and land elevation satellite-2 (ICESat-2); Bathymetry; Cloud; denoising; ice; machine learning (ML); sentinel-2; De-noising; Ice clouds; Ice, cloud, and land elevation satellite-2; Land elevation satellites; Light gradients; Machine learning; Machine-learning; Remote-sensing; Sea surfaces; Sentinel-2; Computers in Earth Sciences; Atmospheric Science
Abstract :
[en] The application of empirical methods for satellite-derived bathymetry is limited by the lack of in situ bathymetric data in remote, inaccessible areas. This challenge has been addressed with the launch of Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). This study provides an accurate bathymetric photon extraction process for ICESat-2 ATL03 data, and the ${{\bm{R}}}^2$ value of the bathymetric photons obtained using this process and airborne bathymetric LiDAR data is up to 99%. Next, based on two types of remote sensing data, ICESat-2 and Sentinel-2, machine learning models, including linear regression (LR), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost), were trained to obtain bathymetric maps. The experimental results show that the mean root mean square error (RMSE), mean absolute error (MAE), and mean relative error (MRE) values of the LR models are less than 3.02 m, 2.38 m, and 86.03%, respectively. The mean RMSE, MAE, and MRE values of the LightGBM and CatBoost models are less than 0.91 m, 0.66 m, and 23.17%, respectively. It is concluded that the proposed denoising process for ICESat-2 ATL03 data is effective, and the results of the bathymetric maps obtained using these data are satisfactory. Thus, the proposed approach is effective, and this strategy can be used to replace conventional bathymetric inversion methods to obtain high-accuracy bathymetric maps.
Disciplines :
Civil engineering
Author, co-author :
Xie, Tao ; Qingdao National Laboratory for Marine Science and Technology, Laboratory for Regional Oceanography and Numerical Modeling, Qingdao, China ; Ministry of Natural Resources, Technology Innovation Center for Integration Applications in Remote Sensing and Navigation, Nanjing, China ; Jiangsu Prov. Eng. Research Center of Collaborative Navigation/Positioning and Smart Application, Nanjing, China ; Nanjing University of Information Science and Technology, School of Remote Sensing and Geomatics Engineering, Nanjing, China
Kong, Ruiyao; Qingdao National Laboratory for Marine Science and Technology, Laboratory for Regional Oceanography and Numerical Modeling, Qingdao, China
NURUNNABI, Abdul Awal Md ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Bai, Shuying; Qingdao National Laboratory for Marine Science and Technology, Laboratory for Regional Oceanography and Numerical Modeling, Qingdao, China
Zhang, Xuehong; Qingdao National Laboratory for Marine Science and Technology, Laboratory for Regional Oceanography and Numerical Modeling, Qingdao, China
External co-authors :
yes
Language :
English
Title :
Machine-Learning-Method-Based Inversion of Shallow Bathymetric Maps Using ICESat-2 ATL03 Data
Publication date :
2023
Journal title :
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ISSN :
1939-1404
eISSN :
2151-1535
Publisher :
Institute of Electrical and Electronics Engineers Inc.
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