## 2 Materials and Methods  
The SPOT 6/7 multispectral and panchromatic dataset over Pretoria, South Africa was used for the study. SPOT 6 and SPOT 7 are identical sun-synchronous optical satellites launched on 12 September 2012 and 30 June 2014, respectively that co-orbit in the constellation at an altitude of 694 km and are phased at 180 degrees (Airbus, Toulouse, France, 2018). The spectral configuration of the satellites consists of blue (450-520 nm), green (530-590 nm), red (625-695 nm) and near-infrared (760-890 nm) multispectral bands with a spatial resolution of 6 m and a panchromatic (450-745 nm) band with a spatial resolution of 1.5 m and dynamic range of 12 bits per pixel. SPOT 6/7 are capable of contiguous image segments of more than 120 km \(\times\) 120 km or 60 km \(\times\) 180 km from a single pass along one orbit. To meet the operational needs of generating a national wall-to-wall mosaic of South Africa, we selected established pansharpening methods for quantitative quality assessment. The Bayesian (BAY), Brovey transform (BRO), color normalized spectral (CNS) sharpening, Ehlers fusion technique (EHLERS), Gram-Schmidt (GRS), local mean and variance matching (LMVM), modified intensity hue saturation (MIHS), Pansharp algorithm (PANSHARP), principal component analysis (PCA), ratio component substitution (RCS), and wavelet resolution merge (WAVELET) techniques were evaluated in the study. The PANSHARP algorithm available in the PCI Geomatica software is a statistics-based fusion technique aimed at maximizing spatial detail while minimizing color distortions [26]. It attempts to preserve the spectral characteristics of the data. Developed by [PERSON] [27], the algorithm uses the least-squares method to approximate the grey value relationship between the original multispectral, panchromatic, and fused images to achieve the best color representation. The modified intensity hue saturation (MIHS) fusion technique merges high-resolution panchromatic data with lower resolution multispectral data to produce a pansharpened image that retains sharp spatial detail and a realistic resemblance of the original multispectral scene colors. This approach assesses the spectral overlap between each multispectral band and the high-resolution panchromatic band and weighs the merge based on these relative wavelengths. The MIHS method was developed to address a shortcoming of the intensity-hue-saturation (IHS) transformation where color distortions occurred due to discrepancies in spectral characteristics between panchromatic and multispectral bands. The IHS fusion transforms the RGB (red, green, and blue) space into the IHS color space and subsequently replaces the intensity band with a high-resolution pan image in the fusion before performing a reverse IHS transformation. The EHLERS) fusion technique uses an IHS transform coupled with Fourier domain filtering and aims to maintain the spectral characteristics of the fused image [22]. This is achieved by using the high-resolution panchromatic image to sharpen the multispectral image while avoiding adding new grey level information to its spectral components by first separating the color and spatial information. The spatial information content is then embedded as an adaptive enhancement to the images using a combination of color and Fourier transforms [22]. The Brovey transform (BRO) algorithm applies a ratio algorithm to combine the images. This is done by first multiplying each multispectral band by a high-resolution pan band and subsequently dividing each product by the sum of the multispectral bands. It is known to preserve the relative spectral contributions of each pixel but substitutes scene brightness with the high-resolution panchromatic (PAN) image [28]. The principal component analysis (PCA) transform converts intercorrelated Multispectral (MS) bands into a new set of uncorrelated components. The first component that resembles a high-frequency band is replaced by a high-resolution panchromatic band for the fusion.