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Estimating gross primary production (GPP), the gross uptake of CO<sub>2</sub> by vegetation, is a fundamental prerequisite for understanding and quantifying the terrestrial carbon cycle. Over the last decade, multiple approaches have been developed to derive spatiotemporal dynamics of GPP combining <italic>in situ</italic> observations and remote sensing data using machine learning techniques or semiempirical models. However, no high spatial resolution GPP product exists so far that is derived entirely from satellite-based remote sensing data. Sentinel-2 satellites are expected to open new opportunities to analyze ecosystem processes with spectral bands chosen to study vegetation between 10- and 20-m spatial resolutions with five-day revisit frequency. Of particular relevance is the availability of red-edge bands that are suitable for deriving estimates of canopy chlorophyll content that are expected to be much better than any previous global mission. Here, we analyzed whether red-edge-based and near-infrared-based vegetation indices (VIs) or machine learning techniques that consider VIs, all spectral bands, and their nonlinear interactions could predict daily GPP derived from 58 eddy covariance sites. Using linear regressions based on classic VIs, including near-infrared reflectance of vegetation (NIRv), we achieved prediction powers of <inline-formula> <tex-math notation="LaTeX">$R^{2}_{\mathrm{10-fold}} = 0.51$ </tex-math></inline-formula> and an <inline-formula> <tex-math notation="LaTeX">$RMSE_{\mathrm{10-fold}} = 2.95 $ </tex-math></inline-formula> [<inline-formula> <tex-math notation="LaTeX">$\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ </tex-math></inline-formula>] in a 10-fold cross validation. Chlorophyll index red (CIR) and the novel kernel NDVI (kNVDI) achieved significantly higher prediction powers of around <inline-formula> <tex-math notation="LaTeX">$R^{2}_{\mathrm{10-fold}} \approx 0.61$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$RMSE_{\mathrm{10-fold}} \approx 2.57$ </tex-math></inline-formula> [<inline-formula> <tex-math notation="LaTeX">$\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ </tex-math></inline-formula>]. Using all spectral bands and VIs jointly in a machine learning prediction framework allowed us to predict GPP with <inline-formula> <tex-math notation="LaTeX">$R^{2}_{\mathrm{10-fold}} = 0.71$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$RMSE_{\mathrm{10-fold}} = 2.68$ </tex-math></inline-formula> [<inline-formula> <tex-math notation="LaTeX">$\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ </tex-math></inline-formula>]. Despite the high-power prediction when machine learning techniques are used, under water-stress scenarios or heat waves, optical information alone is not enough to predict GPP properly. In general, our analyses show the potential of nonlinear combinations of spectral bands and VIs for monitoring GPP across ecosystems at a level of accuracy comparable to previous works, which, however, required additional meteorological drivers.

On the Potential of Sentinel-2 for Estimating Gross Primary Production