[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128324-en":3,"doc-seo-128324-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128324,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Global mangrove soil organic carbon stocks dataset - year 2020 - 30 m resolution","Global soil organic carbon stocks in mangrove forests are provided at 30 m resolution for the year 2020 through spatiotemporal ensemble machine learning. Predictions estimate soil organic carbon content and bulk density at 1 m soil depth, then aggregate results to compute soil organic carbon stocks. Training uses SOC (%) and bulk density points from global mangrove datasets and recently published studies, supported by globally consistent covariate layers. Model validation uses 10,331 samples and incorporates spatial cross-validation with spatial blocking, plus time-series remote sensing for training periods.","Aberystwyth University  \nGlobal mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning  \nMaxwell, Tania L.; Hengl, Tomislav; Parente, Leandro L.; Minarik, Robert; Worthington, Thomas A.; Bunting, Pete; Smart, Lindsey S. ; Spalding, Mark D. ; Landis, Emily  \nPublished in: Data in Brief  \nDOI:  \n10.1016/j.dib.2023.109621 10.5281/zenodo.7729491  \nPublication date:  \n2023  \nCitation for published version (APA):  \nMaxwell, T. L. , Hengl, T. , Parente, L. L. , Minarik, R. , Worthington, T. A. , Bunting, P. , Smart, L. S. , Spalding, M. D. , & Landis, E. (2023) . Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning. Data in Brief, 50 , Article 109621. [https://doi.org/10.1016/j.dib.2023.109621](https://doi.org/10.1016/j.dib.2023.109621) , [https://doi.org/10.5281/zenodo.7729491](https://doi.org/10.5281/zenodo.7729491)  \nDocument License  \nCC BY  \nGeneral rights  \nCopyright and moral rights for the publications made accessible in the Aberystwyth Research Portal (the Institutional Repository) are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the Aberystwyth Research Portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the Aberystwyth Research Portal  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \ntel: +44 1970 62 2400  \nemail: [is@aber.ac.uk](is@aber.ac.uk)  \nDownload date: 05. Aug. 2026  \nData in Brief 50 (2023) 109621  \nContents lists available at ScienceDirect  \nData in Brief  \njournal [homepage:](homepage: www.elsevier.com/locate/dib)[ www.elsevier.com/locate/dib](homepage: www.elsevier.com/locate/dib)  \nData Article  \nGlobal mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning  \nTania L. Maxwell a, 1,∗, Tomislav Henglb, 1,∗, Leandro L. Parenteb, Robert Minarikc, Thomas A. Worthington a, Pete Bunting d, Lindsey S. Smart e, Mark D. Spalding a,f, Emily Landis e  \na Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge, UK bEnvirometriX Ltd, Wageningen 6708 PW, the Netherlands  \nc OpenGeoHub foundation, Wageningen 6708 PW, the Netherlands  \nd Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, SY23 3DB, UK e The Nature Conservancy, Arlington, VA, USA  \nf The Nature Conservancy, Strada delle Tolfe, 14, Siena, 53100, Italy  \na r t i c l e i n f o  \nArticle history:  \nReceived 24 May 2023  \nRevised 26 July 2023  \nAccepted 19 September 2023  \nAvailable online 26 September 2023  \nDataset link: Global mangrove soil carbon data set at 30 m resolution for year 2020 (0-100 cm) (Original data)  \nKeywords:  \nBlue carbon Carbon sequestration Coastal ecosystem Spatial modelling Mangroves  \n∗ Corresponding authors.  \na b s t r a c t  \nThis dataset presents global soil organic carbon stocks in mangrove forests at 30 m resolution, predicted for 2020. We used spatiotemporal ensemble machine learning to produce predictions of soil organic carbon content and bulk density (BD) to 1 m soil depth, which were then aggregated to calculate soil organic carbon stocks. This was done by using training data points of both SOC (%) and BD in mangroves from a global dataset and from recently published studies, and globally consistent predictive covariate layers. A total of 10,331 soil samples were validated to have SOC (%) measurements and were us","cbCaihAWc9xaUXt2","https://ap.wps.com/l/cbCaihAWc9xaUXt2","pdf",1639747,1,11,"English","en",105,"# Dataset overview\n## Study inputs and training data\n## Prediction method and aggregation\n## Validation and uncertainty representation\n## Data access and downloadable products","[{\"question\":\"What does this dataset provide and for which year?\",\"answer\":\"It provides global soil organic carbon stocks in mangrove forests predicted for 2020, at 30 m resolution.\"},{\"question\":\"How were soil organic carbon stocks calculated?\",\"answer\":\"The study predicts soil organic carbon content and bulk density to a 1 m soil depth, then aggregates those predictions to compute soil organic carbon stocks.\"},{\"question\":\"What data and methods were used for modeling and validation?\",\"answer\":\"Training combines SOC (%) and bulk density measurements from global mangrove data and recent studies with globally consistent covariate layers, using spatiotemporal ensemble machine learning and spatial cross-validation via spatial blocking.\"}]","Global mangrove soil organic carbon stocks dataset - 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