Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

Wu, J; Kobayashi, H; Stark, SC; Meng, R; Guan, K; Tran, NN; Gao, S; Yang, W; Restrepo-Coupe, N; Miura, T; Oliviera, RC; Rogers, A; Dye, DG; Nelson, BW; Serbin, SP; Huete, AR; Saleska, SR

Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

Wu, J Kobayashi, H Stark, SC Meng, R Guan, K Tran, NN Gao, S

Yang, W Restrepo-Coupe, N

Miura, T Oliviera, RC Rogers, A Dye, DG Nelson, BW Serbin, SP Huete, AR

Saleska, SR

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Publication Type:: Journal Article
Citation:: New Phytologist, 2018, 217 (4), pp. 1507 - 1520
Issue Date:: 2018-03-01

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Kobayashi, H	en_US
dc.contributor.author	Stark, SC	en_US
dc.contributor.author	Meng, R	en_US
dc.contributor.author	Guan, K	en_US
dc.contributor.author	Tran, NN	en_US
dc.contributor.author	Gao, S https://orcid.org/0000-0001-6429-1917	en_US
dc.contributor.author	Yang, W	en_US
dc.contributor.author	Restrepo-Coupe, N https://orcid.org/0000-0003-3921-1772	en_US
dc.contributor.author	Miura, T	en_US
dc.contributor.author	Oliviera, RC	en_US
dc.contributor.author	Rogers, A	en_US
dc.contributor.author	Dye, DG	en_US
dc.contributor.author	Nelson, BW	en_US
dc.contributor.author	Serbin, SP	en_US
dc.contributor.author	Huete, AR https://orcid.org/0000-0003-2809-2376	en_US
dc.contributor.author	Saleska, SR	en_US
dc.date.available	2017-11-06	en_US
dc.date.issued	2018-03-01	en_US
dc.identifier.citation	New Phytologist, 2018, 217 (4), pp. 1507 - 1520	en_US
dc.identifier.issn	0028-646X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123658
dc.description.abstract	No claim to original US Government works New Phytologist © 2017 New Phytologist Trust Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun–sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate–phenology relationships in the tropics.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140102698
dc.relation.ispartof	New Phytologist	en_US
dc.relation.isbasedon	10.1111/nph.14939	en_US
dc.subject.classification	Plant Biology & Botany	en_US
dc.subject.mesh	Plant Leaves	en_US
dc.subject.mesh	Seasons	en_US
dc.subject.mesh	Biological Phenomena	en_US
dc.subject.mesh	Models, Biological	en_US
dc.subject.mesh	Optical Phenomena	en_US
dc.subject.mesh	Forests	en_US
dc.title	Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	217	en_US
utslib.for	060703 Plant Developmental and Reproductive Biology	en_US
utslib.for	0705 Forestry Sciences	en_US
utslib.for	0607 Plant Biology	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	07 Agricultural and Veterinary Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	217	en_US

Abstract:

No claim to original US Government works New Phytologist © 2017 New Phytologist Trust Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models (RTMs) with field observations of Amazon forest leaf and canopy characteristics to test three hypotheses for satellite-observed canopy reflectance seasonality: seasonal changes in leaf area index, in canopy-surface leafless crown fraction and/or in leaf demography. Canopy RTMs (PROSAIL and FLiES), driven by these three factors combined, simulated satellite-observed seasonal patterns well, explaining c. 70% of the variability in a key reflectance-based vegetation index (MAIAC EVI, which removes artifacts that would otherwise arise from clouds/aerosols and sun–sensor geometry). Leaf area index, leafless crown fraction and leaf demography independently accounted for 1, 33 and 66% of FLiES-simulated EVI seasonality, respectively. These factors also strongly influenced modeled near-infrared (NIR) reflectance, explaining why both modeled and observed EVI, which is especially sensitive to NIR, captures canopy seasonal dynamics well. Our improved analysis of canopy-scale biophysics rules out satellite artifacts as significant causes of satellite-observed seasonal patterns at this site, implying that aggregated phenology explains the larger scale remotely observed patterns. This work significantly reconciles current controversies about satellite-detected Amazon phenology, and improves our use of satellite observations to study climate–phenology relationships in the tropics.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/123658