Journal
NEW PHYTOLOGIST
Volume 217, Issue 4, Pages 1507-1520Publisher
WILEY
DOI: 10.1111/nph.14939
Keywords
canopy phenology; leaf age; leaf optics; LiDAR canopy structure; MODIS EVI; WorldView-2
Categories
Funding
- NASA [NNX11AH24G, NNX17AF56G]
- NSF (PIRE) [0730305]
- US DOE, Office of Science, Office of Biological and Environmental Research [DE-SC0012704]
- JAXA GCOM-C [111]
- JSPS KAKENHI [16H02948]
- NSF [EF1550686, EF-1340604]
- Australian Research Council - Discovery Project [ARC-DP140102698]
- Direct For Biological Sciences
- Division Of Environmental Biology [1340604, 1340624] Funding Source: National Science Foundation
- Emerging Frontiers
- Direct For Biological Sciences [1340649] Funding Source: National Science Foundation
- Office Of The Director
- Office Of Internatl Science &Engineering [0730305] Funding Source: National Science Foundation
- Grants-in-Aid for Scientific Research [16H02948] Funding Source: KAKEN
- NASA [NNX11AH24G, 1002206, NNX17AF56G, 144811] Funding Source: Federal RePORTER
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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.
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