Chlorophyll Fluorescence Better Captures Seasonal and Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of Southwestern North America
AuthorSmith, W. K.
Biederman, J. A.
Scott, R. L.
Moore, D. J. P.
Kimball, J. S.
Barnes, M. L.
Fox, A. M.
Litvak, M. E.
AffiliationUniv Arizona, Sch Nat Resources & Environm
MetadataShow full item record
PublisherAMER GEOPHYSICAL UNION
CitationChlorophyll Fluorescence Better Captures Seasonal and Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of Southwestern North America 2018, 45 (2):748 Geophysical Research Letters
JournalGeophysical Research Letters
Rights©2018. American Geophysical Union. All Rights Reserved.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractSatellite remote sensing provides unmatched spatiotemporal information on vegetation gross primary productivity (GPP). Yet understanding of the relationship between GPP and remote sensing observations and how it changes with factors such as scale, biophysical constraint, and vegetation type remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have characteristic high spatiotemporal variability and are under-represented by long-term field measurements. Here we utilize an eddy covariance (EC) data synthesis for southwestern North America in an assessment of how accurately satellite-derived vegetation proxies capture seasonal to interannual GPP dynamics across dryland gradients. We evaluate the enhanced vegetation index, solar-induced fluorescence (SIF), and the photochemical reflectivity index. We find evidence that SIF is more accurately capturing seasonal GPP dynamics particularly for evergreen-dominated EC sites and more accurately estimating the full magnitude of interannual GPP dynamics for all dryland EC sites. These results suggest that incorporation of SIF could significantly improve satellite-based GPP estimates.
Note6 month embargo; published online: 4 January 2018
VersionFinal published version
SponsorsDOE's Office of Science; NASA [NNH16ZDA001N]; USDA [58-0111-17-013]; DOE's Regional and Global Climate Modeling Program [DE-SC0016011]