Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, 11973, USA.
School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong.
New Phytol. 2019 Dec;224(4):1557-1568. doi: 10.1111/nph.16123. Epub 2019 Sep 17.
Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R = 0.89; root mean square error (RMSE) = 15.45 g m ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.
叶面积比（LMA）是一个关键的植物性状，反映了叶片光合作用功能、寿命和结构投资之间的权衡。捕捉 LMA 的空间和时间变化一直是生态研究的长期目标，也是推进地球系统模型的重要组成部分。尽管在地球的生物群系内和之间存在着巨大的 LMA 变化，但仍然缺乏一种高效、全球通用的 LMA 预测方法。我们探索了从全球 LMA 性状空间的大部分范围内的叶片光谱来预测 LMA 的能力，其值范围从 17 到 393 g m。我们的数据集包含了从北极到热带的广泛生物群系的叶子，包括阔叶和针叶物种，以及上层和下层（即阳光和阴凉）生长环境的叶子。在这里，我们证明了仅使用来自不同生物群系的广泛物种、叶片年龄和冠层位置的光谱测量值，就可以快速估计 LMA 的能力。我们的模型以高精度和低误差（R = 0.89；均方根误差（RMSE）= 15.45 g m）捕捉 LMA 的可变性。我们的发现强调了这样一个事实，即叶片经济谱与叶片光学谱相呼应，为这项技术在全球生物群系的生态系统中预测 LMA 的多样性铺平了道路。
