基于子孔径分解和最小二乘配置区域网平差模型的大范围LT-1林下地形反演

doi:10.11947/j.AGCS.2026.20260085

测绘学报 ›› 2026, Vol. 55 ›› Issue (6): 1058-1071.doi: 10.11947/j.AGCS.2026.20260085

• 大地测量学与导航 • 上一篇

基于子孔径分解和最小二乘配置区域网平差模型的大范围LT-1林下地形反演

胡华参¹(), 朱建军¹(), 付海强¹, 韩启金², 王爱春², 张明霞², 李志伟¹

^1.中南大学地球科学与信息物理学院，湖南　长沙　410083
^2.中国资源卫星应用中心，北京　100094

收稿日期:2026-03-11 修回日期:2026-05-06 发布日期:2026-07-28
通讯作者: 朱建军 E-mail:csuhuacan@csu.edu.cn;zjj@csu.edu.cn
作者简介:胡华参（2000—），男，博士生，研究方向为基于InSAR技术的森林高度和林下地形反演。E-mail：csuhuacan@csu.edu.cn
基金资助:
国家自然科学基金(42227801; 42574048)

LT-1 sub-canopy topography inversion based on sub-aperture decomposition and LSC-block adjustment model

Huacan HU¹(), Jianjun ZHU¹(), Haiqiang FU¹, Qijin HAN², Aichun WANG², Mingxia ZHANG², Zhiwei LI¹

^1.School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
^2.China Centre for Resources Satellite Data and Application, Beijing 100094, China

Received:2026-03-11 Revised:2026-05-06 Published:2026-07-28
Contact: Jianjun ZHU E-mail:csuhuacan@csu.edu.cn;zjj@csu.edu.cn
About author:HU Huacan (2000—), male, PhD candidate, majors in forest height and sub-canopy topography estimation based on InSAR technology. E-mail: csuhuacan@csu.edu.cn
Supported by:
The National Natural Science Foundation of China(42227801; 42574048)

摘要/Abstract

摘要：

LT-1作为L波段双站InSAR系统，穿透能力强，具备获取高精度林下地形的潜力。然而，受森林体散射的干扰，LT-1 SAR回波信号中混合了来自地表和冠层的散射贡献，且单极化的LT-1 InSAR数据观测信息不足，无法分离森林散射信号。此外，当前以星载LiDAR作为控制点构建区域网平差模型进行地形系统误差校正时，未考虑InSAR与LiDAR在森林区散射中心不一致的问题。鉴于此，本文提出了基于子孔径分解的频率域机理解译方法，以解决观测信息不足问题；在此基础上，构建了基于多尺度最小二乘配置的区域网平差模型以解决散射中心不一致及系统误差和散射模型误差耦合问题。本文选取了地形和森林类型差异显著的广州和根河试验区进行了测试和验证。结果表明，本文方法估计的林下地形的均方根误差分别为3.09和1.36 m，相对于传统方法获取的InSAR DEM（5.87和4.56 m）分别提升了47.4%和70.2%。与现有全球DEM和公开林下地形产品相比，本文方法亦表现出更高的测高精度。

关键词: LT-1, 林下地形, 子孔径分解, 区域网平差, 多尺度最小二乘配置

Abstract:

LuTan-1 (LT-1) as the L-band bistatic InSAR satellite system, features strong penetration capabilities and holds great potential for retrieving high-precision sub-canopy topography. However, due to the influence of volume scattering in forests, the LT-1 SAR echo signal contains mixed contributions from both the ground surface and forest canopy. The single-polarization LT-1 InSAR data lacks sufficient observational information to effectively separate forest scattering components. Moreover, in current practices, system errors in terrain estimation are typically corrected using a block adjustment model with spaceborne LiDAR data as ground control points. However, these methods neglect the mismatch in scattering centers between InSAR and LiDAR in forested areas, and the alternative approach of relying solely on bare ground control points suffers from the limited number of such points. To address these issues, this study proposes a frequency-domain physical interpretation method based on sub-aperture decomposition to mitigate the issue of insufficient observational information. On this basis, a block adjustment model based on multi-scale least-squares collocation is developed to solve the problems of inconsistent scattering centers and the coupling of system errors and scattering model errors. The proposed method is tested and validated in two study areas with different terrain and forest conditions, namely Guangzhou and Genhe test site. The root mean square errors of the estimated sub-canopy topography are 3.09 and 1.36 m, representing improvements of 47.4% and 70.2% over the conventional InSAR DEMs (5.87 and 4.56 m), respectively. Furthermore, the proposed method exhibits superior elevation accuracy compared to existing global DEMs and publicly available sub-canopy topography products.

