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

Jin Wu; Hideki Kobayashi; Scott C. Stark; Ran Meng; Kaiyu Guan; Ngoc Nguyen Tran; Sicong Gao; Wei Yang; Natalia Restrepo-Coupe; Tomoaki Miura; Raimundo Cosme Oliviera; Alistair Rogers; Dennis G. Dye; Bruce W. Nelson; Shawn P. Serbin; Alfredo R. Huete; Scott R. Saleska

doi:10.1111/nph.14939

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

Jin Wu, Hideki Kobayashi, Scott C. Stark, Ran Meng, Kaiyu Guan, Ngoc Nguyen Tran, Sicong Gao, Wei Yang, Natalia Restrepo-Coupe, Tomoaki Miura, Raimundo Cosme Oliviera, Alistair Rogers, Dennis G. Dye, Bruce W. Nelson, Shawn P. Serbin, Alfredo R. Huete, Scott R. Saleska

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56 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	1507-1520
Number of pages	14
Journal	New Phytologist
Volume	217
Issue number	4
DOIs	https://doi.org/10.1111/nph.14939
State	Published - Mar 2018

Keywords

LiDAR canopy structure
MODIS EVI
WorldView-2
canopy phenology
leaf age
leaf optics

ASJC Scopus subject areas

Physiology
Plant Science

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10.1111/nph.14939

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Wu, J., Kobayashi, H., Stark, S. C., Meng, R., Guan, K., Tran, N. N., Gao, S., Yang, W., Restrepo-Coupe, N., Miura, T., Oliviera, R. C., Rogers, A., Dye, D. G., Nelson, B. W., Serbin, S. P., Huete, A. R., & Saleska, S. R. (2018). Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest. New Phytologist, 217(4), 1507-1520. https://doi.org/10.1111/nph.14939

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 2018, 'Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest', New Phytologist, vol. 217, no. 4, pp. 1507-1520. https://doi.org/10.1111/nph.14939

@article{277f081ffca04b1d86206b8d5d3fc6d3,

title = "Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest",

abstract = "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.",

keywords = "LiDAR canopy structure, MODIS EVI, WorldView-2, canopy phenology, leaf age, leaf optics",

author = "Jin Wu and Hideki Kobayashi and Stark, {Scott C.} and Ran Meng and Kaiyu Guan and Tran, {Ngoc Nguyen} and Sicong Gao and Wei Yang and Natalia Restrepo-Coupe and Tomoaki Miura and Oliviera, {Raimundo Cosme} and Alistair Rogers and Dye, {Dennis G.} and Nelson, {Bruce W.} and Serbin, {Shawn P.} and Huete, {Alfredo R.} and Saleska, {Scott R.}",

note = "Funding Information: This work was supported by NASA (#NNX11AH24G, #NNX17AF56G and NESSF to J.W.), NSF (PIRE #0730305), and the Next-Generation Ecosystem Experiments–Tropics project supported by the US DOE, Office of Science, Office of Biological and Environmental Research and through contract #DE-SC0012704 to Brookhaven National Laboratory. K.H. was supported by JAXA GCOM-C (RA6 #111) and JSPS KAKENHI (#16H02948). S.C.S. was supported by NSF (EF-1550686 and EF-1340604). A.R.H., N.N.T. and S.G. were supported by an Australian Research Council – Discovery Project (ARC-DP140102698, CI Huete). DigitalGlobe data were provided by NASA{\textquoteright}s NGA Commercial Archive Data (http://cad4na sa.gsfc.nasa.gov) under the National Geospatial Intelligence Agency{\textquoteright}s NextView license agreement. We also thank the three anonymous reviewers for their constructive comments that helped to improve the scientific rigor and clarity of the manuscript. Publisher Copyright: No claim to original US Government works New Phytologist {\textcopyright} 2017 New Phytologist Trust",

year = "2018",

month = mar,

doi = "10.1111/nph.14939",

language = "English (US)",

volume = "217",

pages = "1507--1520",

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T1 - Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest

AU - Wu, Jin

AU - Kobayashi, Hideki

AU - Stark, Scott C.

AU - Meng, Ran

AU - Guan, Kaiyu

AU - Tran, Ngoc Nguyen

AU - Gao, Sicong

AU - Yang, Wei

AU - Restrepo-Coupe, Natalia

AU - Miura, Tomoaki

AU - Oliviera, Raimundo Cosme

AU - Rogers, Alistair

AU - Dye, Dennis G.

AU - Nelson, Bruce W.

AU - Serbin, Shawn P.

AU - Huete, Alfredo R.

AU - Saleska, Scott R.

N1 - Funding Information: This work was supported by NASA (#NNX11AH24G, #NNX17AF56G and NESSF to J.W.), NSF (PIRE #0730305), and the Next-Generation Ecosystem Experiments–Tropics project supported by the US DOE, Office of Science, Office of Biological and Environmental Research and through contract #DE-SC0012704 to Brookhaven National Laboratory. K.H. was supported by JAXA GCOM-C (RA6 #111) and JSPS KAKENHI (#16H02948). S.C.S. was supported by NSF (EF-1550686 and EF-1340604). A.R.H., N.N.T. and S.G. were supported by an Australian Research Council – Discovery Project (ARC-DP140102698, CI Huete). DigitalGlobe data were provided by NASA’s NGA Commercial Archive Data (http://cad4na sa.gsfc.nasa.gov) under the National Geospatial Intelligence Agency’s NextView license agreement. We also thank the three anonymous reviewers for their constructive comments that helped to improve the scientific rigor and clarity of the manuscript. Publisher Copyright: No claim to original US Government works New Phytologist © 2017 New Phytologist Trust

PY - 2018/3

Y1 - 2018/3

N2 - 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.

AB - 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.

KW - LiDAR canopy structure

KW - MODIS EVI

KW - WorldView-2

KW - canopy phenology

KW - leaf age

KW - leaf optics

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JO - New Phytologist

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

Abstract

Keywords

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