Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations

dc.contributor.authorHeinsch, Faith A.
dc.contributor.authorZhao, Maosheng
dc.contributor.authorRunning, Steven W.
dc.contributor.authorKimball, John S.
dc.contributor.authorNemani, Ramakrishna R.
dc.contributor.authorDavis, Kenneth J.
dc.contributor.authorCook, Bruce D.
dc.contributor.authorDesai, Ankur R.
dc.contributor.authorRicciuto, Daniel M.
dc.contributor.authorLaw, Beverly E.
dc.contributor.authorOechel, Walter C.
dc.contributor.authorKwon, Hyojung
dc.contributor.authorWofsy, Steven C.
dc.contributor.authorDunn, Allison L.
dc.contributor.authorMunger, J. William
dc.contributor.authorBaldocchi, Dennis D.
dc.contributor.authorXu, Liukang
dc.contributor.authorHollinger, David Y.
dc.contributor.authorRichardson, Andrew D.
dc.contributor.authorStoy, Paul C.
dc.contributor.authorSiqueira, Mario B. S.
dc.contributor.authorMonson, Russell K.
dc.contributor.authorBurns, Sean P.
dc.contributor.authorFlanagan, Lawrence B.
dc.contributor.authorBolstad, Paul V.
dc.contributor.authorLuo, Hongyan
dc.description.abstractThe Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values overestimated site values. Land cover presented the fewest errors, with most errors within the forest classes, reducing potential error. Tower-based and MODIS estimates of annual GPP compare favorably for most biomes, although MODIS GPP overestimates tower-based calculations by 20%-30%. Seasonally, summer estimates of MODIS GPP are closest to tower data, and spring estimates are the worst, most likely the result of the relatively rapid onset of leaf-out. The results of this study indicate, however, that the current MODIS GPP algorithm shows reasonable spatial patterns and temporal variability across a diverse range of biomes and climate regimes. So, while continued efforts are needed to isolate particular problems in specific biomes, we are optimistic about the general quality of these data, and continuation of the MOD17 GPP product will likely provide a key component of global terrestrial ecosystem analysis, providing continuous weekly measurements of global vegetation productionen_US
dc.identifier.citationHeinsch Faith A., Maosheng Zhao, Steven W. Running, John S. Kimball, Ramakrishna R. Nemani, Kenneth J. Davis, Paul V. Bolstad, Bruce D. Cook, Ankur R. Desai, Daniel M. Ricciuto, Beverly E. Law, Walter C. Oechel, Hyojung Kwon, Hongyan Luo, Steven C. Wofsy, Allison L. Dunn, J. William Munger, Dennis D. Baldocchi, Liukang Xu, David Y. Hollinger, Andrew D. Richardson, Paul C. Stoy, Mario B. S. Siqueira, Russell K. Monson, Sean P. Burns, and Lawrence B. Flanagan (2006) Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing 44: 1908-1925. DOI: 10.1109/TGRS.2005.853936.en_US
dc.rightsThis Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).en_US
dc.titleEvaluation of remote sensing based terrestrial productivity from MODIS using regional tower eddy flux network observationsen_US
mus.citation.journaltitleIEEE Transactions on Geoscience and Remote Sensingen_US
mus.identifier.categoryLife Sciences & Earth Sciencesen_US
mus.relation.collegeCollege of Agricultureen_US
mus.relation.departmentLand Resources & Environmental Sciences.en_US
mus.relation.universityMontana State University - Bozemanen_US


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