Articles | Volume 17, issue 1
https://doi.org/10.5194/essd-17-205-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-205-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Jan 2025
Data description paper |
| 24 Jan 2025
| 24 Jan 2025
Global digital elevation model (GDEM) product generation by correcting ASTER GDEM elevation with ICESat-2 altimeter data
Binbin Li
College of Surveying and Geo-informatics, and Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
JC STEM Lab of Earth Observations, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR 999077, China
Research Centre for Artificial Intelligence in Geomatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR 999077, China
Research Institute for Land and Space, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR 999077, China
College of Surveying and Geo-informatics, and Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
Shijie Liu
College of Surveying and Geo-informatics, and Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
Zhen Ye
College of Surveying and Geo-informatics, and Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
Zhonghua Hong
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
Qihao Weng
JC STEM Lab of Earth Observations, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR 999077, China
Research Centre for Artificial Intelligence in Geomatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR 999077, China
Research Institute for Land and Space, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR 999077, China
Yuan Sun
College of Surveying and Geo-informatics, and Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
Qi Xu
College of Surveying and Geo-informatics, and Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
Xiaohua Tong
College of Surveying and Geo-informatics, and Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai 200092, China
Related authors
No articles found.
Huan Xie, Yifan Wang, Xiongfeng Yan, Ming Yang, Yaqiong Wang, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1589–1594, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1589-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1589-2025, 2025
Peiqi Ye, Rong Huang, Puzuo Wang, Yusheng Xu, Zhen Ye, Yongjiu Feng, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1641–1646, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1641-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1641-2025, 2025
Lingxiao Zhang, Rong Huang, Yusheng Xu, Zhen Ye, Changjiang Xiao, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1699–1705, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1699-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1699-2025, 2025
Yi Zhang, Genyi Wan, Dayong Liu, Tao Tao, Changjiang Xiao, Zhen Ye, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1727–1732, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1727-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1727-2025, 2025
Yuan Sun, Huan Xie, Xiaohua Tong, Qi Xu, Binbin Li, Changda Liu, Min Ji, and Hao Tang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 1421–1426, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1421-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-1421-2025, 2025
Chen Liu, Rong Huang, Huan Xie, Tao Tao, Yongjiu Feng, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-G-2025, 959–966, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-959-2025, https://doi.org/10.5194/isprs-archives-XLVIII-G-2025-959-2025, 2025
Leilei Jiao, Yusheng Xu, Rong Huang, Zhen Ye, Sicong Liu, Shijie Liu, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 629–635, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-629-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-629-2024, 2024
Jiarui Cao, Rong Huang, Zhen Ye, Yusheng Xu, and Xiaohua Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 51–56, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-51-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-51-2024, 2024
Ying Tu, Shengbiao Wu, Bin Chen, Qihao Weng, Yuqi Bai, Jun Yang, Le Yu, and Bing Xu
Earth Syst. Sci. Data, 16, 2297–2316, https://doi.org/10.5194/essd-16-2297-2024, https://doi.org/10.5194/essd-16-2297-2024, 2024
Short summary
Short summary
We developed the first 30 m annual cropland dataset of China (CACD) for 1986–2021. The overall accuracy of CACD reached up to 0.93±0.01 and was superior to other products. Our fine-resolution cropland maps offer valuable information for diverse applications and decision-making processes in the future.
Xin Luo, Hongping Zeng, and Zhen Ye
EGUsphere, https://doi.org/10.5194/egusphere-2023-2389, https://doi.org/10.5194/egusphere-2023-2389, 2023
Preprint archived
Short summary
Short summary
To better understand the glacier melting features in the SETP, multisource satellite observations including ASTER DEM, ICESat, ICESat-2 and CryoSat-2 are used in this study. We found the glacier melting rate of the entire SETP is during 2000–2022. And the glacier melting has accelerated at a rate of 31.2 % in the recent decade. A comprehensive comparison among the related existing studies revealed that our estimates have a finer temporal scale and less estimation uncertainty.
