  Satellite remote sensing has been widely used in the last decades for
agricultural applications, {both for assessing vegetation condition and for
subsequent yield prediction.} Existing remote sensing-based methods to estimate
gross primary productivity (GPP), which is an important variable to indicate
crop photosynthetic function and stress, typically rely on empirical or
semi-empirical approaches, which tend to over-simplify photosynthetic
mechanisms. In this work, we take advantage of all parallel developments in
mechanistic photosynthesis modeling and satellite data availability for
advanced monitoring of crop productivity. In particular, we combine
process-based modeling with the soil-canopy energy balance radiative transfer
model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and
machine learning methods in order to estimate crop GPP. Our model successfully
estimates GPP across a variety of C3 crop types and environmental conditions
even though it does not use any local information from the corresponding sites.
This highlights its potential to map crop productivity from new satellite
sensors at a global scale with the help of current Earth observation cloud
computing platforms.
