{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T22:00:36Z","timestamp":1772056836638,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan)","award":["CUG200619"],"award-info":[{"award-number":["CUG200619"]}]},{"name":"the Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19070202"],"award-info":[{"award-number":["XDA19070202"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41804003"],"award-info":[{"award-number":["41804003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m.<\/jats:p>","DOI":"10.3390\/rs13112147","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T03:45:29Z","timestamp":1622432729000},"page":"2147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Forest Height Estimation from a Robust TomoSAR Method in the Case of Small Tomographic Aperture with Airborne Dataset at L-Band"],"prefix":"10.3390","volume":"13","author":[{"given":"Xing","family":"Peng","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"given":"Xinwu","family":"Li","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1277-564X","authenticated-orcid":false,"given":"Yanan","family":"Du","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-3354","authenticated-orcid":false,"given":"Qinghua","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Simard, M., Pinto, N., Fisher, J.B., and Baccini, A. (2011). Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res., 116.","DOI":"10.1029\/2011JG001708"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1016\/j.rse.2011.03.020","article-title":"The BIOMASS mission: Mapping global forest biomass to better understand the terrestrial carbon cycle","volume":"115","author":"Quegan","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6404","DOI":"10.1109\/TGRS.2013.2296533","article-title":"TanDEM-X Pol-InSAR performance for forest height estimation","volume":"52","author":"Kugler","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1016\/j.asr.2017.04.018","article-title":"Polarimetric SAR Interferometry based Modeling for Tree Height and Aboveground Biomass Retrieval in a Tropical Deciduous Forest","volume":"60","author":"Kumar","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fu, H.Q., Wang, C.C., Zhu, J.J., Xie, Q., and Zhang, B. (2016). Estimation of pine forest height and underlying DEM using multi-baseline P-band PolInSAR data. Remote Sens., 8.","DOI":"10.3390\/rs8100820"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/36.868873","article-title":"First Demonstration of Airborne SAR Tomography Using Multibaseline L-band Data","volume":"38","author":"Reigber","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ho Tong Minh, D., Ngo, Y.N., and L\u00ea, T.T. (2021). Potential of P-Band SAR Tomography in Forest Type Classification. Remote Sens., 13.","DOI":"10.3390\/rs13040696"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MGRS.2019.2963093","article-title":"Forest SAR tomography: Principles and applications","volume":"8","author":"Aghababaei","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1007\/s10712-019-09539-7","article-title":"The status of technologies to measure forest biomass and structural properties: State of the art in SAR tomography of tropical forests","volume":"40","author":"Tebaldini","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1109\/LGRS.2018.2819884","article-title":"Forest biomass retrieval from L-band SAR using tomographic ground backscatter removal","volume":"15","author":"Blomberg","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/LGRS.2018.2808681","article-title":"The role of nonlocal estimation in SAR tomographic imaging of volumetric media","volume":"15","author":"Aghababaee","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Tello, M., CazcarraBes, V., Pardini, M., and Papathanassiou, K. (2016, January 10\u201315). Assessment of forest structure estimation by means of SAR tomography: Potential and limitations. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7728999"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1109\/TGRS.2011.2159614","article-title":"Multibaseline Polarimetric SAR Tomography of a Boreal Forest at P- and L- Bands","volume":"50","author":"Tebaldini","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rse.2015.12.037","article-title":"SAR Tomography for the Retrieval of Forest Biomass and Height: Cross-validation at Two Tropical Forest Sites in French Guiana","volume":"175","author":"Minh","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, L., Chen, E., Li, Z., Zhao, L., and Gu, X. (2016, January 10\u201315). Forest above ground biomass estimation from P-band tomography data. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7728996"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5175","DOI":"10.1109\/JSTARS.2017.2741723","article-title":"Spaceborne PolSAR Tomography for Forest Height Retrieval","volume":"10","author":"Kumar","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1857","DOI":"10.1109\/JSTARS.2021.3051081","article-title":"The Performance of Relative Height Metrics for Estimation of Forest Above-Ground Biomass Using L-and X-Bands TomoSAR Data","volume":"14","author":"Yu","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","unstructured":"Gustavo, D., del Campo, M., Reigber, A., and Shkvarko, Y.V. (2016, January 10\u201315). Resolution enhanced SAR tomography: A Nonparametric Iterative Adaptive Approach. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Peng, X., Wang, C., Li, X., Du, Y., Fu, H., Yang, Z., and Xie, Q. (2018). Three-Dimensional Structure Inversion of Buildings with Nonparametric Iterative Adaptive Approach Using SAR Tomography. Remote Sens., 10.","DOI":"10.3390\/rs10071004"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1109\/LGRS.2015.2402124","article-title":"A novel fast approach for SAR tomography: Two-step iterative shrinkage\/thresholding","volume":"12","author":"Wei","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TGRS.2003.809934","article-title":"Three-dimensional Focusing with Multi-pass SAR Data","volume":"41","author":"Fornaro","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4616","DOI":"10.1109\/TGRS.2011.2147321","article-title":"Three-dimensional imaging and scattering mechanism estimation over urban scenes using dual-baseline polarimetric InSAR observations at L-band","volume":"49","author":"Sauer","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1109\/TGRS.2011.2171494","article-title":"Under-foliage Object Imaging Using SAR Tomography and Polarimetric Spectral Estimators","volume":"50","author":"Huang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, Y., Zhang, Q., and Ferro-Famil, L. (2021). Forest Height Estimation Using a Single-Pass Airborne L-Band Polarimetric and Interferometric SAR System and Tomographic Techniques. Remote Sens., 13.","DOI":"10.3390\/rs13030487"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1109\/TGRS.2010.2054099","article-title":"Three dimensional SAR focusing from multi-pass signals using compressive sampling","volume":"40","author":"Budillon","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5283","DOI":"10.1109\/TGRS.2012.2231081","article-title":"Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas","volume":"51","author":"Aguilera","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bi, H., Liu, J., Zhang, B., and Hong, W. (2018). Baseline distribution optimization and missing data completion in wavelet-based CS-TomoSAR. Sci. China Inf. Sci., 61.","DOI":"10.1007\/s11432-016-9068-y"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"El Moussawi, I., Ho Tong Minh, D., Baghdadi, N., Abdallah, C., Jomaah, J., Strauss, O., Lavalle, M., and Ngo, Y.N. (2019). Monitoring Tropical Forest Structure Using SAR Tomography at L-and P-Band. Remote Sens., 11.","DOI":"10.3390\/rs11161934"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Cazcarra-Bes, V., Tello-Alonso, M., Fischer, R., Heym, M., and Papathanassiou, K. (2017). Monitoring of Forest Structure Dynamics by means of L-band SAR Tomography. Remote Sens., 9.","DOI":"10.3390\/rs9121229"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1109\/TGRS.2015.2451992","article-title":"Compressive Sensing for Multibaseline Polarimetric SAR Tomography of Forested Areas","volume":"54","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.1109\/JSTARS.2020.2970595","article-title":"Statistical Regularization for Enhanced TomoSAR Imaging","volume":"13","author":"Nannini","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Peng, X., Li, X., Wang, C., Zhu, J., Liang, L., Fu, H., Du, Y., Yang, Z., and Xie, Q. (2019). SPICE-based SAR Tomography over Forest Areas Using a Small Number of P-band Airborne F-SAR Dataset Characterized by Non-uniformly Distributed Baselines. Remote Sens., 11.","DOI":"10.3390\/rs11080975"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TAES.2010.5417172","article-title":"Source localization and sensing: A nonparametric iterative adaptive approach based on weighted least squares","volume":"46","author":"Yardibi","year":"2010","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/JSTSP.2009.2038964","article-title":"Iterative Adaptive Approaches to MIMO Radar Imaging","volume":"4","author":"Roberts","year":"2010","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3567","DOI":"10.1016\/j.sigpro.2013.03.033","article-title":"Adaptive clutter suppression based on iterative adaptive approach for airborne radar","volume":"93","author":"Yang","year":"2013","journal-title":"Signal Process."},{"key":"ref_36","unstructured":"European Space Agency (2009). Technical Assistance for the Development of Airborne SAR and Geophysical Measurements during the BioSAR 2008 Experiment, European Space Agency. Final Report."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:08:58Z","timestamp":1760162938000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/11\/2147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,29]]},"references-count":36,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["rs13112147"],"URL":"https:\/\/doi.org\/10.3390\/rs13112147","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,29]]}}}