An InSAR scattering model for multi-layer snow based on Quasi-Crystalline Approximation (QCA) theory
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Snow-cover parameters are important indicator factors for hydrological models and climate change studies and have typical vertical stratification characteristics. Remote sensing can be used for large-scale monitoring of snow parameters. InSAR (Interferometric Synthetic Aperture Radar) technology has advantages in detecting the vertical structure of snow cover. As a basis of snow vertical structure detection using InSAR, a scattering model can reveal the physical process of interaction between electromagnetic waves and snow. In recent years, the InSAR scattering model for single-layer snow has been fully studied; however, it cannot be applied to the case of multi-layer snow. To solve this problem, a multi-layer snow scattering mode is proposed in this paper, which applies the QCA (Quad-Crystal Approximation) theory to describe the coherent scattering characteristics of snow and introduces a stratification factor to describe the influence of snow stratification on the crosscorrelation of SAR echoes. Based on the proposed model, we simulate an InSAR volumetric correlation of different types of multi-layer snow at the X band (9.6 GHz). The results show that this model is suitable for multi-layer snow, and the sequence of sub-layers of snow has a significant influence on the volumetric correlation. Compared to the single layer model, the multi-layer model can predict a polarization difference in the volumetric correlation more accurately and thus has a wider scope of application. To make the model more available for snow parameter inversion, a simplified multi-layer model was also developed. The model did not have polarization information compared to that of the full model but showed good consistency with the full model. The phase of the co-polarization InSAR volumetric correlation difference is more sensitive to snow parameters than that of the phase difference of the co-polarization InSAR volumetric correlation and more conducive to the development of a parameter-inversion algorithm. The model can be applied to deepen our understanding of InSAR scattering mechanisms and to develop a snow parameter inversion algorithm.
KeywordsSnow InSAR QCA Scattering model
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This work was supported by the National Natural Science Foundation of China (Grant Nos. 41471065 & 41471066), the International Partnership Program of Chinese Academy of Sciences (Grant No. 131C11KYSB20160061), the Science & Technology Basic Resources Investigation Program of China (Grant No. 2017FY100502), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA19070201).
- Armstrong R L, Brun E. 2008. Snow and Climate-Physical Processes, Surface Energy Exchange and Modeling. Cambridge: Cambridge University PressGoogle Scholar
- Ding K H, Yang Y E, Shih S E, Kong J A, Davis R E. 1996. Modeling of electromagnetic wave scattering from time-varying snowcover. Proc IGARSS, 1: 757–759Google Scholar
- Leinss S, Hajnsek I. 2012. Opportunities of snow property extraction based on single and multi pass SAR interferometry: TanDEM-X. Proc IGRASS, 2012: 146–149Google Scholar
- Luzi G, Noferini L, Mecatti D, Macaluso G, Pieraccini M, Atzeni C, Schaffhauser A, Fromm R, Nagler T. 2009. Using a ground-based SAR interferometer and a terrestrial laser scanner to monitor a snow-covered slope: Results from an experimental data collection in Tyrol (Austria). IEEE Trans Geosci Remote Sens, 47: 382–393CrossRefGoogle Scholar
- Tan S R, Chang W M, Tsang L, Lemmetyinen J, Proksch M. 2015. Modeling both active and passive microwave remote sensing of snow using dense media radiative transfer (DMRT) theory with multiple scattering and backscattering enhancement. IEEE J Sel Top Appl Earth Observ Remote Sens, 8: 4418–4430CrossRefGoogle Scholar
- Tsang L, Ding K H, Chang A T C. 2003. Scattering by densely packed sticky particles with size distributions and applications to microwave emission and scattering from snow. Proc IGARSS, 4: 2844–2846Google Scholar
- Tsang L, Kong J A, Shin R T. 1985. Theory of Microwave Remote Sensing. New York: WileyGoogle Scholar
- Xu X L, Tsang L, Chang W M, Yueh S. 2014. Bicontinuous DMRT model extracted from multi-size QCA with application to terrestrial snowpack. General Assembly and Scientific Symposium (URSIGASS), 2014 XXXIth URSI. 1–3Google Scholar