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Remote Sens. 2017, 9(5), 418;

Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data

1,†,* , 2,†,* , 2,* , 2
Laboratory of Critical Zone Evolution, School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
These authors contributed equally to this work.
Authors to whom correspondence should be addressed.
Academic Editors: Alfredo R. Huete and Prasad S. Thenkabail
Received: 22 March 2017 / Revised: 17 April 2017 / Accepted: 27 April 2017 / Published: 29 April 2017
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The latest MODIS GPP (gross primary productivity) product, MOD17A2H, has great advantages over the previous version, MOD17A2, because the resolution increased from 1000 m to 500 m. In this study, MOD17A2H GPP was assessed using the latest eddy covariance (EC) flux data (FLUXNET2015 Dataset) at eighteen sites in six ecosystems across the globe. The sensitivity of MOD17A2H GPP to the meteorology dataset and the fractional photosynthetically- active radiation (FPAR) product was explored by introducing site meteorology observations and improved Global Land Surface Satellite (GLASS) Leaf Area Index (LAI) products. The results showed that MOD17A2H GPP underestimated flux-derived GPP at most sites. Its performance in estimating annual GPP was poor (R2 = 0.62) and even worse over eight days (R2 = 0.52). For the MOD17A2H algorithm, replacing the reanalysis meteorological datasets with the site meteorological measurements failed to improve the estimation accuracies. However, great improvements in estimating the site-based GPP were gained by replacing MODIS FPAR with GLASS FPAR. This indicated that in the existing MOD17A2H product, the errors were originated more from FPAR than the meteorological data. We further examined the potential error contributions from land cover classification and maximum light use efficiency (εmax). It was found that the current land cover classification scheme exhibited frequent misclassification errors. Moreover, the εmax value assigned in MOD17A2H was much smaller than the inferred εmax value. Therefore, the qualities of FPAR and land cover classification datasets should be upgraded, and the εmax value needs to be adjusted to provide more accurate GPP estimates using MOD17A2H for global ecosystems. View Full-Text
Keywords: GPP; MOD17A2H; validation; FLUXNET2015; global ecosystems GPP; MOD17A2H; validation; FLUXNET2015; global ecosystems

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, L.; Zhu, H.; Lin, A.; Zou, L.; Qin, W.; Du, Q. Evaluation of the Latest MODIS GPP Products across Multiple Biomes Using Global Eddy Covariance Flux Data. Remote Sens. 2017, 9, 418.

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