Abstract
Interferometric synthetic aperture radar (InSAR) technology can be used to produce a high spatial resolution digital elevation model (DEM) on a global scale. However, above-ground vegetation strongly biases the DEM accuracy in forested areas by shifting the scattering phase center below the top of the canopy and above the underlying topography. Such a bias is related to the radar wavelength, forest structure, dielectric property, radar incidence angle, and terrain slope. In this paper, based on the strong penetration characteristics of the low-frequency (P-band) SAR signal, the time–frequency (TF) analysis is introduced to increase the InSAR observation space, where the decomposed sublook images can also be utilized to perform interferometry. By analyzing and modeling the sublook interferograms, a comprehensive dual-baseline framework for correcting the InSAR phase shifting is proposed to generate accurate digital terrain, surface, and canopy height models (DTM, DSM, and CHM) over forest areas. The proposed method is validated using P-band InSAR datasets acquired above the boreal coniferous forest in Remningstorp, southern Sweden, and the rainforest in Lopé, Gabon, Africa. For the boreal forest, the root-mean-square errors (RMSEs) in terms of the DTM, CHM, and DSM range from 2 to 4 m, while for the tropical rainforest with complicated topography, the RMSEs of the three elevation models range from 5 to 8 m compared with light detection and ranging (LiDAR) references. The high consistency between the InSAR-derived DTM/DSM and references demonstrates the effectiveness and stability of the proposed method and represents an improvement of 49–70% compared to the raw InSAR DEM.
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References
Airbus Defence and Space (2021) WorldDEM™ Neo - The next level of global Elevation Data. https://www.intelligence-airbusds.com/imagery/reference-layers/worlddem/
Arcioni M, Bensi P, Fehringer M, Fois F, Hélière F, Lin CC, Scipal K (2014) The Biomass mission, status of the satellite system. IEEE Int Geosci Remote Sens Symp 1413–1416. https://doi.org/10.1109/IGARSS.2014.6946700
Ballester-Berman JD, Vicente-Guijalba F, Lopez-Sanchez JM (2015) A simple RVoG test for PolInSAR data. IEEE J Sel Topics Appl Earth Observ in Remote Sens 8:1028–1040. https://doi.org/10.1109/JSTARS.2014.2379438
Ballester-Berman JD (2020) Reviewing the role of the extinction coefficient in radar remote sensing. https://doi.org/10.48550/arXiv.2012.02609
Bamler R, Hartl P (1998) Synthetic aperture radar interferometry. Inverse Prob 14:R1
Baugh CA, Bates PD, Schumann G, Trigg MA (2013) SRTM vegetation removal and hydrodynamic modeling accuracy. Water Resour Res 49(9):5276–5289. https://doi.org/10.1002/wrcr.20412
Birge TR (1932) The calculation of errors by the method of least squares. Phys Rev 40:207–227. https://doi.org/10.1103/PhysRev.40.207
Bonan GB (2008) Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320:1444–1449. https://doi.org/10.1126/science.1155121
Bondur VG, Chimitdorzhiev TN, Dmitriev AV, Dagurov PN (2019) Spatial anisotropy assessment of the forest vegetation heterogeneity at various azimuth angles of the radar polarimetric sensing. Issledovanie Zemli Iz Kosmosa 3:92–103. https://doi.org/10.1134/S0001433819090093
Caicoya AT, Kugler F, Hajnsek I, Papathanassiou KP (2016) Largescale biomass classification in boreal forests with TanDEM-X data. IEEE Trans Geosci Remote Sensing 54(10):5935–5951. https://doi.org/10.1109/TGRS.2016.2575542
Chen CW, Zebker HA (2001) Two-dimensional phase unwrapping with use of statistical models for cost function in nonlinear optimization. J Opt Soc Am A 18(2):338–351
Cloude SR (2006) Polarization coherence tomography. Radio Sci 41:1–27. https://doi.org/10.1029/2005RS003436
Cloude SR (2009) Polarisation: applications in remote sensing. Oxford University Press, London, U.K.
