Sentinel-2 captures new data centers in Iceland

In a remote sensing study published in Nature, the authors claimed that they used "101 CPU-core years of computation (..) within the Google data centres". This made me wonder what could be the carbon footprint of such a study?. I estimated that it should be 65 tonnes of carbon dioxide, but a Google engineer replied:

Google purchases enough renewable energy to offset 100% of its energy use for its offices and data centers.

In just a few years, the company has made an impressive move to renewables, true to its famous motto "don't be evil". Google is the largest corporate purchaser of renewable energy on the planet. However, it's better to save energy than to buy renewable energy, as explained by Forbes:

It is true that Google is buying all its electricity from renewable sources, but it is unlikely that all the electricity it is using comes from renewable sources. This is because solar and wind, Google’s choices for renewable sources, are both variable, while Google’s electricity demand is not. In other words, there are times and locations when Google must use electricity that comes from traditional sources, while simultaneously the electricity generated from the renewable projects funded via Google’s PPAs is curtailed and lost.

To save energy (and cost), large tech companies are moving data centers in Nordic countries like Iceland to take advantage of the "free air cooling". Moreover in Iceland the electricity production relies primarily on hydropower and geothermal heat.

Sentinel-2 images of data centers in Reykjanesskagi, Iceland


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Detecting geolocation errors in glacier outlines with Sentinel-2 snow cover duration maps

Two years ago I posted an animation of the snow cover area evolution near Zermatt, Switzerland from Sentinel-2 L2A data processed by LIS.

From this time series of snow maps I generated a snow cover duration map and
added the glacier outlines from the Randolph Glacier Inventory 5.0.

Colors: Snow cover duration between 01 Sep 2016 to 31 Aug 2017 (in days). Black line: Glacier outline from RGI.



I was satisfied by the overall good agreement between the areas with a high snow cover duration and the glacier outlines. However by looking more closely at the small isolated glacier in the eastern part I noticed a mismatch between both datasets..
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THEIA's Sentinel-2 L2A processing on Sahel is progressing

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As we had announced in November, the MUSCATE production centre in Theia is gradually adding areas in the Sahel, which are shown in the image below. The data are processed from December 2016 onwards, which means that we have a large amount of data to process. So we started with the most westerly tiles, in Senegal on the UTM28 zone, then progressed from one zone to another towards the East.


Theia Sentinel-2 processing area on the Sahel

 

In red, the tiles available from Dec 2016 to NRT, in blue , the remaining tiles to be added.

In recent days, Theia has completed the processing of tiles in the UTM29 area, which mainly covers northern Guinea and western Mali, but also partially covers southern Mauritania and Sierra Leone and north-western Côte d'Ivoire. The treatment of the UTM 30 zone, which covers Burkina Faso and Mali, is also well advanced. The east of this area is finished, and the west is progressing well, as shown in the animation below. The UTM31 zone has also been brought into production. Feel free to take a look from time to time at the map of areas covered by MUSCATE. The blue tiles turn red as soon as we switch to run-of-river processing.

 

The data can be downloaded from here:

https://theia.cnes.fr

 

Animation in the region of the city of Mopti, Mali, with about one image per month in 2017. The displayed time series extends between two rainy seasons and covers the dry season. Many fire scars are visible during the dry season. Some shadows appear, which actually correspond to the shadows of cirrus clouds corrected by MAJA. Shadows and cirrus are marked in the products.

More than 1000 downloads of MAJA atmospheric correction software

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We started distributing MAJA as a free software in July 2017, less than 2 years ago, and it already reached 1000 downloads (1043, as a mater of fact).  MAJA was in fact downloaded twice per working day, and in the very last weeks, we had about 4 downloads a day ! Of course it is probably much less than Sen2cor, which is a software with a comfortable  funding to make it easy to install and use on a personal computer, under Windows, IOS or Linux systems. But both software are not in the same category, Sen2cor was designed to run on client side,  while MAJA was designed  to be robust and efficient in production environments.

 

MAJA only works on linux systems, and its multi-temporal features make it less easy to use. MAJA does much more complex computations than Sen2cor, about twice faster thanks to a good parallelization using the Orfeo Toolbox C++ Library. Still, despite this complexity, a lot of users seem to have managed to make it work and seem to be happy with it.

Comparison of MAJA (left) and Sen2cor (right) time series over Naples, Italy. Cloud masks are outlined in green, shadows in yellow. click on the image to enlarge.


We recommend the use of the latest version 3.3 which brings a lot of improvements.

 

Users can run MAJA through 2 main launchers:

  • Sen2Agri, for which MAJA (or its former MACCS version) was downloaded almost 700 times
  • Start-MAJA, which is a little scheduler I developed first for my own usage and then released as open source on github. It has been downloaded 450 times and has received 18 stars on the CNES github platform, and 45 on my github repository, where it was available before moving to CNES's.

