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Biological processes dominate seasonality of remotely sensed canopy greenness in an Amazon evergreen forest
Author(s) -
Wu Jin,
Kobayashi Hideki,
Stark Scott C.,
Meng Ran,
Guan Kaiyu,
Tran Ngoc Nguyen,
Gao Sicong,
Yang Wei,
RestrepoCoupe Natalia,
Miura Tomoaki,
Oliviera Raimundo Cosme,
Rogers Alistair,
Dye Dennis G.,
Nelson Bruce W.,
Serbin Shawn P.,
Huete Alfredo R.,
Saleska Scott R.
Publication year - 2018
Publication title -
new phytologist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.742
H-Index - 244
eISSN - 1469-8137
pISSN - 0028-646X
DOI - 10.1111/nph.14939
Subject(s) - canopy , phenology , seasonality , leaf area index , environmental science , amazon rainforest , evergreen , vegetation (pathology) , atmospheric sciences , satellite , tree canopy , enhanced vegetation index , evergreen forest , crown (dentistry) , remote sensing , ecology , geography , normalized difference vegetation index , vegetation index , biology , geology , medicine , dentistry , pathology , aerospace engineering , engineering
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.