## 4 Discussion
### The Impacts of Nearby Waterbodies on AMD Mapping  
The material at Wheat Maid comprises a fine particle size (dominated by clay-sized fractions) and exhibits low hydraulic conductivity. This results in the formation of one (or a series of) perched lake, with its volume dependent upon the balance between rainfall and evaporation [17]. Inside the lower lagoon, the mine waste embankments slope averages around 26\({}^{\circ}\), with a maximum of 89\({}^{\circ}\). Slope stability can be greatly impacted, where runoff can contaminate and increase nearby water deposits [61; 62; 63]. The lower lagoon therefore acts like a receptor for all incoming rainfall runoff due to steep embankments (as seen in Figure 3, d DEM), impacting soil moisture distribution. The grey and marbled tailings have previously been sampled for moisture, pH, and geochemical analysis at three depths (surface level, 30 cm, and 50 cm) [29]. Both types of tailings had similar pH ranges (marbled tailings: 1.78-2.55; grey tailings: 1.62-2.96) and metal(loid) concentrations typical of AMD (Al, As, Cu, Fe, Pb). The pH ranges of the marbled tailings coincide with the presence of goethite (and possibly another, secondary mineral) spectral observations made across the sensors taken from the grey tailings at Wheat Maid (known to contain goethite, as identified through an XRD analysis [26]), as seen in the spectral signature graphs in Figure 11. The pH ranges also coincide with ranges known to foster goethite and jarosite formation [9; 10; 26]. The marbled tailings, however, had a higher average moisture level (20.9% surface; 25.7% at 50 cm) compared to the grey tailings (7.1% surface; 22.2% at 50 cm depth). This observation agrees with the findings of this study's FWP analysis taken on 30 July, as most of the fractioned water pixels were concentrated around the perimeter of the lower lagoon, indicating how influential waterbodies can be on AMD mapping. Solis with high moisture can lead to the misclassification of pixels and reduce reflectance in the Fe(III) iron bands. Our findings support [25], in that reduction in all wavelengths occurs in soil as soil moisture increases. However, reflectance may also increase when a critical point in soil moisture has been reached with time. Soils with a high moisture content may also have reflectance characteristics with continuous wavelengths with troughs into the water absorption bands [64]. These troughs, or 'drops', were almost absent in dry soils and sands, leading to the conclusion that soil moisture could lead to land being classified as water. This water overestimation is a by-product of hard binary labelling on moist soils, where pixels are labelled either as 1 or 0, when using a water index such as NDWI. Additionally, if the nearby water surface has been impacted by runoff, it can lead to pixels being labelled as Fe(III) iron deposits, as the green band is sensitive to water turbidity, with clearer water having less reflection than turbid water [64]. The water surface can now share the same colour as the surrounding'marbled' tailings mentioned in other studies [26; 27]. This phenomenon occurred in Wheat Maid's central lower lagoon during our field campaign, as seen via the natural colour image taken by the UAV on the 30 July 2020 (Figure 2e). Instruments with lower spatial resolution are more likely to mis-classify these pixels, as well as some post-processed surface reflectance images such as the SenCor2 products. As with Fe(III) iron mapping, UAV Nano-Hyperspec had the highest accuracy (in terms of the FWP analysis). This allowed mixed water and debris pixels to be better distinguished, and thus, allowed for a lower FWP\({}_{total}\). Moving from hyperspectral to multispectral introduced a greater number of misclassified pixels. The larger the pixel dimensions, the more they began to encompass the water-impacted ground.