Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5299-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-5299-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Dec 2023
Research article |
| 14 Dec 2023
| 14 Dec 2023
Co-registration and residual correction of digital elevation models: a comparative study
Tao Li
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, 430077, China
College of Earth and Planetary Science, University of Chinese Academy of Sciences, Beijing 100049, China
Yuanlin Hu
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, 430077, China
School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei, 430074, China
Bin Liu
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Liming Jiang
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, 430077, China
College of Earth and Planetary Science, University of Chinese Academy of Sciences, Beijing 100049, China
Hansheng Wang
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, 430077, China
Xiang Shen
CORRESPONDING AUTHOR
State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan, Hubei, 430077, China
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 1053–1058, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1053-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-1053-2022, 2022
L. Ye, M. Peng, K. Di, B. Liu, and Y. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2020, 1177–1183, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1177-2020, https://doi.org/10.5194/isprs-archives-XLIII-B3-2020-1177-2020, 2020
J. Wang, J. Li, S. Wang, T. Yu, Z. Rong, X. He, Y. You, Q. Zou, W. Wan, Y. Wang, S. Gou, B. Liu, M. Peng, K. Di, Z. Liu, M. Jia, X. Xin, Y. Chen, X. Cheng, X. Feng, C. Liu, S. Han, and X. Liu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 595–602, https://doi.org/10.5194/isprs-annals-V-3-2020-595-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-595-2020, 2020
K. Di, Z. Liu, B. Liu, W. Wan, M. Peng, J. Li, J. Xie, M. Jia, S. Niu, X. Xin, L. Li, J. Wang, Z. Yue, S. Gou, Y. Wang, R. Wang, J. Liu, Z. Bo, C. Liu, T. Yu, L. Xi, and Y. Miao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1383–1387, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1383-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1383-2019, 2019
B. Liu, S. Niu, X. Xin, M. Jia, K. Di, Z. Liu, M. Peng, and Z. Yue
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1413–1417, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1413-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1413-2019, 2019
W. Wan, Z. Liu, B. Liu, K. Di, J. Wang, C. Liu, T. Yu, Y. Miao, M. Peng, Y. Wang, and S. Gou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1457–1461, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1457-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1457-2019, 2019
H. Wang, L. Xiang, H. Steffen, P. Wu, L. Jiang, Q. Shen, D. Piretzidis, M. G. Sideris, M. Hayashi, and L. Jia
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1793–1796, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1793-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1793-2019, 2019
Qiang Shen, Hansheng Wang, C. K. Shum, Liming Jiang, Hou Tse Hsu, Jinglong Dong, Song Mao, and Fan Gao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2018-149, https://doi.org/10.5194/essd-2018-149, 2018
Revised manuscript not accepted
Short summary
Short summary
Thorough and continued monitoring of ice-sheet dynamics is of utmost importance for accurate predictions of ice-sheet behavior in the future. We present a new Antarctic ice velocity map at a 100-m grid spacing inferred from Landsat 8 imagery data. The datasets will allow for a comprehensive continent-wide investigation of ice dynamics and mass balance with the existing and future ice velocity measurements, provide control and calibration for ice-sheet modelling.
K. Di, M. Jia, X. Xin, B. Liu, Z. Liu, M. Peng, and Z. Yue
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 271–276, https://doi.org/10.5194/isprs-archives-XLII-3-271-2018, https://doi.org/10.5194/isprs-archives-XLII-3-271-2018, 2018
K. Di, B. Liu, M. Peng, X. Xin, M. Jia, W. Zuo, J. Ping, B. Wu, and J. Oberst
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 29–34, https://doi.org/10.5194/isprs-archives-XLII-3-W1-29-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-29-2017, 2017
Qiang Shen, Hansheng Wang, Che-Kwan Shum, Liming Jiang, Hou Tse Hsu, and Jinglong Dong
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-34, https://doi.org/10.5194/tc-2017-34, 2017
Preprint withdrawn
Short summary
Short summary
We constructed two present-day continent-wide ice flow maps on Antarctica, and estimated its mass balances over the last decade. An increased mass discharge from Wilkes Land, East Antarctica was found, contrary to the long-standing belief that accelerated mass loss primarily originates from West Antarctica and Antarctic Peninsula. Our maps allow the first continent-wide assessment of mass discharge changes in the last decade, which will contribute to our understanding of Antarctic ice dynamics.
K. Di, B. Xu, B. Liu, M. Jia, and Z. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 369–374, https://doi.org/10.5194/isprs-archives-XLI-B4-369-2016, https://doi.org/10.5194/isprs-archives-XLI-B4-369-2016, 2016
Related subject area
Discipline: Other | Subject: Remote Sensing
Ice thickness and water level estimation for ice-covered lakes with satellite altimetry waveforms and backscattering coefficients
Semi-automated tracking of iceberg B43 using Sentinel-1 SAR images via Google Earth Engine
Mapping potential signs of gas emissions in ice of Lake Neyto, Yamal, Russia, using synthetic aperture radar and multispectral remote sensing data
Brief communication: Glacier run-off estimation using altimetry-derived basin volume change: case study at Humboldt Glacier, northwest Greenland
Recent changes in pan-Antarctic region surface snowmelt detected by AMSR-E and AMSR2
CryoSat Ice Baseline-D validation and evolutions
Theoretical study of ice cover phenology at large freshwater lakes based on SMOS MIRAS data
Xingdong Li, Di Long, Yanhong Cui, Tingxi Liu, Jing Lu, Mohamed A. Hamouda, and Mohamed M. Mohamed
The Cryosphere, 17, 349–369, https://doi.org/10.5194/tc-17-349-2023, https://doi.org/10.5194/tc-17-349-2023, 2023
Short summary
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This study blends advantages of altimetry backscattering coefficients and waveforms to estimate ice thickness for lakes without in situ data and provides an improved water level estimation for ice-covered lakes by jointly using different threshold retracking methods. Our results show that a logarithmic regression model is more adaptive in converting altimetry backscattering coefficients into ice thickness, and lake surface snow has differential impacts on different threshold retracking methods.
