Abstract
Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention. In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. Even though the analysis of snow pack can be conducted in the visible, near-infrared and short-wave infrared spectrum, the presence of clouds during the melting season, which may be pervasive in some parts of the World (e.g., polar regions), renders impossible the regular acquisition of information needed for the operational purposes. Therefore, the use of the microwave sensors, which signal can penetrate the clouds, can be an asset for the detection of snow proprieties. In particular, the SAR images have demonstrated to be effective and robust measurements to identify the wet snow. Among the several methods presented in the literature, the best results in wet snow mapping have been achieved by the bi-temporal change detection approach proposed by Nagler and Rott [1], or its slight improvements presented afterwards (e.g., [2]). Nonetheless, with the introduction of the Sentinel-1 by ESA, which provides free-of-charge SAR images every 6 days over the same geographical area with a resolution of 20m, the scientists have the opportunity to better investigate and improve the state-of-the-art methods for wet snow detection. In this work, we propose a novel method based on a supervised learning approach able to exploit both the experience of the state-of-the-art algorithms and the high multi-temporal information provided by the Sentinel-1 data. In detail, this is done by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., “Retrieval of wet snow by means of multitemporal SAR data,” in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., “Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions”, EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., “A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., “Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2073-2084, Oct. 2004.