Subpixel mapping of snow cover in forests by optical remote sensing

Abstract

Forest represents a challenging problem for snow-cover mapping by optical satellite remote sensing. To investigate reflectance variability and to improve the mapping of snow in forested areas, a method for subpixel mapping of snow cover in forests (SnowFrac) has been developed. The SnowFrac method is based on linear spectral mixing modelling of snow, trees and snow-free ground. The focus has been on developing a physically based reflectance model which uses a forest-cover map as prior information. The method was tested in flat terrain covered by spruce, pine and birch forests, close to the Jotunheimen region of South Norway. Experiments were carried out using a completely snow-covered Landsat Thematic Mapper (TM) scene, aerial photos and in situ reflectance measurements. A detailed forest model was photogrammetrically derived from the aerial photos. Modelled and observed TM reflectances were compared. In the given situation, the results demonstrate that snow and individual tree species, in addition to cast shadows on the snow surface from single trees, are the most influencing factors on visible and near-infrared reflectance. Modelling of diffuse radiation reduced by surrounding trees slightly improve the results, indicating that this effect is less important. The best results are obtained for pine forest and mixed pine and birch forest. Future work will focus on deriving a simplified reflectance model suitable for operational snow-cover mapping in forests.