[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120787-en":3,"doc-seo-120787-105":30,"detail-sidebar-cat-0-en-105":91},{"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},120787,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Advances in Earth Observation and Machine Learning for Quantifying Blue Carbon - Review","Blue carbon ecosystems (mangroves, seagrasses, and saltmarshes) are highly productive coastal habitats and among the most carbon-dense ecosystems, supporting both climate mitigation and adaptation. Quantifying blue carbon stocks and dynamics at large scales using remote sensing is difficult due to cloud and sensor limitations in multispectral data, SAR speckle noise, and coastal environmental factors. Advances in multispectral/SAR and LiDAR imagery, UAV platforms, and machine learning enable multi-spectral, multi-temporal estimation, yet challenges persist in atmospheric correction, water penetration, sample scarcity, and SAR backscattering. This review surveys Earth Observation data and deep learning methods for above- and below-ground carbon and soil carbon, highlighting limitations and future directions including data fusion, optimization, and open-source workflows.","Earth-Science Reviews 243 (2023) 104501  \nContents lists available at ScienceDirect  \nEarth-Science Reviews  \njournal [homepage: www.elsevier.com/locate/earscirev](homepage: www.elsevier.com/locate/earscirev)  \n| Advances in Earth observation and machine learning for quantifying blue carbon |  |  |  |\n| --- | --- | --- | --- |\n| Tien Dat Phama, *, Nam Thang Hab, Neil Saintilana, Andrew Skidmorea, c, Duong Cao Phand, Nga Nhu Lee, Hung Luu Vietf, Wataru Takeuchi g, Daniel A. Friessh\u003Cbr>a School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia\u003Cbr>b Faculty of Fisheries, University of Agriculture and Forestry, Hue University, Hue 530000, Viet Nam\u003Cbr>c Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the Netherlands d Centre for AI and Applied Data Analytics, School of Computer Science, University of Dublin, Belfield, Dublin 4, Ireland\u003Cbr>e Department of Marine Mechanics and Environment, Institute of Mechanics, Vietnam Academy of Science and Technology (VAST), Ba Dinh, Hanoi 100000, Viet Nam f Centre of Multidisciplinary Integrated Technologies for Field Monitoring (FIMO), the University of Engineering and Technology, Vietnam National University (VNU), 144 Xuan Thuy, Cau Giay, Hanoi 100000, Viet Nam\u003Cbr>g Institute of Industrial Science, the University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan h Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA 70118, USA |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Blue Carbon Mangrove Seagrass Saltmarsh Machine learning\u003Cbr>Earth observation Deep learning Optimisation |  | Blue carbon ecosystems (mangroves, seagrasses and saltmarshes) are highly productive coastal habitats, and are considered some of the most carbon-dense ecosystems on Earth. They are an important nature-based solution for both climate change mitigation and adaptation. Quantifying blue carbon stocks and assessing their dynamics at large scales through remote sensing remains challenging due to difficulties of cloud coverage, spectral, spatial and temporal limitations of multispectral sensors and speckle noise of synthetic aperture radar (SAR). Recent advances in airborne and space-borne multispectral and SAR imagery and Light Detection and Ranging (LiDAR) data, sensor platforms such as unmanned aerial vehicles (UAVs), combined with novel machine learning techniques have offered different users with a wide-range of spectral, spatial, and multi-temporal information for quantifying blue carbon from space. However, a large number of challenges are posed by various traits such as atmospheric correction, water penetration, and water column transparency issues in coastal environments, the multi-dimensionality and size of the multispectral and LiDAR data, the limitation of training samples, and backscattering mechanisms of SAR imagery in the acquisition process. As a result, existing methodologies face major difficulties in accurately estimating blue carbon stocks using these datasets. In this context, emerging and innovative machine learning and artificial intelligence methodologies are often required for robustness and reliability of blue carbon estimates, particularly those using open-source software for signal processing and regression tasks. This review provides an overview of Earth Observation data, machine learning and state-of-theart deep learning techniques that are currently being used to quantify above-ground carbon, below-ground carbon, and soil carbon stocks of mangroves, seagrasses and saltmarshes ecosystems. Some key limitations and future directions for the potential use of data fusion combined with advanced machine learning, deep learning, and metaheuristic optimisation techniques for quantifying blue carbon stocks are also highlighted. In summary, the quantification of blue carbon using remote sensing and machine learning approaches holds great potential in contributing to glob","cbCaitWk3847IiZF","https://ap.wps.com/l/cbCaitWk3847IiZF","pdf",5712397,1,19,"English","en",105,"# Introduction\n# Blue carbon ecosystems and carbon storage\n# Remote sensing challenges and data sources\n# Machine learning and deep learning approaches\n# Data fusion and optimisation directions\n# Future challenges and conclusions","[{\"question\":\"Why is quantifying blue carbon from remote sensing difficult?\",\"answer\":\"Remote sensing faces issues such as cloud and multispectral sensor limitations, SAR speckle noise, and coastal-specific problems including atmospheric correction, water penetration, and water column transparency.\"},{\"question\":\"Which Earth Observation data types are highlighted for blue carbon quantification?\",\"answer\":\"The text emphasizes multispectral and SAR imagery, LiDAR data, and platforms such as UAVs, enabling access to spectral, spatial, and multi-temporal information.\"},{\"question\":\"What do recent machine learning and deep learning advances contribute?\",\"answer\":\"They improve robustness and reliability of blue carbon estimates by learning from complex multi-dimensional datasets, including approaches for signal processing and regression tasks.\"}]","Advances in Earth Observation and Machine Learning for Quantifying Blue Carbon - Review | PDF",1785732034,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"advances-in-earth-observation-and-machine-learning-for-quantifying-blue-carbon-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/advances-in-earth-observation-and-machine-learning-for-quantifying-blue-carbon-review/120787/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is quantifying blue carbon from remote sensing difficult?","Question",{"text":75,"@type":76},"Remote sensing faces issues such as cloud and multispectral sensor limitations, SAR speckle noise, and coastal-specific problems including atmospheric correction, water penetration, and water column transparency.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which Earth Observation data types are highlighted for blue carbon quantification?",{"text":80,"@type":76},"The text emphasizes multispectral and SAR imagery, LiDAR data, and platforms such as UAVs, enabling access to spectral, spatial, and multi-temporal information.",{"name":82,"@type":73,"acceptedAnswer":83},"What do recent machine learning and deep learning advances contribute?",{"text":84,"@type":76},"They improve robustness and reliability of blue carbon estimates by learning from complex multi-dimensional datasets, including approaches for signal processing and regression tasks.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},"General","general"]