## 2 Methodology
### SIF Retrieval  
In this study, we applied three frequently applied methods for the proximal sensing of SIF [8, 22], including modified Fraunhofer line discrimination (3 FLD) [23], the spectral fitting method (SFM) [24, 25], and the singular-vector-decomposition-based method (SVD) [19, 26, 27].  
The 3 FLD method assumes a linear variation of reflectance (\(\rho\)) and fluorescence (\(F\)) over the absorption window and uses the following formula to calculate the SIF value at the band \(\lambda_{in}\) in the absorption feature:  
\[F=\frac{L(\lambda_{in})-E(\lambda_{in})\frac{\omega_{l}L(\lambda_{l})+\omega_{ r}L(\lambda_{r})}{\omega_{l}E(\lambda_{l})+\omega_{r}E(\lambda_{r})}}{1-\frac{E( \lambda_{in})}{\omega_{l}E(\lambda_{l})+\omega_{r}E(\lambda_{r})}} \tag{1}\]  
where \(L\) is the upwelling radiance; \(E\) is the incident irradiance; \(\lambda_{l}\) and \(\lambda_{r}\) represent the two reference bands situated on the left and right shoulders of the absorption feature, respectively; and \(\omega_{l}\) and \(\omega_{r}\) are the weighting factors. In this study, we set \(\lambda_{l}\), \(\lambda_{in}\), and \(\lambda_{r}\) as 759.0, 760.0, and 767.0 nm, respectively, to retrieve SIF at the O\({}_{2}\)A absorption band.  
The SFM method [25] uses general mathematical functions representing canopy \(\rho\) and \(F\) within narrow spectral windows centered at absorption features. SFM uses mathematical formulations to describe the spectral shapes of true reflectance (\(\rho\)) and fluorescence (\(F\)).  
\[L(\lambda)=E.\rho_{mod}(\lambda)+F_{mod}(\lambda)+\epsilon(\lambda) \tag{2}\]  
where \(\rho_{mod}\) represents the mathematical formulation to describe the spectral shape of \(\rho\); \(F_{mod}\) represents the mathematical formulation to describe the spectral shape of \(F\); and \(\epsilon\) is the modeling error. In this study, we used quadratic polynomials to model \(\rho\) and \(F\) with a band range of 759.0-767.0 nm.  
Figure 2: The location of the experiment site.  
The SVD method is a data-driven statistical approach based upon the assumption that the SIF-free spectrum can be represented by a linear combination of singular vectors derived from the singular vector decomposition of a given SIF-free training dataset [26; 27].  
\[L\left(\lambda\right)=(\sum_{i=0}^{n\pi p}a_{i}\cdot\lambda^{i})\cdot(\sum_{j=1}^ {n\pi}b_{j}\cdot SV_{j})+F_{S}\cdot h_{F}\cdot\tau^{\dagger} \tag{3}\]  
where \(\lambda\) represents the wavelength; the polynomial of \(\lambda\) represents spectrally smooth terms, such as surface reflectance and atmospheric scattering; np is the order of the polynomial; \(SV\) is the singular vector obtained by singular value decomposition from the training dataset to describe the variability in solar irradiance and atmospheric transmittance; \(nv\) is the number of \(SV\)s describing the high spectral frequency variations; \(h_{F}\) is the fluorescence shape function; \(F_{S}\) is the scaling factor of \(h_{F}\); \(a_{i}\) and \(b_{j}\) represent the coefficients for polynomial and singular vectors, respectively; and \(\tau^{\dagger}\) is an upwelling atmospheric transmittance. In this study, we used the algorithm proposed by [PERSON] [19]. We set the band range to 750.0-765.0 nm and used the daily irradiance spectra as the training dataset.