Gross Primary Production (GPP) is the amount of carbon dioxide (CO2) that is fixed by an ecosystem through photosynthesis. It is a key variable to understand the carbon cycle and biodiversity conservation. In-situ measurements of GPP are possible at a local scale. To estimate GPP on a global scale, models based on remote sensing data are used. One of them is the Penman-Montheith-Leuning (PML) model, which is constructed on physical processes.

In this thesis, a sensitivity analysis of the PML model is performed for the case study of Torgnon subalpine grassland. It is defined around three aspects: the application of the PML model to Torgnon subalpine grassland, the impact of the input data on the output data, and the reliability of the calibration procedure. We found out that the PML model can be applied to the study site, but the value of the parameters presented in the literature are not optimal (R2 = 0.57 using the literature calibration and R2 = 0.94 in the optimised case).

Moreover, the model is extremely sensitive to the Leaf Area Index (LAI), and, to a lesser extent, to temperature and Photosynthetically Active Radiations (PAR). However, it is weakly dependent on the CO2 concentration of the atmosphere. The analysis of the calibration procedure showed that a simplified PML model, that does not consider the CO2 concentration and Vapour Pressure Deficit (VPD), performs similarly to the initial model. An ensemble estimation of GPP is obtained by using an ensemble of optimal parameters. It suggests that errors in the calibration principally impact the spring and summer estimations.