June 9, 2024 Dataset Restricted Access

Chakraborty, Rupsa; Rachdi, Imane; Thiele, Samuel Thomas; Booysen, René; Kirsch, Moritz; Lorenz, Sandra; Gloaguen, Richard; Sebari, Imane

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  <identifier identifierType="DOI">10.14278/rodare.3292</identifier>
  <creators>
    <creator>
      <creatorName>Chakraborty, Rupsa</creatorName>
      <givenName>Rupsa</givenName>
      <familyName>Chakraborty</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7060-1034</nameIdentifier>
      <affiliation>Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf, Untermarkt 20, 02826 Görlitz, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Rachdi, Imane</creatorName>
      <givenName>Imane</givenName>
      <familyName>Rachdi</familyName>
      <affiliation>Department of Photogrammetry and Cartography, School of Geomatics and Surveying Engineering, IAV Hassan II, 6202 Madinat Al Irfane, Rabat 10000, Morocco</affiliation>
    </creator>
    <creator>
      <creatorName>Thiele, Samuel Thomas</creatorName>
      <givenName>Samuel Thomas</givenName>
      <familyName>Thiele</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4169-0207</nameIdentifier>
      <affiliation>Helmholtz-Institute Freiberg for Resource Technology, Helmholtz-Zentrum-Dresden Rossendorf, 09599 Freiberg, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Booysen, René</creatorName>
      <givenName>René</givenName>
      <familyName>Booysen</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-5549-4090</nameIdentifier>
      <affiliation>Helmholtz-Institute Freiberg for Resource Technology, Helmholtz-Zentrum-Dresden Rossendorf, 09599 Freiberg, Germany;</affiliation>
    </creator>
    <creator>
      <creatorName>Kirsch, Moritz</creatorName>
      <givenName>Moritz</givenName>
      <familyName>Kirsch</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-1512-5511</nameIdentifier>
      <affiliation>Helmholtz-Institute Freiberg for Resource Technology, Helmholtz-Zentrum-Dresden Rossendorf, 09599 Freiberg, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Lorenz, Sandra</creatorName>
      <givenName>Sandra</givenName>
      <familyName>Lorenz</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-8464-2331</nameIdentifier>
      <affiliation>Helmholtz-Institute Freiberg for Resource Technology, Helmholtz-Zentrum-Dresden Rossendorf, 09599 Freiberg, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Gloaguen, Richard</creatorName>
      <givenName>Richard</givenName>
      <familyName>Gloaguen</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-4383-473X</nameIdentifier>
      <affiliation>Helmholtz-Institute Freiberg for Resource Technology, Helmholtz-Zentrum-Dresden Rossendorf, 09599 Freiberg, Germany</affiliation>
    </creator>
    <creator>
      <creatorName>Sebari, Imane</creatorName>
      <givenName>Imane</givenName>
      <familyName>Sebari</familyName>
      <affiliation>Department of Photogrammetry and Cartography, School of Geomatics and Surveying Engineering, IAV Hassan II, 6202 Madinat Al Irfane, Rabat 10000, Morocco</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Data: A Spectral and Spatial Comparison of Satellite-Based Hyperspectral Data for Geological Mapping</title>
  </titles>
  <publisher>Rodare</publisher>
  <publicationYear>2024</publicationYear>
  <subjects>
    <subject>Hyperspectral Remote Sensing</subject>
    <subject>EnMAP</subject>
    <subject>EMIT</subject>
    <subject>PRISMA</subject>
    <subject>HyMap</subject>
    <subject>Carbonatite</subject>
    <subject>Comparitive Analysis</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2024-06-09</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://rodare.hzdr.de/record/3292</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsCitedBy">10.3390/rs16122089</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsIdenticalTo">https://www.hzdr.de/publications/Publ-40090</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsReferencedBy">https://www.hzdr.de/publications/Publ-39575</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.14278/rodare.3291</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/casus</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/fwg</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://rodare.hzdr.de/communities/rodare</relatedIdentifier>
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  <rightsList>
    <rights rightsURI="info:eu-repo/semantics/restrictedAccess">Restricted Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The new generation of satellite hyperspectral (HS) sensors provides remarkable potential for regional-scale mineralogical mapping. However, as with any satellite sensor, mapping results are dependent on a typically complex correction procedure needed to remove atmospheric, topographic and geometric distortions before accurate reflectance spectra can be retrieved. These are typically applied by the satellite operators but use different approaches that can yield different results. In this study, we conduct a comparative analysis of PRISMA, EnMAP, and EMIT hyperspectral satellite data, alongside airborne data acquired by the HyMap sensor, to investigate the consistency between these datasets and their suitability for geological mapping. Two sites in Namibia were selected for this comparison, the Marinkas-Quellen and Epembe carbonatite complexes, based on their geological significance, relatively good exposure, arid climate and data availability. We conducted qualitative and three different quantitative comparisons of the hyperspectral data from these sites. These included correlative comparisons of (1) the reflectance values across the visible-near infrared (VNIR) to shortwave infrared (SWIR) spectral ranges, (2) established spectral indices sensitive to minerals we expect in each of the scenes, and (3) spectral abundances estimated using linear unmixing. The results highlighted a notable shift in inter-sensor consistency between the VNIR and SWIR spectral ranges, with the VNIR range being more similar between the compared sensors than the SWIR. Our qualitative comparisons suggest that the SWIR spectra from the EnMAP and EMIT sensors are the most interpretable (show the most distinct absorption features) but that latent features (i.e., endmember abundances) from the HyMap and PRISMA sensors are consistent with geological variations. We conclude that our results reinforce the need for accurate radiometric and topographic corrections, especially for the SWIR range most commonly used for geological mapping.&lt;/p&gt;</description>
  </descriptions>
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