Estimating carbon flux phenology with satellite-derived land surface phenology and climate drivers for different biomes: a synthesis of AmeriFlux observations

• Published in: PloS one Vol. 8; no. 12; p. e84990
• Main Authors: Zhu, Wenquan, Chen, Guangsheng, Jiang, Nan, Liu, Jianhong, Mou, Minjie
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.12.2013; Public Library of Science (PLoS)
• Subjects: Agricultural land; Biology; Biomes; Carbon; Carbon - analysis; Carbon - metabolism; Carbon uptake; Climate; Climate Change; Climatic data; Deciduous forests; Earth Sciences; Ecosystem; Ecosystem biology; ENVIRONMENTAL SCIENCES; Financial planners; Forests; Grasslands; Interannual variability; Laboratories; Methodology; MODIS; Motor vehicle drivers; Phenology; Photosynthesis; Productivity; Remote sensing; Respiration; Root-mean-square errors; Satellite data; Satellites; Science; Seasons; Spacecraft; Terrestrial ecosystems; Terrestrial environments; Vegetation index; Beijing China; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0084990

Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this study, we proposed a methodology to estimate CFP metrics with satellite-derived Land Surface Phenology (LSP) metrics and climate drivers for 4 biomes (i.e., deciduous broadleaf forest, evergreen needleleaf forest, grasslands and croplands), using 159 site-years of NEE and climate data from 32 AmeriFlux sites and MODIS vegetation index time-series data. LSP metrics combined with optimal climate drivers can explain the variability in Start of Carbon Uptake (SCU) by more than 70% and End of Carbon Uptake (ECU) by more than 60%. The Root Mean Square Error (RMSE) of the estimations was within 8.5 days for both SCU and ECU. The estimation performance for this methodology was primarily dependent on the optimal combination of the LSP retrieval methods, the explanatory climate drivers, the biome types, and the specific CFP metric. This methodology has a potential for allowing extrapolation of CFP metrics for biomes with a distinct and detectable seasonal cycle over large areas, based on synoptic multi-temporal optical satellite data and climate data.