## Publication Details

 Reference Category Journals DOI / URL link Creative Commons Licence Title (Primary) On the potential of Sentinel-2 for estimating gross primary production Author Pabon-Moreno, D.E.; Migliavacca, M.; Reichstein, M.; Mahecha, M.D. Journal IEEE Transactions on Geoscience and Remote Sensing Year 2022 Department RS Volume 60 Page From art. 4409412 Language englisch Topic T5 Future Landscapes Supplements https://ieeexplore.ieee.org/ielx7/36/9633014/9715117/tgrs-3152272-mm.zip?arnumber=9715117 Keywords Vegetation mapping, Machine learning, Indexes, Clouds, Biological system modeling, Remote sensing, Spatial resolution Abstract Estimating gross primary production (GPP), the gross uptake of CO2 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 in situ 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 $R^{2}_{\mathrm{10-fold}} = 0.51$ and an $RMSE_{\mathrm{10-fold}} = 2.95$ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ] in a 10-fold cross validation. Chlorophyll index red (CIR) and the novel kernel NDVI (kNVDI) achieved significantly higher prediction powers of around $R^{2}_{\mathrm{10-fold}} \approx 0.61$ and $RMSE_{\mathrm{10-fold}} \approx 2.57$ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ]. Using all spectral bands and VIs jointly in a machine learning prediction framework allowed us to predict GPP with $R^{2}_{\mathrm{10-fold}} = 0.71$ and $RMSE_{\mathrm{10-fold}} = 2.68$ [ $\mu \rm {mol \ CO_{2} m^{-2}s^{-1}}$ ]. 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. Persistent UFZ Identifier https://www.ufz.de/index.php?en=20939&ufzPublicationIdentifier=26382 Pabon-Moreno, D.E., Migliavacca, M., Reichstein, M., Mahecha, M.D. (2022): On the potential of Sentinel-2 for estimating gross primary production IEEE Trans. Geosci. Remote Sensing 60 , art. 4409412