## 3 Results  
### Fe(III) Iron Distribution Maps and Spectral Linear Regression  
A simple linear regression model was used to test if Fe(III) iron spectral values derived from aerial and spaceborne imagery significantly predicted the ground-based Fe(III) iron spectral values at Wheat Maid. The results of the main regression test between the Fe(III) iron values taken by the highest spatial resolution (<50 mm\({}^{2}\)) sensor onboard UAV and ground-based Fe(III) iron reflectance values revealed that the model could explain 77.6% of the total variance (Figure 2c). The overall regression was statistically significant (R\({}^{2}\) = 0.776, F(1, 32) = 110.67, \(p\) < 0.000), and the Fe(III) iron reflectance values from the Nano-Hyperspec sensor onboard the UAV significantly predicted the ground-based Fe(III) iron reflectance values (\(\beta_{1}\) = 0.998, \(p\) < 0.000). When the spatial resolution of the sensors began to decrease (inversely, pixel size began to increase), the explanatory variables' (air- and space-borne Fe(III) iron values) ability to predict the response variable (ground-based Fe(III) iron values) diminished, albeit remaining statistically significant across all sensors (low and moderate correlation for Sentinel-2 and PlanetScope PS2.SD, respectively). The remainder of the regression model results between PS.2 SD, S.2 (simulated), and the ground readings can be found in Figure 2a-c.  
Figure 2: Tiered raster model scenes depicting Fe(III) iron band ratio indices for Sentinel-2 (simulated), PlanetScope 2.SD, UAV, Halo handheld spectrometer Kriging results, and a natural RGB image of Wheat Maid (GSD: 11 mm). Red areas indicate high Fe(III) iron pixel distribution, yellow areas show that Fe(III) iron indicates medium-low Fe(III) iron surface abundance, and very dark green areas denote vegetated areas. A Kriging interpolated model of the Halo handheld spectrometer Fe\({}^{3+}i\) point with an index ranging from low (2.25) to high (9). Below the raster scenes, graphs (**f**-**h**) show corresponding regression scatterplots, with linear regression trend lines fitted. (**f**) Sentinel-2; (**g**) PlanetScope 2.SD; (**h**) UAV.  
The layered raster models' scenes depicting Fe(III) iron pixel distribution maps in Figure 2a-c) demonstrate that Fe(III) iron is concentrated around the perimeter of the central lower lagoon. The central lower lagoon is clearly visible in Figure 2e's natural image of Wheal Maid (taken on 30 July 2020), as is the grey and marbled tailing distribution. The tiered raster models begin with the lowest resolution instrument, labelled (a) Sentinel-2 (simulated), and progress to the medial resolution (b) PlanetScope 2.SD, up to the highest resolution of c) UAV Nano-Hyperspec. The western part of Wheal Maid's lower lagoon (grey tailings area) remained largely uninfluenced by surface Fe(III) iron in these maps, and the area remains largely yellow to green (Figure 2c). Fe(III) iron designated pixels were predominantly located below Wheal Maid's winter water level. The Halo handheld spectrometer's Kriging model in Figure 2d shows interpolation between Fe\({}^{3+}\)i point data taken across Wheal Maid, where an easterly retreat in the winter water level can be seen; the largest reflection occurred on marbled tailings.  
The regression model with the highest ability to predict ground values was recorded between the high-resolution UAV Nano-Hyperspec sensor and Halo handheld spectrometer ground reflectance values. Corresponding regression scatterplots can be found below the tiered raster model scenes (Figure 2f-h). Increased homoscedasticity can be seen within the residuals when progressing from Figure 2f (simulated Sentinel-2) to Figure 2h (UAV Nano-Hyperspec).