Cloude SR, Papathanassiou KP (1998) Polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 36(5):1551–1565. https://doi.org/10.1109/36.718859
Cloude SR, Papathanassiou KP (2003) Three-stage inversion process for polarimetric SAR interferometry. IET Radar Sonar Navig 150:125–134. https://doi.org/10.1049/ip-rsn:20030449
Cloude SR (2002) Robust parameter estimation using dual baseline polarimetric SAR interferometry. In: Geoscience and remote sensing symposium, 2002. ’GARSS'02. 2002 IEEE International. 838–840. https://doi.org/10.1109/IGARSS.2002.1025702
D’Alessandro MM, Tebaldini S (2019) Digital terrain model retrieval in tropical forests through P-band SAR tomography. IEEE Trans Geosci Remote Sens 57(9):6774–6781. https://doi.org/10.1109/TGRS.2019.2908517
D’Annunzio R, Sandker M, Finegold Y, Min Z (2015) Projecting global forest area towards 2030. For Ecol Manage 352:124–133. https://doi.org/10.1016/j.foreco.2015.03.014
Dall J (2007) InSAR elevation bias caused by penetration into uniform volumes. IEEE Trans Geosci Remote Sens 45(7):2319–2324. https://doi.org/10.1109/TGRS.2007.896613
Das A, Kumar R, Rosen P (2021) Nisar mission overview and updates on ISRO science plan. In: 2021 IEEE international India geoscience and remote sensing symposium (InGARSS). https://doi.org/10.1109/InGARSS51564.2021.9791979
Denbina M, Simard M, Hawkins B (2018) Forest height estimation using multibaseline PolInSAR and sparse lidar data fusion. IEEE J Sel Topics Appl Earth Observ in Remote Sens 11:3415–3433. https://doi.org/10.1109/JSTARS.2018.2841388
Dubayah R et al (2020) The global ecosystem dynamics investigation: high resolution laser ranging of the earth’s forests and topography. Sci Remote Sens 1:100002. https://doi.org/10.1016/j.srs.2020.100002
Dubois-Fernandez PC, Souyris JC, Angelliaume S et al (2008) The compact polarimetry alternative for spaceborne SAR at low frequency. IEEE Trans Geosci Remote Sens 46(10):3208–3222. https://doi.org/10.1109/TGRS.2008.919143
Essebtey EIS, Villard L, Borderies P, Koleck T, Burban B, Toan TL (2021) Long-term trends of P-band temporal decorrelation over a tropical dense forest-experimental results for the BIOMASS mission. IEEE Trans Geosci Remote Sens 60:1–15. https://doi.org/10.1109/TGRS.2021.3082395
ESA, “BIOMASS Report for mission selection,” Paris, France, ESA SP-1324/1, May 2012. Online available: https://earth.esa.int/eogateway/documents/20142/37627/BIOMASS-Report-for-Mission-Selection-An-Earth-Explorer-to-observe-forest-biomass.pdf/c8e17ab9-d6a8-6a48-eb9d-9cd4c1209a1c?version=1.0&t=1623407751317
Ferreira VS, Martins SG, Figueira NM, Pochmann PGC (2021) The use of a digital surface model with virtual reality in the Amazonian context. In: International conference on electrical, computer and energy technologies (ICECET). https://doi.org/10.1109/ICECET52533.2021.9698550
Ferro-Famil L, Reigber A, Pottier E, Boerner WM (2003) Scene characterization using subaperture polarimetric SAR data. IEEE Trans Geosci Remote Sens 41:2264–2276. https://doi.org/10.1109/TGRS.2003.817188
Fu HQ, Zhu JJ, Wang CC, Wang HQ, Zhao R (2017) Underlying topography estimation over forest areas using high-resolution P-band single-baseline PolInSAR data. Remote Sens 9(4):363. https://doi.org/10.3390/rs9040363
