Evaluating the Accuracy of Global Bathymetric Models in the Red Sea Using Shipborne Bathymetry

IF 2.2 4区 地球科学 Q3 ENVIRONMENTAL SCIENCES Journal of the Indian Society of Remote Sensing Pub Date : 2024-08-28 DOI:10.1007/s12524-024-01981-4
Ahmed Zaki, Bashar Bashir, Abdullah Alsalman, Basem Elsaka, Mohamed Abdallah, Mohamed El-Ashquer
{"title":"Evaluating the Accuracy of Global Bathymetric Models in the Red Sea Using Shipborne Bathymetry","authors":"Ahmed Zaki, Bashar Bashir, Abdullah Alsalman, Basem Elsaka, Mohamed Abdallah, Mohamed El-Ashquer","doi":"10.1007/s12524-024-01981-4","DOIUrl":null,"url":null,"abstract":"<p>Global bathymetric models derived from satellite altimetry are important for studying the Earth’s oceans. However, the accuracy of these models can vary across different geographic regions. This study evaluates four widely used global bathymetric models ETOPO 2022, GEBCO 2023, SRTM15 + V2.5.5, and DTU18BAT in the Red Sea using 268,071 reference shipborne bathymetric measurements. The analysis compares the models’ depth estimates to the shipborne measurements across different depth ranges between 0 and 3000 m. The results show that overall, the GEBCO 2023 model provides the highest accuracy with the lowest standard deviation of 43.774 m and root mean square error of 43.929 m relative to shipborne data. The ETOPO 2022 model ranks second in accuracy with a standard deviation of 45.316 m and root mean square error of 45.345 m. The frequency distribution of residuals indicates that GEBCO 2023 and ETOPO 2022 models have the most precise depth predictions concentrated tightly around zero difference, while SRTM15 + V2.5.5 and DTU18BAT ones show broader spreads. There is no systematic depth over or under-predictions. Finally, the GEBCO 2023 and ETOPO 2022 models show good accuracy in the Red Sea, outperforming SRTM15 + V2.5.5 and DTU18BAT.</p>","PeriodicalId":17510,"journal":{"name":"Journal of the Indian Society of Remote Sensing","volume":"53 1","pages":""},"PeriodicalIF":2.2000,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Indian Society of Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s12524-024-01981-4","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Global bathymetric models derived from satellite altimetry are important for studying the Earth’s oceans. However, the accuracy of these models can vary across different geographic regions. This study evaluates four widely used global bathymetric models ETOPO 2022, GEBCO 2023, SRTM15 + V2.5.5, and DTU18BAT in the Red Sea using 268,071 reference shipborne bathymetric measurements. The analysis compares the models’ depth estimates to the shipborne measurements across different depth ranges between 0 and 3000 m. The results show that overall, the GEBCO 2023 model provides the highest accuracy with the lowest standard deviation of 43.774 m and root mean square error of 43.929 m relative to shipborne data. The ETOPO 2022 model ranks second in accuracy with a standard deviation of 45.316 m and root mean square error of 45.345 m. The frequency distribution of residuals indicates that GEBCO 2023 and ETOPO 2022 models have the most precise depth predictions concentrated tightly around zero difference, while SRTM15 + V2.5.5 and DTU18BAT ones show broader spreads. There is no systematic depth over or under-predictions. Finally, the GEBCO 2023 and ETOPO 2022 models show good accuracy in the Red Sea, outperforming SRTM15 + V2.5.5 and DTU18BAT.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用船载水深测量法评估红海全球测深模型的准确性
卫星测高法得出的全球测深模型对研究地球海洋非常重要。然而,这些模型的准确性在不同地理区域会有差异。本研究利用 268,071 个参考船载测深数据,评估了在红海广泛使用的四个全球测深模型 ETOPO 2022、GEBCO 2023、SRTM15 + V2.5.5 和 DTU18BAT。结果表明,总体而言,GEBCO 2023 模型的精度最高,与船载数据相比,标准偏差最小,为 43.774 米,均方根误差最小,为 43.929 米。残差的频率分布表明,GEBCO 2023 和 ETOPO 2022 模型具有最精确的深度预测,其深度紧紧集中在零差值附近,而 SRTM15 + V2.5.5 和 DTU18BAT 模型则显示出更大的差值。没有系统性的深度预测偏高或偏低。最后,GEBCO 2023 和 ETOPO 2022 模式在红海显示出良好的精度,优于 SRTM15 + V2.5.5 和 DTU18BAT。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of the Indian Society of Remote Sensing
Journal of the Indian Society of Remote Sensing ENVIRONMENTAL SCIENCES-REMOTE SENSING
CiteScore
4.80
自引率
8.00%
发文量
163
审稿时长
7 months
期刊介绍: The aims and scope of the Journal of the Indian Society of Remote Sensing are to help towards advancement, dissemination and application of the knowledge of Remote Sensing technology, which is deemed to include photo interpretation, photogrammetry, aerial photography, image processing, and other related technologies in the field of survey, planning and management of natural resources and other areas of application where the technology is considered to be appropriate, to promote interaction among all persons, bodies, institutions (private and/or state-owned) and industries interested in achieving advancement, dissemination and application of the technology, to encourage and undertake research in remote sensing and related technologies and to undertake and execute all acts which shall promote all or any of the aims and objectives of the Indian Society of Remote Sensing.
期刊最新文献
A Heuristic Approach of Radiometric Calibration for Ocean Colour Sensors: A Case Study Using ISRO’s Ocean Colour Monitor-2 Farmland Extraction from UAV Remote Sensing Images Based on Improved SegFormer Model Self Organizing Map based Land Cover Clustering for Decision-Level Jaccard Index and Block Activity based Pan-Sharpened Images Improved Building Extraction from Remotely Sensed Images by Integration of Encode–Decoder and Edge Enhancement Models Enhancing Change Detection Accuracy in Remote Sensing Images Through Feature Optimization and Game Theory Classifier
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1