Snow depth inversion based on D-InSAR method

Yang LIU; Lanhai LI; Jinming YANG; Xi CHEN; Run ZHANG

doi:10.11834/jrs.20187125

Technology and Methodology | Views : 0 下载量: 690 CSCD: 5

PDF
Export
Share
Collection
Album

Snow depth inversion based on D-InSAR method
Vol. 22, Issue 5, Pages: 802-809(2018)
Received：03 May 2017，

Accepted：24 December 2017，

Published：2018-09
DOI： 10.11834/jrs.20187125
稿件说明：

移动端阅览

刘洋, 李兰海, 杨金明, 陈曦, 张润. 2018. D-InSAR技术的积雪深度反演. 遥感学报, 22(5): 802–809 DOI： 10.11834/jrs.20187125.

Liu Y, Li L H, Yang J M, Chen X and Zhang R. 2018. Snow depth inversion based on D-InSAR method. Journal of Remote Sensing, 22(5): 802–809 DOI： 10.11834/jrs.20187125.

摘要

积雪深度是大量气候、水文、农业及生态模型的重要输入变量。选用欧空局Sentinel-1主动微波数据，利用合成孔径雷达SAR(Synthetic Aperture Radar)差分干涉测量技术的二轨法，根据积雪相位与雪深之间的转换关系，反演新疆天山中段的巴音布鲁克盆地典型区的积雪雪深分布，提出了基于InSAR二轨差分的雪深估计方法，反演得到2016年12月18日的空间分辨率为13.89 m的雪深分布。研究表明：(1)对Sentinel-1数据进行正确的预处理以后，可以应用SAR差分干涉测量技术的二轨法反演区域雪深分布。但由于像对相干性和积雪状态的差异，积雪深度超过10 cm，可以获取较准确的雪深反演结果，

=0.65，反演误差的均方根误差RMSE=4.52 cm，平均相对误差为22.42%，反演雪深结果均比实测结果略偏低；而当雪深小于10 cm时，雪深反演值较实测值存在较大的误差，相对误差均高于34.52%，且反演雪深值均比实测值偏高。(2)雪深反演精度受高程及实际雪深的差异影响显著，另外雪深反演精度也受限于干涉像对相干性。结果表明，对于获取区域积雪雪深，InSAR技术较光学及被动微波遥感具有非常广阔的应用前景。

Abstract

Snow depth is a general input variable in many models of agriculture

hydrology

climate

and ecology. This study adopts the Sentinel-1 C-band of the European Space Agency using the two-pass method of differential interferometry to conduct an inversion study of the snow depth distribution in typical areas of Bayanbulak Basin in the Middle Tianshan Mountains of Xinjiang

China. Based on Sentinel-1 SAR image

the image of day October 31

2016 is selected as the master image and the image of day December 18

2016 is used as the slave image to form the image pair. After the interferogram is formed

the orbit phases

terrain

ground effect

and noise effect are removed. The phase unwrapping of the remaining phase aims to obtain the distribution of snow depth with the spatial resolution of 13.89 m on day December 18

2016 by relying on the relationship between snow depth and snow phase in the typical Bayanbulak region. The study demonstrates the following: (1) After proper preprocessing of Sentinel-1 data

snow depth distribution inversion is achieved by utilizing the InSAR-based two-pass method. However

owing to the difference of image-pair coherence and snow accumulation conditions

a relatively accurate inversion result of snow depth is available when the snow depth is larger than 10 cm (

=0.65

RMSE=4.52 cm

and average relative error is 22.42%). The estimated snow depth is slightly lower than the actual depth. When the snow depth is less than 10 cm

the inversion result is not accurate: it is larger than the actual depth

and the average relative error is higher than 34.52%. (2) The inversion accuracy of snow depth is also significantly influenced by the height and actual snow depth. Moreover

the inversion result of snow depth is influenced by coherence losses. This study demonstrates that the InSAR method is more promising in obtaining and estimating snow depth compared with optical technology and passive microwave remote sensing.

