Abstract Plant functional diversity, defined as the range of plant chemical, physiological and structural properties within plants, is a key component of biodiversity which controls the ecosystem functioning and stability. Monitoring its variations across space and over time is critical in ecological studies. So far, several reflectance-based metrics have been tested to achieve this objective, yielding different degrees of success. Our work aimed at exploring the potential of a novel metric based on far-red sun-induced chlorophyll fluorescence (F760) to map the functional diversity of terrestrial ecosystems. This was achieved exploiting high-resolution images collected over a mixed forest ecosystem with the HyPlant sensor, deployed as an airborne demonstrator of the forthcoming ESA-FLEX satellite. A reference functional diversity map was obtained applying the Rao's Q entropy metric on principal components calculated on key plant functional trait maps retrieved from the hyperspectral reflectance cube. Based on the spectral variation hypothesis, which states that the biodiversity signal is encoded in the spectral heterogeneity, two moving window-based approaches were tested to estimate the functional diversity from continuous spectral data: i) the Rao's Q entropy metric calculated on the normalized difference vegetation index (NDVI) and ii) the coefficient of variation (CV) calculated on hyperspectral reflectance. Finally, a third moving window approach was used to estimate the functional diversity based on F760 heterogeneity quantified through the calculation of the Rao's Q entropy metric. Results showed a strong underestimation of the functional diversity using the Rao's Q index based on NDVI and the CV of reflectance. In both cases, a weak correlation was found against the reference functional diversity map (r2 = 0.05, p

中文翻译：

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太阳诱导的荧光异质性作为功能多样性的衡量标准

摘要 植物功能多样性是植物体内化学、生理和结构特性的范围，是生物多样性的重要组成部分，控制着生态系统的功能和稳定性。监测其跨空间和随时间的变化在生态研究中至关重要。到目前为止，已经测试了几种基于反射率的指标来实现这一目标，取得了不同程度的成功。我们的工作旨在探索一种基于远红太阳诱导叶绿素荧光 (F760) 的新指标的潜力，以绘制陆地生态系统的功能多样性。这是利用 HyPlant 传感器在混合森林生态系统上收集的高分辨率图像实现的，该传感器部署为即将到来的 ESA-FLEX 卫星的机载演示器。通过对从高光谱反射立方体检索的关键植物功能性状图计算的主成分应用 Rao 的 Q 熵度量，获得参考功能多样性图。基于光谱变异假设，即生物多样性信号编码在光谱异质性中，测试了两种基于移动窗口的方法来估计连续光谱数据的功能多样性：i) 基于归一化差异计算的 Rao 的 Q 熵度量植被指数 (NDVI) 和 ii) 基于高光谱反射计算的变异系数 (CV)。最后，基于通过 Rao 的 Q 熵度量量化的 F760 异质性，使用第三种移动窗口方法来估计功能多样性。结果表明，使用基于 NDVI 和反射率 CV 的 Rao 的 Q 指数严重低估了功能多样性。在这两种情况下，都发现与参考功能多样性图（r2 = 0.05，p