| Summary: | This thesis focuses on the determination of fractional snow cover (FSC) from optical data provided by satellite instruments. It describes the method development, starting from a simple regionally applicable linear interpolation method and ending at a globally applicable, semi-empirical modeling approach. The development work was motivated by the need for an easily implementable and feasible snow mapping method that could provide reliable information particularly for forested areas. The contribution of the work to the optical remote sensing of snow is mainly associated with accounting for boreal forest canopy effect to the observed reflectance, thus facilitating accurate fractional snow retrievals also for ground beneath the tree canopies. The first proposed approach was based on a linear interpolation technique, which relies on a priori known reference reflectances at a) full snow cover and b) snow-free conditions for each calculation unit-area. An important novelty in the methodology was the utilization of a forest sparseness index determined from AVHRR reflectance data acquired at full dry snow cover conditions. This index was employed to describe the similarity between different unit-areas. In practice, the index was used to determine the reference reflectances for such unit-areas for which the reflectance level could not be determined otherwise, e.g. due to frequent cloud cover. This approach was found to be feasible for Finnish drainage basins characterized by fragmented landscape with moderate canopies. Using a more physical approach instead of linear interpolation would allow the model parameterization using physical quantities (reflectances), and would therefore leave space for further model developments based on measuring and/or modeling of these quantities. The semi-empirical reflectance model-based method SCAmod originates from radiative transfer theory and describes the scene-level reflectance as a mixture of three major constituents: opaque forest canopy, snow and snow-free ground, which are ...
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