粗糙面电磁散射建模与嫦娥七号SAR月球极化定标场选取及分析

宋佳乐; 柳钮滔; 金亚秋; 陆萍萍; 王宇

doi:10.12000/JR25194

粗糙面电磁散射建模与嫦娥七号SAR月球极化定标场选取及分析

DOI: 10.12000/JR25194 CSTR: 32380.14.JR25194

1.
电磁波信息科学教育部重点实验室复旦大学上海 200433
2.
中国科学院空天信息创新研究院北京 100190

基金项目: 国家自然科学基金(62495032, 62201154, 62495035)，上海市晨光计划

详细信息

作者简介:
宋佳乐，硕士生，主要研究方向为月球粗糙面电磁散射建模和SAR极化分解

柳钮滔，博士，副研究员，主要研究方向为行星微波遥感、复杂媒质电磁散射模拟和空间电磁信息解译

金亚秋，博士，教授，中国科学院院士，主要研究方向为复杂自然环境与目标电磁散射辐射传输、空间微波遥感和计算电磁学

陆萍萍，博士，研究员，博士生导师，主要研究方向为微波遥感成像技术、遥感图像智能解译和人工智能应用

王　宇，博士，研究员，博士生导师，主要研究方向为星载成像雷达系统与信号处理

通讯作者:
柳钮滔 ntliu@fudan.edu.cn

责任主编：丁大志 Corresponding Editor: DING Dazhi

中图分类号: TN959.74
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出版历程
- 收稿日期: 2025-09-29

Electromagnetic Scattering Modeling of Rough Surfaces and Selection of Chang’e-7 SAR Polarimetric Lunar Calibration Fields

1.
Key Laboratory for Information Science of Electromagnetic Wave, Ministry of Education, Fudan University, Shanghai 200433, China
2.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China

Funds: The National Natural Science Foundation of China (62495032, 62201154, 62495035), The Chenguang Program of Shanghai

More Information

Corresponding author: LIU Niutao, ntliu@fudan.edu.cn

摘要

摘要: 嫦娥七号将搭载全极化合成孔径雷达，对月球极区地形与物质特性开展探测。极化合成孔径雷达要求极化定标，而传统地面定标方法在月球探测任务中难以应用，需要发展新的月面极化相对定标方法。针对月表相对定标问题，该文采用垂直观测，分析同极化比$ {\sigma _{{\text{HH}}}} $/$ {\sigma _{{\text{VV}}}} $，HH和VV相位差与斜坡间关系，提出参数v衡量极化旋转角分布均匀性并用于筛选月球定标场。仿真实验表明，极化旋转角越均匀，极化比分布越趋于1，相位差分布越趋近于0°。该文结合粗糙面电磁散射回波和月船二号实测数据，建立统计模型约束下的定标所需最小观测次数估算方法，为月球极化SAR系统的相对定标提供了理论依据和技术路径。
- 深空探测 /
- 月球 /
- SAR定标 /
- 嫦娥七号 /
- SAR极化
Abstract: Chang’e-7 will carry a fully polarimetric synthetic aperture radar (SAR) to investigate the topography and material properties of the lunar polar regions, which necessitates reliable polarimetric calibration. However, conventional ground-based calibration strategies are infeasible for lunar missions, underscoring the need for new relative polarimetric calibration methods tailored to the lunar surface. To address this challenge, we adopt a normal-incidence observation geometry and analyze how the co-polarization ratio $ {\sigma _{{\text{HH}}}} $/$ {\sigma _{{\text{VV}}}} $ and the HH–VV phase difference vary with local slope, where $ \sigma $ denotes the backscattering coefficients. H and V represent horizontal and vertical polarizations, respectively. We introduce a parameter v to quantify the uniformity of the polarization orientation angle distribution and use it to identify suitable lunar calibration sites. Simulation results show that, as terrain slopes become more uniform, the copolarization ratio distribution converges toward unity and the phase difference distribution approaches 0°. Combining rough-surface electromagnetic scattering simulations with Chandrayaan-2 polarimetric observations, we further develop a statistically constrained estimator for determining the minimum number of observations required for robust calibration. This work provides both a theoretical basis and a practical pathway for achieving relative calibration of lunar polarimetric SAR systems.
- Deep Space Exploration /
- Moon /
- SAR Calibration /
- Chang’e-7 /
- SAR polarization

