基于机器学习联合TanDEM-X InSAR和ICESat-2数据估计大范围林下地形  

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

作　　者：胡华参 朱建军[1] 付海强[1] Lopez-Sanchez Juan Manuel Gómez Cristina 张涛 刘奎 HU Huacan;ZHU Jianjun;FU Haiqiang;LOPEZ-SANCHEZ Juan Manuel;GÓMEZ Cristina;ZHANG Tao;LIU Kui(School of Geosciences and Info-Physics,Central South University,Changsha 410083,China;Institute for Computer Research,University of Alicante,Alicante 03690,Spain;iuFOR-EiFAB,University of Valladolid,Campus Duques de Soria 47002,Spain;School of Geoscience,University of Aberdeen,Aberdeen AB243UE,Scotland,UK)

机构地区：[1]中南大学地球科学与信息物理学院,长沙410083 [2]阿利坎特大学计算机研究院,西班牙阿利坎特03690 [3]巴利亚多利德大学可持续森林管理研究所,西班牙杜克斯德索里亚47002 [4]阿伯丁大学地球科学学院,英国苏格兰阿伯丁AB243UE

出　　处：《遥感学报》2025年第1期191-202,共12页NATIONAL REMOTE SENSING BULLETIN

基　　金：国家自然科学基金(编号:41820104005,42030112);湖南省自然科学基金(编号:2021JJ30808)。

摘　　要：双站TanDEM-X InSAR系统已成功应用于生产全球数字高程模型。然而,受X波段SAR信号的穿透能力限制和森林体积散射的影响,在森林地区提取的DEM包含严重森林信号。因此,为降低TanDEM-X InSAR数据估计林下地形过程中森林体散射对InSAR测高的影响,本研究提出了一种基于机器学习联合TanDEM-X InSAR、ICESat-2和Landsat 8数据估计林下地形的方法。为验证所提方法的有效性,选用了两个具有不同地形条件和森林类型特征的试验区(加蓬热带雨林试验区和西班牙北方试验区)进行了测试,并利用高精度机载LiDAR DTM进行精度评定。结果表明:在加蓬热带雨林试验区,所提方法估计林下地形在2个验证区域的RMSE为5.45 m和5.91 m,与InSAR DEM的估测结果14.70 m和18.58 m相比,地形精度提高了60%以上;在西班牙北方森林试验区,林下地形估测的RMSE也从6.05—9.10 m降低到了3.06—4.42 m。综上,本研究为使用双站X波段InSAR系统准确估计大范围林下地形提供了一种有效且稳健的方案。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 availabl

关 键 词：林下地形 合成孔径雷达干涉测量 相位中心高度 机器学习 TanDEM-X ICESat-2 

分 类 号：P2[天文地球—测绘科学与技术]