A high-precision dynamic and adaptive BRDF and fractional snow-cover monitoring algorithm

The aim of this work is to develop a very precise approach for monitoring the changing spectral snow bidirectional reflectance distribution function (BRDF) and fractional snow cover area (FSCA). Intended use is from local monitoring at the basin level to global monitoring. The concept assumes a day-to-day monitoring of the snow from winter conditions until all snow has melted. The developing reflectance spectrum of the snow is both observed by satellite sensors, giving samples of the BRDF, and modeled by including an empirical snow metamorphosis model and a snow impurity model giving full BRDF. Snow grain size (SGS) and snow surface impurities (SSI) are measured by indices and used to parameterize the models. The predicted snow spectrum and the local bare ground spectrum are applied in a linear spectral unmixing algorithm to estimate the area fraction of snow (SCA) and bare ground. By using predicted spectra for the current situation and not a pool of all possible spectra, the result will in general be more accurate and reliable. The area fractions and the predicted albedos of snow and bare ground are combined in order to predict the BRDF of the observed pixel.