关键词

Keywords

references

Armstrong R L and Brodzik M J. 2002. Hemispheric-scale comparison and evaluation of passive-microwave snow algorithms. Annals of Glaciology, 34: 38–44

Atzori S, Chiarabba C, Devoti R, Bonano M and Lanari R. 2013. Anomalous far‐field geodetic signature related to the 2009 L’Aquila (central Italy) earthquake. Terra Nova, 25(5): 343–351

Bozzano F, Mazzanti P, Perissin D, Rocca A, De Pari P and Discenza M E. 2017. Basin scale assessment of landslides geomorphological setting by advanced InSAR analysis. Remote Sensing, 9(3): 267

Che T, Dai L Y, Zheng X M, Li X F and Zhao K. 2016. Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast china. Remote Sensing of Environment, 183: 334–349

Cui Y R, Xiong C, Lemmetyinen J, Shi J C, Jiang L M, Peng B, Li H X, Zhao T J, Ji D B and Hu T X. 2016. Estimating snow water equivalent with backscattering at X and Ku band based on absorption loss. Remote Sensing, 8(6): 505

Dai L Y, Che T and Ding Y J. 2015. Inter-calibrating SMMR, SSM/I and SSMI/S data to improve the consistency of snow-depth products in China. Remote Sensing, 7(6): 7212–7230

Evans J R and Kruse F A. 2014. Determination of snow depth using elevation differences determined by interferometric SAR (InSAR)//Proceedings of 2014 IEEE International Geoscience and Remote Sensing Symposium. Quebec City, QC, Canada: IEEE: 962–965 [DOI: 10.1109/IGARSS.2014.6946586]

Foster J L, Chang A T C and Hall D K. 1997. Comparison of snow mass estimates from a prototype passive microwave snow algorithm, a revised algorithm and a snow depth climatology. Remote Sensing of Environment, 62(2): 132–142

Guneriussen T, Hogda K A, Johnsen H and Lauknes I. 2001. InSAR for estimation of changes in snow water equivalent of dry snow. IEEE Transactions on Geoscience and Remote Sensing, 39(10): 2101–2108

Leinss S, Lemmetyinen J, Wiesmann A and Hajnsek I. 2015. Interferometric and polarimetric methods to determine SWE, fresh snow depth and the anisotropy of dry snow//Proceedings of 2015 International Geoscience and Remote Sensing Symposium. Milan, Italy: IEEE: 4029–1032 [DOI: 10.1109/IGARSS.2015.7326709]

Leinss S, Parrella G and Hajnsek I. 2014. Snow height determination by polarimetric phase differences in X-band SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3794–3810

李晖, 肖鹏峰, 冯学智, 林金堂, 汪左, 满旺. 2014. 基于重轨Insar的积雪深度反演方法. 冰川冻土, 36(3): 517–526

Li H, Xiao P F, Feng X Z, Lin J T, Wang Z and Man W. 2014. Snow depth derived from repeat-pass InSAR sounding. Journal of Glaciology and Geocryology, 36(3): 517–526 (

Liang J Y, Liu X P, Huang K N, Li X, Shi X, Chen Y N and Li J. 2015. Improved snow depth retrieval by integrating microwave brightness temperature and visible/infrared reflectance. Remote Sensing of Environment, 156: 500–509

刘洋, 李诚志, 刘志辉, 邓兴耀, 朱金焕. 2016. 基于WRF模式的新疆巴音布鲁克盆地强降雨天气数值模拟效果分析. 干旱区研究, 33(1): 28–37

Liu Y, Li C Z, Liu Z H, Deng X Y and Zhu J H. 2016. Analysis on the numerical simulation of heavy rainfall based on WRF model in Bayanbuluk basin. Arid Zone Research, 33(1): 28–37 (

Marshall S, Roads J O and Glatzmaier G. 1994. Snow hydrology in a general circulation model. Journal of Climate, 7(8): 1251–1269

Nagler T, Rott H, Ripper E, Bippus G and Hetzenecker M. 2016. Advancements for snowmelt monitoring by means of sentinel-1 SAR. Remote Sensing, 8(4): 348

