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Comparative validation of the radar interferometry-based SYM12 Using ICESat-2 ATL03 and ground-based GPS measurements

Yıl 2025, Cilt: 7 Sayı: 1, 53 - 68, 30.06.2025

Yunus Kaya

https://doi.org/10.51489/tuzal.1665238

Öz

This study evaluates the performance of the SYM12 digital surface model, derived from radar interferometry between 2011 and 2014, by comparing it with two reference datasets: the ICESat-2 ATL03 lidar altimeter (2018–2023) and 879 GPS ground control points measured in 2024. Relationships between SYM12, ICESat-2 ATL03, and GPS measurements were tested using root mean square error (RMSE), mean absolute error (MAE), and Pearson correlation coefficient analysis. Both unfiltered and interquartile range (IQR) filtered data were analyzed. Results indicate a strong correlation (R > 0.99) between SYM12, ICESat-2 ATL03, and GPS observations in low- and mid-elevation regions, while discrepancies increase in high-elevation and complex terrain areas. Filtering improved the performance of SYM12 relative to ATL03, reducing the RMSE from 35.54 m to 2.24 m and the MAE from 5.10 m to 1.60 m. Notably, RMSE and MAE values remained higher in high-altitude areas. For SYM12-GPS comparisons, the RMSE and MAE were 2.32 m and 1.56 m, respectively, while GPS-ATL03 comparisons yielded RMSE and MAE values of 0.60 m and 0.39 m, respectively. This study underscores the value of integrating newer lidar-based datasets, such as ATL03, to enhance the accuracy of DSMs derived from radar interferometry.

Anahtar Kelimeler

Digital Surface Model (DSM12) ICESat-2 ATL03 radar interferometry lidar altimetry

Teşekkür

I would like to express my gratitude to the General Directorate of Mapping (HGM) for providing the SYM12 and TG20 datasets, and to the National Snow and Ice Data Center (NSIDC) for access to the ICESat-2 ATL03 data.