Fu HQ, Zhu JJ, Wang CC, Li ZW (2018a) Underlying topography extraction over forest areas from multi-baseline PolInSAR data. J Geod 92:727–741. https://doi.org/10.1007/s00190-017-1091-1
Fu HQ, Zhu JJ, Wang CC, Zhao R, Xie QH (2018b) Atmospheric effect correction for InSAR with wavelet decomposition-based correlation analysis between multipolarization interferograms. IEEE Trans Geosci Remote Sens 56(10):5614–5625. https://doi.org/10.1109/TGRS.2018.2821716
Fu HQ, Zhu JJ, Wang CC, Zhao R, Xie QH (2019) Underlying topography estimation over forest areas using single-baseline InSAR data. IEEE Trans Geosci Remote Sens 57(5):2876–2888. https://doi.org/10.1109/TGRS.2018.2878357
Garestier F, Toan LT (2010) Forest modeling for height inversion using single-baseline InSAR/Pol-InSAR data. IEEE Trans Geosci Remote Sens 48(3):1528–1539. https://doi.org/10.1159/000048217
Garestier F, Dubois-Fernandez P, Champion I (2008a) Forest height inversion using high-resolution P-band Pol-InSAR data. IEEE Trans Geosci Remote Sens 46(11):3544–3559. https://doi.org/10.1109/tgrs.2008.922032
Garestier F, Dubois-Fernandez PC, Papathanassiou KP (2008) Pine forest height inversion using single-pass X-band PolInSAR data. IEEE Trans Geosci Remote Sens 46(1):59–68. https://doi.org/10.1109/TGRS.2007.907602
Gómez C, Lopez-Sanchez J; Romero-Puig N et al (2021) Canopy Height Estimation in Mediterranean Forests of Spain With TanDEM-X Data. IEEE J Sel Top Appl Earth Obs Remote Sens 14:2956–2970. https://doi.org/10.1109/JSTARS.2021.3060691
Hajnsek I, Pardini M, Jäger M, Horn R, Kim JS, Jörg H, Papathanassiou K, Dubois-Fernandez P, Dupuis X, Wasik V (2017) Technical assistance for the development of airborne SAR and geophysical measurements during the AfriSAR campaign,” ESA, Paris, France, Final Rep. 4000114293/15/NL/CT, 2011. [Online]. Available: https://earth.esa.int/documents/10174/134665/AfriSAR-Final-Report
Hajnsek I, Scheiber R, et al (2008) Technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2007 experiment. ESA contract No.: 20755/07/NL/CB. [Online available]
Hanssen RF (2001) Radar interferometry: data interpretation and error analysis. Kluwer Academic Publisher, Dordrecht, Netherlands
Järnstedt J, Pekkarinen A, Tuominen S, Ginzler C, Holopainen M, Viitala R (2012) Forest variable estimation using a high-resolution digital surface model. ISPRS-J Photogramm Remote Sens 74:78–84. https://doi.org/10.1016/j.isprsjprs.2012.08.006
Jiang HJ, Zhang L, Wang Y, Liao MS (2014) Fusion of high-resolution DEMs derived from COSMO-SkyMed and TerraSAR-X InSAR datasets. J Geod 88:587–599. https://doi.org/10.1007/s00190-014-0708-x
Jin G et al (2019) An advanced phase synchronization scheme for LT-1. IEEE Trans Geosci Remote Sens 58:1735–1746. https://doi.org/10.1109/TGRS.2019.2948219
Kramer H, Akca A (1995) Leitfaden zur Waldmesslehre, Sauerländer, Frankfurt p. 145
Keenan RJ, Reams GA et al (2015) Dynamics of global forest area: results from the FAO global forest resources assessment 2015. For Ecol Manage 352:9–20. https://doi.org/10.1016/j.foreco.2015.06.014
Krieger G, Moreira A, Fiedler H, Hajnsek I, Werner M, Younis M, Zink M (2007) TanDEM-X: a satellite formation for high-resolution SAR interferometry. IEEE Trans Geosci Remote Sens 45(11):3317–3341. https://doi.org/10.1109/TGRS.2007.900693
Kugler F, Schulze D, Hajnsek I, Pretzsch H, Papathanassiou KP (2014) TanDEM-X Pol-InSAR performance for forest height estimation. IEEE Trans Geosci Remote Sens 52(10):6404–6422. https://doi.org/10.1109/TGRS.2013.2296533
