An approach to estimate tree height using PolInSAR data constructed by the Sentinel-1 dual-pol SAR data and RVoG model

Abstract

We estimate tree heights using polarimetric interferometric synthetic aperture radar (PolInSAR) data constructed by the dual-polarization (dual-pol) SAR data and random volume over the ground (RVoG) model. Considering the Sentinel-1 SAR dual-pol (S_VV, vertically transmitted and vertically received and S_VH, vertically transmitted and horizontally received) configuration, one notes that S_HH, the horizontally transmitted and horizontally received scattering element, is unavailable. The S_HH data were constructed using the S_VH data, and polarimetric SAR (PolSAR) data were obtained. The proposed approach was first verified in simulation with satisfactory results. It was next applied to construct PolInSAR data by a pair of dual-pol Sentinel-1A data at Duke Forest, North Carolina, USA. According to local observations and forest descriptions, the range of estimated tree heights was overall reasonable. Comparing the heights with the ICESat-2 tree heights at 23 sampling locations, relative errors of 5 points were within ±30 ​%. Errors of 8 points ranged from 30 ​% to 40 ​%, but errors of the remaining 10 points were >40 ​%. The results should be encouraged as error reduction is possible. For instance, the construction of PolSAR data should not be limited to using S_VH, and a combination of S_VH and S_VV should be explored. Also, an ensemble of tree heights derived from multiple PolInSAR data can be considered since tree heights do not vary much with time frame in months or one season.

Keywords

Constructed polarimetric SAR data / Dual polarization Sentinel-1 SAR data / Polarimetric interferometric SAR / Random volume over the ground model / Tree height estimation

Declaration of competing interest

The authors declare no conflicts of interest.

Acknowledgments

Sentinel-1A datasets were downloaded at https://vertex.daac.asf.alaska.edu/of Alaska Satellite Facility, Alaska, USA. ICESat-2 data were downloaded from the US/NASA website, https://search.earthdata.nasa.gov/search. Data analyses were primarily conducted using the public PolSARPro software (http://step.esa.int/main/toolboxes/).

References

[1] Y. Wang, Synthetic aperture radar, inversion of aboveground biomass in forested environments: Balancing curiosity- and application-driven acts, J. Remote Sens. 23 (5) (Sept. 2019) 809-812.

[2] C. Choi, V. Cazcarra-Bes, R. Guliaev, et al., Large-scale forest height mapping by combining TanDEM-X and GEDI data, IEEE J.-Stars 16 (Feb. 2023) 2374-2385.

[3] K.P. Papathanassiou, S.R. Cloude, Single-baseline polarimetric SAR interferometry, IEEE T. Geosci. Remote 39 (11) (Nov. 2001) 2352-2363.

[4] R.N. Treuhaft, P.R. Siqueira, Vertical structure of vegetated land surfaces from interferometric and polarimetric radar, Radio Sci. 35 (1) (Jan.–Feb. 2000) 141-177.

[5] S.R. Cloude, K.P. Papathanassiou, Three-stage inversion process for polarimetric SAR interferometry, IEE P.-Radar Son. Nav 150 (3) (Jun. 2003) 125-134.

[6] F.-L. Xue, X. Wang, F. Xu, Y. Wang, Polarimetric SAR interferometry: a tutorial for analyzing system parameters, IEEE Geosc. Rem. Sen. M. 8 (2) (Feb. 2020) 83-107.

[7] C.-C. Wang, L. Wang, H.-Q. Fu, Q.-H. Xie, J.-J. Zhu, The impact of forest density on forest height inversion modeling from polarimetric InSAR data, Remote Sens.-Basel 8 (4) (Mar. 2016) 1-13. 291.

[8] F. Kugler, D. Schulze, I. Hajnsek, H. Pretzsch, K.P. Papathanassiou, TanDEM-X Pol-InSAR performance for forest height estimation, IEEE T. Geosci. Remote 52 (10) (Oct. 2014) 6404-6422.

[9] F. Garestier, P.C. Dubois-Fernandez, K.P. Papathanassiou, Pine forest height inversion using single-pass X-band PolInSAR data, IEEE T. Geosci. Remote 46 (1) (Jan. 2008) 59-68.

