Optimizing hyperspectral imaging based CO2 detection by radiative transfer modeling to mitigate surface albedo and aerosol impacts

Hyperspectral imaging offers cost-effective spatial mapping of CO2 concentrations across expansive areas. Yet, these estimations can be skewed by surface albedo and aerosol scattering. This study utilizes radiative transfer modeling to counteract the distortions arising from aerosols and surface albedo. By generating a synthetic hyperspectral dataset using realistic libraries, this approach was validated. The outcomes highlight a marked enhancement in CO2 detection capabilities, indicating the potential for hyperspectral data to detect CO2 over vast regions. However, the influence of albedo and aerosol scattering must be rectified for reliable estimations.