Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy

SYNOPSIS

This paper explores the utilization of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) in advancing Soil Organic Carbon (SOC) estimation. It presents compelling results, indicating a strong correlation (R² = 0.83) between MIR-based predictions and laboratory measurements. Additionally, it discusses the commercialization prospects of these technologies in Australia, with implications for sustainable agriculture and carbon markets.