S. Xu, R. Huang, Y. Xu, Z. Ye, H. Xie, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W2-2023, 771–776, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-771-2023, 2023
Lixin Lin, Xixi Liu, and Yuan Sun
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-170, https://doi.org/10.5194/bg-2023-170, 2023
Revised manuscript not accepted
Short summary
Short summary
We attempted to disentangle the covers of vegetation and water on soil organic carbon model using fuzzy disentangling. We used the model to simulate the soil organic carbon stocks in western European topsoil. The results show that the per-unit and total SOC stocks in western European topsoil as 99.742 t C ha−1 and 9.373 Pg, respectively. The gap of the results is narrower compared with previous study. The stable simulated values are the result of disentangling of the vegetation and water cover.
Yuelong Xiao, Qunming Wang, Xiaohua Tong, and Peter M. Atkinson
Earth Syst. Sci. Data, 15, 3365–3386, https://doi.org/10.5194/essd-15-3365-2023, https://doi.org/10.5194/essd-15-3365-2023, 2023
Short summary
Short summary
Forest age is closely related to forest production, carbon cycles, and other ecosystem services. Existing stand age products in China derived from remote-sensing images are of a coarse spatial resolution and are not suitable for applications at the regional scale. Here, we mapped young forest ages across China at an unprecedented fine spatial resolution of 30 m. The overall accuracy (OA) of the generated map of young forest stand ages across China was 90.28 %.
H. Zhang, B. Xie, S. Liu, R. Ding, Z. Ye, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 79–84, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-79-2022, 2022
Q. Xu, H. Xie, Y. Sun, X. Liu, Y. Guo, P. Huang, B. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 309–314, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-309-2022, 2022
A. Zhao, Y. Cheng, D. Lv, M. Xia, R. Li, L. An, S. Liu, and Y. Tian
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 805–811, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-805-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-805-2022, 2022
H. Zhang, Y. Shang, X. Tong, J. Chen, W. Ma, M. Li, Y. Lu, and H. Chen
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 619–625, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-619-2022, 2022
Rongxing Li, Hongwei Li, Tong Hao, Gang Qiao, Haotian Cui, Youquan He, Gang Hai, Huan Xie, Yuan Cheng, and Bofeng Li
The Cryosphere, 15, 3083–3099, https://doi.org/10.5194/tc-15-3083-2021, https://doi.org/10.5194/tc-15-3083-2021, 2021
Short summary
Short summary
We present the results of an assessment of ICESat-2 surface elevations along the 520 km CHINARE route in East Antarctica. The assessment was performed based on coordinated multi-sensor observations from a global navigation satellite system, corner cube retroreflectors, retroreflective target sheets, and UAVs. The validation results demonstrate that ICESat-2 elevations are accurate to 1.5–2.5 cm and can potentially overcome the uncertainties in the estimation of mass balance in East Antarctica.
