• Foliar functional traits are widely used to characterize leaf and canopy properties that drive ecosystem processes and to infer physiological processes in Earth system models. Imaging spectroscopy provides great potential to map foliar traits to characterize continuous functional variation and diversity, but few studies have demonstrated consistent methods for mapping multiple traits across biomes.
• With airborne imaging spectroscopy data and field data from 19 sites, we developed trait models using partial least squares regression, and mapped 26 foliar traits in seven NEON (National Ecological Observatory Network) ecoregions (domains) including temperate and subtropical forests and grasslands of eastern North America.
• Model validation accuracy varied among traits (normalized root mean squared error, 9.1–19.4%; coefficient of determination, 0.28–0.82), with phenolic concentration, leaf mass per area and equivalent water thickness performing best across domains. Across all trait maps, 90% of vegetated pixels had reasonable values for one trait, and 28–81% provided high confidence for multiple traits concurrently.
• Maps of 26 traits and their uncertainties for eastern US NEON sites are available for download, and are being expanded to the western United States and tundra/boreal zone. These data enable better understanding of trait variations and relationships over large areas, calibration of ecosystem models, and assessment of continental‐scale functional diversity.

中文翻译：

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来自北美东部生物群落的成像光谱学中的叶面功能性状。

• 叶面功能性状被广泛用于表征驱动生态系统过程的叶和冠层特性，并推断地球系统模型中的生理过程。成像光谱学提供了巨大的潜力来绘制叶性状以表征连续的功能变异和多样性，但是很少有研究表明一致的方法可以绘制跨生物群落的多个性状。
• 利用机载成像光谱数据和来自19个站点的现场数据，我们使用偏最小二乘回归开发了特征模型，并在七个NEON（国家生态观测网络）生态区域（域）中绘制了26个叶状特征，其中包括东北北部的温带和亚热带森林和草地美国。
• 模型性状的准确度因性状而异（归一化均方根误差为9.1-19.4％；确定系数为0.28-0.82），其中酚类浓度，单位面积叶片质量和等效水厚在各个领域中表现最佳。在所有性状图中，90％的植被像素具有一个性状的合理值，而28–81％的植被像素同时提供了多个性状的高置信度。
• 可下载美国东部NEON站点的26个性状及其不确定性的地图，并将其扩展到美国西部和苔原/北方地区。这些数据使人们能够更好地了解大面积地区的性状变异和相互关系，生态系统模型的校准以及大陆规模功能多样性的评估。