Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data based on Machine-Learning Algorithm

Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data based on Machine-Learning Algorithm | 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 Research Article Estimation of Daylily Leaf Area Index by Synergy Multispectral and Radar Remote-Sensing Data based on Machine-Learning Algorithm Minhuan Hu, Jingshu Wang, Peng Yang, Ping Li, Peng He, Rutian Bi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5302684/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rapid and accurate leaf area index (LAI) determination is important to monitoring daylily growth, yield estimation, and field management. Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data, and the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI) and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. Selected features were sensitive to daylily LAI. The RFR algorithm had good anti-noise performance and strong fitting ability; thus, its accuracy was better than the partial least squares regression and artificial neural network models. Synergistic optical and microwave data more comprehensively reflected the physical and chemical properties of daylilies, making the RFR-VI-BC05 model after feature selection better than the others. This study expanded methods for estimating daylily LAI by combining optical and radar data, providing technical support for daylily management. daylily leaf area index optical and microwave remote sensing random forest feature selection machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-5302684","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373475421,"identity":"74ce6ed7-bc1a-4cc5-b854-33b11c47b7ca","order_by":0,"name":"Minhuan Hu","email":"","orcid":"","institution":"Shanxi Agricultural University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Minhuan","middleName":"","lastName":"Hu","suffix":""},{"id":373475422,"identity":"e6f904d3-1698-4645-bb86-bb4cd79447e1","order_by":1,"name":"Jingshu Wang","email":"","orcid":"","institution":"Shanxi Agricultural 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Because of low estimation accuracy of empirical models based on single-source data, we proposed a machine-learning algorithm combining optical and microwave remote-sensing data, and the random forest regression (RFR) importance score to select features. A high-precision LAI estimation model for daylilies was constructed by optimizing feature combinations. The RFR importance score screened the top five important features, including vegetation indices land surface water index (LSWI), generalized difference vegetation index (GDVI), normalized difference yellowness index (NDYI) and backscatter coefficients VV and VH. Vegetation index features characterized canopy moisture and color of daylilies, and the backscatter coefficient reflected dielectric properties and geometric structure. Selected features were sensitive to daylily LAI. 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