Recent advances in remote sensing of solar-induced chlorophyll fluorescence (SIF) have improved the capabilities of monitoring large-scale Gross Primary Productivity (GPP). However, SIF observations are subject to directional effects which can lead to considerable uncertainties in various applications. Practical approaches for normalizing directional SIF observations to nadir viewing, to minimize the directional effects, have not been well studied. Here we developed two practical and physically-solid approaches for removing the directional effects of anisotropic SIF observations: one is based on near-infrared or red reflectance of vegetation (NIR_v and Red_v), and the other is based on the kernel-driven model with multi-angular SIF measurements. The first approach uses surface reflectance while the second approach directly leverages multi-angular SIF measurements. The performance of the two approaches was evaluated using a dataset of multi-angular measurements of SIF and reflectance collected with a high-resolution field spectrometer over different plant canopies. Results show that the relative mean absolute errors between the normalized nadir SIF and the observed SIF at nadir decrease by 3–6% (far-red) and 6–8% (red) for the first approach, and by 7–13% and 6–11% for the second approach, compared to the original data, respectively. The effectiveness and simplicity of our proposed approaches provide great potential to generate long-term and consistent SIF data records with minimized directional effects.

太阳感应叶绿素荧光（SIF）遥感的最新进展提高了监测大规模总初级生产力（GPP）的能力。但是，SIF观测会受到方向性的影响，这可能导致各种应用中的不确定性。尚未充分研究将方向性SIF观测值标准化为最低点观测以最小化方向性影响的实际方法。在这里，我们开发了两种实用且物理上可靠的方法来消除各向异性SIF观测的方向性影响：一种是基于植被的近红外或红色反射率（NIR _v和Red _v），另一个基于具有多角度SIF测量的内核驱动模型。第一种方法使用表面反射率，而第二种方法直接利用多角度SIF测量。使用多角度SIF测量数据集和使用高分辨率现场光谱仪在不同植物冠层上收集的反射率评估这两种方法的性能。结果显示，在第一种方法中，标准化最低点SIF和观测到的最低点之间的相对平均绝对误差对于第一种方法降低了3–6％（远红）和6–8％（红色），而降低了7-13％与原始数据相比，第二种方法分别占6-11％。