## 5 Discussion and Best Practice
### Data Products from UAV Sensing Systems  
Depending on the sensor configuration, different data products can be retrieved from UAV spectral sensing systems. Point spectroradiometers can measure distinct points (e.g., [25]) in space or integrated over an area of interest to get a coarse spatial representation of the spectral properties. With a specialized flying pattern and tilting of the sensor, a multi-angular characterization of an area can be generated [24]. Pushbroom systems allow generating a 2D spectral representation of the surface (e.g., [38; 40; 125]). These hyperspectral images can be overlaid on a digital surface model derived from LiDAR (e.g., [37]) or from RGB SfM. As a result, a spectral digital surface model is generated that represents the surface in 3D space linked with the hyperspectral information emitted and reflected by the objects covered by the surface [43]. The 2D imagers directly allow the generation of spectral and 3D information at the same time, and thus derive spectral 3D point clouds and their derivative spectral digital surface models (c.f. Section 3.3 and e.g., [43; 44; 65]). Moreover, since the spectral information is implicitly connected to the 3D information of every point, 3D spectral point clouds can be generated with the same approach (e.g., Figure 6). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Stable** & **Stable** & **Unstable** & \\
**Radiometric Data Calibration** & **Atmosphere** & **Atmosphere** & **Atmosphere** & \\
**Method** & **Stable** & **Unstable** & **Unstable** & \\  & **Irradiance** & **Irradiance** & **Irradiance** & \\ \hline empirical line method & + & - & - & (P), PP, 2D \\ radiometric block adjustment & + & + & + & (PP *), 2D \\ stationary radiometric tracking & + & + & - & P, PP, 2D \\ on-board radiometric tracking & + & + & + & P, PP, 2D \\ radiative transfer modeling & + & - & - & P, PP, 2D \\ \hline \hline \end{tabular}  
* for radiometric block adjustment, overlapping data is needed. Thus, it also works on multiple scenes (flight strips) of pushbroom systems.  
\end{table} 
Table 4: Overview of the top-of-canopy generation procedures and their applicability to different atmospheric (e.g., cloudiness) and irradiance (e.g., different intensities due to diurnal sun angle change) conditions. Additionally, the applicability to point (P), pushbroom (PP), and 2D imagers (2D) is indicated. +: suitable; -: not suitable. Figure 6: Spectral 3D point cloud (**left**) and 2D orthophoto (**right**) captured with a spectral 2D imager (Rikola FPI) of a spruce-dominated forest area in Finland. The orthophoto has a ground sampling distance (GSD) of 10 cm, and the point cloud has a 5-cm point interval. The spectral bands are green (520 nm; FWHM: 22 nm), red (598.80 nm, FWHM = 24 nm) and near infrared (763.70 nm, FWHM = 32 nm). The 3D point cloud gives a possibility for the spectral analysis of object properties in multiple height levels in each X and Y coordinate, whereas in the orthophoto, only one value is stored in each X and Y coordinate. Additionally, the information from 2D imagers can also be used to generate 2D orthomosaics. However, in comparison with pushbroom systems, their orthorectification is likely to be more precise, since the geometry of the scene has implicitly been taken into account during the mosaicing process (if SfM was used). At the same time, the viewing geometries within the orthomosaics of 2D imagers are more complex than the ones in scenes from pushbroom sensing systems. This is due to the two dimensionality of the data and the high overlap between image frames. In every case where multiple images (or lines for pushbroom systems) overlap, a decision needs to be made on how to 'blend' or mosaic the data into a seamless orthomosaic. This decision has a significant impact on the final data product, as a study by [PERSON] and [PERSON] [42] shows.