{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ding Z"],"funding":["Commonwealth Department of Industry, Science, Energy and Resources","National Natural Science Foundation of China","Chinese Scholarship Council","Young Elite Scientists Sponsorship Program by CAST"],"pagination":["e04152"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12376622"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(31)"],"pubmed_abstract":["Measurement, monitoring, and prediction of soil organic carbon (SOC) are fundamental to supporting climate change mitigation efforts and promoting sustainable agricultural management practices. This review discusses recent advances in methodologies and technologies for SOC quantification, including remote sensing (RS), proximal soil sensing (PSS), artificial intelligence (AI) for SOC modelling (in particular, machine learning (ML) and deep learning (DL)), biogeochemical modelling, and data fusion. Integrating data from RS, PSS, and other sensors usually leads to good SOC predictions, provided it is supported by careful calibration, validation across diverse pedo-climatic and land management, and the use of data processing and modelling frameworks. We also found that the accuracy of AI-driv"],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Advancing Soil Organic Carbon Prediction: A Comprehensive Review of Technologies, AI, Process-Based and Hybrid Modelling Approaches."],"pmcid":["PMC12376622"],"funding_grant_id":["SCICDD000026","32301940","202310930003","2023QNRC001"],"pubmed_authors":["Zhou M","Yin X","Ferreira C","Madgett O","Liu K","Ciais P","Wang B","Wadoux AMJ","Smith P","Grunwald S","Roberts D","Liu Z","Karunaratne S","Duncan S","Shurpali N","Harrison MT","Ding Z"],"additional_accession":[]},"is_claimable":false,"name":"Advancing Soil Organic Carbon Prediction: A Comprehensive Review of Technologies, AI, Process-Based and Hybrid Modelling Approaches.","description":"Measurement, monitoring, and prediction of soil organic carbon (SOC) are fundamental to supporting climate change mitigation efforts and promoting sustainable agricultural management practices. This review discusses recent advances in methodologies and technologies for SOC quantification, including remote sensing (RS), proximal soil sensing (PSS), artificial intelligence (AI) for SOC modelling (in particular, machine learning (ML) and deep learning (DL)), biogeochemical modelling, and data fusion. Integrating data from RS, PSS, and other sensors usually leads to good SOC predictions, provided it is supported by careful calibration, validation across diverse pedo-climatic and land management, and the use of data processing and modelling frameworks. We also found that the accuracy of AI-driv","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-09T19:15:19.611Z","creation":"2026-04-08T01:09:54.153Z"},"accession":"S-EPMC12376622","cross_references":{"pubmed":["40557741"],"doi":["10.1002/advs.202504152"]}}