Ecosystem fluxes simulation with the SCOPE model constrained by Sentinel-3 measurements

The aim of this research is to assess the feasibility of retrieving plant functional traits from Sentinel-3 data by using combined vegetation-atmosphere inversions of radiative transfer models.
Sentinel-3 is a constellation of ESA’s satellites (Sentinel-3A and 3B) that have roughly 30 bands on two sensors (Ocean and Land Colour Instrument, OLCI and Sea and Land Surface Temperature Radiometer, SLSTR) in the visible, near- and mid-infrared regions. The revisit time of about one day makes Sentinel-3 suitable for producing time series and monitoring the ecosystem state. However, the measurements provided by Sentinel-3 are strongly affected by the atmosphere. Atmospheric correction is usually carried out before surface parameter retrievals, but we attempted to retrieve properties from OLCI and SLSTR from the radiative transfer models SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) and 6S so the vegetation and atmospheric parameters are simultaneously modelled. SCOPE is a radiative transfer and micro-meteorological model that simulates reflectance spectra at leaf and canopy levels as well as photosynthesis and the components of the energy balance (net radiation, sensible and latent heat flux) for soil an canopy. We developed an algorithm based on the coupling of the optical module of SCOPE with the atmospheric radiative transfer model 6S to retrieve plant traits (chlorophyll content, leaf area index, water content) from Sentinel-3 measurements. Clouded images, based on a cloud band of SLSTR product, and images without vegetation, based on a low value of the normalized difference vegetation index (NDVI), were excluded from the analysis. The retrieved plant traits were further used as an input for the complete SCOPE model to produce ecosystem fluxes.
Ground measurements from a number of European FLUXNET eddy-covariance towers were used for the validation of the simulated ecosystem fluxes. The plant functional types represented by those towers included savannahs, croplands, grasslands, evergreen broadleaf and needleleaf forests and deciduous forests. To reduce the noise in the data, we compared 3-days mean fluxes.
The project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995.