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Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Review

2025Journal article Open access
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datacite.subject.fosCiências Agrárias::Outras Ciências Agrárias
datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosEngenharia e Tecnologia::Biotecnologia Ambiental
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Naturais::Ciências da Terra e do Ambiente
datacite.subject.sdg02:Erradicar a Fome
datacite.subject.sdg13:Ação Climática
dc.contributor.authorLima, Arthur A. J.
dc.contributor.authorLopes, Júlio Castro
dc.contributor.authorLopes, Rui Pedro
dc.contributor.authorFigueiredo, Tomás d'Aquino
dc.contributor.authorVidal-Vásquez, Eva
dc.contributor.authorHernandez Hernandez, Zulimar
dc.date.accessioned2025-04-09T15:23:56Z
dc.date.available2025-04-09T15:23:56Z
dc.date.issued2025
dc.description.abstractIn the current global change scenario, valuable tools for improving soils and increasing both agricultural productivity and food security, together with effective actions to mitigate the impacts of ongoing climate change trends, are priority issues. Soil Organic Carbon (SOC) acts on these two topics, as C is a core element of soil organic matter, an essential driver of soil fertility, and becomes problematic when disposed of in the atmosphere in its gaseous form. Laboratory methods to measure SOC are expensive and time-consuming. This Systematic Literature Review (SLR) aims to identify techniques and alternative ways to estimate SOC using Remote-Sensing (RS) spectral data and computer tools to process this database. This SLR was conducted using Systematic Review and MetaAnalysis (PRISMA) methodology, highlighting the use of Deep Learning (DL), traditional neural networks, and other machine-learning models, and the input data were used to estimate SOC. The SLR concludes that Sentinel satellites, particularly Sentinel-2, were frequently used. Despite limited datasets, DL models demonstrated robust performance as assessed by R2 and RMSE. Key input data, such as vegetation indices (e.g., NDVI, SAVI, EVI) and digital elevation models, were consistently correlated with SOC predictions. These findings underscore the potential of combining RS and advanced artificial-intelligence techniques for efficient and scalable SOC monitoring.por
dc.description.sponsorshipThe authors would like to thank the Foundation for Science and Technology (FCT, Portugal) and the national funds FCT/MCTES (PIDDAC) for the financial support to CIMO (UIDB/00690/2020 and UIDP/00690/2020), CeDRI (UIDB/05757/2020 and UIDP/05757/2020), and SusTEC (LA/P/0007/2020). The authors would also like to thank the national funding from the FCT, Foundation for Science and Technology, regarding the doctoral scholarships 2022.14010.BD to Arthur Aparecido Janoni Lima and PRT/BD/154594/2023 to Júlio Castro Lopes. The authors would also like to thank the Financial Mechanism of the European Economic Area (EEA) 2014-2021, “Programa Ambiente, Alterações Climáticas e Economia de Baixo Carbono”, “Programa Ambiente”, for financial support for the project “11_CALL#5 – Soluções inovadoras de base natural para restau- ração de serviços dos ecossistemas em áreas degradadas por grande incêndio de incêndio Picões, Portugal_SOILING”.
dc.identifier.citationLima, Arthur A. J.; Lopes, Júlio Castro; Lopes, Rui Pedro:; Figueiredo, Tomás d'Aquino; Vidal-Vásquez, Eva; Hernandez Hernandez, Zulimar (2025). Soil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Review. Remote Sensing. eISSN 2072-4292. 17:5, p. 1-27
dc.identifier.doi10.3390/rs17050882
dc.identifier.eissn2072-4292
dc.identifier.urihttp://hdl.handle.net/10198/34417
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI
dc.relationMountain Research Center
dc.relationMountain Research Center
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation154594/2023
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDeep learning
dc.subjectNeural network
dc.subjectMachine learning
dc.subjectSoil organic carbon
dc.subjectSatellite images
dc.titleSoil Organic Carbon Assessment Using Remote-Sensing Data and Machine Learning: A Systematic Literature Reviewpor
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleMountain Research Center
oaire.awardTitleMountain Research Center
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00690%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F00690%2F2020/PT
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oaire.citation.endPage27
oaire.citation.issue5
oaire.citation.startPage1
oaire.citation.titleRemote Sensing
oaire.citation.volume17
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oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameLima
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person.givenNameArthur A. J.
person.givenNameJúlio Castro
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person.givenNameTomás d'Aquino
person.givenNameZulimar
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person.identifier.ciencia-id5815-8F1B-70F4
person.identifier.orcid0000-0002-5636-022X
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project.funder.identifierhttp://doi.org/10.13039/501100001871
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project.funder.nameFundação para a Ciência e a Tecnologia
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