## 5 Discussion and Best Practice
### Radiometric Processing  
In good conditions, many radiometric calibration approaches are able to provide good quality reflectance data from the spectrometer observations. However, one of the benefits of UAVs that has been stated in the literature is the ability to fly below clouds to capture data (e.g., [196; 197; 44]). At the same time, a study by [PERSON] et al. [198] stated an influence on spectral UAV measurements of more than 100% due to \"fluctuating levels of cloudiness\". Section 4 reviewed the currently available options for transforming the spectral information captured by a sensor to the at-object reflectance. Table 4 summarizes their suitability under different atmospheric conditions. Major challenges hindering the utilization of the ARTM simulation for atmospheric correction are the uncertainties in the sensor absolute radiometric calibration and the inability to parameterize and model the atmospheric influence on the irradiance, in particular under unstable conditions. While a dual spectrometer approach requires cross-calibration of sensors, it can compensate illumination changes, but only if the secondary reference sensor and UAV sensor are close enough. [PERSON] and [PERSON] [158] found that under clear and cloud-free conditions, the approach with a stationary second spectrometer on the ground outperforms methods where a reference measurement is just taken before or after the flight (and then interpolated). A secondary properly stabilized device carried by the UAV allows illumination changes to be corrected for at the place of the measurement if the sun elevation is high enough to allow the cosine receptor to properly capture the illumination conditions. Although the implementation of this approach is more complex, it eases the flight operations, since no ground equipment is needed. We encourage manufacturers to build integrated dual spectrometer systems that provide accurate recordings in the dynamic conditions that are met in UAV remote sensing; challenges include rapid illumination changes, platform vibrations and movement, temperature \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GCPs** & \begin{tabular}{c} **on-Board** \\ **GNSS/IMU** \\ \end{tabular} & \
\begin{tabular}{c} **SfM + GCPs and/or** \\ **GNSS/IMU** \\ \end{tabular} &
\begin{tabular}{c} **Co-Registration** \\ **Co-Registration** \\ \end{tabular} \\ \hline Point & - & ++ & ++ & - \\ spectroradiometer & +/- & ++ & - & + \\ Pushbroom & +/- & ++ & - & + \\
2D imager & + & + & ++ & + \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overview of the suitability (- not suited to ++ very well-suited) of georeferencing techniques for different types of hyperspectral UAV sensors. GCPs: ground control points, GNSS: global navigation satellite system; IMU: inertial measurement unit. effects, and others. For more information on the cross-calibration and reflectance factors retrieved with radiometric reference panels and multiple spectrometers, the interested reader is referred to the works of [PERSON] et al. [3; 151; 199; 200]. Further challenges that remain unresolved with the dual spectrometer systems include the disturbances caused by the object reflectance anisotropy and shadows captured in a measurement (e.g., part of an image) but not by the irradiance sensor and object topography. The ELM is an easy and straightforward approach for the radiometric correction of datasets under constant illumination conditions and if there is sufficient space to place the reference panels. ELM is particularly challenging in forest studies (e.g., [72; 74]), since the panels may have to be deployed in small openings inside the forest. Here, the illumination conditions do not correspond to the conditions at the top-of-canopy due to the scattering and blocking of the direct or diffuse sky radiance of the surrounding canopy.