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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Annals</journal-id>
<journal-title-group>
<journal-title>ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Annals</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9050</issn>
<publisher><publisher-name>Copernicus Publications</publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/isprs-annals-IV-2-W4-131-2017</article-id>
<title-group>
<article-title>ASSESSMENT OF BOTTOM-OF-ATMOSPHERE REFLECTANCE IN LIDAR DATA AS
REFERENCE FOR HYPERSPECTRAL IMAGERY</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Roncat</surname>
<given-names>A.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<ext-link>https://orcid.org/0000-0002-8702-1167</ext-link></contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Pfeifer</surname>
<given-names>N.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Briese</surname>
<given-names>C.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Research Groups Photogrammetry and Remote Sensing, Department of Geodesy and Geoinformation, TU Wien, Vienna, Austria</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>EODC Earth Observation Data Centre for Water Resources Monitoring GmbH, Vienna, Austria</addr-line>
</aff>
<pub-date pub-type="epub">
<day>13</day>
<month>09</month>
<year>2017</year>
</pub-date>
<volume>IV-2/W4</volume>
<fpage>131</fpage>
<lpage>137</lpage>
<permissions>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
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<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/isprs-annals-IV-2-W4-131-2017.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/isprs-annals-IV-2-W4-131-2017.pdf</self-uri>
<abstract>
<p>While airborne lidar has confirmed its leading role in delivering high-resolution 3D topographic information during the last decade, its
radiometric potential has not yet been fully exploited. However, with the increasing availability of commercial lidar systems which (a)
make use of full-waveform information and (b) operate at several wavelengths simultaneously, this potential is increasing as well. Radiometric
calibration of the full-waveform information mentioned before allows for the derivation of physical target surface parameters
such as the backscatter coefficient and a diffuse reflectance value at bottom of atmosphere (BOA), i.e. the target surface.
&lt;br&gt;&lt;br&gt;
With lidar being an active remote sensing technique, these parameters can be derived from lidar data itself, accompanied by the
measurement or estimation of reference data for diffuse reflectance. In contrast to this, such a radiometric calibration for passive
hyperspectral imagery (HSI) requires the knowledge and/or estimation of much more unknowns. However, in case of corresponding
wavelength(s) radiometrically calibrated lidar datasets can deliver an areawide reference for BOA reflectance.
&lt;br&gt;&lt;br&gt;
This paper presents criteria to check where the assumption of diffuse BOA reflectance behaviour is fulfilled and how these criteria are
assessed in lidar data; the assessment is illustrated by an extended lidar dataset. Moreover, for this lidar dataset and an HSI dataset
recorded over the same area, the corresponding reflectance values are compared for different surface types.</p>
</abstract>
<counts><page-count count="7"/></counts>
</article-meta>
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