The V2.0 of SPOT4 (Take5) data set is available.

Voilà ! The new version (V2.0) of SPOT4-Take5 data set is available, for the 45 sites. I would like to thank the development and processing teams of MUSCATE center in CNES, who work for THEIA, the image quality teams at CNES (SI/QI and SI/MO), and of course Mireille Huc at CESBIO, for the production of this new version, which finally required a lot of work.

The product version number is not included in the filenames, but you can recognise a V2.0 product by looking into the xml metadata file :



This reprocessing brings the following new features :

    Quicklooks are now provided with the images. The clouds are circled in green, the shadows in black, water in blue and snow in pink.

  • We provide quicklooks on which you can see the cloud and shadows masks
  • We enhanced the quality of the ortho-rectification :
    • By changing the référence ortho-image for the sites in France (GEOSUD, processing done by the french institute for geography IGN)
    • by replacing the LANDSAT 5 otho-images by LANDSAT 8 images for most other sites outside France. LANDSAT 8 geometric performances are enhanced compared to  LANDSAT 5.
    • however, for a few sites (Borneo, Gabon, Congo (1,CNES), CCRS, Cameroun), no clear LANDSAT 8 images was available yet and we had to keep the LANDSAT 5 reference.
      • It's not too bad for Congo, CCRS et Cameroon, as LANDSAT 5 references where quite good, for Gabon, we used a reference made with the cloud free image obtained with SPOT4-Take5, and finally, we just have Borneo site for which the level 1C obtained are quite bad with large registration errors (I am sorry Jukka)
      • A large enhancement of the performances has been observed for Sumatra, Gabon and Congo (2,ESA), for which the first version was quite bad.
  • SPOT4 radiometric calibration updated
    • A the end of SPOT4's life, my CNES colleagues updated its absolute calibration. Spot calibration is obtained using desert sites, using another satellite as reference. Up to now, it was POLDER, but now it is MERIS/ENVISAT. Moreover, the calibration coefficients we used in the first version had been extrapolated from older measurements, while now recent measurements have been used. The differences are not too big, except for the near infra-red band which varied by 4%..
  • The level 2A have been reprocessed with a new version of the aerosol model, with larger aerosols. The previous model had been tuned for sites in France, but we found that  the larger particles fitted better the in situ data on all the sites.
  • For users of mountain sites, we added a few flags about the correction of terrain effects. If the slope is in the shade, or nearly in the shade, the correction we have to do is infinite ! We limited the value of the correction and flagged the pixels for which we had to limit it in the .DIV files.
  • And at last, the Maricopa site was finally processed. This site was acquired under two angles, one from the East, one from the West. It has therefore been observed twice every 5 days under different viewing angles. Such a case was not anticipated in our prototype, and we had to correct it. The site has been divided in 2 sites Maricopa_J1 for observations from the West, and Maricopa_J5 for observations from the East. This site, which benefits from New Mexico blue skies, is a very interesting one for remote sensing geeks, as it combines multi angular and multi-temporal observations at constant angles !

SPOT4 (Take5) reprocessing

The SPOT4 (Take5) reprocessing nears its end at THEIA. All the Level 2A products have been produced, but for 4 sites (Provence, Alpes, Sudmipy E and W), for which the processing is on-going. We are now transfering the data to the distribution server. It should take just a few days.


Le retraitement de SPOT4 (Take5) à THEIA est presque terminé. Tous les produits de Niveau 2A ont été fournis sauf pour 4 sites (Provence, Alpes, Sudmipy E et O), pour lesquels le traitement est en cours. Nous commençons le transfert des données vers le serveur de distribution, ce qui devrait prendre à peine quelques jours.



The directional effects, how they work

Riddle : from which of these two ballons was the picture taken ? Solution is at the end of the post.

Among Sentinel-2, LANDSAT, Venµs or SPOT4 (Take 5) features, there is one which is frequently forgotten: it is the possibility to observe all lands every 5th day under constant viewing angles. This way of observing limits the directional effects which are one of the most perturbing effects for reflectance time series. Yet, these effects are not always known by the users of time series of remote sensing images.

