## 1 Introduction
The differences between the FLD-based methods are mainly concerned with how the reflectance and SIF are assumed to vary near the absorption band. The standard FLD method assumes that the reflectance and SIF near the absorption band are constant [23], which is quite a weak assumption for data with a Spectral Resolution (SR) of about 1 nm. However, the 3 FLD method assumes that the variation in the reflectance and SIF is linear [25]. In the iFLD method, two correction coefficients estimated from the smoothed apparent reflectance are introduced to deal with the variation in the reflectance and SIF [26]. In addition to the FLD-based methods, [PERSON] _et al._[27] proposed the Spectral Fitting Method (SFM), in which the reflectance and SIF spectra are described with simple mathematical functions such as polynomial and Gaussian functions. In this way, the variation in the reflectance and SIF near the absorption bands can be more accurately estimated. [PERSON] _et al._[28] investigated the performance of the SFM at the O\({}_{2}\)-A and O\({}_{2}\)-B bands at canopy level and found that the SFM was more accurate than FLD under any noise configuration that was considered. [PERSON] _et al._[29] successfully used the SFM-based algorithm for SIF retrieval from satellite data. Compared to the FLD-based methods, the SFM can retrieve the SIF spectrum within the spectral window being used and provides both the SIF intensity and shape information. Therefore, the SFM algorithm has the potential to retrieve the full-spectrum SIF. Recently, the SFM algorithm was used by [PERSON] _et al._[30] for the retrieval of full-spectrum SIF from airborne HyPlant imagery and the performance of different fitting functions for reflectance and SIF was tested. The accuracy of the retrieval of SIF by the SFM algorithm depends on the accurate estimation of the reflectance and SIF spectra. In earlier studies, the reflectance spectra were usually simulated with polynomials [28] or splines [29]. However, the shape of the reflectance spectra around the O\({}_{2}\)-B band is quite complex because this band is located near the \"red-edge\" and the contribution of SIF is relatively large. This means that it is quite difficult to simulate the reflectance spectra accurately at the O\({}_{2}\)-B band. The SIF spectra were often modeled using double Gaussian or Voigt functions [29, 30] but this meant that a lot of parameters needed to be determined and thus the algorithm was not so robust. To deal with these problems, Principal Components Analysis (PCA), which is widely used in the decomposition and reconstruction of signals, can serve as an alternative method for the SIF retrieval because both the canopy reflectance and SIF have some unique spectral characteristics that can be extracted from a proper training dataset. [PERSON] _et al._[31] proposed a Fluorescence Spectrum Reconstruction (FSR) method to reconstruct the full-spectrum SIF based on the Singular Vector Decomposition (SVD) technique, which is also a method for feature extraction similar to PCA. This method uses the SIF values from within five absorption bands--these values having been retrieved by the SFM (with the reflectance and SIF spectra around each band being modeled by polynomials). [PERSON] and [PERSON] [32] presented a pFLD method to improve the accuracy of FLD-based methods for SIF retrieval. The core idea of the pFLD method is to simulate the reflectance at absorption bands by a linear combination of the principal components (PCs) derived from a training dataset. It has been shown that the reflectance at the O\({}_{2}\)-B band can be more accurately simulated in this way especially when the SR or Signal-to-Noise Ratio (SNR) is relatively low. As the PCA-based method gives a satisfactory performance for the simulation of both reflectance and SIF spectra, it has the potential to be a powerful tool for the retrieval of full-spectrum SIF.