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

• Autor(a) principal: Wu, Jin
• Data de Publicação: 2018
• Outros Autores: Kobayashi, Hideki, Stark, Scott C., Meng, Ran, Guan, Kaiyu, Tran, Ngoc Nguyen, Gao, Sicong, Yang, Wei, Restrepo-Coupé, Natalia, Miura, Tomoaki, Oliviera, Raimundo Cosme, Rogers, Alistair, Dye, Dennis G., Nelson, Bruce Walker, Serbin, Shawn P., Huete, Alfredo Ramon, Saleska, Scott Reid
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional do INPA
• Texto Completo: https://repositorio.inpa.gov.br/handle/1/15658
• topic: Annual Variation; Canopy Architecture; Evergreen Forest; Hypothesis Testing; Leaf Area Index; Lidar; Phenology; Radiative Transfer; Remote Sensing; Satellite Data; Seasonality; Worldview; Amazonia; Biological Model; Cellular, Subcellular And Molecular Biological Phenomena And Functions; Forest; Growth, Development And Aging; Light Related Phenomena; Physiology; Plant Leaf; Season; Biological Phenomena; Forests; Models, Biological; Optical Phenomena; Plant Leaves; Seasons

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. No claim to original US Government works New Phytologist © 2017 New Phytologist Trust