Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China

Jiabing Wu; Jinwei Sun; Dexin Guan; Hong Yang; Guanghua Yin; Anzhi Wang; Fenghui Yuan; Changjie Jin

doi:10.1117/1.3624519

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1 January 2011 Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China

Jiabing Wu; Jinwei Sun; Dexin Guan; Hong Yang; Guanghua Yin; Anzhi Wang; Fenghui Yuan; Changjie Jin

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J. of Applied Remote Sensing, 5(1), 053551 (2011). https://doi.org/10.1117/1.3624519

Abstract

To improve the accuracy of the moderate resolution imaging spectroradiometer (MODIS) gross primary production (GPP) algorithm, it is critical to evaluate MODIS GPP production for different land cover types using ground-based measurements. In this paper the MODIS primary production products (MOD17) is evaluated by using site-specific input parameters to the algorithm and compared to the results of eddy covariance measurements over an old-growth forest. Direct comparisons suggest that 8-day GPP predicted by the standard MODIS algorithm was highly correlated with flux tower based GPP (r² = 0.77, P<0.001), while with an average underestimation of 39%. The difference is substantial in magnitude mainly because the inputs of underestimated MODIS biome-specific parameters, maximum light use efficiency εmax and MODIS derived fraction of photosynthetically active radiation. The modified MODIS algorithm GPP calculated with site-specific input parameters compares favorably with ground flux tower observations (r² = 0.92, relative error = 7%). These results suggest that the MODIS GPP production is most likely underpredicted in forest sites with high primary production, and site-specific input parameters could help to improve the accuracy of MODIS GPP algorithm.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Jiabing Wu, Jinwei Sun, Dexin Guan, Hong Yang, Guanghua Yin, Anzhi Wang, Fenghui Yuan, and Changjie Jin "Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China," Journal of Applied Remote Sensing 5(1), 053551 (1 January 2011). https://doi.org/10.1117/1.3624519

Published: 1 January 2011

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