Nikraftar Z, Hasanlou M and Esmaeilzadeh M. 2016. Novel snow depth retrieval method using time series ssmi passive microwave imagery. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. [s.l.]: Copernicus Publications: 525–530 [DOI: 10.5194/isprs-archives-XLI-B8-525-2016]

Oveisgharan S and Zebker H A. 2007. Estimating snow accumulation from InSAR correlation observations. Transactions on Geoscience and Remote Sensing, 45(1): 10–20

Polcari M, Palano M, Fernández J, Samsonov S V, Stramondo S and Zerbini S. 2016. 3D displacement field retrieved by integrating Sentinel-1 InSAR and GPS data: the 2014 South Napa earthquake. European Journal of Remote Sensing, 49(1): 1–13

Robinson D, Kunzi K, Kukla G and Rott H. 1984. Comparative utility of microwave and shortwave satellite data for all-weather charting of snow cover. Nature, 312(5993): 434–435

Shi J and Dozier J. 2000. Estimation of snow water equivalence using SIR-C/X-SAR. I. Inferring snow density and subsurface properties. IEEE Transactions on Geoscience and Remote Sensing, 38(6): 2465–2474

Shi J C, Xiong C and Jiang L M. 2016. Review of snow water equivalent microwave remote sensing. Science China Earth Sciences, 59(4): 731–745

Storvold R, Malnes E, Larsen Y, Høgda K A, Hamran S, Müller K and Langley K. 2006. SAR remote sensing of snow parameters in Norwegian areas—Current status and future perspective. Journal of Electromagnetic Waves and Applications, 20(13): 1751–1759

Sun H, Zhang Q, Zhao C Y, Yang C S, Sun Q F and Chen W R. 2017. Monitoring land subsidence in the southern part of the lower Liaohe plain, China with a multi-track PS-InSAR technique. Remote Sensing of Environment, 188: 73–84

Tarnocai C, Canadell J G, Schuur E AG, Kuhry P, Mazhitova G and Zimov S A. 2009. Soil organic carbon pools in the northern circumpolar permafrost region. Global Biogeochemical Cycles, 23(2): GB2023

Thakur P K, Aggarwal S P, Garg P K, Garg R D, Mani S, Pandit A and Kumar S. 2012. Snow physical parameters estimation using space-based Synthetic Aperture Radar. Geocarto International, 27(3): 263–288

Wang J, Balz T and Liao M. 2016. Absolute geolocation accuracy of high-resolution spotlight TerraSAR-X imagery-validation in Wuhan. Geo-spatial information science, 19(4): 267–272

Wei M and Sandwell D T. 2010. Decorrelation of L-band and C-band interferometry over vegetated areas in California. IEEE Transactions on Geoscience and Remote Sensing, 48(7): 2942–2952

Wu T D, Chen K S, Shi J C and Fung A K. 2001. A transition model for the reflection coefficient in surface scattering. IEEE Transactions on Geoscience and Remote Sensing, 39(9): 2040–2050

Xiao Y S, Cao Y P and Wu Y C. 2012. Improved algorithm for phase-to-height mapping in phase measuring profilometry. Applied Optics, 51(8): 1149–1155

杨金明, 刘志辉. 2016. Sentinel-1卫星数据产品应用探讨. 地理空间信息, 14(12): 18–20

Yang J M and Liu Z H. 2016. Application of Sentinel-1 satellite and data products. Geospatial Information, 14(12): 18–20 (

Zhang T J. 2005. Influence of the seasonal snow cover on the ground thermal regime: an overview. Reviews of Geophysics, 43(4): RG4002

Zimov S A, Schuur E A G and Chapin III F S. 2006. Permafrost and the global carbon budget. Science, 312(5780): 1612–1613

Alert me when the article has been cited

提交

Urban building change detection on the basis of multitemporal LT-1： A case study of Hengqin Town， Zhuhai City

Forest classification via cross-attention fusion of multispectral and SAR remote sensing data

Validation and analysis of FY-3D/MWRI snow depth/water equivalent products in Yichun region

Snow depth downscaling algorithm using coupled deep learning and microwave radiative transfer model driven by multi-source remote sensing data

Analysis of resampling error of FY-3D/MWRI at different orbital positions