| Summary: | Remote sensing of snow is a method to measure snow cover characteristics without direct physical contact with the target from airborne or space-borne platforms. Reliable estimates of snow cover extent and snow properties are vital for several applications including climate change research and weather and hydrological forecasting. Optical remote sensing methods detect the extent of snow cover based on its high reflectivity compared to other natural surfaces. A universal challenge for snow cover mapping is the high spatiotemporal variability of snow properties and heterogeneous landscapes such as the boreal forest biome. The optical satellite sensor’s footprint may extend from tens of meters to a kilometer; the signal measured by the sensor can simultaneously emerge from several target categories within individual satellite pixels. By use of spectral unmixing or inverse model-based methods, the fractional snow cover (FSC) within the satellite image pixel can be resolved from the recorded electromagnetic signal. However, these algorithms require knowledge of the spectral reflectance properties of the targets present within the satellite scene and the accuracy of snow cover maps is dependent on the feasibility of these spectral model parameters. On the other hand, abrupt changes in land cover types with large differences in their snow properties may be located within a single satellite image pixel and complicate the interpretation of the observations. Ground-based in-situ observations can be used to validate the snow parameters derived by indirect methods, but these data are affected by the chosen sampling. This doctoral thesis analyses laboratory-based spectral reflectance information on several boreal snow types for the purpose of the more accurate reflectance representation of snow in mapping method used for the detection of fractional snow cover. Multi-scale reflectance observations representing boreal spectral endmembers typically used in optical mapping of snow cover, are exploited in the thesis. In addition, ...
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