Evidence map›Paper›PMID 37710041›Full record

ArticleNature ecology & evolution2023

Structural complexity biases vegetation greenness measures.

Yelu Zeng, Dalei Hao, Taejin Park, Peng Zhu, Alfredo Huete, Ranga Myneni, Yuri Knyazikhin, Jianbo Qi, Ramakrishna R Nemani, Fa Li and 8 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature ecology & evolution, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Yelu Zeng *College of Land Science and Technology, China Agricultural University, Beijing, China. zengyelu123@gmail.com.ORCID 0000-0003-4267-1841
Dalei Hao *Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA. dalei.hao@pnnl.gov.ORCID 0000-0001-7154-6332
Taejin Park *NASA Ames Research Center, Moffett Field, CA, USA.ORCID 0000-0003-0698-6942
Peng ZhuDepartment of Geography and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China.
Alfredo HueteFaculty of Science, University of Technology Sydney, Sydney, New South Wales, Australia.ORCID 0000-0003-2809-2376
Ranga MyneniDepartment of Earth and Environment, Boston University, Boston, MA, USA.
Yuri KnyazikhinDepartment of Earth and Environment, Boston University, Boston, MA, USA.
Jianbo QiCentre d'Etudes Spatiales de la Biosphere, Toulouse, France.
Ramakrishna R NemaniNASA Ames Research Center, Moffett Field, CA, USA.
Fa LiDepartment of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA.ORCID 0000-0002-0625-5587
Jianxi HuangCollege of Land Science and Technology, China Agricultural University, Beijing, China.ORCID 0000-0003-0341-1983
Yongyuan GaoCollege of Land Science and Technology, China Agricultural University, Beijing, China.ORCID 0009-0004-1231-0564
Baoguo LiCollege of Land Science and Technology, China Agricultural University, Beijing, China.
Fujiang JiDepartment of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA.
Philipp KöhlerEUMETSAT, Darmstadt, Germany.ORCID 0000-0002-7820-1318
Christian FrankenbergJet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA.ORCID 0000-0002-0546-5857
Joseph A BerryDepartment of Global Ecology, Carnegie Institution for Science, Stanford, CA, USA.ORCID 0000-0002-5849-6438
Min ChenDepartment of Forest and Wildlife Ecology, University of Wisconsin-Madison, Madison, WI, USA. min.chen@wisc.edu.ORCID 0000-0001-6311-7124

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ForestsRainforestBiasChlorophyllSeasonsChlorophyll

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.