An InSAR scattering model for multi-layer snow based on Quasi-Crystalline Approximation (QCA) theory

Li, Zhen; Li, Zhixian; Tian, Bangsen; Zhou, Jianmin

doi:10.1007/s11430-017-9178-3

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…snowcovered ice sheets) [18], [19]. Then, the original two-layer model was further adapted to a dense dielectric medium (such as snowpack) by incorporating particle grain size, volume fraction (that relates to density) and depth [20], and has also been extended to model a multi-layer medium [21]. In this paper, we focus on the use of the adapted two-layer RVoG model for simplicity without loss of generality.…”

Section: A Dense-medium Random Volume Over Ground (Rvog) Modelmentioning

confidence: 99%

Dry Snow Parameter Retrieval With Ground-Based Single-Pass Synthetic Aperture Radar Interferometry

Yang

Baldi³

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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In this paper, we investigate the potential of using single-pass InSAR model-based approaches to retrieve dry snow parameters. Two InSAR scattering models of dry snow are considered: the dense-medium RVoG model and the simple variant of Full Penetration (FP) model. A Quasi-Crystalline Approximation (QCA) based extinction analysis confirms the negligible extinction dependence of the InSAR observables at L/C/X-band for fresh dry snow. The FP models the low-frequency (L/C/X-band) InSAR phase as a single constraint of snow depth and density, which can be supplemented by an extra observation (e.g. InSAR coherence or in-situ depth/density). The single-pass InSAR models and inversion approaches were validated using Xband InSAR data collected from a tower-based three-frequency (X/Ku-low/Ku-high) fully polarimetric TomoSAR system, where a multi-frequency polarimetric InSAR analysis and ground-tovolume ratio-based snow condition analysis were conducted. We also analyzed the sensitivity and error propagation of the single-pass InSAR phase and coherence in measuring dry snow depth/density. It was found that the X-band HH-pol FP-modeled single-pass InSAR phase along with RVoG-modeled coherence or in-situ depth is capable of measuring Snow Water Equivalent (SWE) with a 23-26 mm uncertainty (13-15%) and a 20-26 mm bias (12-15%) for dry snow SWE of 0.2 m, and with an optimal perpendicular baseline on the order of a tenth of the snow depth (0.8 m) at our test site. This single-pass InSAR approach with FP model is potentially useful and thus needs further investigation for large-scale dry snow retrieval with a wide range of snow conditions using ground-based/airborne/spaceborne lowfrequency (L/C/X-band) InSAR observations.

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Section: A Dense-medium Random Volume Over Ground (Rvog) Modelmentioning

confidence: 99%

Dry Snow Parameter Retrieval With Ground-Based Single-Pass Synthetic Aperture Radar Interferometry

Yang

Baldi³

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Different approaches for setting τ were proposed in the literature. In several applications τ was set to 0.1 with single [14], [15] or multi-layer [16] snowpack, both in the active and passive electromagnetic cases [17], [18]. In other studies, τ was varied between a minimum and a maximum value, looking for the best value fitting experimental data [19], [20], [21], [22], [23].…”

Section: Introductionmentioning

confidence: 99%

On the Relationship Between Stickiness in DMRT Theory and Physical Parameters of Snowpack: Theoretical Formulation and Experimental Validation With SNOWPACK Snow Model and X-Band SAR Data

Pilia

Baroni

Lapini

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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This study aims at relating the stickiness parameter (τ) of the Dense Media Radiative Transfer theory integrated with Sticky Hard Sphere (SHS) model (DMRT-QMS), to the physical parameters of the layered snowpack. A relationship has been derived to express τ, which modulates the attractive contact force between ice spheres, as a function of ice volume fraction (𝜙) and coordination number (nc). Since τ is not a measurable parameter, this is a step forward with respect to what is commonly made in literature, where τ is assumed as an arbitrary parameter, generally ranging between 0.1 and 0.3, to fit simulated backscattering data with those measured. As a first validation, DMRT-QMS was integrated with SNOWPACK model to simulate backscattering at X band (9.6 GHz) driven by nivo-meteorological data acquired on a test area located in Monti Alti di Ornella, Italy. The simulations were compared with Synthetic Aperture Radar COSMO-SkyMed (CSK) satellite observations. The results show a significant agreement (R 2 =0.68), although for a limited dataset of eight points in a unique winter season.