The way directional effects modify the reflectances is highly visible n the pictures below, which were taken from an helicopter with the same parameters except for the viewing angles. The image on the left was taken with the back to the sun, in the backscattering direction, while the picture on the right was taken at 90 degrees from that direction.


Conifer forest observed from an helicopter, in backscattering direction (the helicopter shadow is visible). Note the nearer from the helicopter shadow, the higher thereflectance, as tree shadow are no more visible Conifer forest observed from an helicopter, at 90 degrees from the backscattering direction. Reflectance is much lower since the shadows cast by the trees are visible as well as the shadows cast by the needles on the needles below (Pictures F.M. Bréon)


Depending on the observation angles and the solar angles, the reflectances measured by a satellite will change a lot, and we can therefore talk of "reflectance anisotropy", even if "directional effects" is the most frequently used locution. The way they change depends on the surface type : a flat sand desert will have little anisotropy (see next figure on the left), and the surface is said "quasi lambertian". On the contrary, a calm water surface will behave as a mirror, and will exhibit a very strong reflectance peak on the direction opposite to the sun direction, with regard to the vertical. Finally, vegetation always exhibits reflectance peak in the back scattering direction, for which the solar and viewing angles are quasi identical (see the plot below, on the right). On this plot, a reflectance variation greater than 30% can be observed in a couple of degrees. This phenomenon is called Hot Spot, and it is due to the fact that from this direction, one can only see the sunlit faces. Finally, the plot shows that for an angle variation of 40 degrees, the surface reflectance may change by a factor two. The directional effects should thus not be neglected.


Reflectances of a desert, observed by the POLDER instrument, as a function of the phase angle (angular distance to the backscattering direction). In red, the Near Infrared band, in green, the red band. Reflectances of a cropland, observed by the POLDER instrument, as a function of the phase angle (angular distance to the backscattering direction). In red, the Near Infrared band, in green, the red band.


The wide field of view instruments, such as MODIS, SPOT/VEGETATION, MERIS, VIIRS or Proba-V, and the high resolution ones with a pointing capability, such as SPOT, Rapid-Eye or Pleiades, deliver time series acquired under changing angles. Their reflectances time series are thus very noisy if no correction is attempted. NDVI time series are less noisy, because both red and Near Infrared spectral bands exhibit similar variations. Several correction methods were implemented, but their results are far from perfect.


In order to avoid all these troubles, my CESBIO colleagues F.Cabot and G. Dedieu invented the RHEA concept, which consists in putting the satellite on an orbit with a short repeat cycle (1 to 5 days), in order to observe a given site under constant angles. The VENµS satellite stems from this concept, and Sentinel-2 and SPOT4 (Take5) as well. Formosat-2 has also a repeat cycle of one day, but this feature is mainly due to the fact that the Taiwan island can be observed every day from that orbit. Regarding LANDSAT, I do not now if its designers wanted to minimize directional effects, but of course their choice was a good choice.

Thanks to the satellites that observe under constant viewing angles, the noise on time series is really decreased, as shown on the plot below, which gives the surface reflectances  of a wheat pixel (24*24 m²) in Morocco, observed by Formosat-2 during a whole growing season.

Surface reflectances as a funcion of time for a wheat pixel in Morocco.

Finally, it is the hot spot phenomenon, which gives the solution to the riddle above, since the balloon on the left is surrounded by a brighter halo. It means that the direction around the left ballon is the backscattering direction, and therefore that the observer was on this ballon. This is also proven by the complete photograph (taken by A. Deramecourt, a CNES colleague).  I think my colleague saw some poetry in the two balloons hugging, which I hope you still can  appreciate, while, because of my professional bias, I only see a mere hot-spot.



Le retraitement de SPOT4 (Take5) est en cours / SPOT4 (Take5) reprocessing on its way.

Le retraitement des données SPOT4 (Take5) est en cours dans le centre MUSCATE de THEIA, au CNES. Les niveaux 1C ont été produits, le traitement des niveaux 2A commence aujourd'hui. Nous avons perdu une semaine avec un bug qui a été trouvé à la dernière minute sur des données FORMOSAT-2, et bien qu'il ne soit pas très probable qu'il se produise sur SPOT4 (Take 5), nous avons décidé de le corriger avant ce retraitement. Le traitement des Niveaux 2A devrait être terminé dans une semaine, et les produits seront disponibles pour la distribution la semaine suivante.

