Quantifying the (mis)match between in-situ and satellite time series: the case of eddy covariance flux observations
Daniel E. Pabon-Moreno
Eddy covariance (EC) systems are commonly used to measure the net exchanges of energy, water carbon dioxide (CO2) and other trace gasses between the ecosystems and the atmosphere. Such measuring systems have been established in different ecosystems and climate regimes across the globe, thereby providing invaluable ground information to understand ecosystem dynamics at global scale. Although the number of EC stations installed worldwide (e.g. FLUXNET sites) are constantly growing with time, their spatial distribution is limited in comparison to the vast complexity of land ecosystems. Furthermore, EC towers track the exchange of energy and matter from an area (often referred to as a footprint) that spans some few hundred meters around and upstream of the measurement site (the so-called fetch), and which can vary according to meteorological conditions. Remote sensing (RS) and in-situ flux datasets are commonly combined to upscale the exchanges of carbon and energy at a global scale (e.g. the FLUXCOM project), as well as for calibration and validation activities. The challenge to do this correctly lies in trying to link the footprints of the EC measurements to those of the satellite measurements, a task that is often disregarded or oversimplified. In this study we designed a methodological approach within the Open-Earth-Monitor (OEMC) project to estimate dynamically the match (or mismatch) between some likely proxies of EC footprints (approximated as circles with radius from 50 to 200 meters) and the footprints of (coarse) spatial resolution RS time series. To quantify the degree of mismatch we collect Sentinel-2 images at 10 meters resolution for several EC sites over Europe. Then, we compute the kNDVI vegetation index for all the sites masking clouds and cloud shadows. We also define proxies for different pixel sizes of satellite data ranging from 500 meters to 1500 meters radius around the tower. To compare the EC footprints with the Satellite pixel resolution we compute the Jensen-Shannon index that quantifies the amount of information (in terms of kNDVI) shared between both scales at every available time step. As a result, we provide initial recommendations of when in the year the sites are more suitable to be matched with satellite data according to the surrounding phenology. We expect these will open the possibility to correct biases in future upscaling fluxes exercises and remote sensing products calibration.
