Evaluating spatial and temporal patterns of MODIS GPP over the conterminous U.S. against flux measurements and a process model
Evaluating spatial and temporal patterns of MODIS GPP over the conterminous U.S. against flux measurements and a process model
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DOI：
10.1016/j.rse.2012.06.023
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发表时间：
2012-09
影响因子：
13.5
通讯作者：
Fangmin Zhang;Jing M. Chen;Jiquan Chen;C. Gough;T. Martin;D. Dragoni
中科院分区：
文献类型：
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作者：
Fangmin Zhang;Jing M. Chen;Jiquan Chen;C. Gough;T. Martin;D. Dragoni
总初级生产力（GPP）是生态系统光合吸收碳的数量，是陆地碳循环的重要组成部分。经验光利用效率（LUE）模型和基于过程的Farquhar，von Caemmerer和Berry（FvCB）光合模型被广泛用于GPP估计。在本文中，MODIS的GPP算法使用的LUE方法和北方生态系统生产力模拟器（BEPS）的基础上的FvCB模式，其中阳光和阴影叶分离方案进行评估对GPP值来自涡动协方差（EC）的测量在各种生态系统。虽然使用这两个模型模拟的总GPP值在对美国进行平均时在89%以内一致，它们在时空分布格局上存在系统性差异。因此，MODIS GPP的空间分布与BEPS产生的空间分布有很大不同。这些差异可能是由于LUE建模方法的固有问题。当对生物群落类型使用恒定的最大LUE值时，这种简化无法正确处理阴影树叶对总树冠水平GPP的贡献。当GPP由BEPS建模为阳光照射和阴影叶GPP的总和时，问题被最小化，即，在低端，遮荫叶对GPP的相对贡献较小，而在高端，遮荫叶的相对贡献较大。与北美40个测站的涡度相关资料计算的月和年GPP相比，BEPS算法的性能优于MODISGPP算法。随着被遮蔽叶片比例的增加，MODIS和BEPS GPP的差异逐渐扩大。因此，应进一步改进更简单的LUE建模方法，以减少这种偏差问题，从而有效估计区域和时间GPP分布。
Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by ecosystems and is an important component of the terrestrial carbon cycle. Empirical light use efficiency (LUE) models and process-based Farquhar, von Caemmerer, and Berry (FvCB) photosynthetic models are widely used for GPP estimation. In this paper, the MODIS GPP algorithm using the LUE approach and the Boreal Ecosystem Productivity Simulator (BEPS) based on the FvCB model in which a sunlit and shaded leaf separation scheme is evaluated against GPP values derived from eddy-covariance (EC) measurements in a variety of ecosystems. Although the total GPP values simulated using these two models agree within 89% when they are averaged for the conterminous U.S., there are systematic differences between them in terms of their spatial and temporal distribution patterns. The spatial distribution of MODIS GPP therefore differs substantially from that produced by BEPS. These differences may be due to an inherent problem of the LUE modeling approach. When a constant maximum LUE value is used for a biome type, this simplification cannot properly handle the contribution of shaded leaves to the total canopy-level GPP. When GPP is modeled by BEPS as the sum of sunlit and shaded leaf GPP, the problem is minimized, i.e., at the low end, the relative contribution of shaded leaves to GPP is small and at the high end, the relative contribution of shaded leaves is large. Compared with monthly and annual GPP derived from eddy covariance data at 40 tower sites in North America, BEPS performed better than the MODIS GPP algorithm. The difference between MODIS and BEPS GPP widens as with the fraction of shaded leaves increases. The simpler LUE modeling approach should therefore be further improved to reduce this bias issue for effective estimation of regional and temporal GPP distributions.