A unified vegetation index for quantifying the terrestrial biosphere-Reference-Cited by-同舟云学术

A unified vegetation index for quantifying the terrestrial biosphere

Published:2021-02-26 Issue:9 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Camps-Valls Gustau¹^ORCID,Campos-Taberner Manuel²^ORCID,Moreno-Martínez Álvaro¹³^ORCID,Walther Sophia⁴^ORCID,Duveiller Grégory⁵^ORCID,Cescatti Alessandro⁵^ORCID,Mahecha Miguel D.⁶⁷⁸^ORCID,Muñoz-Marí Jordi¹^ORCID,García-Haro Francisco Javier²,Guanter Luis⁹^ORCID,Jung Martin⁴,Gamon John A.¹⁰¹¹^ORCID,Reichstein Markus⁴^ORCID,Running Steven W.³^ORCID

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

Reference63 articles.

1. Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

2. J. Rouse Jr R. Haas J. Schell D. Deering Monitoring vegetation systems in the great plains with ERTS (NASA Special Publication 1974).

3. Red and photographic infrared linear combinations for monitoring vegetation

4. The interpretation of spectral vegetation indexes

5. Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture

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