Servizio Comunitario di Informazione in materia di Ricerca e Sviluppo - CORDIS

Optical algorithms for fractional snow cover area, snow temperature and snow wetness

The temporal development of the spectral reflectance of snow due to snow metamorphosis and variable contents of impurities, the anisotropic effects in the snow reflectance and the variable reflectance of the bare ground all make precise measurements of the fractional snow cover area (FSCA) challenging. In addition, variable local acquisition and illumination geometry varies due to sun elevation, topography and observation angle. No algorithm so far has been able to give accurate results through the whole melting season. The new algorithm takes all the effects mentioned into account and thereby makes very precise estimates of the fractional SCA.

The concept assumes a day-to-day monitoring of the snow from winter conditions until all snow has melted. The developing spectrum of the snow is both observed by satellite sensors, giving samples of the BRDF, and modelled by including an empirical snow metamorphosis model and a snow impurity model giving full BRDF. Snow impurity development and snow metamorphosis models are used to infer the development of the snow albedo.

The impurity model is taking into account the typical impurity development based on time, land surface cover type and bare-ground fraction. The metamorphosis model follows a similar scheme based on time. 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 (FSCA) and bare ground. By using predicted spectra for the current situation and not a pool of all possible spectra, the result is more accurate and reliable.

The surface temperature of snow (STS) is a geophysical parameter that is relevant for hydrological modelling of the snow melting as well as for climatic monitoring. Retrieval of STS by means of satellite remote sensing is undertaken in the thermal part of the spectrum. Snow surfaces are close to blackbodies, but because of the atmosphere, the brightness temperature observed from the satellite will typically differ from the real surface temperature of the snow cover. The atmospheric attenuation depends on the wavelength of the observed radiation, the length of the atmospheric path between the surface and the satellite, and the chemical composition of the atmosphere.

Methods for retrieving STS from EO data will typically combine observations in different spectral bands and/or from different view angles. We identified Key’s algorithm as one of the best single-view techniques for polar atmospheres, and it can be applied on MODIS as well as AVHRR data. The algorithm has been tailored to the snow application and validated using field data and meteorological measurement stations.

Snow wetness is an important indicator in hydrology for snowmelt onset and the development of the snowmelt process. The approach we have developed is to infer wet snow from a combination of measurements of snow temperature (STS) and snow grain size (SGS) in a time series of observations. The temperature observations give a good indication of where wet snow potentially may be present, but are in themselves not accurate enough to provide very strong evidence of wet snow. However, a strong indication of a wet snow surface is a rapid increase of the effective grain size observed simultaneously with a snow surface temperature of approximately 0°C. We have developed an algorithm applying this approach to infer coarse snow wetness classes. The algorithm has been validated using field data and meteorological measurement stations.

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