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图 1 定标场筛选与分析流程图

Figure 1. Flowchart of calibration site selection and analysis

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图 2 月球高分辨率DTM数据^[15]

Figure 2. High-resolution Lunar DTM data^[15]

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图 3 选区相关函数分析图

Figure 3. Correlation function analysis of the selected regions

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图 4 全月区域坡度角分布图

Figure 4. Global lunar slope-angle map

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图 5 全月区域的参数v分布图

Figure 5. Global distribution of parameter v

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图 6 不同地貌DEM分布图

Figure 6. DEM distribution of different surface landforms

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图 7 基于参数v阈值的潜在定标场分布图

Figure 7. Distribution map of potential calibration sites based on the v-parameter threshold

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图 8 筛选区域角度分布图

Figure 8. PDF of angle in the selected region

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图 9 极化散射回波对比图

Figure 9. Comparison of simulated polarimetric backscattering echoes

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图 10 不同波段下的介电常数分析图

Figure 10. Comparison of simulated polarimetric backscattering echoes

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图 11 小尺度粗糙影响仿真数据示意图

Figure 11. Illustration of the effects of small-scale roughness

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图 12 不同参数v区域的数据分析示意图

Figure 12. Data analysis for regions with different parameter v

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图 13 选区的极化回波统计分布

Figure 13. Statistical distribution of polarized backscatter in selected region

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表 1 随机粗糙面参数设置

Table 1. Parameters of the randomly generated rough surface model

参数	数值
频率	1.25 GHz
面片个数	250×250×2
面片尺寸	0.005 m
均方根高度s	0.025 m
相关长度L	0.185 m
调制波长$ {\lambda _m} $	0.572 m
介电常数$ \epsilon $^[27]	3.04

下载: 导出CSV

参考文献(28)