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“…Extensive research has been conducted on retrieving the snow grain size using "scaled band area" algorithm and the MODIS snow-covered area grain size (MODSCAG) model [15]. On the basis of the quasi-crystalline approximation (QCA) theory, the vertical structure of snow cover could be detected by the "interferometric synthetic aperture radar (InSAR) scatter model" [25]. The pollutant concentration was estimated by a contamination index based on snow reflectance [26][27][28].…”

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confidence: 99%

An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

Liu

et al. 2020

Remote Sensing

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Detecting the variations in snow cover aging over undulating alpine regions is challenging owing to the complex snow-aging process and shadow effect from steep slopes. This study proposes a novel snow-cover status index, namely shadow-adjusted snow-aging index (SASAI), portraying the integrated aging process within the Manas River Basin in northwest China. The Environment Satellites HJ-1A/B optical images and in-field measurements were used during the snow ablation and accumulation periods. The in-field measurements provide a reference for building a candidate library of snow-aging indicators. The representative aging samples for training and validation were obtained using the proposed time-gap searching method combined with the target zones established based on the altitude of snowline. An analytic hierarchy process was used to determine the snow-aging index (SAI) using multiple optimal snow-aging indicators. After correction by the extreme value optimization algorithm, the SASAI was finally corrected for the effects of shading and assessed. This study provides both a flexible algorithm that indicates the characteristics of snow aging and speculation on the causes of the aging process. The separability of the SAI/SASAI and adaptability of this algorithm on multiperiod remote sensing images further demonstrates the applicability of the SASAI to all the alpine regions. cloud removal techniques, and spatiotemporal data fusion techniques [8][9][10][11]. For example, moderate resolution imaging spectroradiometer (MODIS) data are broadly employed as a reliable data source for detecting the extent of snow cover owing to its high spatial, time, and spectral resolutions [12,13]. Its sensitivity to factors such as aerosol optical properties are also explored in alpine areas [14]. Further, the "subpixel snow-cover information", "empirical relationship assumptions" [15,16], and "spectral unmixing" [17] models have been extensively applied for the inversion of the fractional snow cover. Transient snowline altitude and glacier elevation can be extracted by combining optical and synthetic aperture radar (SAR) imagery as well as DEM data [18]. Passive microwave-based models can penetrate clouds and provide measurements in shadowed regions and hence, are useful for inversion of the snow depth [19,20], snow water equivalent (SWE) [21], and snow cover storage [22]. Based on this, the composite snow cover products ESA GlobSnow SWE dataset [23] and snow data assimilation system (SNODAS) [24] were produced.Microscopically, SAP mainly refers to physical metamorphism as a variation in the grain size and particle structure [1][2][3][4]. The accumulation of pollution in snow, increasing liquid water content, and surface roughness are also typical symptoms of SAP (these also influence snow metamorphism) [5,6]. Extensive research has been conducted on retrieving the snow grain size using "scaled band area" algorithm and the MODIS snow-covered area grain size (MODSCAG) model [15]. On the basis of the quasi-crystalline approximation (QCA) th...

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An InSAR scattering model for multi-layer snow based on Quasi-Crystalline Approximation (QCA) theory

Cited by 5 publications

References 22 publications

Dry Snow Parameter Retrieval With Ground-Based Single-Pass Synthetic Aperture Radar Interferometry

Dry Snow Parameter Retrieval With Ground-Based Single-Pass Synthetic Aperture Radar Interferometry

On the Relationship Between Stickiness in DMRT Theory and Physical Parameters of Snowpack: Theoretical Formulation and Experimental Validation With SNOWPACK Snow Model and X-Band SAR Data

An Integrated Shadow-Adjusted Snow-Aging Index for Alpine Regions

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