Information on snow depth and its spatial distribution
is crucial for numerous applications in snow and
avalanche research as well as in hydrology and ecology. Today,
snow depth distributions are usually estimated using
point measurements performed by automated weather stations
and observers in the field combined with interpolation
algorithms. However, these methodologies are not able to
capture the high spatial variability of the snow depth distribution
present in alpine terrain. Continuous and accurate
snow depth mapping has been successfully performed using
laser scanning but this method can only cover limited areas
and is expensive. We use the airborne ADS80 optoelectronic
scanner, acquiring stereo imagery with 0.25m spatial resolution
to derive digital surface models (DSMs) of winter
and summer terrains in the neighborhood of Davos, Switzerland.
The DSMs are generated using photogrammetric image
correlation techniques based on the multispectral nadir and
backward-looking sensor data. In order to assess the accuracy
of the photogrammetric products, we compare these products
with the following independent data sets acquired simultaneously:
(a) manually measured snow depth plots; (b) differential
Global Navigation Satellite System (dGNSS) points; (c)
terrestrial laser scanning (TLS); and (d) ground-penetrating
radar (GPR) data sets. We demonstrate that the method presented
can be used to map snow depth at 2m resolution with
a vertical depth accuracy of 30 cm (root mean square error)
in the complex topography of the Alps. The snow depth maps
presented have an average accuracy that is better than 15%
compared to the average snow depth of 2.2m over the entire
test site.