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Digital terrain, surface, and canopy height model generation with dual-baseline low-frequency InSAR over forest areas

Zhu, Jianjun, Xie, Yanzhou, Fu, Haiqiang, Wang, Changcheng, Wang, Huiqiang, Liu, Zhiwei, Xie, Qinghua·2023·Journal of Geodesy
DOI 10.1007/s00190-023-01791-5
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  • The most commonly used remote sensing acquisition techniques, such as interferometry and stereometry, are unable to capture elevation information for the bare earth surface.

    “the most commonly used remote sensing acquisition techniques, such as interferometry (e.g., radar sensors, using X-radar instruments) and stereometry (e.g., panchromatic sensors), are unable to capture elevation information for the bare earth surface [17,18].”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [17]

  • DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation.

    “DEM datasets tend to exhibit elevation values influenced by vegetation height and coverage, compromising the accuracy of models in representing terrain elevation.”

    From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [17]

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Research works printed but not linked: 89

  1. 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 · DOI
  2. 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 · DOI
  3. Ballester-Berman JD (2020) Reviewing the role of the extinction coefficient in radar remote sensing. https://doi.org/10.48550/arXiv.2012.02609 · DOI
  4. Bamler R, Hartl P (1998) Synthetic aperture radar interferometry. Inverse Prob 14:R1 · DOI
  5. 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 · DOI
  6. 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 · DOI
  7. 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 · DOI
  8. 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 · DOI
  9. 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 · DOI
  10. 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 · DOI
  11. Cloude SR (2006) Polarization coherence tomography. Radio Sci 41:1–27. https://doi.org/10.1029/2005RS003436 · DOI
  12. Cloude SR (2009) Polarisation: applications in remote sensing. Oxford University Press, London, U.K. · DOI
  13. Cloude SR, Papathanassiou KP (1998) Polarimetric SAR interferometry. IEEE Trans Geosci Remote Sens 36(5):1551–1565. https://doi.org/10.1109/36.718859 · DOI
  14. 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 · DOI
  15. 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 · DOI
  16. 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 · DOI
  17. 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 · DOI
  18. 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 · DOI
  19. 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 · DOI
  20. 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 · DOI
  21. 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 · DOI · Paper page
  22. 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 · DOI
  23. 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 · DOI
  24. 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 · DOI
  25. 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 · DOI
  26. 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 · DOI
  27. 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 · DOI
  28. 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 · DOI
  29. 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 · DOI
  30. 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 · DOI
  31. 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 · DOI
  32. 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 · DOI
  33. 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 · DOI
  34. Hanssen RF (2001) Radar interferometry: data interpretation and error analysis. Kluwer Academic Publisher, Dordrecht, Netherlands · DOI
  35. 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 · DOI
  36. 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 · DOI
  37. 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 · DOI
  38. 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 · DOI
  39. 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 · DOI
  40. 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 · DOI
  41. 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 · DOI
  42. 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 · DOI
  43. 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 · DOI
  44. 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 · DOI
  45. 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 · DOI
  46. 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/.
  47. 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 · DOI
  48. 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
  49. 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 · DOI
  50. 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 · DOI
  51. 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 · DOI
  52. 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 · DOI
  53. 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 · DOI
  54. 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 · DOI
  55. 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 · DOI · Paper page
  56. 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 · DOI
  57. 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 · DOI
  58. 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 · DOI
  59. 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 · DOI
  60. 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 · DOI
  61. 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 · DOI
  62. 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 · DOI
  63. 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 · DOI · Paper page
  64. 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 · DOI
  65. 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 · DOI
  66. 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 · DOI
  67. 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 · DOI
  68. 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 · DOI
  69. 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 · DOI
  70. 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 · DOI
  71. 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 · DOI
  72. 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 · DOI · Paper page
  73. 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
  74. 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 · DOI
  75. 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 · DOI
  76. 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 · DOI
  77. 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 · DOI
  78. Tao B et al (2019) Error theory and foundation of surveying adjustment. The Wuhan University Press, Wuhan
  79. 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 · DOI
  80. 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 · DOI
  81. 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 · DOI
  82. 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 · DOI
  83. 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 · DOI
  84. 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 · DOI
  85. 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 · DOI
  86. 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 · DOI
  87. 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 · DOI · Paper page
  88. 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 · DOI
  89. Zhou Q (2017) Digital elevation model and digital surface model. Int Encyclopedia Geography. https://doi.org/10.1002/9781118786352.wbieg0768 · DOI

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Metadata

Authors
Zhu, J., Xie, Y., Fu, H., Wang, C., Wang, H., Liu, Z., & Xie, Q.
ORCID
Xie, Yanzhou
Venue
Journal of Geodesy
Publisher
Springer Science and Business Media LLC
Published
2023-11
Volume
97
Issue
11
ISSN
  • 0949-7714
  • 1432-1394
DOI
10.1007/s00190-023-01791-5
Cited by
16
References
1
Language
en
Licence
Funders
  • National Natural Science Foundation of China (41820104005, 41904004, 42030112)
  • Natural Science Foundation of Hunan Province (2021JJ30808)
  • Hunan Provincial Innovation Foundation for Postgraduate (CX20200324)
  • Postgraduate Scientific Research Innovation Project of Central South University (2022ZZTS0307)

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