August 19, 2022 Software Open Access

Scarth, Peter; Denham, Robert; Watson, Fiona

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  <identifier identifierType="DOI">10.5281/zenodo.7008343</identifier>
  <creators>
    <creator>
      <creatorName>Scarth,  Peter</creatorName>
      <givenName>Peter</givenName>
      <familyName>Scarth</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-5091-7915</nameIdentifier>
      <affiliation>University of Queensland</affiliation>
    </creator>
    <creator>
      <creatorName>Denham, Robert</creatorName>
      <givenName>Robert</givenName>
      <familyName>Denham</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3342-7970</nameIdentifier>
      <affiliation>University of Queensland</affiliation>
    </creator>
    <creator>
      <creatorName>Watson, Fiona</creatorName>
      <givenName>Fiona</givenName>
      <familyName>Watson</familyName>
      <affiliation>Department of Environment and Science, Queensland, Australia</affiliation>
    </creator>
  </creators>
  <titles>
    <title>JRSRP Fractional cover v3</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>remote sensing</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-08-19</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Software"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/7008343</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.7008342</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/remote-sensing</relatedIdentifier>
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  <version>1.0.10</version>
  <rightsList>
    <rights rightsURI="https://opensource.org/licenses/MIT">MIT License</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;The fractional cover v3.0 model is a Multi Layer Perceptron (neural network) model architecture that uses surface reflectance to estimate the three cover fractions of bare ground, photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV). The MLP model was trained with Tensorflow using Landsat TM, ETM+ and OLI surface reflectance and a collection of 4000 field observations of overstorey and ground cover. The field observations covered a wide variety of vegetation, soil and climate types across Australia, collected between 1997 and 2018.&lt;/p&gt;</description>
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