TY - JOUR
T1 - A unified vegetation index for quantifying the terrestrial biosphere
AU - Camps-Valls, Gustau
AU - Campos-Taberner, Manuel
AU - Moreno-Martínez, Álvaro
AU - Walther, Sophia
AU - Duveiller, Grégory
AU - Cescatti, Alessandro
AU - Mahecha, Miguel D.
AU - Muñoz-Marí, Jordi
AU - García-Haro, Francisco Javier
AU - Guanter, Luis
AU - Jung, Martin
AU - Gamon, John A.
AU - Reichstein, Markus
AU - Running, Steven W.
N1 - Publisher Copyright:
Copyright © 2021 The Authors, some rights reserved;
PY - 2021/2/24
Y1 - 2021/2/24
N2 - Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.
AB - Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.
UR - http://www.scopus.com/inward/record.url?scp=85102022366&partnerID=8YFLogxK
U2 - 10.1126/sciadv.abc7447
DO - 10.1126/sciadv.abc7447
M3 - Article
C2 - 33637524
AN - SCOPUS:85102022366
SN - 2375-2548
VL - 7
JO - Science advances
JF - Science advances
IS - 9
M1 - eabc7447
ER -