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(a) Average CPD and (b) average CCOH by vegetation class with interval of confidence (95 %) for orbit 24 (31∘, descending). Values were extracted from the GPS dataset (see Fig. 2c), in which NColtsfoot=33, NDryas=140, NLupine=118 and NShrub=29. The winter period (mid-September to mid-May) is shown in the shaded area. The window over which vegetation class information was extracted is the same size as a TSX pixel (5×5 m). (c) Meteorological data from Qikiqtaruk/Herschel Island station (dataset from Environment Canada, 2021). The meteorological station is not equipped with a telemetry system, and since the island is inaccessible during the winter, the lack of data during the winters of 2014–2015 and 2017–2018 was caused by a malfunction at the station. Air temperatures during these periods were gap-filled using Komakuk Beach meteorological station and are shown by the dotted red line. Please refer to Appendix A for further details on the method.
Source publication
Changes in snowpack associated with climatic warming has drastic impacts on surface energy balance in the cryosphere. Yet, traditional monitoring techniques, such as punctual measurements in the field, do not cover the full snowpack spatial and temporal variability, which hampers efforts to upscale measurements to the global scale. This variability...
Citations
... Applying polarimetry, the copolar phase difference (CPD) between the vertical VV and horizontal HH copolarized channels can indicate the amount of freshly fallen snow [8], [9], [10]. A physical model has been presented in [11], that uses the CPD to invert the snow depth, by assuming the density and anisotropy of a snow pack. ...
The amount of water in a snow pack can be described by the Snow Water Equivalent (SWE). SWE is a crucial parameter for hydrological models, for example for flood predictions. Previous studies have shown that the interferometric phase between two repeat-pass SAR measurements can be used to determine the change in SWE. However, a limitation of this method are phase wraps. To overcome this, the Copolar Phase Difference (CPD) between the VV and HH channel can be used, which has been proven to be related to the depth of freshly accumulated snow. This study proposes an approach to incorporate the information on the fresh snow accumulation from the CPD into the interferometric SWE retrieval algorithm. The aim is to detect and correct interferometric phase wraps. First results using airborne SAR data indicate that including the CPD improves the accuracy of the SWE retrieval.
... Synthetic aperture radar (SAR) remote sensing provides higher spatial resolution, which has more potential for finely retrieving SD [10]. Polarimetric SAR (PolSAR) utilizes multipolarization channel information for retrieving parameters, which is sensitive to the shape and direction of the target and it has a relatively mature application in retrieving SD [11][12][13]. One of the most common methods is the Co-polar Phase Difference (CPD), which is used to express the phase difference of the signal delay between HH and VV polarization channels [9]. ...
... One of the most common methods is the Co-polar Phase Difference (CPD), which is used to express the phase difference of the signal delay between HH and VV polarization channels [9]. From this, relationships between parameters such as snow depth, inter-axis ratio, and snow density are constructed for retrieving SD based on the propagation path of the two polarization channel signals within the snowpack [9,13]. Snow backscatter modeling can also be used to retrieve snow parameters based on PolSAR [14][15][16]. ...
... where ω is the eigenvector of different scattering types. In practical applications, T11 = T22 [41], hence (12) can be simplified to (13): ...
Snow depth is a fundamental parameter in hydrological and climate models, which is crucial for studying climate change, hydrological cycles, and ecosystem changes. Polarimetric synthetic aperture radar interferometry (PolInSAR) is one of the most promising snow depth retrieval methods, which is sensitive to the shape, direction, and vertical distribution of targets. The dense medium random-volume-over-ground (DM-RVoG) model for PolInSAR has been shown to be workable for snow depth retrieval, it still suffers from the inaccuracy of the parameters representing the phase center and decorrelation. In this study, based on the backscattering mechanism of snow, a novel snow depth retrieval method is proposed to improve the DM-RVoG model using polarization decomposition and decorrelation optimization. First, the polarization decomposition is extended to obtain the ground scattering phase. Then, the coherence region boundary estimation method is put forward to obtain pure volume decorrelation. Finally, the proposed method is validated using Ku-band UAV SAR data, and the accuracy is assessed using in-situ data. The correlation coefficient, root mean square error, and mean absolute error of the proposed method are 0.88, 4.98 cm, and 4.08 cm, respectively, demonstrating significant improvements compared with the original method.
