## 2 Materials and Methods
### Uncertainty Visualizations  
As depicted in Figure 1, four methods of cartographic visualization are applicable for the visualization of uncertainty values in the chosen context. All the following four methods of cartographic visualization of yield data from field harvested were selected as a proof-of-concept within this study:  
* colour hue in the form of an extended traffic lights metaphor;
* lightness;
* saturation;
* perspective height. The methods using colour hue, lightness, and saturation represent intrinsic techniques, while the perspective height is considered extrinsic in the sense of [PERSON] and Senaratne [61]. Application of hue, lightness, and saturation requires a classification scheme to be used. The Jenks natural breaks classification method [92] was used homogeneously in those cases. Colour hue refers to the dominant wavelength of visible light. Colour hue is the only colour variable that is suitable for representing qualitative data [93]. The (extended) \"traffic lights\" metaphor is based on colour hue variations, and it is often used and discussed as an alternative for data quality visualization [55; 56; 59; 73]. Humans often integrate their external knowledge into a decision-making process. Utilising the information from everyday experience (traffic lights) helps users to decode the red-orange-green categories more intuitively (see Figure 5). It is also an easy to understand solution for new and inexperienced users [73]. Moreover, this metaphor enables the \"fitness for use\" classification [54] and users' adjustment using different data representation and contexts. Lightness is also known as a colour value, a light or dark variation of one single hue. Lightness is commonly associated with quantitative data [82; 93]. When only grayscale is available, lightness can be applied to shades of grey. The number of colour values used in a scheme, and the perceptual distance between each lightness value, affect how efficiently a reader can interpret depicted values [94]. Saturation is the purity or intensity of a single hue of colour. Fully saturated colours appear vivid or bold, while fully desaturated hues appear grey. Usually, colour saturation is associated with ordinal data as well as with uncertainty [82; 93]. Both lightness and saturation have been recommended for uncertainty visualization based on user preferences [53; 58] and intuitiveness [57; 58]. Besides the intuitiveness of methods, several scholars addressed the uncertainty level match with higher or lower lightness and saturation. Empirical studies supported higher lightness and lower saturation for higher uncertainty [57; 63]. There are more graphic variables available in 3D visualizations compared with 2D. Locations on the horizontal axis level in 3D can be used for expressing attribute values. This additional graphic variable is known as perspective height [82]. Perspective height serves for the depiction of quantitative data. The perspective height can be combined with the above-mentioned colour graphics variables. Both colour and perspective height can display the same phenomenon, or two different phenomena, in which case users can compare the individual variables (phenomena). Uncertainty can be one such phenomenon. Two-dimensional cartographic representations were conducted using ESRI (Environmental Systems Research Institute) ArcMap software in version 10.6 (ESRI, Redlands, CA, USA), while 3D cartographic visualizations were conducted utilising the Plotly JavaScript library (version 1.54.7; Plotly Technologies Inc., Montreal, Canada) [96]. Spatial reference properties were set equally for all the visualizations conducted. The coordinate reference system with code '32633' was used as defined in the EPSG (European Petroleum Survey Group) Geodetic Parameter Registry [97]. To be more specific, universal transverse mercator (UTM) projection of zone 33 N on WGS (World Geodetic System) 1984 coordinate system with Greenwich prime meridian was used as a common reference.