## 5 Discussion and Best Practice
### Comparability between Sensing Systems  
UAVs have been envisaged to bridge the gap between classical ground, full-size aircraft, and satellite sensing systems. While UAV sensing systems are not per se different from other airborne sensing systems, differences between the sensing system may exist in sensor performance (due to miniaturization), calibration, data processing, measurement geometries (integrated FOV of non-imaging devices versus IFOV of imaging devices, nadir versus oblique), spatial and spectral resolution, and measurement timings (fixed time with satellite versus flexible UAV).  
Several researchers have investigated the comparability of non-imaging ground and imaging and non-imaging UAV spectral data. Most found offsets, which they attributed to calibration issues [217; 218; 219]. [PERSON] and [PERSON] [42] systematically looked at the issue and found that the differences rather resulted from differences in the angular properties of the data. They defined the term specific field of view (SFOV) as a concept to understand the composition of pixels and their angular properties used to characterize a specific area of interest. This SFOV is influenced by the sensor's FOV and the data processing, which explains the differences in the date captured by different sensors, eventually on different platforms, and processing [42]. Another study investigated the cross-validation of field and airborne spectroscopy data, and identified common sources of errors and uncertainties, as well as techniques to collect high-quality spectra under natural illumination conditions and highlighted the importance of appropriate metadata [167].  
Other studies have compared UAV data to satellite observations, i.e., a comparison of Landsat 8 and two calibrated--one modified to NIR--Panasonic DMC-LX5 digital cameras showed that reflectance was not always consistent due to variable illumination conditions [92]. [PERSON] et al. [27] compared a flight track of 210 km UAV nadir reflectance with the MODIS nadir BRDF-adjusted surface reflectance products of a dry snow region near Summit, Greenland. The data show that the UAV  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Pixel** & \multicolumn{2}{c}{**Image**} & \multicolumn{2}{c}{**Scene**} \\ \hline signal-to-noise ratio & (n, m) & capturing position & (n, m) & sensor description & (q, m) \\ radiometric resolution & (n, m) & illumination & (q, m) & (including version) & \\ viewing geometry & (n, m) & conditions & band configuration & (n, m) \\  & & direct and diffuse & (n, a) & (FWHM, band center) & \\  & & illumination ratio & geometric processing & (q, m) \\  & & capturing time & (n, m) & procedures and accuracies & \\  & & & & (including software version and parameters) \\  & & & top-of-canopy & (q, m) \\  & & & reflectance calculation method & \\  & & & reflectance uncertainty & (n, a) \\  & & & environmental & (q, m) \\  & & & conditions during measurement & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Numeric (n) or qualitatively (q) mandatory (m), and advised (a)auxiliary and metadata for spectral data processing. Although the direct and diffuse illumination ratio is important, it is set to advised, since it is not easy to measure.  
measurements were slightly higher than the MODIS NBARs for all of the bands, but agreed within their stated uncertainties. [PERSON] et al. (2015) compared UAV and WorldView-2 imagery for mapping a leaf area index. They found that the high resolution of UAV images was suitable to eliminate influences from the background in low leaf area index situations.  
In addition, many other factors might affect the comparability. One example is the measurement duration of a method that might introduce additional artefacts: satellites may sample many square km in an instant, while it may take the whole day to sample the same area with ground-based measurements; the latter would introduce artefacts from the diurnal illumination change. Research on such subjects has only just started.