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

doi:10.11947/j.AGCS.2026.20260085

Abstract

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

CLC Number:

P207

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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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