Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain
<p> </p> <table> <tbody> <tr> <td> <h2>Metadata information</h2> </td> <td> </td> </tr> <tr> <td><strong>Full Title</strong></td> <td>Band Ratio Mosaics from Airborne Hyperspectral Data at Aramo, Spain</td> </tr> <tr> <td><strong>Abstract</strong></td> <td> <p>This dataset comprises results from the S34I Project, derived from processing airborne hyperspectral data acquired at the Aramo pilot site in Spain. Spectral Mapping Services (SMAPS Oy) conducted the airborne data acquisition in May 2024 using the Specim AisaFENIX sensor (covering VNIR-SWIR spectral ranges) over 17 flight lines. SMAPS performed geometric correction, radiometric calibration to reflectance, and atmospheric correction of the data. Subsequent processing steps included spectral smoothing with a Savitzky-Golay filter, cloud masking, bad pixel corrections, and hull correction (continuum removal).</p> <p>Manual processing and interpretation of hyperspectral data is a challenging, time-consuming, and subjective task, necessitating automated or semi-automated approaches. Therefore, we present a semi-automated workflow for large-scale interpretation of hyperspectral data, based on a combination of state-of-the-art methodologies. This dataset results from the calculation of a series of band ratios applied to the images and their subsequent mosaicking into a TIFF file. The mosaics are delivered as georeferenced TIFF files that cover approximately 97 km² with a spatial resolution of 1.2 m per pixel. The NoData value is set to -9999, representing areas of cloud removal or missing flight lines. The projected coordinate system is UTM Zone 30 Northern Hemisphere WGS 1984, EPSG 4326.</p> <p>Hyperspectral band ratios involve applying mathematical operations (such as division, subtraction, addition, or multiplication) among the reflectance values of different spectral bands. This technique enhances subtle variations in how materials absorb and reflect light across the electromagnetic spectrum. These variations are caused by electronic transitions, vibrations of chemical bonds (including -OH, Si-O, Al-O, and others), and lattice vibrations within the material's crystal structure.</p> <p>By creating these mathematical combinations, specific absorption features are emphasized, generating unique spectral fingerprints for different materials. However, these fingerprints alone cannot definitively identify a mineral, as different minerals may share similar absorption features due to common chemical bonds or crystal structures. Spectral geologists use band ratios as a tool to highlight potential areas of interest, but they must integrate this information with other geological knowledge and analyses to accurately interpret the mineralogy of an area. </p> <p>This dataset includes nine spectral band ratios. The mathematical formulas used to calculate each ratio are provided below:</p> <p> </p> <p>BR1 target Carbonate / Chlorite / Epidote</p> <p>BR1 = ((C7 + C9) / (C8))</p> <p>C7= Mean of bands between 2246.6 and 2257.55 nm</p> <p>C8= Mean of bands between 2339 and 2345 nm</p> <p>C9= Mean of bands between 2400 and 2410 nm</p> <p> </p> <p>BR2 target Chlorite</p> <p>BR2 = ((Cl1 + Cl2) / (Cl2))</p> <p>Cl1 = Mean of bands between 2191.93 and 2197.4 nm</p> <p>Cl2 = Mean of bands between 2246.63 and 2257.55 nm</p> <p> </p> <p>BR3 target Clay</p> <p>BR3 = ((C1 + C2) / (C2))</p> <p>C1 = Mean of bands between 1590.32 and 1612.56 nm</p> <p>C2 = Mean of bands between 2191.93 and 2208.35 nm</p> <p> </p> <p>BR4 target Dolomite</p> <p>BR4 = ((C6 + C8) / (C7))</p> <p>C6= Mean of bands between 2186 and 2191 nm</p> <p>C7= Mean of bands between 2246.6 and 2257.55 nm</p> <p>C8= Mean of bands between 2339 and 2345 nm</p> <p> </p> <p>BR5 target Fe2</p> <p>BR5 = ((Fe2n + Fe2d) / (Fe2d))</p> <p>Fe2n = Mean of bands between 721.85 and 742.48 nm</p> <p> </p> <p>BR6 target Fe3</p> <p>BR6 = ((Fe3n - Fe3d) / (Fe3n + Fe3d))</p> <p>Fe3n = Mean of bands between 776.87 to 811.26 nm</p> <p>Fe3d = = Mean of 3 bands around 610 nm</p> <p> </p> <p>BR7 target = Kaolinite / clays</p> <p>BR7 = ((K1 + K2) / (K3 + K4))</p> <p>K1 = Mean of bands between 2082.27 and 2104.23 nm</p> <p>K2 = Mean of bands between 2104.23 and 2115.2 nm</p> <p>K3 = Mean of bands between 2159.07 and 2164.55 nm</p> <p>K4 = Mean of bands between 2202.88 and 2208.35 nm</p> <p> </p> <p>BR8 target Kaolinite2 / clays</p> <p>BR8 = ((K1_2 + K2_2) / (K2_2))</p> <p>K1_2 = Mean of bands between 2197.4 and 2219.29 nm</p> <p>K2_2 = Mean of bands between 2159.07 and 2170.03 nm</p> <p> </p> <p>BR9 target NDVI (Normalized Difference Vegetation Index)</p> <p>BR9 = ((NIR - Red) / (NIR + Red))</p> <p>NIR= Mean of bands between 776.87 and 811.26 nm</p> <p>Red = Mean of bands between 666.87 to 680.6 nm</p> </td> </tr> <tr> <td>Keywords</td> <td>Earth Observation, Remote Sensing, Hyperspestral Imaging, Automated Processing, Hyperspectral Data Processing, Mineral Exploration, Critical Raw Materials</td> </tr> <tr> <td>Pilot area</td> <td>Aramo</td> </tr> <tr> <td>Language</td> <td> <p>English</p> </td> </tr> <tr> <td>URL Zenodo</td> <td>https://zenodo.org/uploads/14193286</td> </tr> <tr> <td><strong>Temporal reference</strong></td> <td> </td> </tr> <tr> <td>Acquisition date (dd.mm.yyyy)</td> <td>01.05.2024</td> </tr> <tr> <td>Upload date (dd.mm.yyyy)</td> <td>20.11.2024</td> </tr> <tr> <td><strong>Quality and validity</strong></td> <td> </td> </tr> <tr> <td>Fromat</td> <td>GeoTiff</td> </tr> <tr> <td>Spatial resolution</td> <td>1.2m </td> </tr> <tr> <td>Positional accuracy</td> <td>0.5m </td> </tr> <tr> <td>Coordinate system</td> <td>EPGS 4326</td> </tr> <tr> <td><strong>Access and use constrains</strong></td> <td> </td> </tr> <tr> <td>Use limitation</td> <td>None</td> </tr> <tr> <td>Access constraint</td> <td>None</td> </tr> <tr> <td>Public/Private</td> <td>Public</td> </tr> <tr> <td><strong>Responsible organisation</strong></td> <td> </td> </tr> <tr> <td>Responsible Party</td> <td>Beak Consultants GmbH</td> </tr> <tr> <td>Responsible Contact</td> <td>Roberto De La Rosa</td> </tr> <tr> <td><strong>Metadata on metadata</strong></td> <td> </td> </tr> <tr> <td>Contact</td> <td>Roberto.delarosa@beak.de</td> </tr> <tr> <td>Metadata language</td> <td>English</td> </tr> </tbody> </table>
ShareScore
44/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 12
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 4