Paper Info
Title
Spatial Dispersion and Non-Negative Matrix Factorization of SAR Backscattering as Tools for Monitoring Snow Depth Evolution in Mountain Areas: A Case Study at Central Pyrenees (Spain)
Abstract
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 (sigma(VH)/sigma(VV)) 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 sigma(VH)/sigma(VV) spatial dispersion, (ii) spatial distribution of pixelwise sigma(VH)/sigma(VV) temporal standard deviation, and (iii) fundamental modes of sigma(VH)/sigma(VV )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 sigma(VH)/sigma(VV) 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.
Year
DOI
Venue
2022
10.3390/rs14030653
REMOTE SENSING
Keywords
DocType
Volume
snow depth monitoring, SAR backscattering, bakscattering spatial dispersion, non-negative matrix factorization of backscattering
Journal
14
Issue
Citations 
PageRank 
3
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Antonella Amoruso100.34
Luca Crescentini200.34
Riccardo Costa300.34