Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence
Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence
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DOI：
10.1073/pnas.1320008111
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发表时间：
2014-04-08
影响因子：
11.1
通讯作者：
Griffis, Timothy J.
中科院分区：
文献类型：
--
作者：
Guanter, Luis;Zhang, Yongguang;Griffis, Timothy J.
光合作用是植物通过收集阳光从二氧化碳和水中产生糖的过程。它是地球上所有生命的主要能源；因此，重要的是要了解这一过程如何应对气候变化和人类影响。然而，基于模型的初级生产总值(GPP，来自光合作用的产出)的估计是高度不确定的，特别是在管理严密的农业区。光谱学的最新进展使空间监测陆地植物的太阳诱导的叶绿素荧光(SIF)成为可能。在这里，我们证明了星载SIF反演提供了农田和草原生态系统GPP的直接测量。这种与作物光合作用的强烈联系在传统的遥感植被指数中并不明显，对于更复杂的碳循环模型也不明显。我们使用SIF观察来提供关于农业生产力的全球视角。我们基于SIF的作物GPP估计比最先进的碳循环模型的结果高出50%-75%，例如美国玉米带和印度恒河平原，这意味着当前的模型严重低估了管理的作用。我们的结果表明，SIF数据可以帮助我们改进我们的全球模型，以便更准确地预测农业生产率和气候对作物产量的影响。将我们的方法扩展到其他生态系统，以及在不久的将来增加对SIF的观测能力，有望减少当前和未来碳循环建模中的不确定性。
Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.