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dc.contributor.authorYan, D.
dc.contributor.authorScott, R.L.
dc.contributor.authorMoore, D.J.P.
dc.contributor.authorBiederman, J.A.
dc.contributor.authorSmith, W.K.
dc.date.accessioned2019-03-27T19:14:49Z
dc.date.available2019-03-27T19:14:49Z
dc.date.issued2019-03-15
dc.identifier.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.en_US
dc.identifier.issn00344257
dc.identifier.doi10.1016/j.rse.2018.12.029
dc.identifier.urihttp://hdl.handle.net/10150/631991
dc.description.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.en_US
dc.description.sponsorshipStrategic 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 Arizonaen_US
dc.language.isoenen_US
dc.publisherELSEVIER SCIENCE INCen_US
dc.relation.urlhttps://linkinghub.elsevier.com/retrieve/pii/S0034425718305881en_US
dc.rights© 2019 Elsevier Inc. All rights reserved.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectDrylandsen_US
dc.subjectGreenness indicesen_US
dc.subjectGross primary productivityen_US
dc.subjectPhenoCamen_US
dc.subjectLandsaten_US
dc.subjectMODISen_US
dc.titleUnderstanding the relationship between vegetation greenness and productivity across dryland ecosystems through the integration of PhenoCam, satellite, and eddy covariance dataen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Sch Nat Resources & Environmen_US
dc.identifier.journalREMOTE SENSING OF ENVIRONMENTen_US
dc.description.note24 month embargo; published online: 17 January 2019en_US
dc.description.collectioninformationThis 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 repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleRemote Sensing of Environment
dc.source.volume223
dc.source.beginpage50
dc.source.endpage62


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