Coniferous forests are important in the regulation of the Earth’s climate and thus continuous monitoring of these ecosystems is crucial to better understand potential responses to climate change. Optical remote sensing (RS) provides powerful methods for the estimation of essential climate variables and for global forest monitoring.
However, coniferous forests represent challenging targets for RS methods, mainly due to structural features specific for coniferous trees (e.g. narrow needle leaves, shoot clumping) whose effects on the RS signal are not yet known or not yet fully understood.
Recognizing the need for a better adaptation of RS methods to such spatially heterogeneous and structurally complex canopies, this thesis contributes to improving the interpretation of the remotely sensed optical signal reflected from coniferous stands and explores the application of approaches that simplify the way the structural complexity of such an environment is tackled when using canopy-level physical modelling.