The SPOT4-(Take5) reprocessing is on its way in the MUSCATE processing center at THEIA, CNES. The level 1C products have been produced, and the level 2A should start today. A last minute bug on the level 2 was found when processing some Formosat-2 data, and although it was not likely to happen on SPOT4 (Take 5), data, we decided to correct it before launching the reprocessing. The level 2A processing should end next week, and distribution will start the week after.

Sentinel-2 Agriculture

We are very proud to tell that our consortium was selected by ESA for the S2-Agri call for tender.


Our consortium is built from the following partners :


The S2-Agri project, whose website was just created, aims at showing on a large scale project, the capabilities of Sentinel-2 mission for agriculture monitoring, by providing, after consulting several "champion" users, and open source processing software, that will provide the following types of products :


  • periodic synthese of surface reflectances (Level 3A products)
  • a crop mask
  • a map of the main crops (see the image below, and the post on land cover maps)
  • some vegetation indices or biophysical variables

Example of a land cover map automatically generated by a software developed by Isabel Rodes (CESBIO), from LANDSAT 5 and 7 data in 2010. This land cover map was produced by I. Rodes, in the framework of a methodological PhD thesis, it is not as specialized for Agriculture as the ones that will be produced for S2-Agri project. It still already provides 3 agriculture classes : winter crops, summer corps, and meadows.


This project, which started on January 31st, 2014, will be carried out in three phases, each with an approximative duration of 1 year.

  1. A test phase, to develop, tune and validate methods and products, on 13 sites scattered around the world, this phase will mainly rely on SPOT4-Take5 data, complemented by LANDSAT 8 or RapidEye images. Several sites will be selected within the JECAM network.
  2. A development phase, during which the production system will be built, and prototype products will be issued and tested.
  3. A demonstration phase, based on the first year of Sentinel-2 acquisitions, for which 3 entire countries (> 500 000 km²) plus 5 sites of 300x3000 km². At least 2 of selected  the countries are in Africa.

At the end of the project, the production system will be released as an open source software by ESA, and

A l'issue de ce projet, le système de production sera disponible en open source auprès de l'ESA, and given the amount of work, we will have won dark circles around our eyes!


The level 3A products

Among the products prepared to be processed by the THEIA land data center, the level 3A product was not yet described in this blog. The level 3A products provide a monthly synthesis of the level 2A. These products should be very useful for the following reasons :

  • The level 3A, produced once a month, uses up to six times less volume than the level 2A products acquires during a month.
  • The level 3A provides a regular time sampling of the reflectances variation, while the level 2A sampling is dependent on the cloud cover
  • Several processing methods and applications are hindered by the data gaps due to cloud cover. The level 3A product aims at minimizing the residual gaps.


Thanks to SPOT4 (Take5) data set, we were able to try and test several methods to produce level 3A products on varous types of landscapes and climates. This work, suprvised by Mireille Huc and myself, is performed by Mohamed Kadiri, at CESBIO, and is funded by the CNES budget of THEIA Land Data Center. Our method consists in computing, foe each pixel, a weighted average of the surface reflectances of the cloud free observations, obtained within a N day distance frome the central date TO of the level 3A product. For instance, the example below was obtained with N=21, for the 15 th of each month. As a result, the level 2A used in the average for the level 3A product dated on March the 15th, were acquired from Feruary the 24th to April the 4th.



The weighted average gives more weight to

  • the cloud free images
  • the pixels which are far from clouds
  • the images with a low aerosol content
  • the images acquired near the level 3A product date.

Les values of the weight and of the duration N, have a large influence on the product data quality. To tune their values, we set up three quality criteria :

  • The percentage of residual data gaps for which all the observations were cloudy
  • The difference of the level 3A reflectances with the values of a selected level 2A product acquired near the central date T0.
  • A measurement of the artefacts standard deviation. The artefacts appear near the borders of data gaps that affect one of the dates used in the level 3A synthesis.


