Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest
Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest
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DOI：
10.1016/j.rse.2010.07.012
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发表时间：
2010-12
影响因子：
13.5
通讯作者：
Chaoyang Wu;J. W. Munger;Z. Niu;D. Kuang
中科院分区：
文献类型：
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作者：
Chaoyang Wu;J. W. Munger;Z. Niu;D. Kuang
植被总初级生产力（GPP）是植被通过光合作用固定碳的总速率，是碳循环和气候变化研究的重要内容。利用2003 - 2006年哈佛森林的涡度相关（EC）观测数据和中分辨率成像光谱仪（MODIS）卫星图像，比较了植被光合作用模型（VPM）、温度和绿度（TG）模型和植被指数（VI）模型对全球生产力（GPP）的估算。这些模式对全球PP的估算结果都比MODIS全球PP产品的估算结果更可靠。VPM、TG和VI模型的Pearson相关系数分别为0.94、0.92和0.90。GPP与地表温度（LST，R2=0.72）和水汽压亏缺（VPD，R2=0.45）的关系表明气候变量对GPP的估算是重要的。由于三个标量对温度、水分胁迫和叶龄的适当表征，VPM最好地遵循GPP的季节变化。TG模型结合了MODIS地表反射率和LST产品，完全基于遥感观测，是最适合于没有先验知识的地区的选择。结果表明，从VI模型的可能性，使用一个单一的植被指数的光利用效率（LUE）估计在落叶林，是高度的空间异质性。模型的验证和比较将有助于利用气候变量和/或遥感观测的组合开发未来的全球伙伴关系模型。
Gross primary production (GPP) defined as the overall rate of fixation of carbon through the process of vegetation photosynthesis is important for carbon cycle and climate change research. Three models, the Vegetation Photosynthesis Model (VPM), the Temperature and Greenness (TG) model and the Vegetation Index (VI) model have been compared for the estimation of GPP in Harvard Forest from 2003 to 2006 using climate variables acquired by eddy covariance (EC) measurements and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images. All these models provide more reliable estimates of GPP than that of MODIS GPP product. High Pearsons correlation coefficients r equal to 0.94, 0.92 and 0.90 are observed for the VPM, the TG and the VI model, respectively. Relationships between GPP and land surface temperature (LST, R2=0.72), and vapor pressure deficit (VPD, R2=0.45) indicate that climate variables are important for GPP estimation. Due to proper characterization of temperature, water stress and leaf age by three scalars, VPM best follows the seasonal variations of GPP. By incorporation of the MODIS surface reflectance and LST product, the TG model is the most suitable choice for areas without prior knowledge as it is based entirely on remote sensing observations. Results from the VI model demonstrate the possibility of using a single vegetation index for light use efficiency (LUE) estimation in deciduous forest that is of high spatial heterogeneity. The validation and comparison of models will be helpful in development of future GPP models using combinations of climate variables and/or remote sensing observations.