On demand Sentinel-2 L2A processing with MAJA

Good news ! Until now, there were two options to access to the high quality Sentinel-2 L2A products obtained from MAJA processor :

Starting from today, there is another possibility: the French collaborative ground segment PEPS allows on demand processing with MAJA. As PEPS is a global mirror, it means that you can process any Sentinel-2 product. The only restriction is that it has to be a single tile scene (acquired after December 2016, or recently reprocessed).

Update: we have also added a script to submit the processing and download the products with a command line interface and no click: https://github.com/olivierhagolle/maja_peps

More info here: http://www.cesbio.ups-tlse.fr/multitemp/?p=14335


Left L2A products from Theia, Right, L2A products from PEPS. Reflectances and Cloud mask are nearly identical.

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Nice, is there any interest in making the MAJA-processor a plugin of SNAP?

We are open to this, but I do not know what it means in terms of amount of work. Anyway, Snap understands somehow MAJA output format.

Hi Olivier,

As reported by Marcus, if the benefit/effort ratio is convinient, we could evaluate the possibility to import the MAJA-Processor in SNAP.
Concerning the reader for MAJA output, SNAP understands its format because was one of our requirements to the SNAP’s developers. :stuck_out_tongue_winking_eye:

Best Regards,
Fabrizio

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Yes sure, and thanks for that !
Olivier

A new version of the on-demand processing of Sentinel-2 L2A products with MAJA processor has been implemented within PEPS. It should be now much more efficient when used to process time series.

It relies on a python interface, which is very simple:
Here is an example to obtain the nice images below (over Sebastopol, Crimea):

  1. Get an account on PEPS
  2. Get FULL_MAJA on github
  3. Submit processing with :
    python full_maja_process.py -a peps.txt -t 36TWQ -o 64 -d 2019-03-01 -f 2019-09-01 -g 36TWQ_2019-03-01.log
  4. Wait
  5. Download results with
    python full_maja_download.py -a peps.txt -g 36TWQ_2019-03-01.log -w /path/to/36TWQ

For details, please read our new post:

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