One of the major challenges in soil carbon sequestration is measuring it accurately and efficiently. Researchers at the Alliance of Bioversity International and CIAT are using AI to analyze remote sensing data and calculate soil carbon levels across large areas. This approach could make it easier for farmers to participate in carbon markets and receive carbon credits for their sustainable practices.
Measuring Soil Carbon with AI
Traditionally, measuring soil carbon has relied on labor-intensive field sampling and laboratory analysis, which is time-consuming and expensive. The researchers are exploring the use of AI and remote sensing data to overcome these challenges.
Remote Sensing Data Analysis
By analyzing satellite imagery and other remote sensing data, the researchers can extract information about the physical properties of soils, such as color, texture, and moisture content. This data can then be used to estimate the soil carbon levels across large areas.
Artificial Intelligence for Soil Carbon Mapping
The researchers are using machine learning algorithms to develop models that can accurately predict soil carbon levels based on the remote sensing data. These models can be trained on a relatively small number of field samples and then applied to map soil carbon across entire regions or countries.
Enabling Participation in Carbon Markets
The ability to accurately measure and map soil carbon levels using AI and remote sensing could be a game-changer for farmers who want to participate in carbon markets. By providing reliable data on the carbon sequestration potential of their land, farmers can demonstrate the impact of their sustainable practices and potentially earn carbon credits that they can sell.