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

## TL;DR

This paper reviews current and emerging methods for measuring carbon in terrestrial ecosystems to better understand and address climate change.

## Contribution

The paper provides a comprehensive review of carbon quantification methods and highlights the growing role of remote sensing and machine learning.

## Key findings

- The Walkley-Black method and Elemental Analysis are widely used for soil carbon measurement.
- Remote sensing and machine learning are increasingly used alongside traditional methods for carbon modeling.
- Forest and agricultural areas are the most studied, with the U.S. and China leading in research output.

## Abstract

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.

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.

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.