A neural-network based observation operator for the assimilation of microwave satellite observations with LDAS-Monde on global cropland for improving land-surface variables

Vural, Jasmin; Vanderbecken, Pierre; Bonan, Bertrand; Rojas-Muñoz, Oscar; Calvet, Jean-Christophe

doi:10.5194/eo-1-155-2026

Articles | Volume 1, issue 1

https://doi.org/10.5194/eo-1-155-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/eo-1-155-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 1, issue 1

Research article

|

05 Nov 2026

Research article |

| 05 Nov 2026

A neural-network based observation operator for the assimilation of microwave satellite observations with LDAS-Monde on global cropland for improving land-surface variables

Jasmin Vural, Pierre Vanderbecken, Bertrand Bonan, Oscar Rojas-Muñoz, and Jean-Christophe Calvet

Data sets

Leaf Area Index 2014-Present (Raster 300 m), Global, 10-Daily - Version 1 Copernicus Land Monitoring Service and Copernicus Land Monitoring Service Helpdesk https://doi.org/10.2909/219FDC9F-616B-444B-A495-198F527B4722

Quality Assessment Report GEOV2-AVHRR: Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Fraction of Green Vegetation Cover (FCover) from LTDR AVHRR A. Verger https://www.theia-land.fr/en/blog/product/series-of-vegetation-variables-avhrr/

Short summary

New satellite observations are used to improve our understanding of agricultural land on a global scale. Innovative artificial intelligence methods are employed to combine the available information. This improvement is evaluated using independent observations and varies depending on the characteristics of instruments and models.