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<front>
<journal-meta>
<journal-id journal-id-type="publisher">ISPRS-Archives</journal-id>
<journal-title-group>
<journal-title>The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</journal-title>
<abbrev-journal-title abbrev-type="publisher">ISPRS-Archives</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2194-9034</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-archives-XLVI-1-W1-2021-1-2022</article-id>
<title-group>
<article-title>USING DESIS AND EO-1 HYPERION REFLECTANCE TIME SERIES FOR THE ASSESSMENT OF VEGETATION TRAITS AND GROSS PRIMARY PRODUCTION (GPP)</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Campbell</surname>
<given-names>P. E. K.</given-names>

</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<ext-link>https://orcid.org/0000-0002-0505-4951</ext-link></contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Huemmrich</surname>
<given-names>K. F.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Middleton</surname>
<given-names>E. M.</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>Alfieri</surname>
<given-names>J.</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>van der Tol</surname>
<given-names>C.</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Neigh</surname>
<given-names>C. S. R.</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<ext-link>https://orcid.org/0000-0002-5322-6340</ext-link></contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD 21228, USA</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>USDA-ARS Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>ITC, University of Twente, Enschede, The Netherlands</addr-line>
</aff>
<pub-date pub-type="epub">
<day>11</day>
<month>02</month>
<year>2022</year>
</pub-date>
<volume>XLVI-1/W1-2021</volume>
<fpage>1</fpage>
<lpage>8</lpage>
<permissions>
<copyright-statement>Copyright: © 2022 P. E. K. Campbell et al.</copyright-statement>
<copyright-year>2022</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-archives.copernicus.org/articles/isprs-archives-XLVI-1-W1-2021-1-2022.html">This article is available from https://isprs-archives.copernicus.org/articles/isprs-archives-XLVI-1-W1-2021-1-2022.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/isprs-archives-XLVI-1-W1-2021-1-2022.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/isprs-archives-XLVI-1-W1-2021-1-2022.pdf</self-uri>
<abstract>
<p>This study evaluates the potential of the DLR Earth Sensing Imaging Spectrometer (DESIS) visible through near-infrared (VNIR) surface reflectance to augment the EO-1 Hyperion full spectrum (400–2400 nm) reflectance collection over vegetated flux sites, to extend the reflectance time series up to the present. We compared DESIS and Hyperion surface reflectance magnitude and variability at a pseudo-invariant site (PICS) and a vegetated flux site (VFS). VNIR reflectance magnitudes between the two sensors did not significantly differ at the PICS. However, DESIS variability was higher, likely due to differences in the data acquisition time and observation geometry. Using empirical and biophysical models, both DESIS and Hyperion datasets captured the seasonal variations in gross primary production (GPP) and canopy bio-physical parameters such as chlorophyll content, leaf area index (LAI), and senescent material at the VFS. Differences in the magnitudes of the bio-physical parameters were observed, likely due to the differences in the sensors spectral range and resolution. Using together VNIR reflectance from EO-1 Hyperion and DESIS convolved to Hyperion spectral resolution to estimate canopy chlorophyll and GPP, we demonstrate that combining historic and current space-based reflectance data in a common multi-sensor approach is feasible. This is of importance for extending the reflectance record established with EO-1 Hyperion to provide continuity with the current orbital instruments (e.g., DESIS/ISS, PRISMA/ASI) and the forthcoming NASA Surface Biology and Geology (SBG), ESA CHIME and DLR EnMAP satellite missions, which is of key importance for comparisons of current and past trends in the seasonal dynamics of vegetation traits and photosynthetic function.</p>
</abstract>
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