Large-scale sub-canopy topography estimation from TanDEM-X InSAR and ICESat-2 data using machine learning method

被引：0

作者：

Hu, Huacan ^{[1
]}

Zhu, Jianjun ^{[1
]}

Fu, Haiqiang ^{[1
]}

Lopez-Sanchez Juan, M. ^{[2
]}

Cristina, Gómez ^{[3
,4
]}

Zhang, Tao ^{[1
]}

Liu, Kui ^{[1
]}

机构：

[1] School of Geosciences and Info-Physics, Central South University, Changsha

[2] Institute for Computer Research, University of Alicante, Alicante

[3] iuFOR-EiFAB, University of Valladolid, Campus Duques de Soria

[4] School of Geoscience, University of Aberdeen, Aberdeen

来源：

National Remote Sensing Bulletin | 2025年 / 29卷 / 01期

基金：

中国国家自然科学基金;

关键词：

ICESat-2; InSAR; machine learning; phase center height; sub-canopy topography; TanDEM-X;

D O I：

10.11834/jrs.20233152

中图分类号：

学科分类号：

摘要：

Digital Elevation Models (DEMs) are indispensable data sources for natural resource investigation, climate change analysis, and disaster monitoring and assessment. TanDEM-X mission, as the first twin-satellite Interferometric Synthetic Aperture Radar (InSAR) system, has successfully obtained a high-precision global DEM with 12 m resolution. However, limited by the penetration capability of shortwave signals, DEMs acquired in dense forest areas are usually contaminated by forest canopy signals and are difficult to meet practical applications. The Phase Center Height (PCH) caused by forest volume scattering needs to be removed from InSAR-derived DEM to obtain sub-canopy topography. Unfortunately, TanDEM-X acquires single-baseline, single-polarization data in the global standard mode, which is difficult to meet the needs of existing model solutions and requires the introduction of external data. In this study, we propose a machine learning-based method to estimate sub-canopy topography by combining TanDEM-X InSAR, ICESat-2, and Landsat 8 OLI data. The effectiveness of the proposed method was tested and validated in the Gabon rainforest and the Spanish boreal forest. In the Gabon rainforest test site, compared with that of the airborne LiDAR Digital Terrain Model (DTM), the Root-Mean-Square errors (RMSEs) of the InSAR DEMs corresponding to two locations are 14.70 m and 18.58 m. After PCH removal, the accuracy is improved to 5.54 m and 5.86 m, which represents an improvement of over 60%. In the Spanish northern forest test site with complex terrain, the RMSE of sub-canopy topography decreased from 6.05—9.10 m to 3.06—4.42 m. In addition, we investigate the necessity of the proposed method to use InSAR observations and the effect of the accuracy of the ICESat-2 control points used on the sub-canopy topography estimation. These satisfactory results demonstrate the potential of the proposed method in estimating sub-canopy topography for future spaceborne InSAR missions (e. g., TanDEM-L and LT-1) when only single-baseline, single-polarization data are available. Furthermore, by combining the high resolution of TanDEM-X and the strong penetration of BIOMASS, the proposed method has the potential to estimate sub-canopy topography with higher accuracy and resolution in the future. © 2025 Science Press. All rights reserved.

引用

页码：190 / 201

页数：11

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