The capability of very high-resolution satellite imagery for plot-level early wheat stem rust disease detection, monitoring, and phenotyping in Ethiopia

The capability of very high-resolution satellite imagery for plot-level early wheat stem rust disease detection, monitoring, and phenotyping in Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The capability of very high-resolution satellite imagery for plot-level early wheat stem rust disease detection, monitoring, and phenotyping in Ethiopia Gerald Blasch, Ashenafi Gemechu, Yoseph Alemayehu, Adama Ndour, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7652033/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Very high‑resolution satellites (VHRS) have potential for early crop disease detection and enhanced food security. The capability of multispectral SkySat and Pleiades‑Neo imagery for early detection and monitoring was assessed for wheat stem rust (SR) at the plot level. In randomized trials with contrasting fungicide and irrigation treatments, six bread wheat varieties with differing SR susceptibility were monitored using VHRS. 113 multispectral features were evaluated for their association with SR progression and associated yield loss. Several features demonstrated moderate to strong correlations with SR disease levels. Across early disease stages (healthy-mild-moderate), spectral sensitivity was dominated by Blue (B)‑based features, with Red-Blue (R-B) and Green-Blue (G-B) two-band features at mild and moderate levels, respectively. During late stages (severely-fully diseased), spectral sensitivity was driven by R-based features (e.g., R-B, R-G on both sensors) and by Deep Blue (DB), Red-Edge-DB (RE-DB), and R-DB combinations on Pleiades‑Neo. Early SR detection under moderate disease pressure was possible using ratio (RSI) and normalized difference (NDSI) spectral indices with B-G combinations. Key SkySat features such as RSI(NIR,B), RSI(R,B), and RSI(G,B) were sensitive across scenarios. This work delivers the first VHRS-based SR detection, advancing monitoring from plot to regional scales. Health sciences/Diseases Biological sciences/Plant sciences Full Text Additional Declarations No competing interests reported. Supplementary Files ManuscriptDZARC2023v7supplMaterialrd1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 04 May, 2026 Reviewers invited by journal 03 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7652033","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":589014500,"identity":"aeff7d21-72b7-4b46-8d05-b71119ec18a1","order_by":0,"name":"Gerald Blasch","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFACHsYDQDKBH0gcIFYLWGWCZAPJWgyIVc8g78B74MCPP3Z5xsePXzzwcw+DnHn/AvxaDA/wJRzsbUsuNjuTU3Cw5xmDscyNBwS0NPAYHOBtOJC47QZPwgGeAwyJMyQIOBGk5eCfPwcSN8/gSTj4hxgt8gw8Bod52A4kbpBgP3AYbAt/A34tBsx8CYdl25ITZ5zJYTgsc0DCWEICvw4G+fbegw/f/LFL7G8//vjjmwM2chL8BBxmcBjO5DEAEkArJBII2IJwOPsDCE3IllEwCkbBKBhxAABrIk0O8/fS1AAAAABJRU5ErkJggg==","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":true,"prefix":"","firstName":"Gerald","middleName":"","lastName":"Blasch","suffix":""},{"id":589014502,"identity":"dd3fc214-ebe2-46e2-9aff-365d290eef52","order_by":1,"name":"Ashenafi Gemechu","email":"","orcid":"","institution":"Debre Zeit Agricultural Research Center","correspondingAuthor":false,"prefix":"","firstName":"Ashenafi","middleName":"","lastName":"Gemechu","suffix":""},{"id":589014503,"identity":"8008fa0b-f6ee-4414-943b-edd8a7fe7c07","order_by":2,"name":"Yoseph Alemayehu","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":false,"prefix":"","firstName":"Yoseph","middleName":"","lastName":"Alemayehu","suffix":""},{"id":589014507,"identity":"6206382b-eb8c-4e4f-a160-06f32a6b1a46","order_by":3,"name":"Adama Ndour","email":"","orcid":"","institution":"International Maize and Wheat Improvement Center","correspondingAuthor":false,"prefix":"","firstName":"Adama","middleName":"","lastName":"Ndour","suffix":""},{"id":589014510,"identity":"2ec22e3d-a956-44df-9b35-bdafb98785ff","order_by":4,"name":"Pablo Olivera Firpo","email":"","orcid":"","institution":"United States Department of Agriculture","correspondingAuthor":false,"prefix":"","firstName":"Pablo","middleName":"Olivera","lastName":"Firpo","suffix":""},{"id":589014512,"identity":"e2303bc1-22b1-458f-84f3-f2859bb3b277","order_by":5,"name":"Andries Potgieter","email":"","orcid":"","institution":"University of Queensland","correspondingAuthor":false,"prefix":"","firstName":"Andries","middleName":"","lastName":"Potgieter","suffix":""},{"id":589014513,"identity":"0bba2e8e-339f-4170-a56d-bb41334e0699","order_by":6,"name":"David P. 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The capability of multispectral SkySat and Pleiades‑Neo imagery for early detection and monitoring was assessed for wheat stem rust (SR) at the plot level. In randomized trials with contrasting fungicide and irrigation treatments, six bread wheat varieties with differing SR susceptibility were monitored using VHRS. 113 multispectral features were evaluated for their association with SR progression and associated yield loss. Several features demonstrated moderate to strong correlations with SR disease levels. Across early disease stages (healthy-mild-moderate), spectral sensitivity was dominated by Blue (B)‑based features, with Red-Blue (R-B) and Green-Blue (G-B) two-band features at mild and moderate levels, respectively. During late stages (severely-fully diseased), spectral sensitivity was driven by R-based features (e.g., R-B, R-G on both sensors) and by Deep Blue (DB), Red-Edge-DB (RE-DB), and R-DB combinations on Pleiades‑Neo. 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