... Rapid advances in remote sensing technology in recent years provide new opportunities for retrieving large-scale and continuous SD. In the past, many studies have tried to retrieve SD by using microwave remote sensing, which can not only overcome the influence of complex weather, but also provide highfrequency bands interacting with the snowpack effectively (Dai and Che, 2022;Patil et al. 2020b;Shi and Dozier, 2000;Voglimacci-Stephanopoli et al., 2022). ...
... Polarimetric SAR (PolSAR) measurement has been maturely applied in retrieving SD (Leinss et al., 2014;Patil et al. 2020b). The co-polarization phase difference model (CPD) is among the most popular methods of PolSAR for SD retrieval, and was developed based on the principle that the different refractive indices of the VV and HH polarization signals in snow cover lead to phase differences (Leinss et al., 2014;Voglimacci-Stephanopoli et al., 2022). Polarization decomposition were recently investigated to retrieve the SD with promising results (Patil et al. 2020b;Singh et al., 2019). ...
Snow depth retrievals from spaceborne C-band synthetic aperture radar (SAR) backscatter have the potential to fill an important gap in the remote monitoring of seasonal snow. Sentinel-1 (S1) SAR data have been used previously in an empirical algorithm to generate snow depth products with near-global coverage, subweekly temporal resolution and spatial resolutions on the order of hundreds of meters to 1 km. However, there has been no published independent validation of this algorithm. In this work we develop the first open-source software package that implements this Sentinel-1 snow depth retrieval algorithm as described in the original papers and evaluate the snow depth retrievals against nine high-resolution lidar snow depth acquisitions collected during the winters of 2019–2020 and 2020–2021 at six study sites across the western United States as part of the NASA SnowEx mission. Across all sites, we find agreement between the Sentinel-1 snow depth retrievals and the lidar snow depth measurements to be considerably lower than requirements placed for remotely sensed observations of snow depth, with a mean root mean square error (RMSE) of 0.92 m and a mean Pearson correlation coefficient r of 0.46. Algorithm performance improves slightly in deeper snowpacks and at higher elevations. We further investigate the underlying Sentinel-1 data for a snow signal through an exploratory analysis of the cross- to co-backscatter ratio (σVH/σVV; i.e., cross ratio) relative to lidar snow depths. We find the cross ratio increases through the time series for snow depths over ∼ 1.5 m but that the cross ratio decreases for snow depths less than ∼ 1.5 m. We attribute poor algorithm performance to (a) the variable amount of apparent snow depth signal in the S1 cross ratio and (b) an algorithm structure that does not adequately convert S1 backscatter signal to snow depth. Our findings provide an open-source framework for future investigations, along with insight into the applicability of C-band SAR for snow depth retrievals and directions for future C-band snow depth retrieval algorithm development. C-band SAR has the potential to address gaps in radar monitoring of deep snowpacks; however, more research into retrieval algorithms is necessary to better understand the physical mechanisms and uncertainties of C-band volume-scattering-based retrievals.
The dramatic reduction in microwave RADAR corner reflector backscattered power when coated with even a thin layer of snow is investigated and the impact on spaceborne RADAR remote sensing in polar regions is assessed. Time series over two years of Sentinel-1 measurements in Spitsbergen (Arctic Norway, 79°N) are interpreted in view of this analysis. The refraction of the incoming microwave beam preventing the backscattered signal from reaching the monostatic RADAR is reproduced in laboratory controlled experiments demonstrating that low-loss dielectric layers not parallel to the corner reflector conducting sides are the cause of the signal loss.
