Investigating whether considering spatial heterogeneity within Eddy-Covariance tower footprints can better characterise high-frequency changes in GPP

Eddy Covariance (EC) towers measure ecosystem-atmosphere fluxes and are typically installed in homogeneous landscapes to ensure representativeness. However, sometimes the landscapes can exhibit more heterogeneity than desired, especially when the objective is to link these fluxes with other data sources, such as coarse remote sensing observations, notably in efforts to upscale these fluxes. Accurately integrating EC flux measurements with satellite observations or model-based simulations remains a significant challenge due to the inherent spatial heterogeneity that can occur within the flux footprint. This footprint is also dynamic, changing according to meteorological conditions such as wind speed and direction, while many approaches consider it static for simplicity. This study examines whether modelling the footprint dynamics and describing the underlying fine-scale spatial variability information from remote sensing data, specifically using 20 m Sentinel-2 data as a proxy for the spatial heterogeneity in vegetation structure, can help explain the high frequency (e.g., half-hourly) variability of Gross Primary Production (GPP) estimates from EC. To isolate the contribution of spatial heterogeneity from the dominant effect of incoming radiation, we work on light-normalized fluxes (i.e., GPP/PAR, as a proxy for light-use efficiency) measured at the tower. We hypothesize that combining light-normalized EC fluxes with remote sensing information weighted by dynamically modelled flux footprints provides a more accurate representation of the high-frequency variations in GPP than approaches relying on static footprint representations.

To test our hypothesis, we analyze three ICOS sites characterized by distinct ecosystem types: (i) IT-Noe, a Mediterranean maquis in Italy; (ii) ES-LMa, a typical holm oak savanna in Spain; and (iii) IT-Ren, a subalpine forest in Italy. Our methodology integrates half-hourly EC datasets for GPP and meteorological variables with Sentinel-2 data cube at 20 m spatial resolution to compute various Vegetation Indices (VIs), including: NDVI, EVI, CIR, NDWI, and NIRv. We compare three footprint modeling approaches: (i) Static Footprint (SF), a fixed-area approach with radii of 50, 250, and 500m; (ii) Climatological Footprint (CF), based on the Flux Footprint Prediction (FPP) model by Kljun et al. (2015) applied as an average over the growing season; and (iii) Dynamic Footprint (DF), providing a dynamic representation of flux for each Sentinel-2 band every 30 minutes.

Preliminary results indicate that incorporating high-resolution Sentinel-2 data to explicitly account for spatial heterogeneity within the flux footprint provides substantial added value for the ecosystem flux studies. The comparison between footprint-based approaches and simplified assumptions highlights the importance of capturing fine-scale spatial variability to ensure accurate estimates of GPP, particularly in complex and heterogeneous landscapes.