Quantifying terrestrial carbon in the context of climate change: a review of common and novel technologies and methods

• Published in: Carbon balance and management Vol. 20; no. 1; pp. 25 - 19
• Main Authors: Gameiro, Samuel, Ferreira, Manuel Eduardo, Ruiz, Luis Fernando Chimelo, Galford, Gillian L., Zeraatpisheh, Mojtaba, Nascimento, Victor Fernandez, Collevatti, Rosane Garcia
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 07.08.2025; Springer Nature B.V; BMC
• Subjects: Analytical techniques; Bibliometrics; Biomass; Carbon; Carbon cycle; Carbon dioxide; Carbon sequestration; Carbon stock; Climate change; Climate change mitigation; Earth and Environmental Science; Ecosystems; Emissions; Environment; Environmental Management; Forestry; Grasslands; Greenhouse gases; Keywords; Land use; Landsat; Lidar; Machine learning; Mangroves; Methods; PRISMA; Regression analysis; Remote sensing; Review; Satellite imagery; Satellites; Scientometrics; Spectrum analysis; Vegetation; Analytical techniques; Carbon stock; PRISMA; Remote sensing
• DOI: 10.1186/s13021-025-00316-1
• ISSN: 1750-0680

Background Understanding carbon dynamics in Earth’s ecosystem is necessary for mitigating climate change. With recent advancements in technologies, it is important to understand both how carbon quantification in soil and vegetation is measured and how it can be improved. Therefore, this study conducted a bibliometric and bibliographic review of the most common carbon quantification methodologies. Results Among the most widely used techniques, the Walkley-Black method and Elemental Analysis stand out for measuring below-ground carbon, while forest inventories are prominent for assessing above-ground carbon. Additionally, we found that the United States and China have the largest number of publications on this topic, with forest and agricultural areas being the most studied, followed by grasslands and mangroves. However, it should be noted that despite being indirect techniques, remote sensing, regression analysis, and machine learning have increasingly been used to generate geo-environmental carbon models for various areas. Landsat satellite images are the most widely used in remote sensing, followed by LiDAR digital models. Conclusions These results demonstrate that while new technologies do yet not replace analytical techniques, they are valuable allies working in conjunction with the current carbon quantification process.