Key words: LT-1, sub-canopy topography, sub-aperture decomposition, block adjustment, multi-scale least-squares collocation

中图分类号:

P207

胡华参, 朱建军, 付海强, 韩启金, 王爱春, 张明霞, 李志伟. 基于子孔径分解和最小二乘配置区域网平差模型的大范围LT-1林下地形反演[J]. 测绘学报, 2026, 55(6): 1058-1071.

Huacan HU, Jianjun ZHU, Haiqiang FU, Qijin HAN, Aichun WANG, Mingxia ZHANG, Zhiwei LI. LT-1 sub-canopy topography inversion based on sub-aperture decomposition and LSC-block adjustment model[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(6): 1058-1071.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20260085

http://xb.chinasmp.com/CN/Y2026/V55/I6/1058

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试验区	获取时间	入射角/（°）	有效基线/m	模糊高/m	轨道	数量
广州	2022-10-10	32.18	1551.29	61.05	升轨	5
	2022-11-03	37.32	1482.61	70.68	升轨	5
	2022-11-11	40.05	1423.67	80.13	升轨	5
	2022-11-18	20.79	1853.98	30.01	升轨	5
根河	2022-09-19	29.82	1361.79	58.87	升轨	2
	2022-10-05	39.93	609.26	162.39	升轨	3
	2022-10-11	36.18	2052.88	48.28	降轨	3
	2022-11-11	41.92	2040.65	58.22	降轨	5
	2022-11-20	22.39	1994.55	30.93	降轨	4
	2022-11-22	32.76	727.58	122.49	升轨	3
	2022-11-28	32.93	2091.18	42.67	降轨	5

地形产品	广州试验区			根河试验区
地形产品	MAE	STD	RMSE	MAE	STD	RMSE
LT-1 DEM	3.41	4.47	5.62	4.06	2.63	4.83
林下地形	0.31	3.17	3.19	0.04	1.37	1.38

方法	广州试验区			根河试验区
方法	MAE	STD	RMSE	MAE	STD	RMSE
传统方法	4.59	3.67	5.87	3.87	2.40	4.56
本文方法（单一尺度协方差）	0.46	3.58	3.61	0.25	1.52	1.54
本文方法（多尺度协方差）	0.34	3.07	3.09	0.12	1.35	1.36

地形产品	广州试验区						根河试验区
	ICESat-2全局验证			LiDAR DTM局部验证			ICESat-2全局验证			LiDAR DTM局部验证
	MAE	STD	RMSE	MAE	STD	RMSE	MAE	STD	RMSE	MAE	STD	RMSE
SRTM DEM	3.72	6.26	7.28	1.19	6.30	6.41	2.47	3.09	3.96	3.32	2.43	4.12
COP DEM	3.94	4.62	6.07	4.91	4.25	6.49	5.56	3.56	6.60	5.48	3.59	6.55
LT-1 DEM（传统方法）	3.35	4.28	5.44	4.52	3.58	5.76	4.01	2.58	4.76	3.82	2.37	4.50
FAB DEM	0.54	3.97	4.01	0.08	3.88	3.89	0.39	2.15	2.19	0.06	2.57	2.58
林下地形（本文方法）	0.29	3.15	3.16	0.31	3.02	3.04	0.04	1.35	1.35	0.11	1.34	1.35

基于子孔径分解和最小二乘配置区域网平差模型的大范围LT-1林下地形反演

LT-1 sub-canopy topography inversion based on sub-aperture decomposition and LSC-block adjustment model

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