Kaynakça

  • Anders, N., Smith, M., Suomalainen, J., Cammeraat, E., Valente, J., & Keesstra, S. (2020). Impact of flight altitude and cover orientation on digital surface model (DSM) accuracy for flood damage assessment in Murcia (Spain) using a fixed-wing UAV. Earth Science Informatics, 13, 391–404.
  • Courellis, H. S., Iversen, J. R., Poizner, H., & Cauwenberghs, G. (2016). EEG channel interpolation using ellipsoid geodesic length. In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 540–543). IEEE.
  • Dibs, H., Al-Ansari, N., & Laue, J. (2023). Analysis of remotely sensed imagery and architecture environment for modelling 3D detailed buildings using geospatial techniques. Engineering, 15(5), 328–341.
  • Gao, M., Xing, S., Zhang, G., Zhang, X., & Li, P. (2023). Assessment of ICESat-2’s horizontal accuracy using an iterative matching method based on high-accuracy terrains. Remote Sensing, 15(9), 2236. https://doi.org/10.3390/rs15092236
  • Gazioğlu, C., Aipar, B., Yücel, Z. Y., Müftüoğlu, A. E., Güneysu, C., Ertek, T. A., Demir, V., & Kaya, H. (2014). Morphologic features of Kapıdağ Peninsula and its coasts (NW-Turkey) using by remote sensing and DTM. International Journal of Environment and Geoinformatics, 1(1), 48–63.
  • Guo, K., Guan, M., & Yu, D. (2021). Urban surface water flood modelling–A comprehensive review of current models and future challenges. Hydrology and Earth System Sciences, 25(5), 2843–2860. https://doi.org/10.5194/hess-25-2843-2021
  • Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M. R., Kuemmerle, T., Meyfroidt, P., Mitchard, E. T. A., Reiche, J., Ryan, C. M., & Waske, B. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70. https://doi.org/10.3390/rs8010070
  • Karataş, H., & Yaman, A. (2024). Investigation of the usability of Göktürk-2 data and UAV data for pond construction project. The Egyptian Journal of Remote Sensing and Space Sciences, 27(3), 565–576.
  • Kaya, Y., Sanli, F. B., & Abdikan, S. (2023). Determination of long-term volume change in lakes by integration of UAV and satellite data: The case of Lake Burdur in Türkiye. Environmental Science and Pollution Research, 30(55), 117729–117747.
  • Kılıç, B., Gülgen, F., Çelen, M., Öncel, S., Oruç, H., & Vural, S. (2022). Morphometric analysis of Saz-Çayırova drainage basin using geographic information systems and different digital elevation models. International Journal of Environment and Geoinformatics, 9(2), 177–186.
  • Li, B., Xie, H., Liu, S., Ye, Z., Hong, Z., Weng, Q., Sun, Y., Xu, Q., & Tong, X. (2024). Global DEM product generation by correcting ASTER GDEM elevation with ICESat-2 altimeter data. Earth System Science Data Discussions, 2024, 1–24. https://doi.org/10.5194/essd-2024-3
  • Li, B., Xie, H., Tong, X., Tang, H., & Liu, S. (2023). A global-scale DEM elevation correction model using ICESat-2 laser altimetry data. IEEE Transactions on Geoscience and Remote Sensing.
  • Li, H., Zhao, J., Yan, B., Yue, L., & Wang, L. (2022). Global DEMs vary from one to another: An evaluation of newly released Copernicus, NASA and AW3D30 DEM on selected terrains of China using ICESat-2 altimetry data. International Journal of Digital Earth, 15(1), 1149–1168.
  • Li, Y., Fu, H., Zhu, J., Wu, K., Yang, P., Wang, L., & Gao, S. (2022). A method for SRTM DEM elevation error correction in forested areas using ICESat-2 data and vegetation classification data. Remote Sensing, 14(14), 3380. https://doi.org/10.3390/rs14143380
  • Loew, A., Bell, W., Brocca, L., Bulgin, C. E., Burdanowitz, J., Calbet, X., Donner, R. V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C., Lambert, J. C., Schaepman-Strub, G., Schröder, M., & Verhoelst, T. (2017). Validation practices for satellite‐based Earth observation data across communities. Reviews of Geophysics, 55(3), 779–817. https://doi.org/10.1002/2017RG000562
