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

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

doi:10.1111/nph.14939

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

Journal Article · Sat Dec 23 00:00:00 EST 2017 · New Phytologist

DOI:https://doi.org/10.1111/nph.14939· OSTI ID:1413960

Wu, Jin ^[1]; Kobayashi, Hideki ^[2]; Stark, Scott C. ^[3]; Meng, Ran ^[4]; Guan, Kaiyu ^[5]; Tran, Ngoc Nguyen ^[6]; Gao, Sicong ^[6]; Yang, Wei ^[7]; Restrepo-Coupe, Natalia ^[8]; Miura, Tomoaki ^[9]; Oliviera, Raimundo Cosme ^[10]; Rogers, Alistair ^[4]; Dye, Dennis G. ^[10]; Nelson, Bruce W. ^[11]; Serbin, Shawn P. ^[4]; Huete, Alfredo R. ^[6]; Saleska, Scott R. ^[8]

Univ. of Arizona, Tucson, AZ (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
Japan Agency for Marine-Earth Science and Technology, Kanagawa Prefecture (Japan)
Michigan State Univ., East Lansing, MI (United States)
Brookhaven National Lab. (BNL), Upton, NY (United States)
Univ. of Illinois at Urbana Champaign, Urbana, IL (United States)
Univ. of Technology Sydney, Ultimo, NSW (Australia)
Chiba Univ., Chiba (Japan)
Univ. of Arizona, Tucson, AZ (United States)
Univ. of Hawaii, Honolulu, HI (United States)
Northern Arizona Univ., Flagstaff, AZ (United States)
Brazil's National Institute for Amazon Research (INPA), Manaus (Brazil)

Summary Satellite observations of Amazon forests show seasonal and interannual variations, but the underlying biological processes remain debated. Here we combined radiative transfer models ( RTM s) 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 RTM s ( PROSAIL and FL i ES ), 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 FL i ES ‐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.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0012704

OSTI ID:: 1413960

Alternate ID(s):: OSTI ID: 1419694

Report Number(s):: BNL-114821-2017-JA; R&D Project: 2019‐BNL-EE630EECA-Budg; KP1701000

Journal Information:: New Phytologist, Vol. 217, Issue 4; ISSN 0028-646X

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 54 works

Citation information provided by
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54 ENVIRONMENTAL SCIENCES
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