Gross primary productivity (GPP) is a crucial variable for ecosystem dynamics, and it can significantly vary on the small spatial scales of vegetation and environmental heterogeneity. This is especially true for mountain ecosystems, which pose severe difficulties to field monitoring. In addition, the specificity of such ecosystems and the extreme abiotic conditions that they experience often make global and regional models unsuited to predictions. In this case, remote sensing products offer the opportunity to explore the productivity of vegetation communities in remote areas such as Alpine grasslands all year round, and empirical models can help in the challenge of modelling Alpine GPP. Along these lines, we took a hybrid approach, blending several remote sensing data sources (such as a high-definition digital terrain model and moderate- and high- resolution satellite products such as MODIS and Sentinel 2) and gridded datasets such as ERA5 with <em>in situ</em> measurements to implement a specific empirical model. The resulting remote-sensing-based model developed here was suited to represent the measured primary productivity in different areas within a high-altitude grassland at the Nivolet plain, in the north-western Italian Alps at 2700–2500 m amsl. A cross-validation approach allowed us to evaluate to what extent a single empirical model could represent diverse communities and different abiotic factors found in these areas. We finally identified the ratio between MCARI2 and MSAVI2 as a good predictor of light use efficiency, a key factor in the empirical model, probably due to its good correlation with the leaves phenological status, inasmuch it estimates the ratio between chlorophyll and the ensemble of leaf pigments.