Estimating Carbon Flux Phenology with Satellite-Derived Land Surface Phenology and Climate Drivers for Different Biomes: A Synthesis of AmeriFlux Observations

Zhu, Wenquan; Chen, Guangsheng; Jiang, Nan; Liu, Jianhong; Mou, Minjie

doi:10.1371/journal.pone.0084990

Estimating Carbon Flux Phenology with Satellite-Derived Land Surface Phenology and Climate Drivers for Different Biomes: A Synthesis of AmeriFlux Observations

Journal Article · Fri Dec 27 00:00:00 EST 2013 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0084990· OSTI ID:1265293

Zhu, Wenquan ^[1]; Chen, Guangsheng ^[2]; Jiang, Nan ^[3]; Liu, Jianhong ^[3]; Mou, Minjie ^[3]

Beijing Normal University, Beijing (China). State Key Laboratory of Earth Surface Processes and Resource Ecology
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division
Beijing Normal University, Beijing (China). College of Resources Science and Technology

Carbon Flux Phenology (CFP) can affect the interannual variation in Net Ecosystem Exchange (NEE) of carbon between terrestrial ecosystems and the atmosphere. In this paper, 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. In conclusion, 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.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1265293

Journal Information:: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 12 Vol. 8; ISSN 1932-6203

Publisher:: Public Library of ScienceCopyright Statement

Country of Publication:: United States

Language:: English

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