Kugler F, Lee SK, Hajnsek I, Papathanassiou KP (2015) Forest height estimation by means of Pol-InSAR data inversion: the role of the vertical wavenumber. IEEE Trans Geosci Remote Sens 53(10):5294–5311. https://doi.org/10.1109/tgrs.2015.2420996
Lavalle M, Hensley S (2015) Extraction of structural and dynamic properties of forests from polarimetric interferometric SAR data affected by temporal decorrelation. IEEE Trans Geosci Remote Sens 53(9):4752–4767. https://doi.org/10.1109/TGRS.2015.2409066
Lavalle M, Khun K (2014) Three-baseline InSAR estimation of forest height. IEEE Geosci Remote Sens Lett 11:1737–1741. https://doi.org/10.1109/LGRS.2014.2307583
Lavalle M, Simard M, Hensley S (2012) A temporal decorrelation model for polarimetric radar interferometers. IEEE Trans Geosci Remote Sens 50(7):2880–2888. https://doi.org/10.1109/TGRS.2011.2174367
Lee S-K, Kugler F, Papathanassiou KP, Hajnsek I (2013) Quantification of temporal decorrelation effects at L-band for polarimetric SAR interferometry applications. IEEE J Sel Topics Appl Earth Observ in Remote Sens 6:1351–1367. https://doi.org/10.1109/JSTARS.2013.2253448
Lee SK, Fatoyinbo TE, Lagomasino D, Feliciano E, Trettin C (2018) Multibaseline TanDEM-X mangrove height estimation: the selection of the vertical wavenumber. IEEE J Sel Topics Appl Earth Observ Remote Sens 11(10):3434–3442. https://doi.org/10.1109/JSTARS.2018.2835647
Lee SK, Kugler F, Papathanassiou KP, Hajnsek I (2011) Multibaseline polarimetric SAR interferometry forest height inversion approaches. In Proc. ESA POLinSAR Workshop, 1–7. [Online]. Available: http://elib.dlr.de/74010/.
Lefsky MA (2010) A global forest canopy height map from the moderate resolution imaging spectroradiometer and the geoscience laser altimeter system. Geophys Res Lett 37:78–82
Liu K, Wang R et al (2022) LuTan-1: an innovative L-band Spaceborne SAR Mission. In: 14th European Conference on Synthetic Aperture Radar, vol 2022. EUSAR, 614–618. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9944327
Lei Y, Treuhaft R, Gonçalves F (2020) Automated estimation of forest height and underlying topography over a Brazilian tropical forest with single-baseline single-polarization TanDEM-X SAR interferometry. Remote Sens Environ 252:1–17. https://doi.org/10.1016/j.rse.2020.112132
Li Y, Fu H, Zhu J, Wang L et al (2023) A photon cloud filtering method in forested areas considering the density difference between canopy photons and ground photons. IEEE Trans Geosci Remote Sens 61:1–14. https://doi.org/10.1109/TGRS.2023.3267823
Liang D et al (2020) The processing framework and experimental verification for the noninterrupted synchronization scheme of LuTan-1. IEEE Trans Geosci Remote Sens 59:5740–5750. https://doi.org/10.1109/TGRS.2020.3024561
Liao MS, Wang T, Lu LJ, Zhou WJ, Li DR (2007) Reconstruction of DEMs from ERS-1/2 Tandem data in mountainous area facilitated by SRTM data. IEEE Trans Geosci Remote Sens 45(7):2325–2335. https://doi.org/10.1109/TGRS.2007.896546
Liao Z, He B, Van Dijk AI, Bai X, Quan X (2018) The impacts of spatial baseline on forest canopy height model and digital terrain model retrieval using P-band PolInSAR data. Remote Sens Environ 210:403–421. https://doi.org/10.1016/j.rse.2018.03.033
