Multi-sensor/multi-temporal Analysis of Envisat Data for Snow Monitoring

The ENVISAT satellite with its many sensors opens for new, interesting approaches of combined multi-sensor, multi-temporal monitoring. In this study, we have focused on monitoring of snow parameters in the snowmelt seasons of 2003 and 2004 (April-June) in South Norway. The sensors used in this study are ENVISAT MERIS and ASAR and Terra MODIS. The study is motivated by operational prospects for snow hydrology, meteorology and climate monitoring. We have developed a generic multi-sensor/multitemporal approach for monitoring of snow cover area (SCA), snow surface wetness (SSW) and snowmelt onset time (SOT). The objective is to analyse, on a daily basis, a time series of optical and SAR data together producing sensor-independent products. We have defined raster products for each variable and developed a prototype production line. The production line automatically performs data retrieval, pre-processing, parameter retrieval, data aggregation and product generation. A few algorithms for multi-sensor/time-series processing have been developed and are compared. One approach is to analyse 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 analysed. The confidence algorithm is able to take into account, e.g., information about observation geometry, probability of clouds, prior information about snow state and reliability of the classification. The time series of day products are then combined into a multi-sensor/multi-temporal product. The combination of products is done on a pixelby-pixel basis and controlled by each individual pixel’s confidence and a decay function of time for the product. The “multi-product” should then represent the most likely status of the monitored variable.