  • Mesa-Mingorance, J. L., & Ariza-López, F. J. (2020). Accuracy assessment of digital elevation models (DEMs): A critical review of practices of the past three decades. Remote Sensing, 12(16), 2630. https://doi.org/10.3390/rs12162630
  • Moore, I. D., Turner, A. K., Wilson, J. P., Jenson, S. K., & Band, L. E. (1993). GIS and land-surface-subsurface process modeling. In M. F. Goodchild, B. O. Parks, & L. T. Steyaert (Eds.), Environmental Modeling with GIS (pp. 196–230).
  • Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Saba, J., Harbeck, K., ... & Rebold, T. (2020). ATLAS/ICESat-2 L2A global geolocated photon data, version 3. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, United States.
  • Oksanen, J., & Sarjakoski, T. (2005). Error propagation of DEM-based surface derivatives. Computers & Geosciences, 31(8), 1015–1027.
  • Panda, S. S., Rao, M. N., Thenkabail, P. S., Misra, D., & Fitzgerald, J. P. (2016). Remote sensing systems—Platforms and sensors: Aerial, satellite, UAV, optical, radar, and LiDAR. In P. S. Thenkabail (Ed.), Remote Sensing Handbook, Volume I (pp. 3–86). CRC Press.
  • Quamar, M. M., Al-Ramadan, B., Khan, K., Shafiullah, M., & El Ferik, S. (2023). Advancements and applications of drone-integrated geographic information system technology—A review. Remote Sensing, 15(20), 5039. https://doi.org/10.3390/rs15205039
  • Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., & Goldstein, R. M. (2000). Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3), 333–382. https://doi.org/10.1109/5.838084
  • Sahani, J., Kumar, P., Debele, S., Spyrou, C., Loupis, M., Aragão, L., Feredico, P., Shah, M. A. R., & Di Sabatino, S. (2019). Hydro-meteorological risk assessment methods and management by nature-based solutions. Science of the Total Environment, 696, 133936. https://doi.org/10.1016/j.scitotenv.2019.133936
  • Schumann, G. J. P., Muhlhausen, J., & Andreadis, K. M. (2019). Rapid mapping of small-scale river-floodplain environments using UAV SfM supports classical theory. Remote Sensing, 11(8), 982. https://doi.org/10.3390/rs11080982
  • Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., & DiMarzio, J. P. (2005). Overview of the ICESat mission. Geophysical Research Letters, 32(21). https://doi.org/10.1029/2005GL024009
  • Shen, X., Ke, C. Q., Fan, Y., & Drolma, L. (2022). A new digital elevation model (DEM) dataset of the entire Antarctic continent derived from ICESat-2. Earth System Science Data, 14(7), 3075–3089. https://doi.org/10.5194/essd-14-3075-2022
  • Simav, M., Akpınar, İ., Akdoğan, Y. A., & Yıldız, H. (2021). Türkiye’de güncel yersel gravimetri çalışmaları. Harita Dergisi, 166(July), 10–24.
  • Ulvi, A., & Yiğit, A. Y. (2022). Comparison of the wearable mobile laser scanner (WMLS) with other point cloud data collection methods in cultural heritage: A case study of Diokaisareia. ACM Journal on Computing and Cultural Heritage, 15(4), 1–19.
  • Van Westen, C. J. (2013). Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise on Geomorphology, 3(15), 259–298.
  • Yiğit, A. Y., Hamal, S. N. G., Yakar, M., & Ulvi, A. (2023). Investigation and implementation of new technology wearable mobile laser scanning (WMLS) in transition to an intelligent geospatial cadastral information system. Sustainability, 15(9), 7159. https://doi.org/10.3390/su15097159
  • Yıldız, H., Simav, M., Sezen, E., Akpınar, İ., Akdoğan, Y. A., Cingöz, A., & Akabali, O. A. (2021). Determination and validation of the Turkish Geoid Model-2020 (TG-20). Bulletin of geophysics and oceanography, 62, 495–512.
  • Yuan, C., Gong, P., & Bai, Y. (2020). Performance assessment of ICESat-2 laser altimeter data for water-level measurement over lakes and reservoirs in China. Remote Sensing, 12(5), 770. https://doi.org/10.3390/rs12050770

