A unified vegetation index for quantifying the terrestrial biosphere

Author:

Camps-Valls Gustau1ORCID,Campos-Taberner Manuel2ORCID,Moreno-Martínez Álvaro13ORCID,Walther Sophia4ORCID,Duveiller Grégory5ORCID,Cescatti Alessandro5ORCID,Mahecha Miguel D.678ORCID,Muñoz-Marí Jordi1ORCID,García-Haro Francisco Javier2,Guanter Luis9ORCID,Jung Martin4,Gamon John A.1011ORCID,Reichstein Markus4ORCID,Running Steven W.3ORCID

Affiliation:

1. Image Processing Laboratory, Universitat de València, 46980, Paterna, Spain.

2. Environmental Remote Sensing group (UV‑ERS), Universitat de València, 46100, Burjassot, Spain.

3. Numerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, MT, USA.

4. Max Planck Institute for Biogeochemistry, 07745 Jena, Germany.

5. European Commission Joint Research Centre, Ispra, Italy.

6. Remote Sensing Centre for Earth System Research, Leipzig University, Talstr. 35, 04103 Leipzig, Germany.

7. Helmholtz Centre for Environmental Research - UFZ, Permoserstraße 15, 04318 Leipzig, Germany.

8. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig, Germany.

9. Universitat Politècnica de València, 46022 València, Spain.

10. University of Alberta, Edmonton, Alberta, Canada.

11. University of Nebraska–Lincoln, Lincoln NE, USA.

Abstract

Machine learning generalizes vegetation indices to better quantify the terrestrial biosphere.

Funder

H2020 European Research Council

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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