A Novel Method for Salinization Estimation in the Yellow River Delta Based on XGBoost and Three-Dimensional Feature Space Models

A Novel Method for Salinization Estimation in the Yellow River Delta Based on XGBoost and Three-Dimensional Feature Space Models | 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 A Novel Method for Salinization Estimation in the Yellow River Delta Based on XGBoost and Three-Dimensional Feature Space Models Runze Zhang, Bing Guo, Rui Zhang, Jicun Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7296634/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 Soil salinization in the Yellow River Delta (YRD) is becoming more and more serious, and has become one of the key factors restricting the sustainable development of the region. Compared with two-dimensional feature space, three-dimensional feature space is superior in monitoring effectiveness and can more effectively utilize the multispectral information of remote sensing images. At present, the monitoring of soil salinization in the Yellow River Delta based on 3D feature space is not sufficiently reported. In this study, based on the Sentinel-2 remote sensing image data, 24 indices (vegetation index, salinity index and other indices) of different categories were calculated, and the Bayesian optimized XGBoost model was used to optimize the features of the indices for each category, construct the three-dimensional feature space model, and carry out the one-dimensional linear regression analysis to determine the optimal model for salinity estimation. The results showed that: (1) among the vegetation feature coefficients, Normalized Difference Red Edge Index, Extended Ratio Vegetation Index and Normalized Difference Vegetation Index were the most important. Among the salinity feature covariates, the salinity indices SI5, S3 and SI7 had the largest contribution; (2) the salinity monitoring model constructed based on the NDRE-Albedo-SI7 feature space had the highest accuracy, with the coefficient of determination was 0.921; (3) the extremely salinized and heavily salinized areas were mainly distributed in the northern coastal area of Hekou District, Kenli County and the eastern part of Dongying District, while the mildly salinized and non-salinized areas were mainly distributed in Lijin County, the western part of Kenli District, the western part of Dongying District and the southwestern part of Guangrao County. In this study, a three-dimensional feature space model was innovatively applied to monitor soil salinization in the Yellow River Delta by introducing the Sentinel-2-specific red-edge index and using the Bayesian-optimized XGBoost model for feature selection, which significantly improved the accuracy and reliability of the model. The results of the study provide a scientific basis for the monitoring and management of soil salinization in the Yellow River Delta. Three-Dimensional feature space XGBoost Sentinel-2 Soil salinization Yellow River Delta 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. 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-7296634","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":524348315,"identity":"366928be-a935-48b5-8602-23dfde291a7d","order_by":0,"name":"Runze Zhang","email":"","orcid":"","institution":"Shandong University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Runze","middleName":"","lastName":"Zhang","suffix":""},{"id":524348319,"identity":"ddbcc481-123b-40a9-b8c0-af2894480f0c","order_by":1,"name":"Bing 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Compared with two-dimensional feature space, three-dimensional feature space is superior in monitoring effectiveness and can more effectively utilize the multispectral information of remote sensing images. At present, the monitoring of soil salinization in the Yellow River Delta based on 3D feature space is not sufficiently reported. In this study, based on the Sentinel-2 remote sensing image data, 24 indices (vegetation index, salinity index and other indices) of different categories were calculated, and the Bayesian optimized XGBoost model was used to optimize the features of the indices for each category, construct the three-dimensional feature space model, and carry out the one-dimensional linear regression analysis to determine the optimal model for salinity estimation. The results showed that: (1) among the vegetation feature coefficients, Normalized Difference Red Edge Index, Extended Ratio Vegetation Index and Normalized Difference Vegetation Index were the most important. Among the salinity feature covariates, the salinity indices SI5, S3 and SI7 had the largest contribution; (2) the salinity monitoring model constructed based on the NDRE-Albedo-SI7 feature space had the highest accuracy, with the coefficient of determination was 0.921; (3) the extremely salinized and heavily salinized areas were mainly distributed in the northern coastal area of Hekou District, Kenli County and the eastern part of Dongying District, while the mildly salinized and non-salinized areas were mainly distributed in Lijin County, the western part of Kenli District, the western part of Dongying District and the southwestern part of Guangrao County. In this study, a three-dimensional feature space model was innovatively applied to monitor soil salinization in the Yellow River Delta by introducing the Sentinel-2-specific red-edge index and using the Bayesian-optimized XGBoost model for feature selection, which significantly improved the accuracy and reliability of the model. 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