Estimating soil moisture and organic matter contents in salt-affected farmlands using hyperspectral remote sensing machine learning

Estimating soil moisture and organic matter contents in salt-affected farmlands using hyperspectral remote sensing machine learning | 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 Estimating soil moisture and organic matter contents in salt-affected farmlands using hyperspectral remote sensing machine learning Qidong Ding, Huayu Huang, Junhua Zhang, Yijing Wang, Keli Jia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4781691/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 salinisation and alkalinisation are a major constraint to sustainable agricultural development, especially in arid and semi-arid areas. Hyperspectral remote sensing enables rapid and dynamic monitoring of soil properties, but it is still a challenge to improve the estimation accuracy. The aim of this study was to improve the accuracy of estimating soil moisture content (SMC) and soil organic matter (SOM) in salt-affected farmlands based on multi-source data. Nine study sites in the Hetao Plain, northwestern China were selected to acquire field hyperspectral data and measure soil properties. Spectral transformations were performed after preprocessing of the original hyperspectral reflectance data. Feature bands were selected by competitive adaptive reweighted sampling and multi-band spectral index development. Topographic, climatic and edaphic covariates were introduced to build models for SMC and SOM estimation based on four machine learning algorithms. The results showed that standard normal variate and fractional-order derivative transformations effectively captured subtle information in spectral data. Three-band spectral indices showed stronger correlations with SMC and SOM than two-band spectral indices. For the two soil properties, extremely randomised tree (ERT) models achieved the highest accuracy, followed by random forest, support vector machine and partial least squares regression models. The ERT models yielded R 2 values of 0.91 and 0.96 for SMC and SOM, respectively. Interpretation of the ERT models using SHapley Additive exPlanations revealed that soil total nitrogen, followed by climatic factors, was the leading factor contributing to both SMC and SOM estimation. While the contribution of three-band spectral indices to model estimation was no greater than that of two-band spectral indices, there were notable differences in the contribution of single spectral bands. This study provides a new perspective to accurately estimate SMC and SOM in salt-affected farmlands. Recommendations for site-specific farmland management are given to facilitate soil amelioration. Remote sensing monitoring Fractional-order derivative Multi-band spectral indices Extremely randomised tree SHapley Additive exPlanations Inverse distance weighting 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-4781691","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":339946737,"identity":"85d88a18-37f3-4192-bd31-4268b35c7c70","order_by":0,"name":"Qidong Ding","email":"","orcid":"","institution":"School of Ecology and Environment, Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Qidong","middleName":"","lastName":"Ding","suffix":""},{"id":339946738,"identity":"d3e27254-a355-43bb-bf9f-436ce7f61f19","order_by":1,"name":"Huayu Huang","email":"","orcid":"","institution":"School of Ecology and Environment, Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Huayu","middleName":"","lastName":"Huang","suffix":""},{"id":339946739,"identity":"b66483cb-b1dd-457a-9866-f3d5137d5e59","order_by":2,"name":"Junhua Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACCRDBw8BgwMDA/uNDhQ0PP38D8VoYJGecSZORnHGAGC0MEC3SvG2HbQwaEvDrkJ/d/OzhF5nDeeYSyQ8MeM6c5zFgOMD44WMObi2Mc46ZG8vwHC62nJFmkCBRcZvHnLmBWXLmNtxamCUSzKQleA4nbriRYHDA4MxtHsuGA2zMvHi0sEmkf4NqSf/YkNh2jsfgQAJ+LTwSOWaSH8BacowZDrYdIKxFQiKnTJqBJz1xw5k3ZYwNZ5J5JGccbMbrF/kZ6dskf/ZYJ244nr6N+U+FnT0/f/PBDx/xaAEHAW8PCp+xAb96kJIfPwiqGQWjYBSMgpEMACj6VQrZBPhaAAAAAElFTkSuQmCC","orcid":"","institution":"School of Ecology and Environment, Ningxia University","correspondingAuthor":true,"prefix":"","firstName":"Junhua","middleName":"","lastName":"Zhang","suffix":""},{"id":339946740,"identity":"ac67f506-6b4d-447b-93fd-ead206b1070e","order_by":3,"name":"Yijing Wang","email":"","orcid":"","institution":"School of Geography and Planning, Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Yijing","middleName":"","lastName":"Wang","suffix":""},{"id":339946741,"identity":"510c26d3-ecc4-445b-b5e6-def79b377b49","order_by":4,"name":"Keli Jia","email":"","orcid":"","institution":"School of Geography and Planning, Ningxia University","correspondingAuthor":false,"prefix":"","firstName":"Keli","middleName":"","lastName":"Jia","suffix":""}],"badges":[],"createdAt":"2024-07-22 12:01:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4781691/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4781691/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64437321,"identity":"3be1d1e9-94ef-41ff-8f70-d0402eb0e2a9","added_by":"auto","created_at":"2024-09-13 07:32:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1681287,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4781691/v1_covered_d3178e46-cb53-46de-a837-651e135452da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimating soil moisture and organic matter contents in salt-affected farmlands using hyperspectral remote sensing machine learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Remote sensing monitoring, Fractional-order derivative, Multi-band spectral indices, Extremely randomised tree, SHapley Additive exPlanations, Inverse distance weighting","lastPublishedDoi":"10.21203/rs.3.rs-4781691/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4781691/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSoil salinisation and alkalinisation are a major constraint to sustainable agricultural development, especially in arid and semi-arid areas. Hyperspectral remote sensing enables rapid and dynamic monitoring of soil properties, but it is still a challenge to improve the estimation accuracy. The aim of this study was to improve the accuracy of estimating soil moisture content (SMC) and soil organic matter (SOM) in salt-affected farmlands based on multi-source data. Nine study sites in the Hetao Plain, northwestern China were selected to acquire field hyperspectral data and measure soil properties. Spectral transformations were performed after preprocessing of the original hyperspectral reflectance data. Feature bands were selected by competitive adaptive reweighted sampling and multi-band spectral index development. Topographic, climatic and edaphic covariates were introduced to build models for SMC and SOM estimation based on four machine learning algorithms. The results showed that standard normal variate and fractional-order derivative transformations effectively captured subtle information in spectral data. Three-band spectral indices showed stronger correlations with SMC and SOM than two-band spectral indices. For the two soil properties, extremely randomised tree (ERT) models achieved the highest accuracy, followed by random forest, support vector machine and partial least squares regression models. The ERT models yielded \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values of 0.91 and 0.96 for SMC and SOM, respectively. Interpretation of the ERT models using SHapley Additive exPlanations revealed that soil total nitrogen, followed by climatic factors, was the leading factor contributing to both SMC and SOM estimation. While the contribution of three-band spectral indices to model estimation was no greater than that of two-band spectral indices, there were notable differences in the contribution of single spectral bands. This study provides a new perspective to accurately estimate SMC and SOM in salt-affected farmlands. 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