For instance, here are the performances obtained on the Versailles site, which was heavily clouded in the spring of 2013. For this site, one can note, that the residual gap percentage is very low despite the bad weather, confirming that Sentinel-2 should be able to provide cloud free Level 3A products each month. For this site, the optimal duration of the synthesis is somewhere between 2* 21 and 2*28 days.


Performances obtained for Versailles SPOT4(Take5) site, for several values of the half-period N. In red, the residual percentage of data gaps (scale on the right), in yellow and green, the maximum value of the difference of the level 3A to the central level 2A, for resp the best 70% and 95% of pixels. In blue, the residual error standard deviation.




For Sentinel-2, the level 3A will have to include a correction for directional effects, in order to use in the same level 3A product, the data acquired from different satellite tracks, from different viewing angles. Finally, as an option, we might include a gap-filling method to fill the residual gaps.

In short, we still have work to do. A comparison with the classical NDVI Maximum Value Composite is provided in this post.

The product level names, how they work ?

Simulation of Sentinel-2 products from Formosat-2 data (CESBIO)


Many users are confused by the earth observation product names. Maybe detailing the logic behind the names will help recall them. Here is how the THEIA Land Data Center product names were chosen.

  • we decided to use Sentinel-2 product names 1C, 2A, 3A, since we are sure Sentinel-2 will become a reference in high resolution earth observation.
    • Level 1C is a monodate ortho-rectified image expressed in TOA reflectance
    • Level 2A is a monodate ortho-rectified image expressed in surface reflectance, provided with a cloud/cloud shadow/snow/water mask
    • Level 3A is a monthly composite of Level2A Cloud/Cloud shadows free pixels
  • this nomenclature, defined by ESA and CNES, complies with the norms of the Committee on Earth Observation Satellites (CEOS)


CEOS naming rules are quite difficult to find, and I had searched them several times unsuccessfully. But recently, I found the list of members of the CEOS product harmonization committee, and two of its members (Frédéric Baret (INRA, France), and Kenneth McDonald (NOAA, USA)) replied very quickly to my questions.


The CEOS norm is based on a nomenclature defined by NASA in 1996, which is available on  Wikipedia :


Data Level NASA-EOSDIS Definition
Level 0 Reconstructed, unprocessed instrument and payload data at full resolution, with any and all communications artifacts (e.g., synchronization frames, communications headers, duplicate data) removed.
Level 1A Reconstructed, unprocessed instrument data at full resolution, time-referenced, and annotated with ancillary information, including radiometric and geometric calibration coefficients and georeferencing parameters (e.g., platform ephemeris) computed and appended but not applied to Level 0 data.
Level 1B Level 1A data that have been processed to sensor units (not all instruments have Level 1B source data).
Level 2 Derived geophysical variables at the same resolution and location as Level 1 source data.
Level 3 Variables mapped on uniform space-time grid scales, usually with some completeness and consistency.
Level 4 Model output or results from analyses of lower-level data (e.g., variables derived from multiple measurements).


I do not know what you think of it, but my sense is that it is quite vague in some aspects (what is a  "sensor unit") and too directive in some other aspects : a resampling of data on a cartographic grid is only allowed at level 3. If a uses does not want to handle the always complex reprocessing of data, he has to use the level 3 data.


The CEOS norm provided below was clearly inspired by NASA's norm, but it allows a data resampling  starting at level 1, and the data can be expressed in Physical units and not only "Sensor units". The CEOS norm does not detail the sub-levels (1A, 2B...). However, the distinction between Level 1 and Level 2 is still a bit fuzzy, as it is not always easy to tell a physical unit from a geophysical unit. We often consider a top of atmosphere reflectance as a Level 1 product and a surface reflectance after atmospheric correction a Level 2 product. Is a surface reflectance a physical unit or a geophysical unit?


