A unified vegetation index for quantifying the terrestrial biosphere
Gustau Camps‐VallsImage Processing Laboratory, Universitat de València, 46980, Paterna, SpainManuel Campos‐TabernerEnvironmental Remote Sensing group (UV‑ERS), Universitat de València, 46100, Burjassot, SpainÁlvaro Moreno‐MartínezImage Processing Laboratory, Universitat de València, 46980, Paterna, SpainSophia WaltherMax Planck Institute for Biogeochemistry, 07745 Jena, GermanyGrégory DuveillerEuropean Commission Joint Research Centre, Ispra, ItalyAlessandro CescattiEuropean Commission Joint Research Centre, Ispra, ItalyMiguel D. MahechaGerman Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Puschstr. 4, 04103 Leipzig, GermanyJordi Muñoz-Marı́Image Processing Laboratory, Universitat de València, 46980, Paterna, SpainFrancisco Javier Garcı́a-HaroEnvironmental Remote Sensing group (UV‑ERS), Universitat de València, 46100, Burjassot, SpainLuis GuanterUniversitat Politècnica de València, 46022 València, SpainMartin JungMax Planck Institute for Biogeochemistry, 07745 Jena, GermanyJohn A. GamonUniversity of Alberta, Edmonton, Alberta, CanadaMarkus ReichsteinMax Planck Institute for Biogeochemistry, 07745 Jena, GermanySteven W. RunningNumerical Terradynamic Simulation Group (NTSG), University of Montana, Missoula, MT, USA
2021en
ABI
Annotatsiya
Machine learning generalizes vegetation indices to better quantify the terrestrial biosphere.
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