Estimation of snow pack characteristics by means of polarimetric SAR data

Abstract

Characterization of the snow-pack is fundamental for several applications in hydrology, such as modelling and\ud forecasting of snow melt runoff, water resource management and risk analysis. Thanks to its night/day capabilities and\ud weather conditions independence, Synthetic Aperture Radar (SAR) represents a valuable tool for snow monitoring,\ud especially in mountain areas often covered by clouds.\ud The goal of the research project presented in this communication is to investigate the sensitivity of fully polarimetric Cband\ud satellite SAR data to different conditions of the snow-pack.\ud The work is based on the use of RADARSAT-2 C-band SAR data and collocated in-situ measurements acquired during\ud two ground campaigns over an area located in the Catalan Pyrenees, that took place between February to October 2011.\ud The main outcome of this study is the definition of two new polarimetric parameters sensitive to the snow presence, able\ud to distinguish between dry-snow and non snow cover, allowing a qualitative remote sensing with C-band polarimetric\ud space-borne SAR data. The importance of developing an application based on remote sensed data will be discussed.\ud Results of the activity scheduled during the first year of the project will be highlighted. Observed deviations between\ud SAR measurements and in situ measurement shall be analyzed and discussed.Postprint (published version

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

“…Singh et al [30] found that snow-covered regions show lower entropy, H(1-A) as well as higher polarimetric anisotropy; thus they proposed a threshold method to detect snow cover, which resulted in an accuracy comparable to a supervised Wishart classification. Reppucci et al [167] observed that dry snow is characterized by higher values in the Pauli surface parameter and lower value in the double-bounce parameter. Therefore, a combination of the two parameters enables to calculate the difference and then to map dry snow cover.…”

“…Hence, almost all studies calculated the mask during the geocoding step and later re-used it to mask out the results, as illustrated in Figure 6. However, these terrain-induced radiometric effects and the local illuminated brightness should be In addition to conventional confusion matrix-based accuracy evaluations, Luojus et al [139,175] proposed a quantitative analysis approach based on the RMSE to check the improvement made by each refining algorithm; Reppucci et al [167] compared the resultant SCE with an elevation map to check whether the distribution of snow is reasonable.…”

“…Furthermore, the capabilities to detect snow relying on different polarizations attracted more attention. Among all snow cover detecting studies based on polarimetric information, almost all employed SAR images in full-polarimetry mode including PALSAR-1 [30,163,169,170] and Radarsat-2 [35,165,167,168,191]. Early airborne SAR and recent spaceborne studies already suggested that multi-polarization SAR-and especially fully polarimetric SAR-is more suitable to detect snow and ice than single/dual-polarization [102,191,192].…”

“…The speckle noise in SAR images affects all SAR-based snow cover detection algorithms, especially the backscattering-based approaches. The available options for filtering algorithms to overcome this problem include Frost filter [30,126,137,142,186,187,195], refined Lee filter [96,132,163,205,206], median filter [102,103,131], low pass filter [135,207], multichannel intensity filter [140], binary partition tree [167], De Grandi filter [208], multi-scale multilooking [209], and Kuan filter [136]. Some studies attempted to compare the ability of different filters.…”