H. Cui, R. Li, H. Li, T. Hao, G. Qiao, Y. He, G. Hai, H. Xie, Y. Cheng, and B. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 443–448, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-443-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-443-2021, 2021
S. Luo, Y. Cheng, Z. Li, Y. Wang, K. Wang, X. Wang, G. Qiao, W. Ye, Y. Li, M. Xia, X. Yuan, Y. Tian, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 491–496, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-491-2021, 2021
Y. Gong, H. Xie, X. Tong, Y. Jin, X. Xv, and Q. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 103–108, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-103-2020, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-103-2020, 2020
S. L. Jiang, G. Li, W. Yao, Z. H. Hong, and T. Y. Kuc
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 605–610, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-605-2020, https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-605-2020, 2020
H. Zhang, S. Liu, Z. Ye, X. Tong, H. Xie, S. Zheng, and Q. Du
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 149–155, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-149-2020, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-149-2020, 2020
J. Han, S. L. Zhang, and Z. Ye
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 17–23, https://doi.org/10.5194/isprs-annals-V-1-2020-17-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-17-2020, 2020
Z. Ye, Y. Xu, C. Wei, X. Tong, and U. Stilla
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 157–163, https://doi.org/10.5194/isprs-annals-V-1-2020-157-2020, https://doi.org/10.5194/isprs-annals-V-1-2020-157-2020, 2020
R. Huang, W. Yao, Z. Ye, Y. Xu, and U. Stilla
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 235–242, https://doi.org/10.5194/isprs-annals-V-2-2020-235-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-235-2020, 2020
Y. Lu, J. Zhang, X. Tong, X. Lu, W. Han, H. Zhang, H. Zhao, and X. Liu
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 141–148, https://doi.org/10.5194/isprs-annals-V-3-2020-141-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-141-2020, 2020
Y. Wang, X. Tong, H. Xie, M. Jiang, Y. Huang, S. Liu, X. Xu, Q. Du, Q. Wang, and C. Wang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 603–608, https://doi.org/10.5194/isprs-annals-V-3-2020-603-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-603-2020, 2020
Q. Fu, S. Liu, X. Tong, and H. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W16, 91–94, https://doi.org/10.5194/isprs-archives-XLII-2-W16-91-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W16-91-2019, 2019
S. Gao, Z. Ye, C. Wei, X. Liu, and X. Tong
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 33–38, https://doi.org/10.5194/isprs-annals-IV-2-W7-33-2019, https://doi.org/10.5194/isprs-annals-IV-2-W7-33-2019, 2019
Y. Lu, J. Zhang, X. Tong, W. Han, and H. Zhao
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1243–1247, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1243-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1243-2019, 2019
Y. Cheng, X. Li, G. Qiao, W. Ye, Y. Huang, Y. Li, K. Wang, Y. Tian, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1735–1739, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1735-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1735-2019, 2019
R. Li, D. Lv, H. Xiao, S. Liu, Y. Cheng, G. Hai, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1759–1763, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1759-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1759-2019, 2019
R. Li, H. Xie, Y. Tian, W. Du, J. Chen, G. Hai, S. Zhang, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1765–1769, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1765-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1765-2019, 2019
Z. Ye, Y. Xu, L. Hoegner, X. Tong, and U. Stilla
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 147–153, https://doi.org/10.5194/isprs-archives-XLII-2-W13-147-2019, https://doi.org/10.5194/isprs-archives-XLII-2-W13-147-2019, 2019
Y. Tian, S. Zhang, W. Du, J. Chen, H. Xie, X. Tong, and R. Li
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1657–1660, https://doi.org/10.5194/isprs-archives-XLII-3-1657-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1657-2018, 2018
Wenping Song, Shijie Liu, Xiaohua Tong, Changling Niu, Zhen Ye, Han Zhang, and Yanmin Jin
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 193–196, https://doi.org/10.5194/isprs-annals-IV-3-193-2018, https://doi.org/10.5194/isprs-annals-IV-3-193-2018, 2018
Xin Zhang, Shijie Liu, Haifeng Yu, Xiaohua Tong, and Guoman Huang
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-3, 267–271, https://doi.org/10.5194/isprs-annals-IV-3-267-2018, https://doi.org/10.5194/isprs-annals-IV-3-267-2018, 2018
W. Du, L. Chen, H. Xie, G. Hai, S. Zhang, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1513–1516, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1513-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1513-2017, 2017
G. Hai, H. Xie, J. Chen, L. Chen, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1517–1520, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1517-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1517-2017, 2017
M. Xia, G. Tang, Y. Tian, W. Ye, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1569–1573, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1569-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1569-2017, 2017
H. Xiao, S. Liu, R. Li, and X. Tong
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W7, 1575–1577, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1575-2017, https://doi.org/10.5194/isprs-archives-XLII-2-W7-1575-2017, 2017
Rongxing Li, Haifeng Xiao, Shijie Liu, and Xiaohua Tong
The Cryosphere Discuss., https://doi.org/10.5194/tc-2017-178, https://doi.org/10.5194/tc-2017-178, 2017
Revised manuscript not accepted
Short summary
Short summary
Fracturing in the RFIS was slightly increased, particularly at its front, from 2003 to 2015. They do not seem to suggest an immediate significant impact on the stability of the shelf. However, with the rapid changes and 3D measurements of Rifts 1 and 2, the most active activities occurred at the front of the FIS from 2001 to 2016. A potential upcoming major calving event in FIS is estimated to occur in 2051. The stability of the ice shelf, particularly Rifts 1 and 2, should be closely monitored.