Last fortnight clones of Start-MAJA scheduler

However, it is not easy to know how many users managed to install it properly, and how many failed. We know some users manage to install it easily. We often receive feedback and questions when users do not succeed at once, and we do not know the proportion who give-up. We would be happy to receive feedback emails and know if you succeeded, and if you are happy with the results.

 

 

Near real time detection of deforestation in French Guiana

Marie Ballère started in October 2018 a Ph.D. funded by WWF and CNES. The aim of her Ph.D. is to characterize animal habitats in tropical forest using radar and optical data. The first results on near real time forest disturbances assessment using radar Sentinel-1 data in French Guiana were shown at the ESA Living Planet Symposium 2019 in Milano, and they are striking !

 

The near real time forest disturbances detection method used by Marie has been described in Bouvet et al. (2018) and successfully tested over a test site in Peru. Classical methods are based on the hypothesis that the radar backscatter decreases when disturbances occur. However, the backscatter does not necessarily decrease, because rainfalls and/or trees remaining on the ground for example, lead to an increase of radar backscatter.

 

To get around this problem, the method from Bouvet et al. (2018) is based on the detection of radar shadowing. Shadowing occurs in radar images because of the particular side-looking viewing geometry of radar systems. A shadow in a radar image is an area that cannot be reached by any radar pulse. Shadows created by trees at the borders between forest and non-forest areas can be observed in high-resolution radar images (Figure 1), depending on the viewing direction. Shadows that appear are characterized by a sudden drop of backscatter in the radar time series. Thanks to the purely geometrical nature of the shadowing effects, this decrease of backscatter is expected to be persistent over time. New shadows should consequently remain visible for a long time and are easily detectable when dense time series of radar data, such as Sentinel-1 time series, are available.

Figure 1 Illustration of the SAR shadowing effect at the border between forests and deforested areas

 

This method has been tested over various sites in South American, African and Asian tropical forests for three years now and significantly improved. Marie Ballère participated to the improvement of the method, applied it over the whole French Guiana using Sentinel-1 data acquired from 2016 to 2018, and validated the resulting maps. Slashing deforestation (farming method that involves the cutting and burning of trees) detection has been validated using 94 reference data (surface area of 48.2 ha) kindly shared by Pierre Joubert and Eloise Grebic from the Parc Amazonien de Guyane. Producer and user accuracies related to disturbed forests reached 83% and 99% respectively. Gold mining detection has been validated using 36 reference data (surface area of 76 ha), leading to producer and user accuracies of 86% and 99% respectively.

 

In addition, we compared our results with the deforestation patches detected in the University of Maryland (UMD) Global Land Analysis and Discovery (GLAD) Forest Alert dataset (Hansen et al., 2016), a Landsat-based humid tropical forest disturbance alert system over the tropics (http://glad.geog.umd.edu/alerts). Producer accuracies of 24% and 44% were found for slashing deforestation and gold mining respectively. A small area showing the comparison between the CESBIO and UMD-GLAD methods is shown in the maps in Figure 2.

Figure 2 Comparison between the CESBIO and UMD-GLAD near real time forest disturbances detection methods in French Guiana in 2018. Gold mining reference plots are shown in green. Producer accuracies of 86% and 44% were found using the CESBIO and UMD-GLAD respectively

Figure 3 shows the number of disturbed areas detected per month using the CESBIO and UMD-GLAD methods (note that disturbed plots that were not detected using the UMD-GLAD method were not taken into account). Slashing deforestation, occurring mainly during the dry season, was detected timely using both methods. However, because clouds hamper the GLAD optical-based forest disturbances detection during the rainy season, gold mining occurring all year long was detected 72±58 days in advance using the CESBIO method.

Figure 3 Slashing deforestation, occurring mainly during the dry season, was detected timely using CESBIO and UMD-GLAD methods. Gold mining occurring all year long was detected 72±58 days in advance using the CESBIO method

Figure 4 Forest disturbances detection using the CESBIO method in French Guiana from 2016 to 2018

The CESBIO method has been applied over the whole French Guiana for the years 2016, 2017 and 2018 (Figure 4). The deforestation rates were found to be -0.7%, -0.5% and -0.5% respectively, relatively to French Guyana area.

 

A lot of exciting research can now be performed based on these results (e.g. for understanding the causes related to the spatial and temporal evolution of disturbances patterns). In addition, Sentinel-1 and Sentinel-2 data are being currently used by Marie to identify the drivers of deforestation.

References:
  • Bouvet, A., Mermoz, S., Ballère, M., Koleck, T., & Le Toan, T. (2018). Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series. Remote Sensing, 10(8), 1250.
  • Hansen, M. C., Krylov, A., Tyukavina, A., Potapov, P. V., Turubanova, S., Zutta, B., ... & Moore, R. (2016). Humid tropical forest disturbance alerts using Landsat data. Environmental Research Letters, 11(3), 034008.

