Snow cover monitoring using multitemporal ERS-1 SAR data

Piesbergen, J.; Holecz, F.; Haefner, H.

doi:10.1109/igarss.1995.524015

Cited by 18 publications

(3 citation statements)

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“…The applicability of remote sensing for snow monitoring and has been investigated for years. Remote sensing instruments offer a method for monitoring the snow cover, as successfully demonstrated in [1], [2], [3], [4], [5] and [6]. Remote sensing provides logistical advantages compared with gauging networks.…”

Section: Introductionmentioning

confidence: 99%

“…However, few studies have analyzed the effect of various snow characteristics to the accuracy of SAR-based snow extent estimation during the melting season. Previous studies have mainly shown results obtained by comparing SAR-derived snow extent estimates with ground surveys or satellite-borne optical images [3], [4], [6], [8]. This paper presents a simple snow backscattering model combined with the HUT semiempirical forest backscattering model.…”

Section: Introductionmentioning

confidence: 99%

“…Modeled behavior of backscattering coefficient difference between (a) dry snow and wet snow and (b) wet snow and snow-free ground as a function of snow wetness. The calculation is based on various snow depths(20-200 cm), snow densities (0.2-0.5 g/cm3 ) and effective snow crystal sizes (2.0-4.0 mm). The vertical bars indicate the standard deviation of the mean backscattering differences.…”

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confidence: 99%

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Effect of snow wetness to C-band backscatter-a modeling approach

Koskinen¹,

Pulliainen²,

Hallikainen³

IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remot

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A simple backscattering model is presented and its application to radar remote sensing of snow is discussed. Our snow model consists of three parts, (a) IEM surface scattering model for snow surface scattering, (b) a discrete scatterer volume scattering model and (c) the Michigan empirical surface scattering model for soil backscattering. Together with the snow-covered terrain backscattering model we use the HUT boreal forest semi-empirical backscattering model in order to analyze the effect of forest canopy for snow monitoring. The modeling results are compared with HUTSCAT airborne scatterometer and ERS-1 SAR data obtained during the winters of 1992 (wet snow conditions) and 1993 (dry snow conditions) in the Sodankylä test site in northern Finland. The model predictions agree with the experimental data even in the presence of forest canopies. However, according to our model simulations the effective snow crystal size is much larger that the measured mean snow crystal size. The models are used to analyze the effect of various snow parameters to C-band backscattering and to define the accuracy of two snow melt radar algorithms.

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