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Remote Sens. 2016, 8(5), 395;

Evaluation of MODIS Gross Primary Production across Multiple Biomes in China Using Eddy Covariance Flux Data

1,* , 2,†,* , 1
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
These authors contributed equally to this work.
Authors to whom correspondence should be addressed.
Academic Editors: Richard L. Miller, Cheng-Chien Liu, Clement Atzberger and Prasad S. Thenkabail
Received: 22 March 2016 / Revised: 14 April 2016 / Accepted: 2 May 2016 / Published: 13 May 2016
(This article belongs to the Special Issue Remote Sensing of Biogeochemical Cycles)
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MOD17A2 provides near real-time estimates of gross primary production (GPP) globally. In this study, MOD17A2 GPP was evaluated using eddy covariance (EC) flux measurements at eight sites in five various biome types across China. The sensitivity of MOD17A2 to meteorological data and leaf area index/fractional photosynthetically active radiation (LAI/FPAR) products were examined by introducing site meteorological measurements and improved Global Land Surface Satellite (GLASS) LAI products. We also assessed the potential error contributions from land cover and maximum light use efficiency (εmax). The results showed that MOD17A2 agreed well with flux measurements of annual GPP (R2 = 0.76) when all biome types were considered as a whole. However, MOD17A2 was ineffective for estimating annual GPP at mixed forests, evergreen needleleaf forests and croplands, respectively. Moreover, MOD17A2 underestimated flux derived GPP during the summer (R2 = 0.46). It was found that the meteorological data used in MOD17A2 failed to properly estimate the site measured vapor pressure deficits (VPD) (R2 = 0.31). Replacing the existing LAI/FPAR data with GLASS LAI products reduced MOD17A2 GPP uncertainties. Though land cover presented the fewest errors, εmax prescribed in MOD17A2 were much lower than inferred εmax calculated from flux data. Thus, the qualities of meteorological data and LAI/FPAR products need to be improved, and εmax should be adjusted to provide better GPP estimates using MOD17A2 for Chinese ecosystems. View Full-Text
Keywords: MODIS; gross primary production (GPP); validation; eddy covariance; China MODIS; gross primary production (GPP); validation; eddy covariance; China

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Zhu, H.; Lin, A.; Wang, L.; Xia, Y.; Zou, L. Evaluation of MODIS Gross Primary Production across Multiple Biomes in China Using Eddy Covariance Flux Data. Remote Sens. 2016, 8, 395.

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