Servicio de Información Comunitario sobre Investigación y Desarrollo - CORDIS

Multi-sensor time-series snow cover algorithms

Frequent mapping of snow parameters, like snow cover area (SCA), is important for applications in hydrology and climatology. The objective is to analyse on a daily basis a time series of optical and Synthetic Aperture Radar (SAR) data together producing sensor-independent products. A multi-sensor SCA product has been defined. A prototype production line has been developed to automatically perform data retrieval, pre-processing, parameter retrieval and product generation.

The approach is to analyse each satellite image individually and then first to combine them into a day product. How each image contributes to the day product is controlled by a pixel-by-pixel confidence value that is computed for each image analysed. 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” is then to represent the most likely status of the SCA.

Reported by

Norut IT
P.O. 6434 Forskningsparken
9294 Tromsø
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