Identification and, possibly, removal of hydrology-associated crustal deformation is indispensable to produce reliable displacement and deformation time series for geological disaster monitoring and precursor identification. For example, in the case of the 2009 L'Aquila (Italy) earthquake, ground displacements, originally interpreted as seismic precursors, are actually of hydrologic origin (see Amoruso et al., 2017; Devoti et al., 2018, for one-week and multiyear signals, respectively). Hydrology-associated deformation is transformed from noise to a useful signal if local or regional aquifer dynamics (e.g., Amoruso et al., 2013;Hu & Bürgmann, 2020) or rock hydrological properties (e.g., Barbour & Wyatt, 2014) are investigated and focus of study.
Accurate knowledge of snow cover extent, depth (SD), and water equivalent is essential for studying the global water cycle, climate, and energy–mass exchange in the Earth–atmosphere system, as well as for water resources management. Ratio between SAR cross- and co-polarization backscattering (σVH/σVV) was used to monitor SD during snowy months in mountain areas; however, published results refer to short periods and show lack of correlation during non-snowy months. We analyze Sentinel-1A images from a study area in Central Pyrenees to generate and investigate (i) time series of σVH/σVV spatial dispersion, (ii) spatial distribution of pixelwise σVH/σVV temporal standard deviation, and (iii) fundamental modes of σVH/σVV evolution by non-negative matrix factorization. The spatial dispersion evolution and the first mode are highly correlated (correlation coefficients larger than 0.9) to SD evolution during the whole seven-year-long period, including snowy and non-snowy months. The local incidence angle strongly affects how accurately σVH/σVV locally follows the first mode; thus, areas where it predominates are orbit-dependent. When combining ascending- and descending-orbit images in a single data matrix, the first mode becomes predominant almost everywhere snow pack persists during winter. Capability of our approach to reproduce SD evolution makes it a very effective tool.
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