Lopez-Sanchez JM, Vicente-Guijalba F, Erten E, Campos-Taberner M, Garcia-Haro FJ (2017) Retrieval of vegetation height in rice fields using polarimetric SAR interferometry with TanDEM-X data. Remote Sens Environ 192:30–44. https://doi.org/10.1016/J.RSE.2017.02.004
Markus T, Neumann T, Martino A, Abdalati W, Brunt K, Csatho B et al (2017) The ice, cloud, and land elevation satellite-2 (ICESat-2): science requirements, concept, and implementation. Remote Sens Environ 190:260–273. https://doi.org/10.1016/j.rse.2016.12.029
Meddensa AJH, Vierling LA, Eitel JUH, Jennewein JS, White JC, Wulder MA (2018) Developing 5m resolution canopy height and digital terrain models from WorldView and ArcticDEM data. Remote Sens Environ 218:174–188. https://doi.org/10.1016/j.rse.2018.09’.10
Mette T, Papathanassiou K P, Hajnsek I (2004) Biomass estimation from polarimetric SAR interferometry over heterogeneous forest terrain. Proceeding of IEEE Geosci Remote Sens Symp (IGARSS 2004), 511–514. https://doi.org/10.1109/IGARSS.2004.1369076
Michele M, Paola et al (2018) The global forest/non-forest map from TanDEM-X interferometric SAR data. Remote Sens Environ 205:352–373. https://doi.org/10.1016/j.rse.2017.12.002
Moreira A, Krieger G, Hajnsek I, Papathanassiou KP, Younis M, Lopez-dekker P, Huber S, Villano M, Pardini M (2015) Tandem-L: A highly innovative bistatic SAR mission for global observation of dynamic processes on the Earth’s surface. IEEE Geosci Remote Sens Mag 3(2):8–23. https://doi.org/10.1109/MGRS.2015.2437353
Neuenschwander A, Eric G, White JC, Duncanson L, Montesanod P (2020) Validation of ICESat-2 terrain and canopy heights in boreal forests. Remote Sens Environ 251:1–15. https://doi.org/10.1016/j.rse.2020.1121104
Neumann M, Ferro-Famil L, Reigber A (2010) Estimation of forest structure, ground, and canopy layer characteristics from multibaseline polarimetric interferometric SAR data. IEEE Trans Geosci Remot Sens 48(3):1086–1104. https://doi.org/10.1109/TGRS.2009.2031101
Olesk A, Praks J, Antropov O, Zalite K, Arumäe T, Voormansik K (2016) Interferometric SAR coherence models for characterization of hemiboreal forests using TanDEM-X data. Remote Sens 8:1–23. https://doi.org/10.3390/rs8090700
O’Loughlin FE, Paiva R, Durand M, Alsdorf DE, Bates P (2016) A multi-sensor approach towards a global vegetation corrected SRTM DEM product. Remote Sens Environ 182:49–59. https://doi.org/10.1016/J.RSE.2016.04.018
Papathanassiou KP, Cloude SR (2003) The effect of temporal decorrelation on the inversion of forest parameters from PoI-InSAR data. In: International geoscience and remote sensing symposium, 1429–1431. https://doi.org/10.1109/IGARSS.2003.1294134
Papathanassiou KP, Cloude SR (2001) Single-baseline polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 39(11):2352–2363. https://doi.org/10.1109/36.964971
Poon J, Fraser C, Zhang C (2007) Digital surface models from high resolution satellite imagery. Photogramm Eng Remote Sens. https://doi.org/10.14358/PERS.73.11.1225
Praks J, Antropov O, Hallikainen MT (2012) Lidar-aided SAR interferometry studies in boreal forest: scattering phase center and extinction coefficient at X- and L-band. IEEE Trans Geosci Remote Sens 20(10):3831–3843. https://doi.org/10.1109/TGRS.2012.2185803
