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      <title>Shallow water bathymetry retrieval using a band-optimization iterative approach : application to New Caledonia coral reef lagoons using Sentinel-2 data</title>
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    <abstract>To achieve high accuracy bathymetry retrieval using remote sensing images with robust performance in a 0 to 25 m-deep lagoon with sharp bottom depth variations, a new Iterative Multiple Band Ratio (IMBR) algorithm is tested against known Multiple Band Ratio (MBR) and Single Band Ratio (SBR) algorithms. The test was conducted using the five multispectral bands, at 10 to 60 m resolution, of a Sentinel-2 image of the 25 km(2) Poe lagoon, a UNESCO World Heritage Area. The IMBR approach requires training datasets for the definitions of depth threshold at which optimal band ratios vary. IMBR achieved accuracy, quantified with an original block cross-validation procedure across the entire depth range reached a mean absolute error of 46.0 cm. It compares very favorably against MBR (78.3 cm) and the various SBR results (188-254 cm). The method is suitable for generalization to other sites pending a minimal ground-truth dataset crossing all the depth range being available. We stress that different users may need different precisions and can use MBR or SBR algorithms for their applications. For the hydrodynamic modelling applications that are developing in New Caledonia, the IMBR solutions applied to Sentinel imagery are optimal.</abstract>
    <targetAudience authority="marctarget">specialized</targetAudience>
    <subject>
      <topic>satellite derived bathymetry</topic>
      <topic>rugose lagoon</topic>
      <topic>adaptive iterative model</topic>
      <topic>band ratio</topic>
      <topic>UNESCO World Heritage Area</topic>
      <topic>multispectral image</topic>
    </subject>
    <subject authority="local">
      <geographic>NOUVELLE CALEDONIE</geographic>
    </subject>
    <classification authority="local">126</classification>
    <classification authority="local">032</classification>
    <classification authority="local">064</classification>
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      <titleInfo>
        <title>Remote Sensing</title>
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      <part>
        <detail type="volume">
          <number>13</number>
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        <detail type="volume">
          <number>20</number>
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        <extent unit="pages">
          <list>4108 [20 ]</list>
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        <dateIssued>2021</dateIssued>
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    <identifier type="uri">https://www.documentation.ird.fr/hor/fdi:010083329</identifier>
    <identifier type="doi">10.3390/rs13204108</identifier>
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      <recordCreationDate encoding="w3cdtf">2021-12-03</recordCreationDate>
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