Multi-sensor and time-series approaches for monitoring of snow parameters

Frequent mapping of snow parameters, like snow cover area (SCA) and snow surface wetness (SSW), is important for applications in hydrology, meteorology and climatology. In this study, we have developed a few general multi-sensor/time-series approaches for such monitoring. The objective is to analyze, on a daily basis, a time series of optical and Synthetic Aperture Radar (SAR) data together producing sensor-independent products. A few algorithms for multi-sensor/time-series processing have been developed and are compared in this study. A typical approach is to analyze each image individually and combine them into a day product. How each image contributes to the day products is controlled by a pixel-by-pixel confidence value that is computed for each image analyzed. The confidence algorithm may take into account information about the local observation angle/IFOV size, probability of clouds, prior information about snow state, etc. The time series of day products are then combined into a multi-sensor/multi-temporal product. The combination of products is done on a pixel-by-pixel basis and controlled by each individual product/pixel's confidence and a decay function of time. The "multi product" should then represent the most likely status of the monitored variable. The sensors applied in this algorithm study are MODIS for optical data and ENVISAT ASAR for SAR data. The study area is South Norway, and the study focuses on the snowmelt seasons in 2003 and 2004.