## 4 Radiometric Processing Workflow
### Scene Reflectance Generation
#### 4.3.2 Empirical Line Method (ELM)  
The ELM is a commonly used image calibration method in which a set of radiometric reference panels with a known spectral reflectance are used to calculate reflectance factors (HDRFs). After the relative or absolute radiometric calibration of images, a line is fitted with the least-squares method between the image DNs and the measured target reflectance factors [152]. The sensor may be radiometrically calibrated, or, if not, then the method combines the linear conversions from the DNs to the reflectance factor into a single linear transformation. Implementations of the method can be found in software packages such as ENVI (Exelis Visual Information Solutions, Bolder, CO, USA).  
Several researchers have applied the ELM for their UAV operations (e.g., [129; 130]). [PERSON] et al. [38] used five near-Lambertian gray panels with 5% to 70% reflectance built with a special paint that provided a reasonably flat spectral response. [PERSON] et al. [129] used five artificial near-Lambertian tarps placed on flat ground for the ELM. [PERSON] and [PERSON] [153] described the ELM for transforming raw RGB images to reflectance based on nine characterized gray panels with different intensities (although they did not perform relative radiometric correction as would be recommended). Additionally, the ELM has been used with (modified) RGB consumer-grade cameras [154; 155]. Several UAV studies have also used a simplified ELM with only one panel; the reflectance factors are calculated by rationing the target and the white reference measurements [43; 129; 144]. However, [PERSON] and [PERSON] [42] found some issues when using this simplified ELM for UAVs. When placing the sensor and the UAV above the panel, a large part of the hemisphere is (invisibly) shaded. This might introduce a severe wavelength-dependent bias to the measurements that also affects the retrieval of vegetation parameters. The bias is strongest under cloudy conditions, and can account for up to 15% [42]. Thus, we recommend not using the simplified ELM for UAV research when the UAV is placed above the panel at a short distance.  
The ELM is simple and accurate if all of the assumptions of the method are met. When ELM is used in its simple form, many factors can deteriorate the accuracy, such as for example, variations in atmospheric conditions over the area of interest, topographic variations, and atmospheric BRDF. A minimum of two reference targets covering the range of reflectance values of interest should be used; typically, the range is 0-50% for vegetation. Adding more than two targets reduces uncertainties, enables an assessment of sensor linearity, and allows evaluation of the ELM results by using some panels only for verification. The calibration targets should be flat and leveled, without obstructions, and should be large enough (preferably more than five times the image GSD) to reduce adjacency effects by only selecting the middle part of the panel. Targets should have uniform intensity and be close to Lambertian reflectance characteristics. If it is not possible to deploy targets, reflectance of ground objects with an appropriate reflectance range and Lambertian reflectance can be measured and used, for example, gravel, sand, or asphalt surfaces. The assumption of Lambertian reflectance can be released if the reflectance anisotropy of the target is characterized and considered [156; 157].  
[PERSON] and [PERSON] [158] stated that the ELM was suitable for flight times shorter than 30 min under stable weather conditions (clear sky) when the results of the ELM of panel measurements at the beginning and end of a flight were linearly interpolated. However, the main disadvantage is that the ELM cannot adapt for illumination changes during the flight, since the panels are not within every image. Thus, the ELM alone is not useful under variable conditions.