{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T20:09:22Z","timestamp":1775678962343,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Commonwealth Department of Industry, Science, Energy and Resources","award":["SCICDD00002"],"award-info":[{"award-number":["SCICDD00002"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Understanding and monitoring soil organic carbon (SOC) stocks is crucial for ecosystem carbon cycling, services, and addressing global environmental challenges. This study employs the BERTopic model and bibliometric trend analysis exploration to comprehensively analyze global SOC estimates. BERTopic, a topic modeling technique based on BERT (bidirectional encoder representatives from transformers), integrates recent advances in natural language processing. The research analyzed 1761 papers on SOC and remote sensing (RS), in addition to 490 related papers on machine learning (ML) techniques. BERTopic modeling identified nine research themes for SOC estimation using RS, emphasizing spectral prediction models, carbon cycle dynamics, and agricultural impacts on SOC. In contrast, for the literature on RS and ML it identified five thematic clusters: spatial forestry analysis, hyperspectral soil analysis, agricultural deep learning, the multitemporal imaging of farmland SOC, and RS platforms (Sentinel-2 and synthetic aperture radar, SAR). From 1991 to 2023, research on SOC estimation using RS and ML has evolved from basic mapping to topics like carbon sequestration and modeling with Sentinel-2A and big data. In summary, this study traces the historical growth and thematic evolution of SOC research, identifying synergies between RS and ML and focusing on SOC estimation with advanced ML techniques. These findings are critical to global ecosystem SOC assessments and environmental policy formulation.<\/jats:p>","DOI":"10.3390\/rs16173168","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T09:26:51Z","timestamp":1724750811000},"page":"3168","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Soil Organic Carbon Estimation via Remote Sensing and Machine Learning Techniques: Global Topic Modeling and Research Trend Exploration"],"prefix":"10.3390","volume":"16","author":[{"given":"Tong","family":"Li","sequence":"first","affiliation":[{"name":"School of Agriculture and Food Sustainability, The University of Queensland, St Lucia, QLD 4072, Australia"},{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD 4111, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4977-7577","authenticated-orcid":false,"given":"Lizhen","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yu","family":"Wu","sequence":"additional","affiliation":[{"name":"Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5257-7755","authenticated-orcid":false,"given":"Timothy I.","family":"McLaren","sequence":"additional","affiliation":[{"name":"School of Agriculture and Food Sustainability, The University of Queensland, St Lucia, QLD 4072, Australia"}]},{"given":"Anquan","family":"Xia","sequence":"additional","affiliation":[{"name":"Development and Research Center (National Geological Archives of China), China Geological Survey, Beijing 100037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4849-775X","authenticated-orcid":false,"given":"Rajiv","family":"Pandey","sequence":"additional","affiliation":[{"name":"Indian Council of Forestry Research & Education, Dehradun 248006, India"}]},{"given":"Hongdou","family":"Liu","sequence":"additional","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD 4111, Australia"}]},{"given":"Weijin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Agriculture and Food Sustainability, The University of Queensland, St Lucia, QLD 4072, Australia"}]},{"given":"Zhihong","family":"Xu","sequence":"additional","affiliation":[{"name":"Centre for Planetary Health and Food Security, School of Environment and Science, Griffith University, Nathan, Brisbane, QLD 4111, Australia"}]},{"given":"Xiufang","family":"Song","sequence":"additional","affiliation":[{"name":"National Science Library, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2381-9601","authenticated-orcid":false,"given":"Ram C.","family":"Dalal","sequence":"additional","affiliation":[{"name":"School of Agriculture and Food Sustainability, The University of Queensland, St Lucia, QLD 4072, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6357-3146","authenticated-orcid":false,"given":"Yash P.","family":"Dang","sequence":"additional","affiliation":[{"name":"School of Agriculture and Food Sustainability, The University of Queensland, St Lucia, QLD 4072, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.jenvman.2014.05.017","article-title":"Dynamics and climate change mitigation potential of soil organic carbon sequestration","volume":"144","author":"Sommer","year":"2014","journal-title":"J. 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