## 3 Discussion
Besides the already implemented calculation of reflectance indices related to important plant properties, another goal for the future is to directly invert physically based radiative transfer models (RTMs), making use of the entire spectral data space of HyPlant DUAL. In this context, the ARTMO toolbox [63; 64] is a powerful software package, providing access to different canopy RTMs, such as PROSAIL [2], which can be used to derive biophysical and chemical vegetation parameters from HyPlant DUAL TOC reflectance data. In contrast to the DUAL data processing, the processing of the FLUO data required the adjustment of available and the development of specific new software tools. Since SIF is a very small signal, the following aspects have been considered in the processing of HyPlant FLUO data, in order to allow for an accurate SIF retrieval:  
(i) A precise sensor calibration (radiometric calibration, wavelength calibration and FWHM determination) is of the utmost importance for the conversion of DNs to at-sensor radiance values, which form the basis for the subsequent SIF retrieval. As reported in different studies (e.g., [51; 65]), a sensor miscalibration (e.g., spectral shift) can lead to systematic and random SIF-retrieval errors. Furthermore, to correct for the effect of spatial and spectral cross-talk caused by the dispersion of light across the sensor matrix, the precise characterization of the sensor's PSF is an essential step. Different PSF deconvolution approaches have recently been tested to investigate their impact on the FLUO at-sensor radiance quality and the final SIF products. This is part of ongoing research, yet to be published. An initial comparison of HyPlant SIF products derived from deconvolved and non-deconvolved at-sensor radiance by [66], however, has clearly shown the impact of the PSF on the SIF retrieval. (ii) The accuracy of the SIF retrieval is also strongly affected by atmospheric absorption and scattering effects [14; 67]. For this reason, the availability of measurements such as AOT, WVC and surface pressure, describing the atmospheric conditions at the time of a HyPlant overpass, is a crucial factor. This is particularly important for the SFM and NA SIF retrieval, since both methods require the aforementioned atmospheric parameters as input data for the radiative transfer modeling. The sensor surface distance (optical path length) is another important parameter, which has a high impact on the SIF retrieval and thus needs to be considered. While an average surface sensor distance is currently used, it is planned to include elevation information in the form of a DSM in the SFM SIF-retrieval scheme. This will be particularly helpful in rugged terrain to improve the quality of the derived SIF products. (iii) Another important aspect, addressed in the processing of HyPlant FLUO (and also DUAL) data, is the precise geometric processing of the final products. The positioning and orientation information recorded by the Oxford GPS/INS unit in parallel to the HyPlant data acquisition are used for the geometric correction of both sensor modules. This led to a precise internal image geometry and a high absolute positional accuracy, with only minor deviations of less than \(\pm\)2 pixel (cf. Figures 8-10). In order to further enhance the positional accuracy, a high-resolution RGB snapshot camera has been operated in the aircraft together with HyPlant in recent years. The spatially highly resolved and precisely located image data recorded by this camera will be used in the future to improve the absolute geometric accuracy using different feature-detection and image-matching techniques, such as the scale invariant feature transform (SIFT) and speed up robust feature (SURF) methods [68]. (iv) The four SIF-retrieval schemes implemented in the HyPlant processing chain for the first time allow a cross-comparison of the different methods on a spatially larger scale and can help to identify which approach performs best under certain conditions.