First demonstration of spaceborne L-band bistatic single-polarization single-baseline SAR interferometry on the retrieval of forest vertical structural information
Yang Lei , Weiliang Li , Yanghai Yu , Xiaotong Liu , Jie Xu , Anmin Fu , Jie Wan , Changcheng Wang , Wenli Huang , Zixuan Qiu , Tao Li , Haiqiang Fu , Yu Liu , Jiancheng Shi
{"title":"First demonstration of spaceborne L-band bistatic single-polarization single-baseline SAR interferometry on the retrieval of forest vertical structural information","authors":"Yang Lei , Weiliang Li , Yanghai Yu , Xiaotong Liu , Jie Xu , Anmin Fu , Jie Wan , Changcheng Wang , Wenli Huang , Zixuan Qiu , Tao Li , Haiqiang Fu , Yu Liu , Jiancheng Shi","doi":"10.1016/j.rse.2025.114916","DOIUrl":null,"url":null,"abstract":"<div><div>This paper shows the first demonstration of spaceborne L-band bistatic InSAR from the Chinese Lutan-1 mission for forest vertical structural information retrieval (in this work, namely, vertical profile, forest height, and underlying topography). With the single-polarization/baseline bistatic InSAR mode of Lutan-1, the measured few-look InSAR phase height histograms compare very well with the GEDI lidar waveforms, both capturing similar characteristics of the forest vertical structural profile. The ground finding approach based on the few-look InSAR phase height histogram is further adapted to incorporate spaceborne lidar measurements from GEDI and ICESat-2/ATLAS for more robust calibration. As for the DTM estimation, two ground finding strategies are developed: one using ample spaceborne lidar samples (with the lidar height as the feature), and the other using limited spaceborne lidar samples (with the few-look InSAR phase height standard deviation as the feature), both of which rely on the statistical model relating the underlying terrain elevation to the statistics of the few-look InSAR histogram. Then, forest height is inverted using the few-look histogram that mimics using lidar waveform to derive height metrics. The large-scale DTM and forest height mosaics of 2.74 million hectares are produced over tropical rainforest of the entire Hainan island of China. Through validation with airborne lidar data, the forest height is estimated to an accuracy of ∼5 m for tropical forest up to 45 m tall (relative error 10–15 %). The InSAR-derived DTM has a negligible bias (mean value of the radar-lidar DTM deviation) as referenced to airborne lidar DTM, with the uncertainties (median absolute deviation or MAD) being dependent on topographic surface slopes: 3 m (<2°), 4 m (2°-6°), 7 m (6°-25°), and 9 m (>25°). This approach sheds light on combining ascending/descending viewing geometries of spaceborne L-band bistatic InSAR data with single polarization/baseline (e.g. Lutan-1 and its follow-on) for large-scale wall-to-wall mapping of forest vertical structural profile, height metrics/biomass, underlying topography, as well as the changes of these forest parameters.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"329 ","pages":"Article 114916"},"PeriodicalIF":11.1000,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425725003207","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
This paper shows the first demonstration of spaceborne L-band bistatic InSAR from the Chinese Lutan-1 mission for forest vertical structural information retrieval (in this work, namely, vertical profile, forest height, and underlying topography). With the single-polarization/baseline bistatic InSAR mode of Lutan-1, the measured few-look InSAR phase height histograms compare very well with the GEDI lidar waveforms, both capturing similar characteristics of the forest vertical structural profile. The ground finding approach based on the few-look InSAR phase height histogram is further adapted to incorporate spaceborne lidar measurements from GEDI and ICESat-2/ATLAS for more robust calibration. As for the DTM estimation, two ground finding strategies are developed: one using ample spaceborne lidar samples (with the lidar height as the feature), and the other using limited spaceborne lidar samples (with the few-look InSAR phase height standard deviation as the feature), both of which rely on the statistical model relating the underlying terrain elevation to the statistics of the few-look InSAR histogram. Then, forest height is inverted using the few-look histogram that mimics using lidar waveform to derive height metrics. The large-scale DTM and forest height mosaics of 2.74 million hectares are produced over tropical rainforest of the entire Hainan island of China. Through validation with airborne lidar data, the forest height is estimated to an accuracy of ∼5 m for tropical forest up to 45 m tall (relative error 10–15 %). The InSAR-derived DTM has a negligible bias (mean value of the radar-lidar DTM deviation) as referenced to airborne lidar DTM, with the uncertainties (median absolute deviation or MAD) being dependent on topographic surface slopes: 3 m (<2°), 4 m (2°-6°), 7 m (6°-25°), and 9 m (>25°). This approach sheds light on combining ascending/descending viewing geometries of spaceborne L-band bistatic InSAR data with single polarization/baseline (e.g. Lutan-1 and its follow-on) for large-scale wall-to-wall mapping of forest vertical structural profile, height metrics/biomass, underlying topography, as well as the changes of these forest parameters.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.