An index-based insurance solution is developed by Pacifica Crédit Agricole Assurances and Astrium GEO-Information to estimate and monitor the near real-time forage production in France. In this system, payouts are indexed on an indicator, called Forage Production Index (FPI), calculated using a biophysical characterization of the grassland from medium spatial resolution remote sensing time series. We used the Fraction of green Vegetation Cover (fCover) integral as a surrogate of the forage production. fCover is a biophysical parameter estimating the cover of green vegetation, looking in a vertical direction, independently of the actual image sensor viewing or illuminations conditions. It is computed performing overall inversion of existing radiative transfer models. Validation is required to check the reliability of the fCover. High spatial resolution images are used as an intermediate scale. This paper presents the first step of our validation process: local ground measurements of biomass production are compared to FPI values obtained from high spatial resolution space-based images.  It describes the grasslands parcels, the field protocol established to collect biomass production data, the method to get the fCover biophysical variable and the statistical analysis. In spring 2013, 6 parcels of grassland were selected to represent variations on pasture management practices and to consider different types of grassland species. On each parcel, a 10 meters grid is created corresponding to the spatial resolution of the remote sensing images. Plots were sampled to maximize distance between 2 measurements and to avoid border area. From March, 7^th to June, 17^th, biomass was measured every 15 days using a sickle bar mower with a 110 cm cutter bar. One of the 6 grasslands was already monitored in 2012. Finally, the dataset contains 488 plots. It corresponds to one production data per hectare per parcel every two weeks in average. Simultaneously, four sensors SPOT-4, SPOT-6, Formosat-2 and Landsat-8 were used to build a HSR images time series and to check that the fCover is computed in a consistent way. From February, 16^th to June, 26^th 2013, 55 images were acquired over the 6 parcels every 5 days, in particular thanks to the SPOT4/Take5 time series (http://www.cesbio.ups-tlse.fr/multitemp/) distributed freely by CNES and produced by THEIA land data center. Among them, 34 were selected regarding the cloud cover and resampled at 10 meters. For the parcel monitored in 2012, the dataset contained 10 Formosat-2 images. On each plot where biomass is measured, the fCover's profile was produced at the pixel level using a linear interpolation between the dates of acquisition. The analysis mainly consists in studying the relation of biomass ground measurements with grassland production estimated by fCover. Discrepancies between both variables are quantified by the coefficient of determination, the systematic bias and the root mean square error. First, fCover values derived from the four sensors are coherent. It demonstrates the ability of the algorithm used in this study to provide a consistent way to calculate biophysical variable. Then, for the whole dataset, the scatter plot between FPI and biomass shows an acceptable correlation (R²=0,724; α < 0,0001) with a correct systematic bias of 0,016. However, it remains dispersion with a RMSE going up to 0.128. If we take into account only data recorded until the maximum of production, the results are improved (R²= 0,811; α < 0,0001 and RMSE 0,101). This can be explained by the way FPI is designed: by definition, when the fCover integral is calculated, the brown fraction of the cover is not considered. Finally, the analysis carried out either at parcels, period of mowing and grasses species scales reveals variability on the regression coefficients. It indicates that, in addition to the fCover, others explanatory variables should be integrated to better compute the FPI. In the framework of the research activities developed to create the index-based insurance product, all these different results are discussed to draw recommendations to improve the FPI index. They also enable to conclude that High Spatial Resolution images can be used to perform an indirect validation of the FPI produced from medium spatial resolution remote sensing time series.