Disruptions in the availability of Theia products

In English MAJA

Jerome Colin, 26 septembre 2022

=>

You may have noticed an interruption in the availability of level 2 and 3 products on the THEIA portal. This is due to ongoing disruptions in the availability of CAMS (Copernicus Atmosphere Monitoring Service) data used by the MAJA atmospheric correction chain. These disruptions are linked to the migration of the ECMWF data centre, which began on 8 September, and could affect all downstream users for another 3 to 4 weeks. We were expecting some disruptions, but we are currently experiencing a complete shutdown of the service.

As a reminder, the use of CAMS products greatly improves the estimation of the atmospheric optical thickness (AOT) in MAJA, by providing at a 12-hour time resolution and a 0.4° spatial resolution the relative contribution of 7 aerosol species to the total AOT. These data are usually retrieved on the fly by the production centre upstream of the level 1 product processing, and are currently missing.

The CNES production team will catch up with the processing as soon as the CAMS service is back in operation.

Thank you for your patience

Plus d'actualités

Rapid analysis of the GLOF in Gupis, Gilgit-Baltistan

Pakistan news media reported that a glacier lake outburst flood (the failure of a dam containing a glacial lake) occurred near Gupis, Gilgit-Baltistan, a highly mountainous region administered by Pakistan. Satellite images show that the flood triggered a debris flow, which reached the Gupis valley where it hit the Roshan (Rashon) village and blocked the […]

BIOMASS, the third launched satellite mission designed at CESBIO !

After SMOS in 2009, and VENµS in 2017, the CESBIO laboratory is very proud to see its third proposed mission, Biomass, reach orbit. As always, it has been a long journey from the idea, at the beginning of the century, to the selection in 2013 as the seventh Earth Explorer Mission by ESA, to the […]

Biophysical parameter retrieval from Sentinel-2 images using physics-driven deep learning for PROSAIL inversion

The results presented here are based on published work: Y. Zérah, S. Valero, and J. Inglada. « Physics-constrained deep learning for biophysical parameter retrieval from sentinel-2 images: Inversion of the prosail model« , in Remote Sensing of Environment, doi: 10.1016/j.rse.2024.114309. This work is part of the PhD of Yoël Zérah, supervised by Jordi Inglada and Silvia Valero. […]

Rechercher