Ability of remotely sensed solar-induced chlorophyll fluorescence (SIF) to serve as a vegetation productivity and stress indicator is impaired by confounding factors, such as varying crop-specific canopy structure, changing solar illumination angles, and SIF-soil optical interactions. This study investigates two normalisation approaches correcting diurnal top-of-canopy SIF observations retrieved from the O2-A absorption feature at 760 nm (F760 hereafter) of summer barley crops for these confounding effects. Nadir SIF data was acquired over nine breeding experimental plots simultaneously by an airborne imaging spectrometer (HyPlant) and a drone-based high-performance point spectrometer (AirSIF). Ancillary measurements, including leaf pigment contents retrieved from drone hyperspectral imagery, destructively sampled leaf area index (LAI), and leaf water and dry matter contents, were used to test the two normalisation methods that are based on: i) the fluorescence correction vegetation index (FCVI), and ii) three versions of the near-infrared reflectance of vegetation (NIRV). Modelling in the discrete anisotropic radiative transfer (DART) model revealed close matches for NIRv-based approaches when corrected canopy SIF was compared to simulated total chlorophyll fluorescence emitted by leaves (R2 = 0.99). Normalisation with the FCVI also performed acceptably (R2 = 0.93), however, it was sensitive to variations in LAI when compared to leaf emitted chlorophyll fluorescence efficiency. Based on the results modelled in DART, the NIRvH1 normalisation was found to have a superior performance over the other NIRv variations and the FCVI normalisation. Comparison of the SIF escape fractions suggests that the escape fraction estimated with NIRvH1 matched escape fraction extracted from DART more closely. When applied to the experimental drone and airborne nadir canopy SIF data, the agreement between NIRvH1 and FCVI produced chlorophyll fluorescence efficiency was very high (R2 = 0.93). Nevertheless, NIRvH1 showed higher uncertainties for areas with low vegetation cover indicating an unaccounted contribution of SIF-soil interactions. The diurnal courses of chlorophyll fluorescence efficiency for both approaches differed not significantly from simple normalisation by incoming and apparent photosynthetically active radiation. In conclusion, SIF normalisation with NIRvH1 more accurately compensates the effects of canopy structure on top of canopy far red SIF, but when applied to top of canopy in-situ data of spring barley, the effects of NIRvH1 and FCVI on the diurnal course of SIF had a similar influence.

中文翻译：

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比较使用辐射传输模型和大麦作物的空气昼夜测量进行太阳诱导荧光效率估计的方法


遥感太阳诱导叶绿素荧光 （SIF） 作为植被生产力和胁迫指标的能力受到混杂因素的影响，例如改变作物特异性的冠层结构、改变的太阳照射角度以及 SIF-土壤光学相互作用。本研究调查了两种归一化方法，以校正从夏大麦作物 760 nm 处的 O2-A 吸收特征（以下简称 F760）检索到的昼夜冠顶 SIF 观察结果，以了解这些混杂效应。通过机载成像光谱仪 （HyPlant） 和基于无人机的高性能点光谱仪 （AirSIF） 同时在 9 个育种实验地上获取最低点 SIF 数据。辅助测量，包括从无人机高光谱图像中检索的叶色素含量、破坏性采样叶面积指数 （LAI） 以及叶水和干物质含量，用于测试基于以下两种归一化方法：i） 荧光校正植被指数 （FCVI），以及 ii） 植被近红外反射率 （NIRV） 的三个版本。离散各向异性辐射传输 （DART） 模型中的建模显示，当将校正的冠层 SIF 与树叶发出的模拟总叶绿素荧光 （R2 = 0.99） 进行比较时，基于 NIRv 的方法非常匹配。使用 FCVI 进行归一化也表现良好 （R2 = 0.93），但是，与叶片发射的叶绿素荧光效率相比，它对 LAI 的变化很敏感。根据 DART 中建模的结果，发现 NIRvH1 归一化比其他 NIRv 变体和 FCVI 归一化具有更好的性能。 SIF 逃逸分数的比较表明，用 NIRvH1 估计的逃逸分数与从 DART 中提取的逃逸分数更接近。当应用于实验无人机和机载最低点冠层 SIF 数据时，NIRvH1 和 FCVI 产生的叶绿素荧光效率非常高 （R2 = 0.93）。然而，对于植被覆盖度低的区域，NIRvH1 显示出更高的不确定性，表明 SIF-土壤相互作用的贡献未得到解释。两种方法的叶绿素荧光效率的昼夜过程与通过入射和明显的光合作用有效辐射进行的简单归一化没有显着差异。综上所述，NIRvH1 的 SIF 归一化更准确地补偿了冠层结构对冠层远红 SIF 顶部的影响，但当应用于春大麦冠层顶部原位数据时，NIRvH1 和 FCVI 对 SIF 日程的影响也具有相似的影响。