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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-XLVIII-4-W6-2022-217-2023</article-id>
<title-group>
<article-title>TRANSFORMATION OF THE NORMALIZED DIFFERENCE CHLOROPHYLL INDEX TO RETRIEVE CHLOROPHYLL-A CONCENTRATIONS IN MANILA BAY</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Manuel</surname>
<given-names>A.</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>Blanco</surname>
<given-names>A. C.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Department of Geodetic Engineering, University of the Philippines Diliman, Philippines</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Training Center for Applied Geodesy and Photogrammetry, University of the Philippines Diliman, Philippines</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Space Information Infrastructure Bureau, Philippine Space Agency, Diliman, Quezon City, Philippines</addr-line>
</aff>
<pub-date pub-type="epub">
<day>07</day>
<month>02</month>
<year>2023</year>
</pub-date>
<volume>XLVIII-4/W6-2022</volume>
<fpage>217</fpage>
<lpage>221</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2023 A. Manuel</copyright-statement>
<copyright-year>2023</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/XLVIII-4-W6-2022/217/2023/isprs-archives-XLVIII-4-W6-2022-217-2023.html">This article is available from https://isprs-archives.copernicus.org/articles/XLVIII-4-W6-2022/217/2023/isprs-archives-XLVIII-4-W6-2022-217-2023.html</self-uri>
<self-uri xlink:href="https://isprs-archives.copernicus.org/articles/XLVIII-4-W6-2022/217/2023/isprs-archives-XLVIII-4-W6-2022-217-2023.pdf">The full text article is available as a PDF file from https://isprs-archives.copernicus.org/articles/XLVIII-4-W6-2022/217/2023/isprs-archives-XLVIII-4-W6-2022-217-2023.pdf</self-uri>
<abstract>
<p>Manila Bay is one of the most significant harbors in the region, enabling commerce and trade between the Philippines and other countries. Its abundant natural resources have provided for generations of its inhabitants and have driven socio-economic development for centuries. Like other water bodies adjacent to highly urbanized cities, the increased organic and nutrient loading from untreated domestic, industrial, and agricultural wastes resulted to further degradation of its water quality. While frequent water quality monitoring is ideal, data from traditional field sampling methods might not be sufficient to assess the spatial and temporal variations of water quality in Manila Bay. Remote sensing fills the need for a frequent and full overview of the bay’s water quality. Sentinel-3 images were initially processed through the Case 2 Regional CoastColour (C2RCC) model to retrieve the remote sensing reflectance. The Normalized Difference Chlorophyll Index (NDCI) was computed for the images and were modeled against the C2RCC-derived chlorophyll-a estimates. From this, we were able to get a general equation (TNDCI – transformed NDCI) to retrieve chlorophyll-a concentrations from two reflectance bands (Oa8 and Oa11). TNDCI gave an R&lt;sup&gt;2&lt;/sup&gt; of 0.9 and RMSE = 5.12 &amp;micro;g/L as compared to C2RCC values, and an R&lt;sup&gt;2&lt;/sup&gt; = 0.85 and RMSE = 2.44 &amp;micro;g/L with field data.</p>
</abstract>
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