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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 the 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-XI-1-2026-61-2026</article-id>
<title-group>
<article-title>First Field Validation of a New VNIR–SWIR-Based Six-Band Multi-Camera System for UAVs over Winter Wheat</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Jenal</surname>
<given-names>Alexander</given-names>
<ext-link>https://orcid.org/0000-0002-1890-4839</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Reddig</surname>
<given-names>Fabian</given-names>
<ext-link>https://orcid.org/0000-0003-4958-1208</ext-link>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Bolten</surname>
<given-names>Andreas</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Vehlken</surname>
<given-names>Leon</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hüging</surname>
<given-names>Hubert</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Nguyen</surname>
<given-names>Thuy Huu</given-names>
<ext-link>https://orcid.org/0000-0003-3870-986X</ext-link>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Bongartz</surname>
<given-names>Jens</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>Bareth</surname>
<given-names>Georg</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Application Center for Machine Learning and Sensor Technologies, University of Applied Sciences Koblenz, Germany</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Institute of Geography, GIS &amp; Remote Sensing Group, University of Cologne, Germany</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Germany</addr-line>
</aff>
<pub-date pub-type="epub">
<day>03</day>
<month>07</month>
<year>2026</year>
</pub-date>
<volume>XI-1-2026</volume>
<fpage>61</fpage>
<lpage>69</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Alexander Jenal et al.</copyright-statement>
<copyright-year>2026</copyright-year>
<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>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/XI-1-2026/61/2026/isprs-annals-XI-1-2026-61-2026.html">This article is available from https://isprs-annals.copernicus.org/articles/XI-1-2026/61/2026/isprs-annals-XI-1-2026-61-2026.html</self-uri>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/XI-1-2026/61/2026/isprs-annals-XI-1-2026-61-2026.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/XI-1-2026/61/2026/isprs-annals-XI-1-2026-61-2026.pdf</self-uri>
<abstract>
<p>Shortwave infrared (SWIR) UAV imaging remains uncommon despite its sensitivity to canopy water and protein. We report, to our knowledge, the first field validation of a six-band, simultaneously exposed VNIR/SWIR multicamera for plot-scale winter wheat. The payload used narrow bandpass filters at 910, 980, 1100, 1200, 1510, and 1650 nm (FWHM 10&amp;ndash;12 nm) and was flown at 30 m AGL, yielding 4 cm GSD. Radiometric calibration used in-flight empirical line calibration with an in-scene gray panel set, followed by independent validation on a material-distinct gray set. ASD spectroradiometer measurements were convolved with Gaussian proxy spectral response functions matched to the nominal filter passbands. Empirical line fits were near-perfect (R2 &amp;asymp; 1.000; RMSE = 0.003&amp;ndash;0.009). Independent panel validation showed near-unity slopes for five bands from 980&amp;ndash;1650 nm (R2 = 0.998&amp;ndash;0.999; RMSE = 0.005&amp;ndash;0.013). Across 36 canopy plot ROIs, camera-to-ASD agreement remained strong for five bands, with slopes of 0.943&amp;ndash;1.079, R2 = 0.58&amp;ndash;0.85, and RMSE = 0.010&amp;ndash;0.023. Two SWIR normalized ratio indices showed tight cross-sensor agreement: NRI[1100,1200] (R2 &amp;asymp; 0.93; RMSE &amp;asymp; 0.010) and NRI[1650,1510] (R2 &amp;asymp; 0.90; RMSE &amp;asymp; 0.017&amp;ndash;0.018). Post-hoc filter transmittance measurements revealed secondary long-wavelength throughput in the 910 nm channel, causing compressed slopes and elevated error (MAPE &amp;asymp; 33%); this band was excluded from accuracy claims. Panel-anchored, bandpass-aware calibration enables quantitative UAV SWIR reflectance and robust SWIR indices for precision agriculture applications. The workflow also identifies hardware-specific failure modes, supporting reproducible validation and informed redesign of filter-reconfigurable SWIR payloads.</p>
</abstract>
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