Advanced Carbon Monitoring in Agriculture Using Remote Sensing and Machine Learning Techniques

Innovative strategies are imperative to address climate change, driven by the escalating global carbon emissions. Agriculture, as both a significant source and potential sink of greenhouse gases (GHGs), presents two critical avenues: reducing emissions and sequestering carbon. This paper utilizes advanced technologies, including big data, remote sensing, and machine learning, to improve the measurement and monitoring of carbon fluxes and stocks within agricultural systems. While traditional techniques, such as soil sampling, offer accuracy, they are timeconsuming and inadequate for large-scale applications. This paper proposes a data-driven approach that integrates multiple data sources—such as soil characteristics, satellite imagery, and climate data—to develop real-time, scalable tools for carbon monitoring. These technologies facilitate the precise estimation of carbon footprints, emissions, and sequestration potential across agricultural landscapes. The paper introduces a cloud-based system that incorporates machine learning algorithms for landuse analysis, anomaly detection, and predictive modeling. Through an intuitive dashboard, stakeholders can visualize carbon data and implement sustainable practices, such as crop rotation and conservation tillage, to optimize carbon management. The outcomes of this paper include a comprehensive analysis of agricultural carbon dynamics, forecasting tools for mitigation strategies, and actionable insights to support the realization of global climate goals, including those outlined in the Paris Agreement. This work advances sustainable agricultural practices and enhances climate resilience by addressing pivotal challenges in carbon monitoring.