## 5 Discussion and Best Practice  
### Sensors  
During the last 10 years, the number of (commercial) sensors tailored for UAV sensing systems has rapidly increased. Today, sensors are able to capture data faster and with much higher spatial resolution, which allows flying higher and faster, and covering a much larger area. While most of the UAV sensors still cannot compete with their bigger counterparts carried on airplanes such as the CASI [181], airborne prism experiment (APEX; [182]), HyPlant [183], the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS; [184]) or NASA Goddard's LiDAR, Hyperspectral, and Thermal Airborne Imager (G-LiHT; [185]), UAV point and pushbroom sensing systems, in particular, have been demonstrated to fill a unique niche for a large variety of applications and research purposes. We expect this trend to continue, since manufacturers of professional airborne sensors such as HySpex and Specim have now also started to build UAV sensors [41; 186]. At the same time, UAV remote sensing has grown to form its own discipline with research particularly directed to investigating and improving the quality of small and lightweight sensors [22; 50] and further developing data processing algorithms to fit the ultra-high resolution data, including quality assurance approaches [43; 68]. Moreover, innovative approaches empowered by the new technology are developed that go beyond the classical capabilities of remote sensing platforms, such as rapid BRDF quantification [24; 70; 161; 187] and simultaneous spectral and 3D mapping [43; 44; 60; 72]. Table 2 reflects these developments by summarizing key publications on novel sensors, concepts, or methods for calibration, integration, or data pre-processing for UAV spectral sensors and data. Furthermore, the interested reader is referred to [15] for a comprehensive list of spectral imaging sensors that extend the examples in this manuscript. Due to the variety of sensors and their configuration, the classical discrimination between hyperspectral and multispectral is becoming blurred. Thus, every study published should contain information on the specific band configuration. Since so many spectral sensors with different configurations have appeared, it becomes hard to compare the results between different studies. Making the sensor configuration transparent is the very first step in addressing this issue. However, a prerequisite for such information is a comprehensive characterization and calibration of the sensing system. While the interested reader is referred to the calibration studies in Table 2 and [PERSON] et al. [123], we see the main responsibility as being with the camera manufacturers. At the same time, this also includes the correct usage of common terminology (e.g., spectral sampling interval versus FWHM). It is important to note that there is most likely no sensor that is able to meet all needs. Additionally, when selecting a sensor, users are usually confronted with the spatial resolution versus spectral resolution versus coverage challenge. Generally, a higher spatial resolution leads to a lower spectral resolution, due to physical constraints in sensor design. Additionally, if larger areas should be covered, this is mostly achieved by flying higher, which will in turn lower the ground sampling distance. We expect that more UAV sensors will become available in the near future and identify two trends. On one hand, there are the more complex and expensive cameras that are able to capture many bands or implement new techniques to capture spectral information (e.g., [31; 188; 189]) with even more lightweight and small sensors. These sensors allow researchers to conduct research on spectral sensing and identify promising bands for different applications. Another trend is toward more consumer-oriented cameras that are relatively easy to use and allow standardized tasks to be carried out, such as acquisition of NDVI imagery.