C. Guo, X. Tong, S. Liu, S. Liu, X. Lu, P. Chen, Y. Jin, and H. Xie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 49–53, https://doi.org/10.5194/isprs-archives-XLII-3-W1-49-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-49-2017, 2017
W. Zhao, X. Tong, H. Xie, Y. Jin, S. Liu, D. Wu, X. Liu, L. Guo, and Q. Zhou
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 213–218, https://doi.org/10.5194/isprs-archives-XLII-3-W1-213-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-213-2017, 2017
Q. Zhou, X. Tong, S. Liu, X. Lu, S. Liu, P. Chen, Y. Jin, and H. Xie
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W1, 219–224, https://doi.org/10.5194/isprs-archives-XLII-3-W1-219-2017, https://doi.org/10.5194/isprs-archives-XLII-3-W1-219-2017, 2017
W. Cao, X. H. Tong, S. C. Liu, and D. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 25–31, https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016, https://doi.org/10.5194/isprs-archives-XLI-B8-25-2016, 2016
T. Feng, Z. Hong, Q. Fu, S. Ma, X. Jie, H. Wu, C. Jiang, and X. Tong
Nat. Hazards Earth Syst. Sci., 14, 2165–2178, https://doi.org/10.5194/nhess-14-2165-2014, https://doi.org/10.5194/nhess-14-2165-2014, 2014
Cited articles
Abrams, M., Tsu, H., Hulley, G., Iwao, K., Pieri, D., Cudahy, T., and Kargel, J.: The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) after fifteen years: Review of global products, Int. J. Appl. Earth Obs. Geoinf., 38, 292–301, https://doi.org/10.1016/j.jag.2015.01.013, 2015.
Abrams, M., Crippen, R., and Fujisada, H.: ASTER Global Digital Elevation Model (GDEM) and ASTER Global Water Body Dataset (ASTWBD), Remote Sens.-Basel, 12, 1156, https://doi.org/10.3390/rs12071156, 2020.
Ahn Viewer: Het Actueel Hoogtebestand Nederland (AHN) 3, https://www.ahn.nl/ahn-viewer, last access: 1 October 2023.
Al-Areeq, A. M., Sharif, H. O., Abba, S. I., Chowdhury, S., Al-Suwaiyan, M., Benaafi, M., Yassin, M. A., and Aljundi, I. H.: Digital elevation model for flood hazards analysis in complex terrain: Case study from Jeddah, Saudi Arabia, Int. J. Appl. Earth Obs. Geoinf., 119, 103330, https://doi.org/10.1016/j.jag.2023.103330, 2023.
Ao, Z., Hu, X., Tao, S., Hu, X., Wang, G., Li, M., Wang, F., Hu, L., Liang, X., Xiao, J., Yusup, A., Qi, W., Ran, Q., Fang, J., Chang, J., Zeng, Z., Fu, Y., Xue, B., Wang, P., Zhao, K., Li, L., Li, W., Li, Y., Jiang, M., Yang, Y., Shen, H., Zhao, X., Shi, Y., Wu, B., Yan, Z., Wang, M., Su, Y., Hu, T., Ma, Q., Bai, H., Wang, L., Yang, Z., Feng, Y., Zhang, D., Huang, E., Pan, J., Ye, H., Yang, C., Qin, Y., He, C., Guo, Y., Cheng, K., Ren, Y., Yang, H., Zheng, C., Zhu, J., Wang, S., Ji, C., Zhu, B., Liu, H., Tang, Z., Wang, Z., Zhao, S., Tang, Y., Xing, H., Guo, Q., Liu, Y., and Fang, J.: A national-scale assessment of land subsidence in China's major cities, Science, 384, 301–306, https://doi.org/10.1126/science.adl4366, 2024.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Cook, A. J., Murray, T., Luckman, A., Vaughan, D. G., and Barrand, N. E.: A new 100-m Digital Elevation Model of the Antarctic Peninsula derived from ASTER Global DEM: methods and accuracy assessment, Earth Syst. Sci. Data, 4, 129–142, https://doi.org/10.5194/essd-4-129-2012, 2012.
