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.

PABON-MORENO Daniel;  MIGLIAVACCA Mirco;  REICHSTEIN Markus;  MAHECHA Miguel; 

2022-04-19

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

JRC128489

0196-2892 (online),   

https://ieeexplore.ieee.org/document/9715117,    https://publications.jrc.ec.europa.eu/repository/handle/JRC128489,   

10.1109/TGRS.2022.3152272 (online),