YoungHyun Koo, Hongjie Xie, Stephen F. Ackley, Alberto M. Mestas-Nuñez, Grant J. Macdonald, and Chang-Uk Hyun
The Cryosphere, 15, 4727–4744, https://doi.org/10.5194/tc-15-4727-2021, https://doi.org/10.5194/tc-15-4727-2021, 2021
Short summary
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This study demonstrates for the first time the potential of Google Earth Engine (GEE) cloud-computing platform and Sentinel-1 synthetic aperture radar (SAR) images for semi-automated tracking of area changes and movements of iceberg B43. Our novel GEE-based iceberg tracking can be used to construct a large iceberg database for a better understanding of the behavior of icebergs and their interactions with surrounding environments.
Georg Pointner, Annett Bartsch, Yury A. Dvornikov, and Alexei V. Kouraev
The Cryosphere, 15, 1907–1929, https://doi.org/10.5194/tc-15-1907-2021, https://doi.org/10.5194/tc-15-1907-2021, 2021
Short summary
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This study presents strong new indications that regions of anomalously low backscatter in C-band synthetic aperture radar (SAR) imagery of ice of Lake Neyto in northwestern Siberia are related to strong emissions of natural gas. Spatio-temporal dynamics and potential scattering and formation mechanisms are assessed. It is suggested that exploiting the spatial and temporal properties of Sentinel-1 SAR data may be beneficial for the identification of similar phenomena in other Arctic lakes.
Laurence Gray
The Cryosphere, 15, 1005–1014, https://doi.org/10.5194/tc-15-1005-2021, https://doi.org/10.5194/tc-15-1005-2021, 2021
Short summary
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A total of 9 years of ice velocity and surface height data obtained from a variety of satellites are used to estimate the water run-off from the northern arm of the Humboldt Glacier in NW Greenland. This represents the first direct measurement of water run-off from a large Greenland glacier, and it complements the iceberg calving flux measurements also based on satellite data. This approach should help improve mass loss estimates for some large Greenland glaciers.
Lei Zheng, Chunxia Zhou, Tingjun Zhang, Qi Liang, and Kang Wang
The Cryosphere, 14, 3811–3827, https://doi.org/10.5194/tc-14-3811-2020, https://doi.org/10.5194/tc-14-3811-2020, 2020
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Snowmelt plays a key role in mass and energy balance in polar regions. In this study, we report on the spatial and temporal variations in the surface snowmelt over the Antarctic sea ice and ice sheet (pan-Antarctic region) based on AMSR-E and AMSR2. Melt detection on sea ice is improved by excluding the effect of open water. The decline in surface snowmelt on the Antarctic ice sheet was very likely linked with the enhanced summer Southern Annular Mode.
Marco Meloni, Jerome Bouffard, Tommaso Parrinello, Geoffrey Dawson, Florent Garnier, Veit Helm, Alessandro Di Bella, Stefan Hendricks, Robert Ricker, Erica Webb, Ben Wright, Karina Nielsen, Sanggyun Lee, Marcello Passaro, Michele Scagliola, Sebastian Bjerregaard Simonsen, Louise Sandberg Sørensen, David Brockley, Steven Baker, Sara Fleury, Jonathan Bamber, Luca Maestri, Henriette Skourup, René Forsberg, and Loretta Mizzi
The Cryosphere, 14, 1889–1907, https://doi.org/10.5194/tc-14-1889-2020, https://doi.org/10.5194/tc-14-1889-2020, 2020
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This manuscript aims to describe the evolutions which have been implemented in the new CryoSat Ice processing chain Baseline-D and the validation activities carried out in different domains such as sea ice, land ice and hydrology.
This new CryoSat processing Baseline-D will maximise the uptake and use of CryoSat data by scientific users since it offers improved capability for monitoring the complex and multiscale changes over the cryosphere.
Vasiliy Tikhonov, Ilya Khvostov, Andrey Romanov, and Evgeniy Sharkov
The Cryosphere, 12, 2727–2740, https://doi.org/10.5194/tc-12-2727-2018, https://doi.org/10.5194/tc-12-2727-2018, 2018
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The paper presents a theoretical analysis of seasonal brightness temperature variations at a number of large freshwater lakes retrieved from data of the Soil Moisture and Ocean Salinity satellite. Three distinct seasonal time regions corresponding to different phenological phases of the lake surfaces, complete ice cover, ice melt and deterioration, and open water, were revealed. The paper demonstrates the possibility of determining the beginning of ice cover deterioration from satellite data.
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Short summary
Raw DEMs are often misaligned with each other due to georeferencing errors, and a co-registration process is required before DEM differencing. We present a comparative analysis of the two classical DEM co-registration and three residual correction algorithms. The experimental results show that rotation and scale biases should be considered in DEM co-registration. The new non-parametric regression technique can eliminate the complex systematic errors, which existed in the co-registration results.
Raw DEMs are often misaligned with each other due to georeferencing errors, and a...