Crippen, R., Buckley, S., Agram, P., Belz, E., Gurrola, E., Hensley, S., Kobrick, M., Lavalle, M., Martin, J., Neumann, M., Nguyen, Q., Rosen, P., Shimada, J., Simard, M., and Tung, W.: Nasadem global elevation model: Methods and progress, ISPRS – International Archives of the Photogrammetry, Rem. Sens. Spat. Inf. Sci., XLI-B4, 125–128, https://doi.org/10.5194/isprs-archives-XLI-B4-125-2016, 2016.
del Rosario González-Moradas, M., Viveen, W., Andrés Vidal-Villalobos, R., and Carlos Villegas-Lanza, J.: A performance comparison of SRTM v. 3.0, AW3D30, ASTER GDEM3, Copernicus and TanDEM-X for tectonogeomorphic analysis in the South American Andes, CATENA, 228, 107160, https://doi.org/10.1016/j.catena.2023.107160, 2023.
Dubayah, R., Blair, J. B., Goetz, S., Fatoyinbo, L., Hansen, M., Healey, S., Hofton, M., Hurtt, G., Kellner, J., Luthcke, S., Armston, J., Tang, H., Duncanson, L., Hancock, S., Jantz, P., Marselis, S., Patterson, P. L., Qi, W., and Silva, C.: 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, 2020.
Fahrland, E., Paschko, H., Jacob, P., and Kahabka, H.: Copernicus DEM Product Handbook, Airbus Defense and Space GmbH, Alemania AO/1-9422/18/I-LG, 2022.
Fan, Y., Ke, C.-Q., and Shen, X.: A new Greenland digital elevation model derived from ICESat-2 during 2018–2019, Earth Syst. Sci. Data, 14, 781–794, https://doi.org/10.5194/essd-14-781-2022, 2022.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Fernandez-Diaz, J. C., Velikova, M., and Glennie, C. L.: Validation of ICESat-2 ATL08 Terrain and Canopy Height Retrievals in Tropical Mesoamerican Forests, IEEE J. Sel. Top. Appl., 15, 2956–2970, https://doi.org/10.1109/JSTARS.2022.3163208, 2022.
Franks, S., Storey, J., and Rengarajan, R.: The New Landsat Collection-2 Digital Elevation Model, Remote Sens.-Basel, 12, 3909, https://doi.org/10.3390/rs12233909, 2020.
García-Álvarez, D. and Lara Hinojosa, J.: Global Thematic Land Use Cover Datasets Characterizing Vegetation Covers, in: Land Use Cover Datasets and Validation Tools: Validation Practices with QGIS, edited by: García-Álvarez, D., Camacho Olmedo, M. T., Paegelow, M., and Mas, J. F., Springer International Publishing, Cham, 373–398, https://doi.org/10.1007/978-3-030-90998-7_19, 2022.
Geoscience Australia: Digital Elevation Model (DEM) of Australia derived from LiDAR 5 Metre Grid, Geoscience Australia, Canberra [data set], https://doi.org/10.26186/89644, 2015.
Gong, P., Liu, H., Zhang, M., Li, C., Wang, J., Huang, H., Clinton, N., Ji, L., Li, W., Bai, Y., Chen, B., Xu, B., Zhu, Z., Yuan, C., Suen, H., Guo, J., Xu, N., Li, W., Zhao, Y., and Song, L.: Stable classification with limited sample: transferring a 30 m resolution sample set collected in 2015 to mapping 10 m resolution global land cover in 2017, Sci. Bull., 64, 370–373, https://doi.org/10.1016/j.scib.2019.03.002, 2019 (data available at: https://data-starcloud.pcl.ac.cn/zh/resource/1, last access: 17 January 2025).