[10] S. Kumar, S.K. Joshi, H. Govil, Spaceborne PolSAR tomography for forest height retrieval, IEEE J.-Stars 10 (12) (Dec. 2017) 5175-5185.

[11] U. Khati, G. Singh, S. Kumar, Potential of space-borne polinsar for forest canopy height estimation over India—a case study using fully polarimetric L-, C-, and X-band SAR data, IEEE J.-Stars 11 (7) (Jul. 2018) 2406-2416.

[12] X. Wang, F. Xu, Determination of the optimum k z for L-band PolInSAR forest height estimation, IEEE Geosci. Remote S. 20 (Jul. 2023) 4007705:1-5.

[13] D.-D. Li, H.-L. Lu, C. Li, L. Mohaisen, W.-P. Jing, Forest height inversion by convolutional neural networks based on L-band PolInSAR data without prior knowledge dependency, IEEE J.-Stars 16 (Oct. 2023) 10394-10405.

[14] M.J. Soja, S. Quegan, M.M. d, et al., Mapping above-ground biomass in tropical forests with ground-cancelled P-band SAR and limited reference data, Remote Sens. Environ. 253 (Feb. 2021) 112153:1-16.

[15] Z.-M. Liao, B.-B. He, X.-W. Quan, A multibaseline forest height inversion method to solve three general problems in P-band repeat-pass PolInSAR data, IEEE T. Geosci.Remote 61 (Jul. 2023) 4404915: 1–15.

[16] W.-X. Fu, H.-D. Guo, X.-W. Li, B.-S. Tian, Z.-C. Sun, Extended three-stage polarimetric SAR interferometry algorithm by dual-polarization data, IEEE T. Geosci. Remote 54 (5) (May 2016) 2792-2802.

[17] H.-M. Li, Y. Wang, X.-L. Luo, Tree height estimation at plateau mountains, northwestern Sichuan, China using dual pol-InSAR data, in: Proc. of IEEE Intl. Geoscience and Remote Sensing Symposium, Beijing, China, (2016), pp. 4698-4701.

[18] Y. Chen, Y. Wang, Y. Yan, An algorithm to estimate tree height with insar technique and dual-pol ALOS/PALSAR datasets, in: Proc. of IEEE Intl. Geoscience and Remote Sensing Symposium, Brussels, Belgium, (2021), pp. 5937-5940.

[19] D.-F. Duan, Y. Wang, X.-L. Luo, A modified ESPRIT algorithm to estimate tree height using dual-polarization InSAR datasets, in: Proc. of 12th European Conf. on Synthetic Aperture Radar, Aachen, German, (2018), pp. 1-4.

[20] D.-F. Duan, Y. Wang, H. Li, Tree height estimation using the three-stage algorithm and HH+HV dual-polarization data, in: Proc. of IEEE Intl. Geoscience and Remote Sensing Symposium, Yokohama, Japanese, (2019), pp. 3229-3232.

[21] D.-Y. Liu, Y. Wang, Estimation of tree height using the ESPRIT algorithm with ALOS/PALSAR dual-pol InSAR datasets, in: Proc. of 13th European Conf. on Synthetic Aperture Radar, Leipzig, German, (2021), pp. 1-4.

[22] C.F. Bohren, D.R. Huffman, Absorption and Scattering of Light by Small Particles, John Wiley and Sons, New York, USA, (1983).

[23] W.-T. An, Y. Cui, J. Yang, Three-component model-based decomposition for polarimetric SAR data, IEEE T. Geosci. Remote 48 (6) (Jun. 2010) 2732-2739.

[24] Y. Wang, E.S. Kasischke, J.M. Melack, F.W. Davis, N.L. Jr. Christensen, The effects of changes in loblolly pine biomass and soil moisture on ERS-1 SAR backscatter, Remote Sens. Environ. 49 (1) (Jul. 1994) 25-31.

[25] Y. Wang, F.W. Davis, J.M. Melack, Simulated and observed backscatter at P-, L-, and C-bands from ponderosa pine stands, IEEE T. Geosci. Remote 31 (4) (Jul. 1993) 871-879.