web pages:

VENµS

http://www.cesbio.ups-tlse.fr/multitemp/?page_id=14229

SENTINEL-2

https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/document-library/-/asset_publisher/Wk0TKajiISaR/content/sentinel-2a-spectral-responses

LANDSAT

https://landsat.usgs.gov/spectral-characteristics-viewer

https://landsat.usgs.gov/landsat/spectral_viewer/bands/Ball_BA_RSR.xlsx

 

 

 

Exploitation combinée de VENµS, Sentinel-2 and Landsat-8 : les bandes spectrales

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L’utilisation combinée des données de VENµS, Sentinel-2 et Landsat-8 peut permettre d’augmenter la probabilité d’obtenir des images sans nuage ou de suivre de manière détaillée des phénomènes à évolution rapide.

Afin de faciliter cette combinaison, le tableau ci-dessous présente de manière résumée les correspondances entre les bandes spectrales des instruments. VENµS ne comporte pas de bande spectrale dans le moyen infrarouge.

La figure ci-dessous présente les bandes spectrales de VENµS et Sentinel-2 dans le domaine 400 à 1000 nm. Les bandes SWIR de Sentinel-2 ne sont incluses.Le tableau ci-dessous présente les combinaisons de bandes usuelles

La figure ci-après permet d'apprécier le degré de similarité des réponses spectrales de ces bandes usuelles.

Les réponses spectrales détaillées de chaque instrument sont disponibles via les pages web suivantes :

VENµS

http://www.cesbio.ups-tlse.fr/multitemp/?page_id=14229

SENTINEL-2

https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/document-library/-/asset_publisher/Wk0TKajiISaR/content/sentinel-2a-spectral-responses

LANDSAT

https://landsat.usgs.gov/spectral-characteristics-viewer

https://landsat.usgs.gov/landsat/spectral_viewer/bands/Ball_BA_RSR.xlsx

 

 

 

Our blog's audience in 2018

A seventh year begins for the "Séries temporelles" blog, and as usual, it is an opportunity to review its audience, and to get a little self-satisfaction. We usually publish this post early January, but it seems there was no January this year (or was I too busy ?). The blog is still receiving more visits every year, with a sharp growth this year : +35% of visits ...

Blog traffic from December 2012 to January 2019

Blog traffic from December 2012 to January 2019 (the trends were computed using the Theil–Sen estimator), computed by Simon Gascoin.

So, if we look at the trends on the plots above, the audience growth is remarkably linear, but if we sum-up everything per year, we see a sharp increase. The cause is that outlier in the top-right corner or each graph above, related to a big buzz in Japan for SImon's article about Xe-Namnoy lake dam failure in July, that flooded several villages, and killed too many people.

 

2013 2014 2015 2016 2017 2018
Number of visits 13985 22928 34723 47773 57692 79243
Number of viewed pages 30922 46940 66947 89555 105846 131846

 

French visitors only counted for 25% of visits, much less than the other years. Japan ranked second with 16%,  United States ranked third, followed by European countries (UK, Germany, Italy, Spain) and by India, Canada and Morocco.

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4 thèses en cours à Toulouse pour étudier les forêts tempérées par télédétection

Le pôle toulousain de recherche publique en télédétection est surtout connu sur la thématique forestière grâce à la mission BIOMASS, qui sera lancée vers 2021 et qui est portée par le CESBio, mais qui concerne surtout les forêts tropicales. L’objectif de cet article est de présenter les travaux en cours sur les forêts tempérées, dans le contexte de la France métropolitaine, qui sont portées en synergie par l’UMR Dynafor (collègues INRA, Ensat et EI Purpan) et par l’UMR CESBio. En effet, 4 thèses sont actuellement en cours dont 2 qui seront soutenues fin 2019. Le point commun à ces 4 thèses comme aux travaux qui les ont précédées est l’utilisation de séries temporelles, d’abord basse résolution (Modis), puis, depuis 2015, en haute résolution spatiale avec Sentinel 1 et 2 (‘S1’ et ‘S2’).

 

Différence de phenologie entre chênes

Figure 1. Différences de phénologie entre espèces de chênes.

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Sentinel-2 Level3A time series (July, August, September 2018)

If you are not afraid to spend too much time while you have urgent things to do, you may have a look to the mosaic of Sentinel-2 monthly syntheses for September over France. You may access to each monthly synthesis using the following links :

 
Or you may also use the viewer below to compare with the previous months and see how France became brown in September :
 

See it full screen

The monthly syntheses are produced using the WASP processor, which is described here.

By comparing the various syntheses, you will see the evolution of the landscape, generally much brownler in September, but this representation will also help you spot the composite artefacts. These are not very numerous, but you will see them :

  • on some web browsers (firefox V58), geometrical differences appear even at a low resolution. Other browsers and versions do not have this defect. It is really not due to Sentinel-2 or Theia products
  • above water and snow (we must work on this defect)
  • where clouds have covered a place during the whole month of July or August. These pixels are flagged as invalid in the products (but not on the mosaic).
  • where clouds or shadows were not properly detected by MAJA
  • at the edges of Sentinel-2 swath. For the first time, in october, a swath edge is clearly visible near Cambrai. The area must have been quite cloudy, and we observe here a greener part on the right, observed later in October, that the browner part on the left. The only way to correct this kind of atefact while keeping a physical meaning to the reflectances, would be to improve Sentinel-2 revisit time
  • some tile edges in July, due to the fact that Level 3A products were not all generated for the 15th of July, but for dates between the 8th and the 26th. This has been corrected for the next months

 

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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La vectorisation du produit OSO, comment ça marche ?

Le produit vecteur d'OSO 2017 est enfin sorti ! Après plusieurs semaines de traitements, les vecteurs de chaque département sont disponibles ici. La production requiert la mobilisation d'une grande quantité de ressources de calcul et une stratégie de traitements un peu particulière. Nous voulions vous expliquer comment parvient-on à produire cette couche d'information.

Exemple du raster initial (10 m), régularisé (20m) et vectorisé

A priori, le plus simple serait de prendre la couche raster issue de la chaine de traitements iota² de l'intégrer dans notre logiciel SIG préféré et d'appuyer sur le bouton "Vectorisation" ! Mais les choses ne sont pas si simples, certaines contraintes et besoins nous obligent à quelques tours de passe-passe :

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