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.

The derivation of water quality indicators is of importance, especially in coastal areas, as most of the economic activities are located here. However, the availability of high-spatial-resolution water quality information in coastal zones is limited. Nowadays, high-resolution satellite data is becoming available and can fill in this knowledge gap. This satellite data contains spectral reflectances, so a model needs to be designed to map these reflectances to water quality indicators. In this thesis, a Gaussian process regression (GPR) method will be introduced and analyzed extensively in terms of covariance functions, hyperparameters and computational costs. Remote sensing data is collected from the Sentinel-2 mission and the in-situ data is obtained from the ODYSSEA programme. The Matérn 3/2 kernel produces the best results and these are compared with the current models that rely on machine learning techniques. GPR shows promising results in terms of estimation accuracy and chlorophyll-a maps are made for different areas and depths. Various approximation methods are tested to speed up the computation time. Singular value decomposition shows promising results for doing predictions to reduce the computation time. Moreover, GPR can handle limited availability of in-situ data well and uncertainty quantification is induced by the Bayesian framework.