Hawker, L., Neal, J., and Bates, P.: Accuracy assessment of the TanDEM-X 90 Digital Elevation Model for selected floodplain sites, Remote Sens. Environ., 232, 111319, https://doi.org/10.1016/j.rse.2019.111319, 2019.
Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., and Neal, J.: A 30 m global map of elevation with forests and buildings removed, Environ. Res. Lett., 17, 024016, https://doi.org/10.1088/1748-9326/ac4d4f, 2022.
Huang, J. and Yu, Y.: Vertical Accuracy Assessment of the ASTER, SRTM, GLO-30, and ATLAS in a Forested Environment, https://doi.org/10.3390/f15030426, 2024.
Instituto Geográfico Nacional: Argentina, Ministerio de Defensa [data set], https://www.ign.gob.ar/NuestrasActividades/Geodesia/ModeloDigitalElevaciones/Mapa, last access: 1 October 2023.
Land Information New Zealand: Land Information New Zealand [data set], https://data.linz.govt.nz/data, last access: 1 October 2023.
Li, B., Xie, H., Tong, X., Tang, H., Liu, S., Jin, Y., Wang, C., and Ye, Z.: High-Accuracy Laser Altimetry Global Elevation Control Point Dataset for Satellite Topographic Mapping, IEEE T. Geosci. Remote, 60, 4411416, https://doi.org/10.1109/TGRS.2022.3177026, 2022.
Li, B., Xie, H., Liu, S., Sun, Y., Xu, Q., and Tong, X.: Correction of ICESat-2 terrain within urban areas using a water pump deployment criterion with the vertical contour of the terrain, Remote Sens. Environ., 298, 113817, https://doi.org/10.1016/j.rse.2023.113817, 2023a.
Li, B., Xie, H., Tong, X., Liu, S., Xu, Q., and Sun, Y.: Extracting accurate terrain in vegetated areas from ICESat-2 data, Int. J. Appl. Earth Obs., 117, 103200, https://doi.org/10.1016/j.jag.2023.103200, 2023b.
Li, B., Xie, H., Tong, X., Tang, H., and Liu, S.: A Global-Scale DEM Elevation Correction Model Using ICESat-2 Laser Altimetry Data, IEEE T. Geosci. Remote, 61, 1–15, https://doi.org/10.1109/TGRS.2023.3321956, 2023c.
Li, B. B., Xie, H., Liu, S. J., Tong, X. H., Tang, H., and Wang, X.: A Method of Extracting High-Accuracy Elevation Control Points from ICESat-2 Altimetry Data, Photogramm. Eng. Rem. S., 87, 821–830, https://doi.org/10.14358/PERS.21-00009R2, 2021.
Li, P., Shi, C., Li, Z., Muller, J.-P., Drummond, J., Li, X., Li, T., Li, Y., and Liu, J.: Evaluation of ASTER GDEM using GPS benchmarks and SRTM in China, Int. J. Remote Sens., 34, 1744–1771, https://doi.org/10.1080/01431161.2012.726752, 2013.
Magruder, L., Neuenschwander, A., and Klotz, B.: Digital terrain model elevation corrections using space-based imagery and ICESat-2 laser altimetry, Remote Sens. Environ., 264, 112621, https://doi.org/10.1016/j.rse.2021.112621, 2021.
Markus, T., Neumann, T., Martino, A., Abdalati, W., Brunt, K., Csatho, B., Farrell, S., Fricker, H., Gardner, A., Harding, D., Jasinski, M., Kwok, R., Magruder, L., Lubin, D., Luthcke, S., Morison, J., Nelson, R., Neuenschwander, A., Palm, S., Popescu, S., Shum, C. K., Schutz, B. E., Smith, B., Yang, Y. K., and Zwally, J.: 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, 2017.
