Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data
AffiliationUniv Arizona, Sch Nat Resources & Environm
MetadataShow full item record
PublisherELSEVIER SCIENCE INC
CitationYan, D., Scott, R. L., Moore, D. J. P., Biederman, J. A., & Smith, W. K. (2019). Understanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance data. Remote Sensing of Environment, 223, 50-62.
JournalREMOTE SENSING OF ENVIRONMENT
Rights© 2019 Elsevier Inc. 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.
AbstractDrylands account for approximately 40% of the global land surface and play a dominant role in the trend and variability of terrestrial carbon uptake and storage. Gross ecosystem photosynthesis termed gross primary productivity (GPP) is a critical driver of terrestrial carbon uptake and remains challenging to be observed directly. Currently, vegetation indices that largely capture changes in greenness are the most commonly used datasets in satellite-based GPP modeling. However, there remains significant uncertainty in the spatiotemporal relationship between greenness indices and GPP, especially for relatively heterogeneous dryland ecosystems. In this paper, we compared vegetation greenness indices from PhenoCam and satellite (Landsat and MODIS) observations against GPP estimates from the eddy covariance technique, across three representative ecosystem types of the southwestern United States. We systematically evaluated the changes in the relationship between vegetation greenness indices and GPP: i) across spatial scales of canopy-level, 30-meter, and 500-meter resolution; and ii) across temporal scale of daily, 8-day, 16-day, and monthly resolution. We found that greenness-GPP relationships were independent of spatial scales as long as land cover type and composition remained relatively constant. We also found that the greenness-GPP relationships became stronger as the time interval increased, with the strongest relationships observed at the monthly resolution. We posit that the greenness-GPP relationship breaks down at short timescales because greenness changes more slowly than plant physiological function, which responds rapidly to changes in key biophysical drivers. These findings provide insights into the potential for and limitations of modeling GPP using remotely sensed greenness indices across dryland ecosystem types.
Note24 month embargo; published online: 17 January 2019
VersionFinal accepted manuscript
SponsorsStrategic Environmental Research and Development Program (SERDP) [RC18-1322]; United States Department of Agriculture (USDA) [58-0111-17-013]; United States Department of Energy (DOE) [DE-SC0016011]; DOE's Ameriflux Management Program; Northeastern States Research Cooperative; NSF's Macrosystems Biology Program [EF-1065029, EF-1702697]; DOE's Regional and Global Climate Modeling program [DE-SC0016011]; US National Park Service Inventory and Monitoring Program; USA National Phenology Network from the United States Geological Survey [G10AP00129]; USA National Phenology Network; North Central Climate Science Center from the United States Geological Survey [G16AC00224]; PhenoCam; USDA Agricultural Research Service; University of Arizona