Consistent Multi-Mission Time Series for Global Assessment of Ecosystems Dynamics and Disturbance

Long-term remote sensing observations enable systematic assessment of terrestrial ecosystem dynamics and land-surface change across continents and climate zones. This contribution presents recent developments in global vegetation and energy, products generated within the Copernicus Land Monitoring Service (CLMS) using harmonized satellite time series. The portfolio delivers global land products, including canopy biophysical variables, land surface phenology, ecosystem productivity metrics, burned area, soil moisture and land surface temperature, all explicitly derived from long-term satellite observations acquired by successive spaceborne sensors. 

NDVI (Normalized Difference Vegetation Index), LAI (Leaf Area Index), FAPAR (Fraction of Absorbed Photosynthetically Active Radiation), and FCover (Fractional Vegetation Cover) are provided at 300 m spatial resolution with methodological continuity—with some adaptations—with earlier 1 km products derived from SPOT-VEGETATION and PROBA-V observations.  Applying a largely consistent retrieval framework supports the joint use of the 1 km and 300 m records for the analysis of vegetation dynamics since 1999, including both long-term trends and short-term anomalies across diverse ecosystems, with appropriate caution when interpreting sensor and resolution transitions. Together, LAI, FAPAR, and FCover describe canopy structure and radiative properties, providing a physically consistent basis for ecosystem functioning and surface–atmosphere interactions. Seasonal vegetation dynamics are further captured through Land Surface Phenology metrics, reflecting shifts in the timing and duration of growth cycles associated with climate variability and extreme events. Productivity indicators, like Dry Matter Productivity and Net Primary Production, quantify biomass accumulation and carbon uptake at global scale, offering insight into terrestrial carbon cycling and ecosystem functioning. In parallel, CLMS delivers daily updated global maps of fire-affected areas. With its low latency (<24h) and its quality almost equal to non-time-critical products quality, available many months after satellite acquisition, this Burned Area product supports timely assessment of wildfire extent and post-fire recovery.  

Other key products delivered by CLMS are surface soil moisture (SSM 1km) and soil water index (SWI 1km and SWI 0.1°). Recently significant improvements have been made to these products integrating algorithmic advances from previous evolution activities but also ingesting improved input data. The SSM 1km product has been substantially upgraded through the implementation of a new radiative transfer model retrieval algorithm, which improves vegetation dynamics and reduces seasonal bias, as well as an enhanced preprocessing and filtering workflow mitigating subsurface scattering anomalies. These improvements directly benefit the SWI 1km, which also inherits these advancements. Furthermore, both SSM 1km and SWI 1km now feature improved masking of frozen soil conditions where retrievals are ill-posed. Finally, the 0.1° Soil Water Index (SWI 0.1°) has been enhanced by integrating the latest ASCAT surface soil moisture product, which offers superior vegetation modelling, long-term trend correction, and subsurface scattering mitigation. 

 

In addition to the vegetation and soil moisture products, a new version of the global Land Surface Temperature (LST) product is currently under development, incorporating several updates to enhance its quality and stability. These updates include improvements to multiple components, such as (i) dynamic vegetation‑based surface emissivity, (ii) high‑frequency cloud information from SAF‑Nowcasting, (iii) a comprehensive eight‑year reprocessing effort (2018–2025) to ensure temporal consistency with near‑real‑time updates, and (iv) increased spatial resolution to 3km. The LST processing chain integrates data from the GOES‑16/19 and Himawari satellites, which are merged with the products generated by the Satellite Application Facility on Land Surface Analysis (LSA SAF) for the Meteosat Second Generation (MSG) prime and Indian Ocean (IODC) missions. 

 

The integration of these complementary products demonstrates the value of sustained, harmonized optical satellite records for large-scale analysis of ecosystem dynamics, climate-related impacts, and operational land monitoring applications.