Yıl 2025, Cilt: 7 Sayı: 1, 53 - 68, 30.06.2025

Yunus Kaya

https://doi.org/10.51489/tuzal.1665238

Öz

Kaynakça

  • Anders, N., Smith, M., Suomalainen, J., Cammeraat, E., Valente, J., & Keesstra, S. (2020). Impact of flight altitude and cover orientation on digital surface model (DSM) accuracy for flood damage assessment in Murcia (Spain) using a fixed-wing UAV. Earth Science Informatics, 13, 391–404.
  • Courellis, H. S., Iversen, J. R., Poizner, H., & Cauwenberghs, G. (2016). EEG channel interpolation using ellipsoid geodesic length. In 2016 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 540–543). IEEE.
  • Dibs, H., Al-Ansari, N., & Laue, J. (2023). Analysis of remotely sensed imagery and architecture environment for modelling 3D detailed buildings using geospatial techniques. Engineering, 15(5), 328–341.
  • Gao, M., Xing, S., Zhang, G., Zhang, X., & Li, P. (2023). Assessment of ICESat-2’s horizontal accuracy using an iterative matching method based on high-accuracy terrains. Remote Sensing, 15(9), 2236. https://doi.org/10.3390/rs15092236
  • Gazioğlu, C., Aipar, B., Yücel, Z. Y., Müftüoğlu, A. E., Güneysu, C., Ertek, T. A., Demir, V., & Kaya, H. (2014). Morphologic features of Kapıdağ Peninsula and its coasts (NW-Turkey) using by remote sensing and DTM. International Journal of Environment and Geoinformatics, 1(1), 48–63.
  • Guo, K., Guan, M., & Yu, D. (2021). Urban surface water flood modelling–A comprehensive review of current models and future challenges. Hydrology and Earth System Sciences, 25(5), 2843–2860. https://doi.org/10.5194/hess-25-2843-2021
  • Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M. R., Kuemmerle, T., Meyfroidt, P., Mitchard, E. T. A., Reiche, J., Ryan, C. M., & Waske, B. (2016). A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring. Remote Sensing, 8(1), 70. https://doi.org/10.3390/rs8010070
  • Karataş, H., & Yaman, A. (2024). Investigation of the usability of Göktürk-2 data and UAV data for pond construction project. The Egyptian Journal of Remote Sensing and Space Sciences, 27(3), 565–576.
  • Kaya, Y., Sanli, F. B., & Abdikan, S. (2023). Determination of long-term volume change in lakes by integration of UAV and satellite data: The case of Lake Burdur in Türkiye. Environmental Science and Pollution Research, 30(55), 117729–117747.
  • Kılıç, B., Gülgen, F., Çelen, M., Öncel, S., Oruç, H., & Vural, S. (2022). Morphometric analysis of Saz-Çayırova drainage basin using geographic information systems and different digital elevation models. International Journal of Environment and Geoinformatics, 9(2), 177–186.
  • Li, B., Xie, H., Liu, S., Ye, Z., Hong, Z., Weng, Q., Sun, Y., Xu, Q., & Tong, X. (2024). Global DEM product generation by correcting ASTER GDEM elevation with ICESat-2 altimeter data. Earth System Science Data Discussions, 2024, 1–24. https://doi.org/10.5194/essd-2024-3
  • Li, B., Xie, H., Tong, X., Tang, H., & Liu, S. (2023). A global-scale DEM elevation correction model using ICESat-2 laser altimetry data. IEEE Transactions on Geoscience and Remote Sensing.
  • Li, H., Zhao, J., Yan, B., Yue, L., & Wang, L. (2022). Global DEMs vary from one to another: An evaluation of newly released Copernicus, NASA and AW3D30 DEM on selected terrains of China using ICESat-2 altimetry data. International Journal of Digital Earth, 15(1), 1149–1168.
  • Li, Y., Fu, H., Zhu, J., Wu, K., Yang, P., Wang, L., & Gao, S. (2022). A method for SRTM DEM elevation error correction in forested areas using ICESat-2 data and vegetation classification data. Remote Sensing, 14(14), 3380. https://doi.org/10.3390/rs14143380
  • Loew, A., Bell, W., Brocca, L., Bulgin, C. E., Burdanowitz, J., Calbet, X., Donner, R. V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C., Lambert, J. C., Schaepman-Strub, G., Schröder, M., & Verhoelst, T. (2017). Validation practices for satellite‐based Earth observation data across communities. Reviews of Geophysics, 55(3), 779–817. https://doi.org/10.1002/2017RG000562
  • Mesa-Mingorance, J. L., & Ariza-López, F. J. (2020). Accuracy assessment of digital elevation models (DEMs): A critical review of practices of the past three decades. Remote Sensing, 12(16), 2630. https://doi.org/10.3390/rs12162630