[1]	LEE J S, GRUNES M R, and DE GRANDI G. Polarimetric SAR speckle filtering and its implication for classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2363–2373. doi: 10.1109/36.789635.
[2]	CLOUDE S R and POTTIER E. A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498–518. doi: 10.1109/36.485127.
[3]	CAMPBELL D B, CAMPBELL B A, CARTER L M, et al. No evidence for thick deposits of ice at the lunar south pole[J]. Nature, 2006, 443(7113): 835–837. doi: 10.1038/nature05167.
[4]	GAO Yao, ZHAO Fei, HOU Wentao, et al. Analysis of rock abundance on lunar surface and near-surface using Mini-RF SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 9590–9605. doi: 10.1109/JSTARS.2023.3323510.
[5]	SPUDIS P D, BUSSEY D B J, BALOGA S M, et al. Initial results for the north pole of the Moon from Mini-SAR, Chandrayaan-1 mission[J]. Geophysical Research Letters, 2010, 37(6): L06204. doi: 10.1029/2009GL042259.
[6]	RANEY R K, SPUDIS P D, BUSSEY B, et al. The Lunar Mini-RF radars: Hybrid polarimetric architecture and initial results[J]. Proceedings of the IEEE, 2011, 99(5): 808–823. doi: 10.1109/JPROC.2010.2084970.
[7]	SPUDIS P D, BUSSEY D B J, BALOGA S M, et al. Evidence for water ice on the Moon: Results for anomalous polar craters from the LRO Mini-RF imaging radar[J]. Journal of Geophysical Research: Planets, 2013, 118(10): 2016–2029. doi: 10.1002/jgre.20156.
[8]	BHIRAVARASU S S, CHAKRABORTY T, PUTREVU D, et al. Chandrayaan-2 dual-frequency synthetic aperture radar (DFSAR): Performance characterization and initial results[J]. The Planetary Science Journal, 2021, 2(4): 134. doi: 10.3847/PSJ/abfdbf.
[9]	WANG Yingjie, WANG R, LIU Kaiyu, et al. Lunar microwave imaging radar (LMIR)[C]. The 14th European Conference on Synthetic Aperture Radar, Leipzig, Germany, 2022: 1–3.
[10]	WANG Chi, JIA Yingzhuo, XUE Changbin, et al. Scientific objectives and payload configuration of the Chang’E-7 mission[J]. National Science Review, 2024, 11(2): nwad329. doi: 10.1093/nsr/nwad329.
[11]	VAN ZYL J J. Calibration of polarimetric radar images using only image parameters and trihedral corner reflector responses[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(3): 337–348. doi: 10.1109/36.54360.
[12]	SARABANDI K. Calibration of a polarimetric synthetic aperture radar using a known distributed target[C]. IEEE International Geoscience and Remote Sensing Symposium, Tokyo, Japan, 1993: 968–970. doi: 10.1109/IGARSS.1993.322167.
[13]	MCKERRACHER P L, JENSEN J R, SEQUEIRA H B, et al. Mini-RF calibration, a unique approach to on-orbit synthetic aperture radar system calibration[C]. The 41st Lunar and Planetary Science Conference (LPSC XLI), The Woodlands, USA, 2010: 2352.
[14]	TANG Zhencheng, LIU Jianjun, WANG Xing, et al. Physical and mechanical characteristics of lunar soil at the Chang’E-4 landing site[J]. Geophysical Research Letters, 2020, 47(22): e2020GL089499. doi: 10.1029/2020GL089499.
[15]	WU Bo, LI Yuan, LIU W C, et al. Centimeter-resolution topographic modeling and fine-scale analysis of craters and rocks at the Chang’E-4 landing site[J]. Earth and Planetary Science Letters, 2021, 553: 116666. doi: 10.1016/j.jpgl.2020.116666.
[16]	LI Yueyang and FA Wenzhe. Centimeter-scale surface roughness of the Chang’E landing regions and implications for radar scattering mechanisms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 4510316. doi: 10.1109/TGRS.2024.3454034.
[17]	ULABY F T and LONG D G. Microwave Radar and Radiometric Remote Sensing[M]. Ann Arbor, USA: University of Michigan Press, 2014: 451–452.
[18]	FUNG A K. Microwave Scattering and Emission Models and Their Applications[M]. Boston, USA, Artech House, 1994: 232–245.
[19]	PANCIERA R, TANASE M A, LOWELL K, et al. Evaluation of IEM, Dubois, and Oh radar backscatter models using airborne L-band SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4966–4979. doi: 10.1109/TGRS.2013.2286203.
[20]	CHEN Kunshan, WU T D, and FUNG A K. A study of backscattering from multiscale rough surface[J]. Journal of Electromagnetic Waves and Applications, 1998, 12(7): 961–979. doi: 10.1163/156939398X01187.
[21]	YANG Ying, CHEN Kunshan, and JIANG Rui. Modeling and analysis of microwave emission from multiscale soil surfaces using AIEM model[J]. Remote Sensing, 2022, 14(22): 5899. doi: 10.3390/rs14225899.
[22]	GUO Mingde, CHEN Kunshan, YANG Ying, et al. On the MCF model for predicting radar ocean backscatter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 2002715. doi: 10.1109/TGRS.2024.3370630.
[23]	JIN Yaqiu and XU Feng. Polarimetric Scattering and SAR Information Retrieval[M]. Hoboken, USA: John Wiley & Sons Singapore Pte. Ltd., 2013: 1–27. doi: 10.1002/9781118188149.
[24]	TSANG L and KONG J A. Scattering of Electromagnetic Waves: Advanced Topics[M]. New York, USA: John Wiley & Sons, Inc., 2001: 66–72. doi: 10.1002/0471224278.
[25]	NGHIEM S V, YUEH S H, KWOK R, et al. Symmetry properties in polarimetric remote sensing[J]. Radio Science, 1992, 27(5): 693–711. doi: 10.1029/92RS01230.
[26]	SMITH D E, ZUBER M T, JACKSON G B, et al. The lunar orbiter laser altimeter investigation on the lunar reconnaissance orbiter mission[J]. Space Science Reviews, 2010, 150(1/4): 209–241. doi: 10.1007/s11214-009-9512-y.
[27]	SU Yan, WANG Ruigang, DENG Xiangjin, et al. Hyperfine structure of regolith unveiled by Chang’E-5 lunar regolith penetrating radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5110414. doi: 10.1109/TGRS.2022.3148200.
[28]	CASELLA G and BERGER R L. Statistical Inference[M]. 2nd ed. Pacific Grove, USA: Thomson Learning, 2002: 432–442.