Publication Date:
2018-01-05
Description:
Satellite 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 (EVI), Solar-Induced Fluorescence (SIF), and the Photochemical Reflectivity Index (PRI). We find evidence that SIF is capturing unique features of seasonal GPP dynamics, particularly for evergreen-dominated EC sites, and interannual GPP dynamics, across all dryland EC sites. These results suggest that incorporation of SIF could significantly improve satellite-based GPP estimates.
Print ISSN:
0094-8276
Electronic ISSN:
1944-8007
Topics:
Geosciences
,
Physics
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