GPP Estimation Based on CatBoost and Analysis of Change Driving Factors in Shanxi Province, China

GPP Estimation Based on CatBoost and Analysis of Change Driving Factors in Shanxi Province, China | 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 GPP Estimation Based on CatBoost and Analysis of Change Driving Factors in Shanxi Province, China Yujie Li, Xuanguang Liu, Zhenchao Zhang, Jun Lang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5700690/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The gross primary productivity (GPP) of Shanxi Province, China, plays an important role in the carbon cycle of the Loess Plateau ecosystem. However, Shanxi Province lacks carbon flux stations, leading to imprecise GPP estimation results. Additionally, few studies have explored the drivers of long-term GPP change in Shanxi Province. Therefore, in this study, we aimed to estimate the GPP in Shanxi Province from 2001 to 2022 and determine the driving factors of long-term GPP trends. To this end, we proposed an improved GPP estimation method based on the CatBoost model. Our CatBoost GPP model reduces model overfitting in few-shot scenarios and effectively captures the time dependence in time-series data. In addition, it integrates the change characteristics of vegetation ecological indicators and topography constraints, improving GPP estimation accuracy. Subsequently, we explore the spatial and temporal variations driving force through Theil-Sen Median trend analysis, Geodetectors, et al. Results show that (1) Compared with existing methods, the proposed CatBoost GPP method achieved superior site-level accuracy with an R2 value of 0.888, Root mean square error (RMSE) of 1.162 gCm −2 ·day −1 , and mean absolute error (MAE) of 0.772 gCm −2 ·day −1. Furthermore, we compare our results with previous GPP products to further assess the regional-level accuracy; (2) The GPP in Shanxi Province displayed a fluctuating increase, with a growth rate of 20.58 gCm −2 ·yr −1 from 2001 to 2022. The overall spatial variation was characterized by low GPP in the northwest and high GPP in the southeast. The GPP change was mainly characterized by weak anti-persistence; thus, approximately 58.8% of the area may experience degradation in the future; and (3) Land use type significantly influenced GPP changes in Shanxi, with the restoration and improvement of grassland being the main contributor to the increase in GPP. The interaction between precipitation and temperature had the most complex and significant impact on GPP, affecting approximately 62.05% of the study area. The results of this study provide a theoretical basis for ecological protection and sustainable development in Shanxi Province. Earth and environmental sciences/Climate sciences/Climate change Earth and environmental sciences/Ecology/Ecological modelling Earth and environmental sciences/Environmental sciences/Environmental impact Gross primary productivity CatBoost Geodetector Driving factor Spatiotemporal distribution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 27 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviews received at journal 15 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers agreed at journal 05 Apr, 2025 Reviewers invited by journal 02 Apr, 2025 Editor assigned by journal 02 Apr, 2025 Editor invited by journal 02 Jan, 2025 Submission checks completed at journal 31 Dec, 2024 First submitted to journal 23 Dec, 2024 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. 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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-5700690","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":396150106,"identity":"c0af3c03-ae4a-444c-8175-2236262f8386","order_by":0,"name":"Yujie Li","email":"","orcid":"","institution":"Lyuliang University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Li","suffix":""},{"id":396150107,"identity":"c4126f26-63d3-4ffa-919e-95f24e15a8aa","order_by":1,"name":"Xuanguang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYBACPiA+8KHCRo69AcQ1sCCshY2BgfHgjDNpxjwHwFokiNLCfJiz7XBiD1gLAzFaJNIvHGY4k5bew95juuFHgQQDf3t3An4tPGcKDhdU2OT28BxLu9kDdJjEmbMb8Gth70k4DPRL7n6J5GM3eIBaDCRyCWhh5kk4zNt2OJ1HIrHt5h+itLC3HwBpSeAB2nKbOFt4zjCAAtkQ5JfbMgYSPAT9wi+R/vgDMCrledh7zG6++WMjx9/ei18LAwOPASqXgHIQYH9AhKJRMApGwSgY0QAAgkBJOrS0jy4AAAAASUVORK5CYII=","orcid":"","institution":"Information Engineering University","correspondingAuthor":true,"prefix":"","firstName":"Xuanguang","middleName":"","lastName":"Liu","suffix":""},{"id":396150109,"identity":"6bca1a0f-b888-430e-9cab-4da664b97792","order_by":2,"name":"Zhenchao Zhang","email":"","orcid":"","institution":"Information Engineering University","correspondingAuthor":false,"prefix":"","firstName":"Zhenchao","middleName":"","lastName":"Zhang","suffix":""},{"id":396150110,"identity":"215e84b1-25fa-45e4-93c2-1258028fb235","order_by":3,"name":"Jun Lang","email":"","orcid":"","institution":"Lyuliang University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Lang","suffix":""}],"badges":[],"createdAt":"2024-12-23 15:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5700690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5700690/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-08927-x","type":"published","date":"2025-07-01T15:57:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86179770,"identity":"c52a54e3-339b-47d4-a8ad-f2e67a6f097e","added_by":"auto","created_at":"2025-07-07 16:19:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":46747109,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5700690/v1_covered_83fe2617-eb96-46c7-b121-f189bd90c7e6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"GPP Estimation Based on CatBoost and Analysis of Change Driving Factors in Shanxi Province, China","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gross primary productivity, CatBoost, Geodetector, Driving factor, Spatiotemporal distribution","lastPublishedDoi":"10.21203/rs.3.rs-5700690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5700690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The gross primary productivity (GPP) of Shanxi Province, China, plays an important role in the carbon cycle of the Loess Plateau ecosystem. However, Shanxi Province lacks carbon flux stations, leading to imprecise GPP estimation results. Additionally, few studies have explored the drivers of long-term GPP change in Shanxi Province. Therefore, in this study, we aimed to estimate the GPP in Shanxi Province from 2001 to 2022 and determine the driving factors of long-term GPP trends. To this end, we proposed an improved GPP estimation method based on the CatBoost model. Our CatBoost GPP model reduces model overfitting in few-shot scenarios and effectively captures the time dependence in time-series data. In addition, it integrates the change characteristics of vegetation ecological indicators and topography constraints, improving GPP estimation accuracy. Subsequently, we explore the spatial and temporal variations driving force through Theil-Sen Median trend analysis, Geodetectors, et al. Results show that (1) Compared with existing methods, the proposed CatBoost GPP method achieved superior site-level accuracy with an R2 value of 0.888, Root mean square error (RMSE) of 1.162 gCm −2 ·day −1 , and mean absolute error (MAE) of 0.772 gCm −2 ·day −1. Furthermore, we compare our results with previous GPP products to further assess the regional-level accuracy; (2) The GPP in Shanxi Province displayed a fluctuating increase, with a growth rate of 20.58 gCm −2 ·yr −1 from 2001 to 2022. The overall spatial variation was characterized by low GPP in the northwest and high GPP in the southeast. The GPP change was mainly characterized by weak anti-persistence; thus, approximately 58.8% of the area may experience degradation in the future; and (3) Land use type significantly influenced GPP changes in Shanxi, with the restoration and improvement of grassland being the main contributor to the increase in GPP. The interaction between precipitation and temperature had the most complex and significant impact on GPP, affecting approximately 62.05% of the study area. The results of this study provide a theoretical basis for ecological protection and sustainable development in Shanxi Province.","manuscriptTitle":"GPP Estimation Based on CatBoost and Analysis of Change Driving Factors in Shanxi Province, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-02 03:57:17","doi":"10.21203/rs.3.rs-5700690/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-29T04:09:44+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T09:57:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-16T03:51:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-15T15:20:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166121591441660517744866957311803372860","date":"2025-04-05T09:13:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111767742469058076139367063634820983234","date":"2025-04-05T09:03:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66966852466292853866411979169178290289","date":"2025-04-05T08:46:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T13:32:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-02T13:29:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-02T13:14:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-31T10:25:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-12-23T15:00:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c429ca27-2af7-476f-a62a-ca9e32081a49","owner":[],"postedDate":"January 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":42204617,"name":"Earth and environmental sciences/Climate sciences/Climate change"},{"id":42204618,"name":"Earth and environmental sciences/Ecology/Ecological modelling"},{"id":42204619,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"}],"tags":[],"updatedAt":"2025-07-07T16:11:10+00:00","versionOfRecord":{"articleIdentity":"rs-5700690","link":"https://doi.org/10.1038/s41598-025-08927-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:57:49","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-01-02 03:57:17","video":"","vorDoi":"10.1038/s41598-025-08927-x","vorDoiUrl":"https://doi.org/10.1038/s41598-025-08927-x","workflowStages":[]},"version":"v1","identity":"rs-5700690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5700690","identity":"rs-5700690","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}