Quegan S, Le Toan T, Chave J, Dall J, Exbrayat J-F, Minh DHT, Lomas M, D’Alessandro MM, Paillou P, Papathanassiou K, Rocca F, Saatchi S, Scipal K, Shugart H, Smallman TL, Soja MJ, Tebaldini S, Ulander L, Villard L, Williams M (2019) The European space agency BIOMASS mission: Measuring forest above-ground biomass from space. Remote Sens Environ 227:44–60. https://doi.org/10.1016/j.rse.2019.03.032
Reigber A, Moreira A (2000) First demonstration of airborne SAR tomography using multibaseline L-band data. IEEE Trans Geosci Remote Sens 38(5):2142–2152. https://doi.org/10.1109/36.868873
Riel B, Denbina M, Lavalle M (2018) Uncertainties in forest canopy height estimation from polarimetric interferometric SAR data. IEEE J Sel Topics Appl Earth Observ in Remote Sens 11:3478–3491. https://doi.org/10.1109/JSTARS.2018.2867789
Rizzoli P, Martone M, Gonzalez C, Wecklich C, Tridon DB, Braeutigam B et al (2017) Generation and performance assessment of the global TanDEM-X digital elevation model. ISPRS-J Photogramm Remote Sens 132:119–139. https://doi.org/10.1016/j.isprsjprs.2017.08.008
Schlund M, Baron D, Magdon P, Erasmi S (2019) Canopy penetration depth estimation with TanDEM-X and its compensation in temperate forests. ISPRS-J Photogramm Remote Sens 147:232–241. https://doi.org/10.1016/j.isprsjprs.2018.11.021
Sedehi M, Carbone A, Imbembo E, Hélière F et al (2021) Biomass - A fully polarimetric P-band SAR ESA mission. 13th European Conf Synth Aperture Radar, EUSAR 2021, 238–242. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9472541
Shiroma GHX, Lavalle M (2020) Digital terrain, surface, and canopy height models from InSAR backscatter-height histograms. IEEE Trans Geosci Remote Sens 58(6):3754–3777. https://doi.org/10.1109/TGRS.2019.2956989
Spigai M, Tison C, Souyris JC (2011) Time-frequency analysis in high-resolution SAR imagery. IEEE Trans Geosci Remote Sens 49(7):2699–2711. https://doi.org/10.1109/TGRS.2011.2107914
Stoica P, Babu P, Li J (2011a) SPICE a sparse covariance based estimation method for array processing. IEEE Trans Signal Process 59(2):629–638. https://doi.org/10.1109/TSP.2010.2090525
Stoica P, Babu P, Li J (2011b) New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data. IEEE Trans Signal Process 59(1):35–47. https://doi.org/10.1109/TSP.2010.2086452
Tao B et al (2019) Error theory and foundation of surveying adjustment. The Wuhan University Press, Wuhan
Toan TL, Quegan S, Davidson MWJ, Balzter H, Paillou P, Papathanassiou K, Plummer S, Rocca F, Saatchi S, Shugart H, Ulander L (2011) The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle. Remote Sens Environ 115:2850–2860. https://doi.org/10.1016/j.rse.2011.03.020
Treuhaft RN, Siqueira PR (2000) Vertical structure of vegetated land surfaces from interferometric and polarimetric data. Radio Sci 35(1):141–217. https://doi.org/10.1029/1999RS900108
Treuhaft RN, Madsen SN, Moghaddam M, Zyl JJ (1996) Vegetation characteristics and underlying topography from interferometric data. Radio Sci 31(6):1449–1495. https://doi.org/10.1029/96RS01763
Ulander MHL, Gustavsson A et al. (2011) Final report: technical assistance for the development of airborne SAR and geophysical measurements during the BioSAR 2010 Experiment. ESA contract No. 4000102285/10/NL/JA/ef [Online available] https://earth.esa.int/eogateway/search?text=biosar+2010&category=Document+library