Data Level CEOS Definition
Level 0 Reconstructed unprocessed instrument data at full space time resolution with all available supplemental information to be used in subsequent processing (e.g., ephemeris, health and safety) appended.
Level 1 Unpacked, reformatted level 0 data, with all supplemental information to be used in subsequent processing appended. Optional radiometric and geometric correction applied to produce parameters in physical units. Data generally presented as full time/space resolution. A wide variety of sub level products are possible.
Level 2 Retrieved environmental variables (e.g., ocean wave height, soil moisture, ice concentration) at the same resolution and location as the level 1 source data.
Level 3 Data or retrieved environmental variables which have been spatially and/or temporally re-sampled (i.e., derived from level 1 or 2 products). Such re-sampling may include averaging and compositing.
Level 4 Model output or results from analyses of lower level data (i.e., variables that are not directly measured by the instruments, but are derived from these measurements).


Having had some difficulties finding the famous CEOS norm, I had build my own idea of what the CEOS norm should be, probably, from dscussions with colleagues during the POLDER.project preparation. So here is my own version of the product level names norm (but I know a personal norm is not a norm anymore)


Data Level Product nomenclature according to... myself
Level 0 Reconstructed unprocessed instrument data at full space time resolution with all available supplemental information to be used in subsequent processing (e.g., ephemeris, health and safety) appended.
Level 1 All pixels were acquired at the same time (within a few instants, during one satellite overpass), and their processing does not make assumptions on the nature of the observed pixel. Each pixel is processed in the same way, whatever it is made of (cloud, forest, sea...). The values are expressed in physical units or the product provides all the necessary information to convert the values to physical units. The product may be resampled onto a cartographic grid, or may just provide the necessary information to resample it.
Level 2 All pixels were acquired all at the same time (within a few instants, during one satellite overpass), but here, the processing may include assumptions on the nature of the pixel, for instance concerning the atmosphere, vegetation of sea state. The pixels may be processed differently according to their nature.
Level 3 The product is obtained from data acquired at different dates, often with different footprints. As for Level 2, processing may differ according to the pixel nature, and assumptions on this nature are allowed. 

Level 3 products are often composite products based on the level 2 data acquired during a certain period of time (10 days, one month...)


The CEOS norm does not define sub-levels, and for that aspect, NASA's norm has some influence. For instance, with Sentinel-2, Level 1A and Level 1B exist as internal products and are quite similar to what is in NASA norm, while the Level 1C is ortho-rectified and expressed in top of atmosphere reflectance. The level 1C only will be distributed to standard users.


Sentinel-2 Mission requirement document also defines a level 2A, expressed in surface reflectance, and a Level 2B that provides biophysical variables such as LAI, fAPAR.


Finally, let's recall that several Earth observing missions do not respect the CEOS norms, often because their nomenclature was defined before the norm existed. It is the case of SPOT and followers (Pleiades, Rapid Eye...)



Production of LANDSAT L2A data at THEIA to begin shortly.

At CNES, the prototype MUSCATE production facility of THEIA land data centre will soon start the production and distribution of Level 2A Landsat 5 and 7 data, and shortly after of LANDSAT 8 data covering the entire surface of France.


Mosaic of LANDSAT 5 & 7 data produced at CESBIO, from both ESA and USGS data. These data are cut in 110 x 110 km² tiles. For each tile, each LANDSAT acquisition with at least a little clear sky corner is provided.


For Landsat 5 and 7, we use data from both USGS and ESA : indeed, up to now only ESA has the LANDSAT 5 data that were acquired over the receiving stations of Mas Palomas (Canary Islands), Matera (Italy), and Svalbard (Norway). A transfer to USGS of ESA's data is expected, it may have started in Svalbard, but it has not yet begun for the Matera station, which covers France.

Level 1C

The USGS data are orthorectified, but those from ESA are not, so, as for SPOT4 (Take5), we set up an ortho-rectification processing using the SIGMA tool provided by CNES.  The ESA's products we received 2 years ago also have some flaws (which may have been corrected by now, but given it took months to obtain the data we did not ask for a reprocessing): the thermal band is unusable and you will find here and there colourful bright spots, such as those produced by your neighbour moped on your TV when you were a child. Nevertheless, we can produce correct Level 1 products, although we look forward to the reprocessing of these products by USGS. ESA has now it own processing of LANDSAT data, but it stops at level 1C.