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

 

 

 

MAJA 3.3 is available, with a LOT of improvements

What's new ?

Pfew ! It has been quite long, but MAJA 3.3 is available, and it improves a LOT of things !

  • Some bugs have been fixed, like the one which caused detection of cloud or cloud shadows on the edges of the images
  • It seems we have finally solved the bugs that plagued the CAMS option since we released MAJA V3.0. Since V3.0, this option uses the Copernicus Atmosphere aerosol forecasts to set the aerosol type before retrieving the aerosol optical thickness (AOT) from Sentinel-2 data
  • We now also use CAMS AOT as a default value, when it is not possible to estimate AOT using the images, for instance above a snow covered landscape or for small gaps in a large cloud cover. Before that, we used 0.1 everywhere as a default value. The default value is used in the cost function with a very low weight, it has no impact when conditions for AOT estimates are good, but a large impact in bad conditions.
  • The cirrus correction module was over correcting the impact of thick cirrus clouds, providing images with dark clouds. We have limited the correction in order to get more realistic values
  • We have improved the cloud detection, with a better compromise between false positives and false negatives. We also handle better the variation against altitude of the cirrus cloud detection with band 10 (1.38 µm). MAJA 3.3 is the version with which we obtained the results of our recently published article. This paper shows that MAJA has slightly better performances than FMask 4.0, and much better performances than Sen2Cor.

Moreover, when we validated the results, we figured out that one of the parameters in our settings had a wrong value (10 instead of 1). It is easy to make such errors, because there are about 150 parameters in MAJA, and it's easy to make an error. We have set up a version management of MAJA settings since 2017, but the erroneous value was already there before that. And this value has a big impact ! The standard deviation of errors in AOT estimates is reduced by 30 to 40% !!

The W_dark parameter controls the weight of the dark pixel method in the AOT estimation. This method is just supposed to be used as a safeguard in case the multi-temporal or multi-spectral methods provide wrong results. It should therefore have a low weight, but with a weight of 10, it was in fact the method which had the highest weight in our estimates. As this method provides a maximum value of the AOT, it tended to reduce the provide too low AOTs. This improvement is therefore a great piece of news, but it comes with some shame not to have found this error before.

 

AOT validation against AERONET for 10 sites with the wrong W_dark value AOT validation against AERONET for 10 sites with the correct W_dark value

The blue dots correspond to validation obtained in good conditions, while red dots correspond to less reliable validation points.

These are the results of comparison of version 3.3 with CAMS activated, changing only the W_dark parameter, but if we compare with the results of version 3.1, the improvement is even more impressive :

AOT validation against AERONET for 10 sites, version 3.1/td> AOT validation against AERONET for 10 sites version 3.3, with the correct W_dark value

How to access MAJA ?

Here is how to access MAJA 3.3 :

  • MAJA 3.3 is distributed as a free software for non commercial purposes from CNES free software site (select the 3.3 TM version in the download tab). If you need it for commercial purposes, you just have to ask me for a different licence, but it still will be free of charge.
  • The best way to use MAJA is to run it with Start_MAJA, which is a simple python code that runs MAJA for a whole time series for a given Sentinel-2 tile. The Strart MAJA readme also explains how to get the good settings, with the good W_dark value, how to prepare the DEM or how to get CAMS data.
  • PEPS on-demand processing facility will be updated soon, but it is still working with MAJA 3.2 so far
  • THEIA is also running MAJA 3.2. We will update first the wdark parameter, and then MAJA 3.3 and then start production with CAMS, hopefully before summer. If everything goes well, we will then start a reprocessing of all our data set. So, stay tuned on this information channel.

Sentinel-2 Level-3A time series

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April, despite a certain amount of cloudiness, once again offered us a beautiful, cloudless synthesis. In fact, Theia's Level 3A products use 46 days periods centered on the 15th of the month, and the good weather of the last days of March or the first days of May compensates the cloudy April. As every month, Peter Kettig from CNES produced the Level 3A syntheses from the previous month's Sentinel-2 data. As you can see, a column of tiles is degraded in western France. This is due to a problem during the download of Sentinel data by PEPS. We're going to reprocess it.

Translated with www.DeepL.com/Translator

 
The full resolution data, and the corresponding data quality masks, can be downloaded from Theia's distribution server at CNES.
 
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 each month since July over France. Each monthly synthesis is accessible using the following links :

Or you may also use the nice viewer below (merci Michel Lepage !) to compare with the previous months.

See it full screen
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Spot the odd one out

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There is an odd image in this time series of L2A products of the 31TCJ Sentinel-2 tile (Toulouse region). Can you guess which one ?

 

Yes it is the last one, acquired on the 26th of February. But what's odd with it ?

  • The black South East corner ? No, just a different orbit
  • The date ? Close enough...
  • Two images separated by one day (25 th, 26 th of January) ? You're boiling !

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