Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.
Beyond greenness: Detecting temporal changes in photosynthetic capacity with hyperspectral reflectance data.
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DOI：
10.1371/journal.pone.0189539
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Moore DJP
中科院分区：
文献类型：
--
作者：
Barnes ML;Breshears DD;Law DJ;van Leeuwen WJD;Monson RK;Fojtik AC;Barron-Gafford GA;Moore DJP
地球未来的碳平衡和区域碳交换动态与植物光合作用密不可分。光谱植被指数被广泛用作植被绿色度的替代指标，并用于估计植被覆盖和叶面积指数等状态变量。然而，绿色叶子吸收碳的能力可能会在整个季节发生变化。我们将光合能力量化为RuBP羧化（Vcmax）和再生（Jmax）的最大速率。Vcmax和Jmax因个体发育过程和气象变量之间的相互作用而在季节内变化。遥感为基础的估算Vcmax和Jmax使用叶片反射光谱是有前途的，但这些关键的决定因素之间的关系，光合能力，叶片反射光谱的时间变化，并连接这些变量的模型还没有得到评估。为了解决这个问题，我们研究了杂种白杨（Populus spp.）在为期7周的仲夏期间，以量化Vcmax，Jmax和叶光谱之间的季节动态关系。我们比较了原位估计的Vcmax和Jmax的气体交换测量的估计Vcmax和Jmax来自偏最小二乘回归（PLSR）和新鲜叶片的反射光谱。PLSR模型是强大的，尽管动态的时间变化，在整个研究期间的Vcmax和Jmax。植物胁迫的种群内变化适度降低了PLSR模型的预测能力。高光谱植被指数与Vcmax和Jmax有很好的相关性，包括广泛使用的归一化差异植被指数。我们的研究结果表明，植物生理性状的高光谱估计PLSR可能是强大的时间变化。此外，高光谱植被指数可能足以检测光合能力的时间变化的背景下，类似于这里研究的。总的来说，我们的研究结果突出了高光谱遥感的潜力，以估计光合能力的决定因素与季节性和植物胁迫相关的动态时间变化期间，从而提高植物生产力的估计和相关的碳预算的特征。
Earth's future carbon balance and regional carbon exchange dynamics are inextricably linked to plant photosynthesis. Spectral vegetation indices are widely used as proxies for vegetation greenness and to estimate state variables such as vegetation cover and leaf area index. However, the capacity of green leaves to take up carbon can change throughout the season. We quantify photosynthetic capacity as the maximum rate of RuBP carboxylation (Vcmax) and regeneration (Jmax). Vcmax and Jmax vary within-season due to interactions between ontogenetic processes and meteorological variables. Remote sensing-based estimation of Vcmax and Jmax using leaf reflectance spectra is promising, but temporal variation in relationships between these key determinants of photosynthetic capacity, leaf reflectance spectra, and the models that link these variables has not been evaluated. To address this issue, we studied hybrid poplar (Populus spp.) during a 7-week mid-summer period to quantify seasonally-dynamic relationships between Vcmax, Jmax, and leaf spectra. We compared in situ estimates of Vcmax and Jmax from gas exchange measurements to estimates of Vcmax and Jmax derived from partial least squares regression (PLSR) and fresh-leaf reflectance spectroscopy. PLSR models were robust despite dynamic temporal variation in Vcmax and Jmax throughout the study period. Within-population variation in plant stress modestly reduced PLSR model predictive capacity. Hyperspectral vegetation indices were well-correlated to Vcmax and Jmax, including the widely-used Normalized Difference Vegetation Index. Our results show that hyperspectral estimation of plant physiological traits using PLSR may be robust to temporal variation. Additionally, hyperspectral vegetation indices may be sufficient to detect temporal changes in photosynthetic capacity in contexts similar to those studied here. Overall, our results highlight the potential for hyperspectral remote sensing to estimate determinants of photosynthetic capacity during periods with dynamic temporal variations related to seasonality and plant stress, thereby improving estimates of plant productivity and characterization of the associated carbon budget.
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作者：
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通讯作者：
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