For Landsat 7, this processing is not necessary because the data are already ortho-rectified. We interpolate only a small portion of the missing data due to LANDSAT 7 SLC breakdown, and then we discard the parts of the image where the gaps are too large. For LANDSAT 8, none of these processings are needed.


Level 2A

The Level 2A products (Cloud Mask, Atmospheric correction) will be produced by the prototype of MACCS software developed and maintained by Mireille Huc (CESBIO, CNRS). Two years ago, I had already produced such a data set on the most Southern part of France, at CESBIO. These products are already distributed on THEIA web site and are also used to illustrate this post.


LANDSAT 5 and 7 :

Starting in April, we will process the LANDSAT 5 & 7 data acquired above France from 2009 to 2011.



From April or May, we will process the LANDSAT 8 data acquired since april 2013, and we will try to keep the pace so as to produce the incoming new LANDSAT 8 acquisitions with a short delay.


Data Format :

We will reuse the data format of SPOT4 (Take5). France will be split into 110*110 km² tiles with a 10 km overlap with their neighbours. (See the image mosaic obtained for the South of France).

Depending on the success of the distribution of these data, we will decide if it is worth producing older time periods or other regions. Please tell us if you need such data.

Exemple : available LANDSAT images from July to October 2009 for the tile centered on Toulouse. For each date, we provide the level 1C image tTOA reflectance), on the left, and the level 2A image on the right (surface reflectance). The detected clouds are circled in red.

A python module for batch downloads of LANDSAT data.


The Python routine, provided via the link at the end of this post, enables batch downloads of LANDSAT data, on USGS earth explorer site.


It works for LANDSAT 8 and LANDSAT 7 (and could work for LANDSAT 5), it just requires that the data are available on line. It is the case for all LANDSAT 8 data, but for LANDSAT 5&7, it may be necessary to order the data first on The routine of course requires that you have an account and password on earth explorer, and you will have to configure your accounts and directories within the routine.


This routine may be used in two ways :

  • You select a LANDSAT scene defined by its WRS-2 coordinates (for instance, (198,030) pour Toulouse). The -d option requires that you provide a day of year for which the scene was observed (1 overpass every 16 days)
    • example :
    • python -o scene -t LC8 -a 2013 -d 113 -f 365 -s 199030
  • You provide a list of products to download, with a site name for each product.
    • Example :
      python -o list -l landsat8_list.txt
    • landsat8_list.txt is as follows : of course, you need to get the references on Earth Explorer.
    • Tunisia LC81910352013160LGN00
      Tunisia LC81910362013160LGN00


The routine is available here :


Once you have downloaded the data, you will probably want to convert them to reflectances or temperatures for the thermal bands. One of our colleagues at CESBIO provides a Python module to do the conversion (The post is in French, but Python is a universal language...).

A new year of time series


Dear readers of this blog, all my best wishes for 2014 !


May this new year bring you nice image time series, but spare you some time to process those recorded last year (among which SPOT4 (Take5) ;) ). Even if 2014 will be another year without Sentinel-2 and VENµS, let's guess that with LANDSAT-8 et SPOT4 (Take5),  the data sets produced by THEIA et maybe the new experiment SPOT5 (Take5), we won't have time to get bored.

This new year is also the first birthday of this blog, and though I only imagined to keep it during SPOT4 (Take5) experiment,  the large number of readers incite me to go on.  And a blog about time series should at least last for some time...


At least one visit from all the countries in blue has been recorded

Country Pages Visits
19 228 4 272
1 306 177
1 250 515
781 218
698 290
684 243
682 162
549 220
493 277
458 84


During its first year, this blog received 14000 visits, which is 60 visits per day of work (some of you even read it during week-ends, but I hope this blog is not the reason why you are not going for  a walk, to the movies or don't read good books !). 31000 pages were visited (with a click), but as it is a blog you may read several posts with only one click. 60% of the visits come from France and 40% from abroad, and the blog received at least a visitor from nearly every country.

Apart from the main pages, the most successful posts are :