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Structural complexity biases vegetation greenness measures.
Zeng, Yelu; Hao, Dalei; Park, Taejin; Zhu, Peng; Huete, Alfredo; Myneni, Ranga; Knyazikhin, Yuri; Qi, Jianbo; Nemani, Ramakrishna R; Li, Fa; Huang, Jianxi; Gao, Yongyuan; Li, Baoguo; Ji, Fujiang; Köhler, Philipp; Frankenberg, Christian; Berry, Joseph A; Chen, Min.
Afiliação
  • Zeng Y; College of Land Science and Technology, China Agricultural University, Beijing, China. zengyelu123@gmail.com.
  • Hao D; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA. zengyelu123@gmail.com.
  • Park T; Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA. dalei.hao@pnnl.gov.
  • Zhu P; NASA Ames Research Center, Moffett Field, CA, USA.
  • Huete A; Bay Area Environmental Research Institute, Moffett Field, CA, USA.
  • Myneni R; Department of Geography and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China.
  • Knyazikhin Y; Faculty of Science, University of Technology Sydney, Sydney, New South Wales, Australia.
  • Qi J; Department of Earth and Environment, Boston University, Boston, MA, USA.
  • Nemani RR; Department of Earth and Environment, Boston University, Boston, MA, USA.
  • Li F; Centre d'Etudes Spatiales de la Biosphere, Toulouse, France.
  • Huang J; NASA Ames Research Center, Moffett Field, CA, USA.
  • Gao Y; Bay Area Environmental Research Institute, Moffett Field, CA, USA.
  • Li B; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA.
  • Ji F; College of Land Science and Technology, China Agricultural University, Beijing, China.
  • Köhler P; College of Land Science and Technology, China Agricultural University, Beijing, China.
  • Frankenberg C; College of Land Science and Technology, China Agricultural University, Beijing, China.
  • Berry JA; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA.
  • Chen M; EUMETSAT, Darmstadt, Germany.
Nat Ecol Evol ; 7(11): 1790-1798, 2023 Nov.
Article em En | MEDLINE | ID: mdl-37710041
ABSTRACT
Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Florestas / Floresta Úmida Idioma: En Revista: Nat Ecol Evol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Florestas / Floresta Úmida Idioma: En Revista: Nat Ecol Evol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China