Hyperspectral remote sensing and machine learning for greenhouse gas flux prediction in a semi-arid savannah: key spectral features and their biophysical interpretations
Dryland ecosystems are important yet understudied sinks of carbon dioxide (CO₂) and methane (CH₄), and sources of nitrous oxide (N₂O). Hence, monitoring greenhouse gas (GHG) fluxes and their responses to climate change in these systems is increasingly important. This thesis investigates the potential of hyperspectral reflectance data (between 390 and 1750nm) to predict greenhouse gas fluxes during
