<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "https://jats.nlm.nih.gov/nlm-dtd/publishing/3.0/journalpublishing3.dtd">
<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="3.0" xml:lang="en">
<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-3-2026-973-2026</article-id>
<title-group>
<article-title>Remote Sensing of Maize Physiological and Nutrient Dynamics in Response to Fall Armyworm Infestation</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Dzurume</surname>
<given-names>Tatenda</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>Darvishzadeh</surname>
<given-names>Roshanak</given-names>
<ext-link>https://orcid.org/0000-0001-7512-0574</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>Dube</surname>
<given-names>Timothy</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>Isleem</surname>
<given-names>Wafaa Rushdi Saleh</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>Ibrahim</surname>
<given-names>Ismail</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>Nelson</surname>
<given-names>Andrew</given-names>
<ext-link>https://orcid.org/0000-0002-7249-3778</ext-link>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, the Netherlands</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Institute for Water Studies, University of the Western Cape, Bellville, South Africa</addr-line>
</aff>
<aff id="aff3">
<label>3</label>
<addr-line>Department of Plant Protection, Ministry of Agriculture, Amman, Jordan</addr-line>
</aff>
<aff id="aff4">
<label>4</label>
<addr-line>Department of Plant Protection, School of Agriculture, The University of Jordan, Amman, Jordan</addr-line>
</aff>
<pub-date pub-type="epub">
<day>09</day>
<month>07</month>
<year>2026</year>
</pub-date>
<volume>XI-3-2026</volume>
<fpage>973</fpage>
<lpage>979</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2026 Tatenda Dzurume 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-3-2026/973/2026/isprs-annals-XI-3-2026-973-2026.html">This article is available from https://isprs-annals.copernicus.org/articles/XI-3-2026/973/2026/isprs-annals-XI-3-2026-973-2026.html</self-uri>
<self-uri xlink:href="https://isprs-annals.copernicus.org/articles/XI-3-2026/973/2026/isprs-annals-XI-3-2026-973-2026.pdf">The full text article is available as a PDF file from https://isprs-annals.copernicus.org/articles/XI-3-2026/973/2026/isprs-annals-XI-3-2026-973-2026.pdf</self-uri>
<abstract>
<p>Fall Armyworm (FAW) is a major pest, threatening maize production and food security. FAW preferentially attacks nitrogen-rich maize due to its nutritional composition, which accelerates larval development. Understanding the effects of FAW infestation on maize growth and nutrient status is critical for effective crop management. This study (i) examines the impact of FAW infestation on maize using key physiological metrics, including fresh/dry weight, chlorophyll content (as measured by SPAD), leaf area index (LAI), stem length, and leaf dimensions (length, width, and area); (ii) investigates differences in carbon, hydrogen, nitrogen, and sulphur concentrations between healthy and infested maize, and (iii) analyses spectral variability between healthy and infested maize using leaf-level hyperspectral data. Hyperspectral data at the leaf scale were used to identify and distinguish the spectral reflectance between healthy and infested FAW maize crops. Results indicate that infested crops exhibit lower fresh weight, reduced LAI, shorter stems, and smaller leaves compared to healthy crops, highlighting a substantial negative effect on above-ground biomass and overall crop vigour. Infested crops showed higher nitrogen levels than healthy crops. This trend could be attributed to nitrogen redistribution following FAW damage, where nitrogen decreases in attacked leaves and increases in roots, with partial recovery after the pest moves on to another crop. The study establishes a methodological framework for linking laboratory, field, and remote sensing approaches, providing a foundation for future predictive modelling of pest impacts on maize nutrient content and productivity.</p>
</abstract>
<counts><page-count count="7"/></counts>
</article-meta>
</front>
<body/>
<back>
</back>
</article>