Wang Y, Li G, Ding J, Guo Z, Tang S, Wang C, Huang Q, Liu R, Chen JM (2016) A combined GLAS and MODIS estimation of the global distribution of mean forest canopy height. Remote Sens Environ 174:24–43. https://doi.org/10.1016/j.rse.2015.12.005
Wang HQ, Zhu JJ, Fu HQ, Feng GC, Wang CC (2019) Modeling and robust estimation for the residual motion error in airborne SAR interferometry. IEEE Geosci Remote Sens Lett 16(1):65–69. https://doi.org/10.1109/LGRS.2018.2867868
Wang HQ, Fu HQ, Zhu JJ, Feng GC, Yang ZF et al (2020) Correction of time-varying baseline errors based on multibaseline airborne interferometric data without high-precision DEMs. IEEE Trans Geosci Remote Sens. https://doi.org/10.1109/TGRS.2020.3041056
Xie Q, Zhu J, Wang C, Fu H, Lopez-Sanchez JM, Ballester-Berman JD (2017) A modified dual-baseline PolInSAR method for forest height estimation. Remote Sens 9:819. https://doi.org/10.3390/rs9080819
Xie Y, Fu H, Zhu J, Wang C, Xie Q (2020) A LiDAR-aided multibaseline PolInSAR method for forest height estimation: with emphasis on dual-baseline selection. IEEE Geosci Remote Sens Lett 17:1807–1811. https://doi.org/10.1109/LGRS.2019.2951805
Yamazaki D, Ikeshima D et al (2017) A high-accuracy map of global terrain elevations. Geophys Res Lett 44(11):5844–5853. https://doi.org/10.1002/2017GL072874
Zhang B, Fu HQ, Zhu JJ, Peng X, Xie QH, Lin DF, Liu ZW (2020) A multibaseline PolInSAR forest height inversion model based on fourier-legendre polynomials. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/LGRS.2020.2984129
Zhou Q (2017) Digital elevation model and digital surface model. Int Encyclopedia Geography. https://doi.org/10.1002/9781118786352.wbieg0768
Acknowledgements
This work was partly supported by the National Key Research and Development Program of China (No. 2022YFB3902605), the National Natural Science Foundation of China (No. 41820104005, 41904004, 42030112), the Provincial Natural Science Foundation of Hunan Province (No. 2021JJ30808, No. 2023JJ20061). The Hunan Provincial Innovation Foundation for Postgraduate (No. CX20200324), the Fundamental Research Funds for the Central Universities of Central South University (No. 2022ZZTS0307, 2020zzts668), and the joint doctoral program of China Scholarship Council (202106370124). The authors would also like to thank the German Aerospace Center (DLR) and the European Space Agency (ESA) for providing the P-band SAR data and LiDAR acquisitions. We also thank the LVIS team in Code 61A at NASA Goddard Space Flight Center with support from the University of Maryland, College Park, for providing the LVIS LiDAR data.
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JZ proposed the initial ideas for this study. YX designed and conducted the experiments and wrote the original manuscript for this research. HF also carried out parts of the experiments, discussed and analyzed the results of this work, and provided a large number of modifications to the original manuscript. CW, HW, ZL, and QX helped analyze the results and presented valuable suggestions to improve the manuscript.
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Zhu, J., Xie, Y., Fu, H. et al. Digital terrain, surface, and canopy height model generation with dual-baseline low-frequency InSAR over forest areas. J Geod 97, 100 (2023). https://doi.org/10.1007/s00190-023-01791-5
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DOI: https://doi.org/10.1007/s00190-023-01791-5