The co-polarized phase difference model for dry snow depth inversion

doi:10.11947/j.AGCS.2021.20200125

Abstract

Abstract: Snow depth is an important structure parameter of snow cover. Obtaining high-precision spatial distribution of snow depth is significant to regional water resources management, climate change research, and disaster prediction. Recently, the co-polarized phase difference (CPD) model based on the polarimetric synthetic aperture radar (PolSAR) technique has shown promising results regarding the dry snow depth estimation. The model is established based on the birefringent properties of snow and on the Maxwell-Garnett mixing formulas providing a link between the snow microstructure and CPD. In this study, the dry snow depth is computed using the PolSAR CPD method with C band GF-3 quad-polarization data and measured samples. The study area is selected from the upstream of Kelan River basin, which is located in the north Altai Mountains in Xinjiang, China. To improve the retrieving accuracy, we divide the study area into deep snow area and shallow snow area. The results show that: ①In the ideal case with constant snow anisotropic relative permittivity, CPD is only a function of snow depth. The semi-empirical linear fit model can be used to invert snow depth and the inversion accuracy is related to the window size of the Gaussian low-pass filter used in the CPD calculation. The optimal filter window in shallow snow area is 55×55 pixels and the corresponding accuracy is R=0.83 and RMSE=2.72 cm, and the optimal filter window in deep snow area is 37×37 pixels and the corresponding accuracy is R=0.54 and RMSE=11.69 cm. ②With the increase of slope, the inversion error of snow depth shows a trend of increasing. The inversion uncertainty is affected by the degree of snow metamorphism, water content of snow and the incidence angle. The inversion method is more applicable to the snow layers with dry, homogeneous and low metamorphic crystallization and the SAR with larger incidence angle. ③Compared with the existing CPD model-based snow depth inversion methods, the proposed inversion method has higher accuracy and reduces the required parameters for inversion. Therefore, this study shows the practicability of CPD model in the dry snow depth estimation over mountain areas and provides a new idea for improving snow depth accuracy using CPD models.

Key words: Kelan River, snow depth, SAR, snow microstructure, co-polarized phase difference

CLC Number:

P227

SONG Yina, XIAO Pengfeng, ZHANG Xueliang, ZHUO Yue, MA Wei. The co-polarized phase difference model for dry snow depth inversion[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(7): 905-915.

