the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global DEM Product Generation by Correcting ASTER GDEM Elevation with ICESat-2 Altimeter Data
Abstract. Advancements in scientific inquiry and practical applications have put forward a higher demand for the accuracy of global digital elevation models (GDEMs), especially for GDEMs whose main data source is optical imagery. To address this challenge, integrating GDEM and satellite laser altimeter data (global coverage and high-accuracy ranging) is one of the important research directions, in addition to the technological enhancement of the main data source. In this paper, we describe the datasets and algorithms used to generate a GDEM product (IC2-GDEM) by correcting ASTER GDEM elevation data with ICESat-2 altimeter data. The algorithm scheme presents the details of the strategies used for the various challenges, such as the processing of DEM boundaries, the fusion of the different data, the geographical layout of the satellite laser altimeter data, etc. We used a high-accuracy global elevation control point dataset and multiple high-accuracy local DEMs as the validation data for a comprehensive assessment at a global scale. The results from the validation comparison present that the elevation accuracy of IC2-GDEM is evidently superior to that of the ASTER GDEM product. The root-mean-square error (RMSE) reduction ratio of the corrected GDEM elevation is between 16 % and 82 %, and the average reduction ratio is about 47 %. From the analysis of the different topographies and land covers, it was also found that this error reduction is effective even in areas with high topographic relief (>15°) and high vegetation cover (>60 %). ASTER GDEM has been in use for more than a decade, and many historical datasets and models are based on its elevation data. IC2-GDEM facilitates seamless integration with these historical datasets, which is essential for longitudinal studies examining long-term environmental change, land use dynamics, and climate impacts. Meanwhile, IC2-GDEM can serve as a new complementary data source to existing DEMs (Copernicus DEM, etc.) mainly sourced from synthetic aperture radar (SAR) observation. By cross-validating qualities, filling data gaps, conducting multi-scale analyses,etc., it can lead to more reliable and comprehensive scientific discoveries, thereby improving the overall quality and reliability of earth science research. IC2-GDEM product is openly released via https://doi.org/10.11888/RemoteSen.tpdc.301229 (Xie et al., 2024).
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RC1: 'Comment on essd-2024-277', Anonymous Referee #1, 25 Aug 2024
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Q1. Can the authors numerically label the findings in the abstract, e.g., (1), (2), etc.?
Q2. L35-100: Some points within this paragraph need improvement, as follows:
First, the authors discuss the significance of refining ASTER GDEM but do not clearly explain what specific improvements or innovations this study introduces compared to previous studies.
Second, the paragraph starts by emphasizing the importance of high-quality DEMs, but the latter part seems to drift towards specific technical details about the ASTER GDEM correction method without clearly linking these details back to the broader impact or significance.
I found that the authors assumed that integrating ASTER GDEM with other DEM products will inherently lead to better outcomes but did not provide evidence or references to justify this assumption. Please revisit and address this point carefully.
I found phrases such as “high-accuracy global control point dataset” and “automatic processing scheme” are used without clear definitions or explanations of what makes them superior or innovative. This is very important for the product’s validation in this work.
The significance of the study is stated multiple times (e.g., “of great significance,” “beneficial supplement”), but without concrete examples or data to support these claims, the statements lack impact.
In addition, the literature review conducted on the use of remote-sensing DEMs Earth science research and scientific applications, including hydrological modeling, climate change research, natural hazard assessment, and ecosystem management was not well reviewed, suggesting accuracy of watershed delineation (10.1016/j.ejrh.2022.101282), flood risk assessment (10.3389/fenvs.2023.1304845), water resources management (10.1016/j.scitotenv.2024.174289 and 10.1007/s00382-024-07319-7), disaster preparedness (10.1109/jstars.2024.3380514), and promote human resilience for coastal communities (10.1016/j.jenvman.2024.121375).
Q3. The study specifically excludes polar regions from the correction process due to challenges like high variability in ice sheets and flow rates. This exclusion limits the global applicability of the IC2-GDEM product and leaves a coverage gap, particularly for researchers focused on polar studies.
Q4. The potential for temporal inconsistencies between the ASTER GDEM data and the more recent ICESat-2 data is not fully discussed. In dynamic landscapes, such as areas experiencing rapid coastal erosion or land use changes, these temporal discrepancies could lead to inaccuracies in the corrected DEM, which the authors did not quantify or address adequately. Please revisit and provide reasonable discussions to address this point.
Q5. The authors acknowledged that the density of ICESat-2 observations varies significantly with latitude, but it does not thoroughly investigate how this variation impacts the accuracy of the DEM corrections. In low-latitude regions, where ICESat-2 data are sparser, the correction results might be less reliable, a factor that needs more detailed examination.
Q6. The authors briefly mention the challenges posed by dynamic landscapes, where changes between the times of data collection could lead to inconsistencies. However, it does not provide a detailed analysis or propose methods to mitigate these issues, which is crucial for applications in rapidly changing environments.
In general, please separate the Discussion from the Conclusion section and provide a more in-depth discussion based on qualitative results.
Q7. Please include a section on limitations and future work.
Q8. In the conclusion, please highlight the main findings with a brief description (suggest highlighting qualitative results), but please keep them short, direct, and concise. The current form is lengthy and difficult to follow.
Citation: https://doi.org/10.5194/essd-2024-277-RC1
Data sets
ICESat-2 corrected GDEM product (IC2-GDEM): Global digital elevation model refined by ICESat-2 laser altimeter data corrections to the ASTER GDEM H. Xie et al. https://doi.org/10.11888/RemoteSen.tpdc.301229
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