Martino, A. J., Neumann, T., Kurtz, N., and McLennan, D.: ICESat-2 mission overview and early performance, Proc. SPIE 11151, Sensors, Systems, and Next-Generation Satellites XXIII, 111510C (10 October 2019), https://doi.org/10.1117/12.2534938, 2019.
Meadows, M., Jones, S., and Reinke, K.: Vertical accuracy assessment of freely available global DEMs (FABDEM, Copernicus DEM, NASADEM, AW3D30 and SRTM) in flood-prone environments, Int. J. Digit. Earth, 17, 2308734, https://doi.org/10.1080/17538947.2024.2308734, 2024.
NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team: ASTER Global Digital Elevation Model V003, distributed by NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/ASTER/ASTGTM.003, 2019.
Neuenschwander, A. and Pitts, K.: The ATL08 land and vegetation product for the ICESat-2 Mission, Remote Sens. Environ., 221, 247–259, https://doi.org/10.1016/j.rse.2018.11.005, 2019.
Neuenschwander, A., Guenther, E., White, J. C., Duncanson, L., and Montesano, P.: Validation of ICESat-2 terrain and canopy heights in boreal forests, Remote Sens. Environ., 251, 112110, https://doi.org/10.1016/j.rse.2020.112110, 2020.
Neuenschwander, A. L., Pitts, K. L., Jelley, B. P., Robbins, J., Klotz, B., Popescu, S. C., Nelson, R. F., Harding, D., Pederson, D., and Sheridan, R.: ATLAS/ICESat-2 L3A Land and Vegetation Height (ATL08, Version 5), Boulder, Colorado USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/ATLAS/ATL08.005, 2021.
NOAA Office for Coastal Management: Digital Coast: Elevation, NOAA Office for Coastal Management [data set], https://coast.noaa.gov/dataviewer/#/lidar/search, last access: 1 October 2023.
Okolie, C. J. and Smit, J. L.: A systematic review and meta-analysis of Digital elevation model (DEM) fusion: pre-processing, methods and applications, ISPRS J. Photogramm., 188, 1–29, https://doi.org/10.1016/j.isprsjprs.2022.03.016, 2022.
Okolie, C. J., Mills, J. P., Adeleke, A. K., Smit, J. L., Peppa, M. V., Altunel, A. O., and Arungwa, I. D.: Assessment of the global Copernicus, NASADEM, ASTER and AW3D digital elevation models in Central and Southern Africa, Geospat. Inf. Sci., 1–29, https://doi.org/10.1080/10095020.2023.2296010, 2024.
Pham, H. T., Marshall, L., Johnson, F., and Sharma, A.: A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data, Remote Sens. Environ., 210, 229–241, https://doi.org/10.1016/j.rse.2018.03.026, 2018.
Purinton, B. and Bookhagen, B.: Beyond Vertical Point Accuracy: Assessing Inter-pixel Consistency in 30 m Global DEMs for the Arid Central Andes, Front. Earth Sci., 9, 758606, https://doi.org/10.3389/feart.2021.758606, 2021.
Schutz, B., Zwally, H., Shuman, C. A., and Hancock, D.: Overview of the ICESat mission, Geophys. Res. Lett., 32, L21S01, https://doi.org/10.1029/2005GL024009, 2005.
Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim, D.-H., Collins, K. M., Channan, S., DiMiceli, C., and Townshend, J. R.: Global, 30 m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error, Int. J. Digit. Earth, 6, 427–448, https://doi.org/10.1080/17538947.2013.786146, 2013.
Shen, X., Ke, C.-Q., Fan, Y., and Drolma, L.: A new digital elevation model (DEM) dataset of the entire Antarctic continent derived from ICESat-2, Earth Syst. Sci. Data, 14, 3075–3089, https://doi.org/10.5194/essd-14-3075-2022, 2022.
