## 4 Discussion  
### Remote Sensing as Means of Detecting AMD  
The results show that all sensors were able to detect AMD and delineate it independently or in a multi-sensor configuration. However, UAV had the highest success. Recent attempts in AMD monitoring have seen similar success, with similar correlation rates between UAV and ground-based readings (R\({}^{2}\approx 0.7\)) [15]. This demonstrates the efficacy of UAV-based band ratio operations to detect AMD at higher accuracy, highlighting its importance in the field of geosciences. The notion of hyperspectral imaging superiority in AMD discrimination has previously gained traction [16]. This is partly due to the capability to deal with mixed pixels between vegetation and tailings material [16], a problem magnified with multispectral data, especially when mapping AMD near water bodies. A UAV mounted with a hyperspectral  
Figure 3: NDWI results for Wheat Maid (30 July 2020). TOA images increased in spectral and spatial resolution downwards on the image, starting with the simulated Sentinel-2 image, then PS2.SD, and ending with the UAV Nano-Hyperspec image. Corresponding FWP\({}_{total}\) percentages are displayed next to each image. The coloured gradient depicts NDWI spectral intensity index. (**d**) DEM of Wheat Maid. NDWI histogram classifications are displayed for (**e**) Sentinel-2 (simulated); (**f**) PlanetScope 2.SD; (**g**) UAV Nano-Hyperspec. The histogram’s land level, FWP, and water regions are labelled accordingly. sensor had the additional benefit of being able to bypass the potential adverse impacts of atmospheric interference [57], which passive space-borne sensors must circumnavigate with additional post-processing steps (which can cause unforeseen problems whilst detecting AMD). This study echoes the findings of [16] that hyperspectral imaging had the highest coincidence between the spatial extent of secondary Fe minerals and high surface Fe index values but goes one step further in investigating what impacts spatial resolution has on AMD mapping. Even with the use of next-generation MSI instruments such as Sentinel-2, we saw a diminishing ability to forecast surface conditions as the spatial resolution decreased. This, however, does not neglect multispectral sensors as an AMD detection tool. Another mutual conclusion with [16] is that multispectral sensors, such as those affixed on the Sentinel-2 and PlanetScope Dove-R satellites, can be used in conjunction with hyperspectral imaging (such as UAV Nano-Hyperspec) to effectively execute a multi-scale analysis of AMD. The current belief that medial-to-high resolution sensors should be prioritized over coarser resolution images such as Sentinel-2 can, therefore, be amended. Satellite imagery at a coarser spatial resolution that is freely accessible, has a high revisit window, and can cover large swathes of land (such as Sentinel-2) should not be neglected during AMD detection studies [58]. Each instrument has its use and is applicable in different scenarios when monitoring AMD sites. Acceptable correlation and determination coefficients were identified for each instrument when conducting ground verification during the multi-sensor analysis (see R\({}^{2}\) values in Figure 2). These values can be used in future multi-scale studies as reference points during ground-truthing and campaigns and sensor verification. Fe(III) Iron Distribution at Wheal Maid  
The known spectral response curve of goethite [13] was used to identify comparable goethite spectral signatures from Wheal Maid's marbled tailings across two out of the four instruments (Halo handheld spectrometer and Sentinel-2; see Figure A1a,d).