  • Moore, I. D., Turner, A. K., Wilson, J. P., Jenson, S. K., & Band, L. E. (1993). GIS and land-surface-subsurface process modeling. In M. F. Goodchild, B. O. Parks, & L. T. Steyaert (Eds.), Environmental Modeling with GIS (pp. 196–230).
  • Neumann, T. A., Brenner, A., Hancock, D., Robbins, J., Saba, J., Harbeck, K., ... & Rebold, T. (2020). ATLAS/ICESat-2 L2A global geolocated photon data, version 3. NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, CO, United States.
  • Oksanen, J., & Sarjakoski, T. (2005). Error propagation of DEM-based surface derivatives. Computers & Geosciences, 31(8), 1015–1027.
  • Panda, S. S., Rao, M. N., Thenkabail, P. S., Misra, D., & Fitzgerald, J. P. (2016). Remote sensing systems—Platforms and sensors: Aerial, satellite, UAV, optical, radar, and LiDAR. In P. S. Thenkabail (Ed.), Remote Sensing Handbook, Volume I (pp. 3–86). CRC Press.
  • Quamar, M. M., Al-Ramadan, B., Khan, K., Shafiullah, M., & El Ferik, S. (2023). Advancements and applications of drone-integrated geographic information system technology—A review. Remote Sensing, 15(20), 5039. https://doi.org/10.3390/rs15205039
  • Rosen, P. A., Hensley, S., Joughin, I. R., Li, F. K., Madsen, S. N., Rodriguez, E., & Goldstein, R. M. (2000). Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3), 333–382. https://doi.org/10.1109/5.838084
  • Sahani, J., Kumar, P., Debele, S., Spyrou, C., Loupis, M., Aragão, L., Feredico, P., Shah, M. A. R., & Di Sabatino, S. (2019). Hydro-meteorological risk assessment methods and management by nature-based solutions. Science of the Total Environment, 696, 133936. https://doi.org/10.1016/j.scitotenv.2019.133936
  • Schumann, G. J. P., Muhlhausen, J., & Andreadis, K. M. (2019). Rapid mapping of small-scale river-floodplain environments using UAV SfM supports classical theory. Remote Sensing, 11(8), 982. https://doi.org/10.3390/rs11080982
  • Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., & DiMarzio, J. P. (2005). Overview of the ICESat mission. Geophysical Research Letters, 32(21). https://doi.org/10.1029/2005GL024009
  • Shen, X., Ke, C. Q., Fan, Y., & Drolma, L. (2022). A new digital elevation model (DEM) dataset of the entire Antarctic continent derived from ICESat-2. Earth System Science Data, 14(7), 3075–3089. https://doi.org/10.5194/essd-14-3075-2022
  • Simav, M., Akpınar, İ., Akdoğan, Y. A., & Yıldız, H. (2021). Türkiye’de güncel yersel gravimetri çalışmaları. Harita Dergisi, 166(July), 10–24.
  • Ulvi, A., & Yiğit, A. Y. (2022). Comparison of the wearable mobile laser scanner (WMLS) with other point cloud data collection methods in cultural heritage: A case study of Diokaisareia. ACM Journal on Computing and Cultural Heritage, 15(4), 1–19.
  • Van Westen, C. J. (2013). Remote sensing and GIS for natural hazards assessment and disaster risk management. Treatise on Geomorphology, 3(15), 259–298.
  • Yiğit, A. Y., Hamal, S. N. G., Yakar, M., & Ulvi, A. (2023). Investigation and implementation of new technology wearable mobile laser scanning (WMLS) in transition to an intelligent geospatial cadastral information system. Sustainability, 15(9), 7159. https://doi.org/10.3390/su15097159
  • Yıldız, H., Simav, M., Sezen, E., Akpınar, İ., Akdoğan, Y. A., Cingöz, A., & Akabali, O. A. (2021). Determination and validation of the Turkish Geoid Model-2020 (TG-20). Bulletin of geophysics and oceanography, 62, 495–512.
  • Yuan, C., Gong, P., & Bai, Y. (2020). Performance assessment of ICESat-2 laser altimeter data for water-level measurement over lakes and reservoirs in China. Remote Sensing, 12(5), 770. https://doi.org/10.3390/rs12050770
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Fotogrametri ve Uzaktan Algılama
Bölüm Araştırma Makaleleri
Yazarlar

Yunus Kaya 0000-0003-2319-4998

Yayımlanma Tarihi 30 Haziran 2025
Gönderilme Tarihi 25 Mart 2025
Kabul Tarihi 24 Nisan 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: 1

Kaynak Göster

IEEE Y. Kaya, “Comparative validation of the radar interferometry-based SYM12 Using ICESat-2 ATL03 and ground-based GPS measurements”, TJRS, c. 7, sy. 1, ss. 53–68, 2025, doi: 10.51489/tuzal.1665238.
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