The principal components (PCs) datasets of the leaf and canopy levels used for full-spectrum SIF reconstruction in FSIF-iPCA method.

A principal components (PCs) dataset (640–850 nm) generated using a principal component analysis approach to reconstruct the shape of the reflectance spectrum for the leaf and canopy levels. For the leaf level, a novel SIF-free leaf spectra dataset (n = 849, species = 95) collected at three sites in Beijing, China during June and August 2023, was used. For the canopy level, a total of 305,640 SIF-free spectra generated using a SCOPE model based on the measured leaf reflectance and transmittance was employed.本数据集为主成分（Principal Components，PCs）数据集，波段范围为640–850 nm，通过主成分分析方法构建，用于重构叶片与冠层尺度的反射光谱形态。在叶片尺度层面，本研究采用一套全新的日光诱导叶绿素荧光（Solar-Induced Chlorophyll Fluorescence，SIF）缺失型叶片光谱数据集，该数据集包含849条样本、涵盖95个物种，于2023年6月至8月期间在中国北京的3个采样点采集完成。在冠层尺度层面，本研究采用了基于实测叶片反射率与透射率，通过SCOPE模型生成的共计305,640条SIF缺失型光谱数据。