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The recent availability of high spatial resolution sensors offers new perspectives for terrestrial applications (agriculture, risks). The aim of this work is to develop a methodology for deriving biophysical variables (Green Leaf Area Index - GLAI, phytomass) from multi-temporal observations at high spatial resolution in order to run a crop model at a regional scale. Accurate predictive crop models require a large set of input parameters, which are not easily available over large area. Spatial upscaling of such models is thus difficult. The use of simple model avoids spatial upscaling issues. This study is focused on SAFY model (Simple Algorithm For Yield estimates) developed. Key SAFY parameters were calibrated using temporal GLAI profiles, empirically estimated from FORMOSAT-2 time series of images. Most of the SAFY parameters are crop related and have been fixed according to literature. However some parameters are more specific and have been calibrated based on GLAI derived from FORMOSAT-2 observations at a field scale. Two calibration strategies are evaluated as a function of sampling (frequency and temporal distribution) of remote sensing data. Spatial upscaling simulations are assessed based on biomass in-situ measurements taken over maize. Good agreement between modelled and measured phytomass have been found on maize (RMSE =20 g.m-2).