References

[1] 施雅风, 程国栋. 冰冻圈与全球变化[J]. 中国科学院院刊, 1991, 6(4): 287-291. SHI Yafeng, CHENG Guodong. Cryosphere and global change[J]. Bulletin of Chinese Academy of Science, 1991, 6(4): 287-291.
[2] HENDERSON G R, PEINGS Y, FURTADO J C, et al. Snow-atmosphere coupling in the Northern Hemisphere[J]. Nature Climate Change, 2018, 8(11): 954-963.
[3] MARKS D, DOZIER J. Climate and energy exchange at the snow surface in the Alpine Region of the Sierra Nevada: 2. snow cover energy balance[J]. Water Resources Research, 1992, 28(11): 3043-3054.
[4] BARNETT T P, ADAM J C, LETTENMAIER D P. Potential impacts of a warming climate on water availability in snow-dominated regions[J]. Nature, 2005, 438(7066): 303-309.
[5] BORMANN K J, BROWN R D, DERKSEN C, et al. Estimating snow-cover trends from space[J]. Nature Climate Change, 2018, 8(11): 924-928.
[6] GAO Jingmin. Analysis and assessment of the risk of snow and freezing disaster in China[J]. International Journal of Disaster Risk Reduction, 2016, 19: 334-340.
[7] 陈春艳, 李毅, 李奇航. 新疆乌鲁木齐地区积雪深度演变规律及对气候变化的响应[J]. 冰川冻土, 2015, 37(3): 587-594. CHEN Chunyan, LI Yi, LI Qihang. Snow cover depth in Vrümqi region, Xinjiang: evolution and response to climate change[J]. Journal of Glaciology and Geocryology, 2015, 37(3): 587-594.
[8] ZHAO Yi, JIANG Mi, MA Zhangfeng. Integration of SAR polarimetric features and multi-spectral data for object-based land cover classification[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(4): 64-72. DOI: 10.11947/j.JGGS.2019.0407.
[9] THAKUR P K, AGGARWAL S P, GARG P K, et al. Snow physical parameters estimation using space-based synthetic aperture radar[J]. Geocarto International, 2012, 27(3): 263-288.
[10] 李震, 田邦森, 张平, 等. 合成孔径雷达积雪参数反演研究进展[J]. 南京信息工程大学学报(自然科学版), 2020, 12(2): 159-169. LI Zhen, TIAN Bangsen, ZHANG Ping, et al. Overview of the snow parameters inversion from synthetic aperture radar[J]. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2020, 12(2): 159-169.
[11] STORVOLD R, MALNES E, LARSEN Y, et al. SAR remote sensing of snow parameters in Norwegian areas—current status and future perspective[J]. Journal of Electromagnetic Waves and Applications, 2006, 20(13): 1751-1759.
[12] SHI J, DOZIER J. Estimation of snow water equivalence using SIR-C/X-SAR. II. inferring snow depth and particle size[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(6): 2475-2488.
[13] LEE J S, POTTIER E. Polarimetric radar imaging: from basics to applications[M]. Boca: CRC Press, 2009.
[14] SINGH G, VENKATARAMAN G, YAMAGUCHI Y, et al. Capability assessment of fully polarimetric ALOS-PALSAR data for discriminating wet snow from other scattering types in mountainous regions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(2): 1177-1196.
[15] SHI Jiancheng, DOZIER J. Inferring snow wetness using C-band data from SIR-C’s polarimetric synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995, 33(4): 905-914.
[16] SINGH G, VERMA A, KUMAR S, et al. Snowpack density retrieval using fully polarimetric TerraSAR-X data in the Himalayas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(11): 6320-6329.
[17] PARRELLA G, HAJNSEK I, PAPATHANASSIOU K P. Polarimetric decomposition of L-band PolSAR backscattering over the Austfonna ice cap[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(3): 1267-1281.
[18] LEINSS S, PARRELLA G, HAJNSEK I. Snow height determination by polarimetric phase differences in X-band SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(9): 3794-3810.
[19] MAJUMDAR S. Snow depth and SWE estimation using spaceborne polarimetric and interferometric synthetic aperture radar[D]. Ernshard: University of Twente, 2019.
[20] PATIL A, SINGH G, RVDIGER C. Retrieval of snow depth and snow water equivalent using dual polarization SAR data[J]. Remote Sensing, 2020, 12(7): 1183.
[21] SINGH G, VENKATARAMAN G. Snow density estimation using polarimetric ASAR data[C]//Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town, South Africa: IEEE, 2009.
[22] 胡汝骥, 魏文寿. 试论中国的雪害区划[J]. 冰川冻土, 1987, 9(S1): 1-12. HU Ruji, WEI Wenshou. On the zoning of snow damage in China[J]. Journal of Glaciology and Geocryology, 1987, 9(S1): 1-12.
[23] 沈永平, 王国亚, 苏宏超, 等. 新疆阿尔泰山区克兰河上游水文过程对气候变暖的响应[J]. 冰川冻土, 2007, 29(6): 845-854. SHEN Yongping, WANG Guoya,SU Hongchao, et al. Hydrological processes responding to climate warming in the upper reaches of Kelan River basin with snow-dominated of the Altay Mountains Region, Xinjiang, China[J]. Journal of Glaciology and Geocryology, 2007, 29(6): 845-854.
[24] 侯小刚. 基于多源数据的阿勒泰地区积雪深度研究[D]. 乌鲁木齐: 新疆师范大学, 2013. HOU Xiaogang. Study of snow depth based on multi-source data about Altay area[D]. Vrümqi: Xinjiang Normal University, 2013.
[25] LEINSS S. Depth, anisotropy, and water equivalent of snow estimated by radar interferometry and polarimetry[D]. Zurich: ETH Zurich, 2015.
[26] 张庆君. 高分三号卫星总体设计与关键技术[J]. 测绘学报, 2017, 46(3): 269-277. DOI: 10.11947/j.AGCS.2017.20170049. ZHANG Qinjun. System design and key technologies of the GF-3 satellite[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 269-277. DOI: 10.11947/j.AGCS.2017.20170049.
[27] SIHVOLA A, TIURI M. Snow fork for field determination of the density and wetness profiles of a snow pack[J]. IEEE Transactions on Geoscience and Remote Sensing,1986, GE-24(5): 717-721.
[28] 郑雷. 北疆地区积雪时空变化特征[D]. 兰州: 兰州大学, 2015. ZHENG Lei. The temporal-spatial distribution of snow properties in North Xinjiang[D]. Lanzhou: Lanzhou University, 2015.
[29] ULABY F T, STILES W H. Microwave response of snow[J]. Advances in Space Research, 1981, 1(10): 131-149.
[30] SCHLEEF S, LÖWE H. X-ray microtomography analysis of isothermal densification of new snow under external mechanical stress[J]. Journal of Glaciology, 2013, 59(214): 233-243.
[31] RICHE F, MONTAGNAT M, SCHNEEBELI M. Evolution of crystal orientation in snow during temperature gradient metamorphism[J]. Journal of Glaciology, 2013, 59(213): 47-55.
[32] PINZER B R, SCHNEEBELI M, KAEMPFER T U. Vapor flux and recrystallization during dry snow metamorphism under a steady temperature gradient as observed by time-lapse micro-tomography[J]. The Cryosphere, 2012, 6(5): 1141-1155.
[33] ALLEY R B, BOLZAN J F, WHILLANS I M. Polar firn densification and grain growth[J]. Annals of Glaciology, 1982, 3: 7-11.
[34] HALLIKAINEN M T, ULABY F T, VAN DEVENTER T E V. Extinction behavior of dry snow in the 18-to 90-GHz range[J]. IEEE Transactions on Geoscience and Remote Sensing, 1987, GE-25(6): 737-745.
[35] WEST R, TSANG L, WINEBRENNER D P. Dense medium radiative transfer theory for two scattering layers with a Rayleigh distribution of particle sizes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1993, 31(2): 426-437.
[36] PARRELLA G, HAJNSEK I, PAPATHANASSIOU K. On the interpretation of L- and P-band PolSAR signatures of polythermal glaciers[C]//Proceedings of ESA PolInSAR Workshop. Frascati, Italy: ESA Communications, 2013: 713.
[37] LEINSS S, LOWE H, PROKSCH M, et al. Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series[J]. The Cryosphere, 2016, 10(4): 1771-1797.
[38] SIHVOLA A. Mixing rules with complex dielectric coefficients[J]. Subsurface Sensing Technologies and Applications, 2000, 1(4): 393-415.
[39] MASSONNET D, FEIG K L. Radar interferometry and its application to changes in the Earth’s surface[J]. Reviews of Geophysics, 1998, 36(4): 441-500.