On the potential of Sentinel-2 for estimating Gross Primary Production
2022
PABON-MORENO Daniel | MIGLIAVACCA Mirco | REICHSTEIN Markus | MAHECHA Miguel
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 spatio-temporal dynamics of GPP combining in-situ observations and remote sensing data using machine learning techniques or semi-empirical 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 10m to 20m spatial resolution with 5-days 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 non-linear interactions could predict daily GPP derived from 58 eddy covariance sites. In general, our analyses show the potential of non-linear combinations of spectral bands and VIs for monitoring GPP across ecosystems.
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