[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128511-en":3,"doc-seo-128511-105":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},128511,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning","This dataset provides global mangrove forest soil organic carbon stocks predicted for 2020 at 30 m resolution. Spatiotemporal ensemble machine learning is used to estimate soil organic carbon content and bulk density to a 1 m depth, which are aggregated to produce soil organic carbon stocks. Training relies on validated mangrove measurements of SOC (%) and bulk density from global and published sources, together with globally consistent covariate layers. Model evaluation includes spatial cross-validation, and 2020 maps report mean predictions with prediction intervals to quantify uncertainty. Downloadable outputs include raster products and cloud-optimized GeoTIFFs under a CC-BY license.","Data 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 used for predictive soil mapping. We used time-series remote sensing data specific to time periods when the training data were sampled, as well as long-term (static) layers to train an ensemble of machine learning model. Ensemble models were used to improve  \nE-mail addresses: [taniamaxwell7@gmail.com](taniamaxwell7@gmail.com) (T.L. Maxwell), [tom.hengl@envirometrix.net](tom.hengl@envirometrix.net) (T. Hengl) .  \nSocial media:  @tania_maxwell7 (T.L. Maxwell),  @opengeohub (T. Hengl)  \n1 Shared first-authorship.  \n[https://doi.org/10.1016/j.dib.2023.109621](https://doi.org/10.1016/j.dib.2023.109621)  \n2352-3409/© 2023 Published by Elsevier Inc. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))  \n2 T.L. Maxwell, T. Hengl and L.L. Parente et al. /Data in Brief 50 (2023) 109621  \nperformance, robustness and unbiasedness as opposed to just using one learner. In addition, we performed spatial cross-validation by using spatial blocking of training data points to assess model performance. We predicted SOC stocks for the 2020 time period and applied them to a 2020 mangrove extent map, presenting both mean predictions and prediction intervals to represent the uncertainty around our predictions. Predictions are available for download under CC-BY license from 10.5281/zenodo.7729491 and also as Cloud-Optimized GeoTIFFs (global mosaics).  \n© 2023 Published by Elsevier Inc.  \nThis is an open access article under the CC BY license  \n([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))  \nSpecifications Table  \nSubject  \nSpecific subject area Type of data  \nHow the data were acquired  \nData format  \nDescription of data collection  \nData source location  \nData accessibility  \nAgricultural Sciences (Soil Science), Environmental Science, Computer Science (Computer Science Applications)  \nSoil carbon in mangroves, remote sensing signal processing, spatio","cbCaijxuyuelSDc3","https://ap.wps.com/l/cbCaijxuyuelSDc3","pdf",1567035,1,10,"English","en",105,"# Dataset overview\n## Spatial resolution and target year\n## Modeling approach and predictors\n## Training data and validation\n# Data processing and outputs\n## Depth estimation and aggregation\n## Spatial cross-validation and uncertainty\n## Download options and formats","[{\"question\":\"What does the dataset provide for mangrove forests?\",\"answer\":\"It provides global soil organic carbon stocks predicted for 2020 in mangrove forests, mapped at 30 m resolution.\"},{\"question\":\"How are soil organic carbon stocks predicted?\",\"answer\":\"Spatiotemporal ensemble machine learning predicts SOC content and bulk density to 1 m depth, then aggregates these estimates to calculate SOC stocks.\"},{\"question\":\"How is uncertainty handled in the 2020 predictions?\",\"answer\":\"Predicted soil carbon stocks are mapped for 2020 with mean predictions and prediction intervals, reflecting uncertainty around model outputs.\"}]","Global mangrove soil organic carbon stocks dataset at 30 m resolution for the year 2020 based on spatiotemporal predictive machine learning | 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does the dataset provide for mangrove forests?","Question",{"text":76,"@type":77},"It provides global soil organic carbon stocks predicted for 2020 in mangrove forests, mapped at 30 m resolution.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How are soil organic carbon stocks predicted?",{"text":81,"@type":77},"Spatiotemporal ensemble machine learning predicts SOC content and bulk density to 1 m depth, then aggregates these estimates to calculate SOC stocks.",{"name":83,"@type":74,"acceptedAnswer":84},"How is uncertainty handled in the 2020 predictions?",{"text":85,"@type":77},"Predicted soil carbon stocks are mapped for 2020 with mean predictions and prediction intervals, reflecting uncertainty around model 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