## 2 Methods
### Data Collections and Data Processing Procedures
#### 2.4.2 _HyPlant_ Data  
Preprocessing of the _HyPlant_ Data  
Preprocessing of the _HyPlant_ calibrated images was performed by [PERSON] team members using the software package CaliGeoPro (Spectral Imaging Ltd., Oulu, Finland). CaliGeoPro provides most of the functionality that is necessary for pre-processing, i.e., radiometric correction, georectification, correction for spectral smile and band broadening as well as keystone correction. It became clear during this campaign that pixel-cross talk and stray light in the _HyPlant_ FLUO sensor affect the \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Measurements and Data Collections**} & **Parker Tract, NC (_Pinus taeda_)** \\ \hline Site location (degrees latitude, longitude) & 35.8031, \(-\)76.6679 \\ \hline \multirow{2}{*}{Mature Loblolly Site (US-NC2)} & Canopy function (Gross Ecosystem Production): \\  & AmeriFlux Eddy covariance tower \\ \hline Multiple Stands & \\ Field measurement dates & Continuous, 23 September to 27 October 2013 \\ Forest survey and GPS of measurements & VT, field guides/GPS \\ \hline Vegetation height & VT, Clinometers \\ Leaf Area Index (LAI) & VT, LAI-2000 Plant canopy analyzers \\ Tree diameter at breast height, DBH & VT, Tree diameter at 1.30 m \\ Canopy (forest) density & VT, Within 1/10 care plot, Prism \\ Stand age & VT, Field determination \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Survey Measurements at the Parker Tract Site. fluorescence retrieval. Therefore, the FLUO images were radiometrically calibrated and corrected for the point spread function using an algorithm developed in [PERSON] in collaboration with [PERSON] (U. Valencia). Additionally, it was decided to spatially bin data to increase the signal to noise ratio and to reduce pixel adjacency effects. A data-driven fluorescence retrieval method based on a semi-empirical radiative transfer formulation (as opposed to full-physics modeling) was used to obtain fluorescence from the high resolution _HyPlant_ FLUO data. This retrieval method was first developed by [PERSON] et al. (2015, 2016) and previously used for fluorescence retrieval from _HyPlant_ FLUO in [PERSON] et al. 2015 [PERSON] et al. (2015). Fluorescence was retrieved at the two peaks of the fluorescence spectrum, in energy units (W m\({}^{-2}\) nm\({}^{-2}\) sr\({}^{-1}\)). The red fluorescence peak is around 685 nm, while the far-red peak is located around 740 nm, thus we term the fluorescence retrievals of the _HyPlant_ instrument at F685 and F740, respectively. The data-driven fluorescence retrieval approach represents the measured at-sensor radiance spectrum as a linear combination of reflected solar radiance and fluorescence emission spectra. The reflected solar radiance is formulated as a linear combination of singular vectors (SVs), which are derived after performing a Singular Vector Decomposition (SVD) of a set of reference (fluorescence-free) spectra. The combination of the derived SVs is able to reproduce any fluorescence-free spectra. This data-driven formulation of the forward model avoids the explicit modeling of atmospheric radiative transfer and the instrument's spectral and radiometric responses, which are typically prone to errors larger than the fluorescence signal itself. The SVs are derived using non-fluorescence training pixels, which are determined by a threshold on the Normalized Difference Vegetation Index (NDVI). We typically used 4-5 SVs to model the \"at-sensor\" radiance signal. A few adjustments were applied to improve the inversion results, such as the removal of the strongest absorption features or the spectral normalization (continuum removal) of canopy and reference radiances. Surface reflectance was calculated from calibrated \"at-sensor\" radiances measured with the _HyPlant_ DUAL module by using the Atmospheric & Topographic Correction, ATCOR-4 (ReSe Applic. Schlapfer, Langeggweg, Switzerland) program for wide FOV airborne optical scanner data ([PERSON] et al., 2016; [PERSON] et al., 2016).