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Biomass and net primary production can be quantified at the landscape level using a combination of field methodologies and remote sensing. Spatial models can be very useful to acquire this information. Although there are several methodologies that have been developed at regional and global scales, there are very few studies that assess biomass and net primary production using very high resolution imagery. Knowing the good fit between vegetation indices and photosynthetic processes, this study aimed at developing methodologies to integrate field data with remote sensing and landscape ecology approaches to quantify biomass and net primary production in bofedales (high altitude rangelands) in the Andes. To achieve our objectives, 13 bofedales were selected in the Andes region near La Paz (Bolivia). Pleiades imagery (2 m-resolution) and LANDSAT (15-m resolution) were used to calculate vegetation indices. Daily biomass data were estimated using a logistic function based on the interception of photosynthetically active radiation, thermal time and radiation use efficiency (RUE). The NPP was considered as 0.45 of RUE. Simulated and field data were compared using a linear regression and then compared to satellite imagery vegetation indices values. Vegetation indices values from satellite imagery showed a significant relationship (Pleiades, r�=0.405, p<0.001; LANDSAT, r�=0.650, p<0.001) with biomass and net primary production. Our results show that it is possible to quantify biomass and net primary production using satellite imagery. The results from the high resolution imagery (2 meters) show the value and importance of considering spatial heterogeneity when collecting field data in order to improve spatial models and get better estimators.