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<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-X-4-W8-2025-487-2026</article-id>
<title-group>
<article-title>Modelling the Structure and Temporal Dynamics of the Moscow Surface Urban Heat Island Using Medium-Resolution Satellite Data</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Malyuta</surname>
<given-names>Oleg R.</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>National Research University Higher School of Economics (HSE), Faculty of Geography and Geoinformation Technologies, Moscow, Russia</addr-line>
</aff>
<pub-date pub-type="epub">
<day>29</day>
<month>05</month>
<year>2026</year>
</pub-date>
<volume>X-4/W8-2025</volume>
<fpage>487</fpage>
<lpage>492</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Oleg R. Malyuta</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/X-4-W8-2025/487/2026/isprs-annals-X-4-W8-2025-487-2026.html">This article is available from https://isprs-annals.copernicus.org/articles/X-4-W8-2025/487/2026/isprs-annals-X-4-W8-2025-487-2026.html</self-uri>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/X-4-W8-2025/487/2026/isprs-annals-X-4-W8-2025-487-2026.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/X-4-W8-2025/487/2026/isprs-annals-X-4-W8-2025-487-2026.pdf</self-uri>
<abstract>
<p>Urban heat island (UHI) amplification poses growing risks to public health and energy demand, yet long-term, city-scale diagnostics remain scarce for rapidly transforming metropolises. Here we present the first 40-year reconstruction of Moscow&amp;rsquo;s surface UHI (SUHI) using 125 cloud-screened summer Landsat scenes (1984&amp;ndash;2024, 30 m). Land surface temperature was retrieved with a single-channel algorithm constrained by MERRA-2 water vapor fields and emissivity from NDVI-based method; accuracy is &amp;plusmn;2◦C. SUHI intensity aggregated into annual and eight 5-year epochs. Eight Local Climate Zones (LCZs) were mapped from very high-resolution imagery and spectral indices to analyse morphology&amp;ndash;temperature links.&amp;nbsp;&lt;br /&gt;The results show that the thermally significant SUHI footprint (&amp;gt; 2◦C) expanded by 35 % (&amp;asymp; 1040 km2) and now covers just under 4000 km2. Growth was highly uneven: New Moscow added 125 %, Old Moscow 19 %, and the suburban belt 35 %. LCZ trends reveal the fastest warming in new development (+0.19◦C yr&amp;minus;1) and industrial areas (+0.13◦C yr&amp;minus;1); forest and landscape parks warmed least (&amp;lt; 0.06◦C yr&amp;minus;1). Built-up index correlates strongly with mean LST (r = 0.68). Scenario mapping, informed by official land use plans, projects further intensification of SUHI along the Kaluzhskoye and Kievskoye highways and adjacent streets by 2035.&amp;nbsp;&lt;br /&gt;The openly available Google Earth Engine &amp;amp; Python implemented workflow is transferable to other cities, providing a template for climate-resilient planning and targeted green infrastructure interventions.</p>
</abstract>
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