Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China
Site-level evaluation of MODIS-based primary production in an old-growth forest in Northeast China
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基于 MODIS 的东北原始林初级生产站点水平评价
DOI：
10.1117/1.3624519
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发表时间：
2011
影响因子：
1.7
通讯作者：
Jin, Changjie
中科院分区：
文献类型：
--
作者：
Wu, Jiabing;Sun, Jinwei;Guan, Dexin;Yang, Hong;Yin, Guanghua;Wang, Anzhi;Yuan, Fenghui;Jin, Changjie
为了提高中分辨率成像光谱辐射计（MODIS）总初级生产量（GPP）算法的精度，利用地基测量对MODIS不同土地覆盖类型的总初级生产量进行评估至关重要。本文对MODIS初级产品（MOD17）进行了评价，并将该算法的特定站点输入参数与原始森林的涡动相关测量结果进行了比较。直接比较表明，标准MODIS算法预测的8天GPP与基于通量塔的GPP高度相关（r2 = 0.77， P<0.001），平均低估39%。这主要是由于低估的MODIS生物群系特征参数、最大光利用效率εmax和MODIS衍生的光合有效辐射分数的输入所致。基于站点特定输入参数计算的改进MODIS算法GPP与地面通量塔观测值比较，r2 = 0.92，相对误差= 7%。这些结果表明，在初级生产力高的样地，MODIS GPP产量最可能被低估，而特定样地的输入参数有助于提高MODIS GPP算法的精度。
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.
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影响因子：
13.5
作者：
S. Running;D. Baldocchi;D. Turner;S. Gower;P. Bakwin;K. Hibbard
通讯作者：
S. Running;D. Baldocchi;D. Turner;S. Gower;P. Bakwin;K. Hibbard
影响因子：
13.5
作者：
N. Coops;T. Black;R. Jassal;J. A. Trofymow;K. Morgenstern
通讯作者：
N. Coops;T. Black;R. Jassal;J. A. Trofymow;K. Morgenstern
影响因子：
6.2
作者：
Hutyra, Lucy R.;Munger, J. William;Wofsy, Steven C.
通讯作者：
Wofsy, Steven C.
影响因子：
6.2
作者：
Guan, De-Xin;Wu, Jia-Bing;Jin, Chang-Jie
通讯作者：
Jin, Chang-Jie
DOI：
--
发表时间：
2017
期刊：
--
影响因子：
--
作者：
D. Baldocchi;E. Falge;L. Gu;R. Olson;D. Hollinger;S. Running;P. Anthoni;C. Bernhofer;K. Davis-K.-Da
通讯作者：
D. Baldocchi;E. Falge;L. Gu;R. Olson;D. Hollinger;S. Running;P. Anthoni;C. Bernhofer;K. Davis-K.-Da