Time-series fusion of optical and SAR data for snow cover area mapping

The first experiments trying to combine SAR and optical data for snow cover area mapping took place about 15 years ago. So far, no published approach has worked very well due to the very different characteristics of the two sensor types. While the SAR signal is dominated by the dielectric properties of the medium measured and its geometrical properties at the scale of the wavelength, the optical sensor is sensitive to reflection, absorption and scattering properties of the snow grains in the top level of the snow pack. Hence, the sensors are measuring entirely different physical phenomena. Any straightforward approach to combine the signals for the retrieval of the snow covered area has therefore been, at best, moderately successful.

We have developed a new approach based on modelling and assimilation. A time series of data is processed, as is typical for a monitoring situation (e.g., relevant for snow hydrology and numerical weather forecasting). SAR data are acquired a few times a week (2-3 times), and optical data is acquired daily but is limited by clouds. The algorithm we use analyse the current time series to predict the current Fractional Snow Cover (FSC) per pixel. A set of snow states is defined. Each snow state has a corresponding reflectance model for optical data and a backscatter model for SAR data. The snow states defined are ‘dry snow, full snow cover’, ‘wet snow, full snow cover’, ‘partial snow cover’ and ‘snow-free ground’. A Hidden Markov Model (HMM) has been established to compute the likelihood of a transition from one state to another, given the current observations.

The backscatter and reflectance observations are processed by an algorithm comparing them to their respective models given by the current state. Based on this, the most likely FSC is calculated for each pixel being analysed. Each pixel is processed independently and might therefore be in different stages (which is typical for alpine terrain).

The approach has been tested in a mountainous region in South Norway combining Terra MODIS and ENVISAT ASAR from five snowmelt seasons (2003-2007). The results demonstrates that it is possible to obtain consistent results of high accuracy (FSC error < 10%) from the combination of the two sensors.