Towards near-real-time Leaf Area Index assimilation in LDAS-Monde

It is now widely recognized that assimilating consolidated satellite-derived vegetation products improves the representation of land surface variables and fluxes. This raises the question of their suitability for both reanalysis products and near-real-time (NRT) applications. Among these variables, Leaf Area Index (LAI) plays a key role in controlling exchanges of water, energy, and carbon between the land surface and the atmosphere. While reprocessed and consolidated products are well suited for retrospective reanalyses, NRT applications require observational streams with low latency and sufficient consistency with delayed, higher-quality products.

In this study, we assess the potential of the Copernicus Land Monitoring Service (CLMS) 300 m LAI products for land surface analysis and NRT applications within the LDAS-Monde system coupled to the ISBA land surface model for the year 2021 in a global scale. We investigate the assimilation of the different CLMS processing streams, namely RT0 and RT1, available in NRT, and RT6, which is provided as a consolidated product with a 60-day latency. A set of global offline experiments forced by ERA5 is conducted to evaluate their consistency and suitability for land data assimilation.

The results show that RT1 captures the main temporal variability of the consolidated RT6 product while benefiting from a substantially reduced latency. Compared with RT0, RT1 provides a more stable and consistent signal, offering a robust compromise between timeliness and data quality. Assimilation experiments within LDAS-Monde indicate that analyses driven by RT1 are consistent with those obtained using RT6, supporting its use in systems targeting NRT land monitoring. In addition, an in situ evaluation based on observations from the ICOS FR-Tou site is conducted for selected surface variables, providing an independent benchmark to further assess whether the use of RT1 leads to comparable performance to RT6.