Tang, X., Xie, J., Liu, R., Huang, G., Zhao, C., Zhen, Y., Tang, H., and Dou, X.: Overview of the GF-7 Laser Altimeter System Mission, Earth Space Sci., 7, e2019EA000777, https://doi.org/10.1029/2019EA000777, 2020.
Townshend, J.: Global Forest Cover Change (GFCC) Tree Cover Multi-Year Global 30 m V003, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MEaSUREs/GFCC/GFCC30TC.003, 2016.
Tran, T.-N.-D., Nguyen, B. Q., Vo, N. D., Le, M.-H., Nguyen, Q.-D., Lakshmi, V., and Bolten, J. D.: Quantification of global Digital Elevation Model (DEM) – A case study of the newly released NASADEM for a river basin in Central Vietnam, J. Hydrol.: Reg. Stud., 45, 101282, https://doi.org/10.1016/j.ejrh.2022.101282, 2023.
Tran, T. N. D., Do, S. K., Nguyen, B. Q., Tran, V. N., Grodzka-Łukaszewska, M., Sinicyn, G., and Lakshmi, V.: Investigating the Future Flood and Drought Shifts in the Transboundary Srepok River Basin Using CMIP6 Projections, IEEE J. Sel. Top. Appl., 17, 7516–7529, https://doi.org/10.1109/JSTARS.2024.3380514, 2024a.
Tran, T.-N.-D., Tapas, M. R., Do, S. K., Etheridge, R., and Lakshmi, V.: Investigating the impacts of climate change on hydroclimatic extremes in the Tar-Pamlico River basin, North Carolina, J. Environ. Manage., 363, 121375, https://doi.org/10.1016/j.jenvman.2024.121375, 2024b.
Xie, H., Li, B., Tong, X., Tang, H., Liu, S., Jin, Y., Wang, C., Ye, Z., Chen, P., Xu, X., Liu, S., and Feng, Y.: Global satellite-borne laser altimeter elevation control point data set (2003–2009), National Tibetan Plateau/Third Pole Environment Data Center [data set], https://doi.org/10.11888/Geogra.tpdc.271727, 2021a.
Xie, H., Li, B., Tong, X., Zhang, X., He, T., Dai, J., Huang, G., Zhang, Z., and Liu, S.: A Planimetric Location Method for Laser Footprints of the Chinese Gaofen-7 Satellite Using Laser Spot Center Detection and Image Matching to Stereo Image Product, IEEE T. Geosci. Remote, 59, 9758–9771, https://doi.org/10.1109/TGRS.2020.3048042, 2021b.
Xie, H., Li, B., Liu, S., Ye, Z., Hong, Z., Sun, Y., Xu, Q., and Tong, X.: ICESat-2 corrected GDEM product (IC2-GDEM): Global digital elevation model refined by ICESat-2 laser altimeter data corrections to the ASTER GDEM, National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/RemoteSen.tpdc.301229, 2024.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Yue, L., Shen, H., Zhang, L., Zheng, X., Zhang, F., and Yuan, Q.: High-quality seamless DEM generation blending SRTM-1, ASTER GDEM v2 and ICESat/GLAS observations, ISPRS J. Photogramm., 123, 20–34, https://doi.org/10.1016/j.isprsjprs.2016.11.002, 2017.
Zhu, J., Yang, P.-F., Li, Y., Xie, Y.-Z., and Fu, H.-Q.: Accuracy assessment of ICESat-2 ATL08 terrain estimates: A case study in Spain, J. Cent. South Univ., 29, 226–238, https://doi.org/10.1007/s11771-022-4896-x, 2022.
Short summary
We refined the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) with Ice, Cloud, and Land Elevation Satellite 2 data to release a new dataset (IC2-GDEM). It has superior global elevation quality to ASTER GDEM. Its seamless integration with historical ASTER GDEM datasets is essential for longitudinal environmental studies. As a complementary data source to other GDEMs, it enables more reliable and comprehensive scientific discoveries.
We refined the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital...
Altmetrics
Final-revised paper
Preprint