# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration  
[PERSON]  
1 Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; [EMAIL_ADDRESS] (J.C.-F); [EMAIL_ADDRESS] (A.L. ); [EMAIL_ADDRESS] (A.C.T.) [PERSON]  
1 Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; [EMAIL_ADDRESS] (J.C.-F); [EMAIL_ADDRESS] (A.L. ); [EMAIL_ADDRESS] (A.C.T.) [PERSON]  
1 Department of Geosciences, Environment and Spatial Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal; [EMAIL_ADDRESS] (J.C.-F); [EMAIL_ADDRESS] (A.L. ); [EMAIL_ADDRESS] (A.C.T.) However, traditional RS methods faced limitations due to the low resolution of early satellite sensors [6; 7]. Recent developments in hyperspectral missions (e.g., PRISMA, EnMAP) have significantly improved mineral mapping capabilities. However, high-resolution satellites like WorldView-3 remain prohibitively expensive for many academic applications, despite offering limited free access. In contrast, the SuperDove (PSB.SD) sensor of the PlanetScope (PS) constellation, launched in 2021, presents an affordable alternative. It provides eight bands in the visible and near-infrared (VNIR) range--including coastal blue and red edge--and a 3 m spatial resolution [8]. PS imagery is also available under free academic quotas, increasing its accessibility to the scientific community. The inclusion of specific VNIR bands, particularly those sensitive to iron oxides (Fe\({}^{3+}\)), which are key indicators of hydrothermal alteration, makes PSB.SD a promising tool for mineral exploration. Despite this potential, its capabilities have not been thoroughly benchmarked against more established sensors such as Sentinel-2, ASTER, and Landsat 9. This study evaluates PSB. SD's ability to map iron oxides, linked to Au-Sb mineralization in Freixeda, northern Portugal, where Sb is found in association with gold (Au), another important mineral for the economy. Au-Sb deposits are found in quartz veins with ferrous sulfides. In hydrothermal systems, these veins often have iron oxide caps at the surface, which can be a good sign of quartz veins below [9]. Regarding RS studies related to antimony, we can mention the work of [PERSON] et al. [10], who integrate images from different multispectral satellites (Landsat 8 OLI, Sentinel-2, and ASTER) with lineament extraction to identify antimony occurrences. [PERSON] et al. [11] used the integration of hyperspectral and thermal data to detect antimony concentrations. [PERSON] et al. [12] applied Convolutional Neural Networks (CNNs) to map antimony in northern Portugal, and [PERSON] et al. [13] used hyperspectral data (PRISMA satellite) to calculate Relative Band Depth (RBD) to map alteration minerals associated with antimony in Freixeda, Portugal. While previous studies have integrated hyperspectral data or machine learning algorithms for Sb detection, this work uniquely evaluates PS's cost-efficient VNIR capabilities against established multispectral sensors. BR was selected for this study as it is a method widely validated in the literature for Fe\({}^{3+}\) detection, due to its simplicity, reproducibility, and proven effectiveness across various mineral systems, including pegmatites [14; 15], carbonate-hosted Zn-Pb [16; 17], copper [4; 18; 19], and gold deposits [16; 20; 21]. The performance of PSB.SD using BRs was then compared with Sentinel-2, ASTER, and Landsat 9, using only the VNIR bands from all sensors to ensure a consistent and fair comparison. The methodology applied in this study focuses on using specific BRs to identify Fe\({}^{3+}\), followed by a comparative analysis of results obtained from different satellites against PS data. The BRs were processed using the equivalent red and blue bands of each sensor. After processing, spectral outliers were identified and masked following the approach of [PERSON] et al. [16].