Potential retrieval of biophysical parameters from FLORIS, S3-OLCI and its synergy

The main objective of FLEX is the measurement of vegetation chlorophyll fluorescence (Fs) from space and the exploitation of this signal to better understand the carbon cycle. FLuORescence Imaging Spectrometer (FLORIS) is the main instrument of the FLEX mission concept. ESA´s Earth Science Advisory Committee recommended the investigation of the FLEX concept as an in-orbit demonstrator to be flown as a tandem mission with Sentinel-3 (S-3). S-3 is amongst others equipped with the Ocean Land Colour Instrument (OLCI). When flown in tandem these instruments are expected to provide an accurate characterization of key atmospheric and surface parameters to facilitate Fs retrieval for FLORIS. In this work the performance of FLORIS and S3-OLCI sensors and their synergy was evaluated on their capability of retrieving relevant biophysical parameters using simulated top-of-atmosphere radiance data (L_TOA). For both sensors, L_TOA data were simulated across a wide range of vegetation, atmospheric and geometry parameters by coupling leaf, canopy and atmospheric radiative transfer models. The pursued analysis was to train for each retrievable parameter (here: Chl, LAI, soil type and F_total) a regression model using the simulated datasets and then evaluate its performance. Two regression types were chosen, a conventional linear regressor and a more advanced nonlinear regressor, and two types of training/validation strategies were followed: a local strategy (at least 2 parameters fixed) and a generic strategy (uniform random subset of the complete dataset). The simulation study led to the following conclusions: 1) FLORIS is well equipped for accurate retrieval of biophysical parameters; 2) however, advanced nonlinear regressors may be needed to achieve robust results, and 3) the large number of bands can lead to redundancy in the nonlinear regressors which can be overcomed by band optimization strategies. Finally, 4) it was demonstrated that a synergy- of both FLORIS and S3-OLCI datasets leads to improved biophysical parameter retrieval.