Satellite-derived bathymetry combined with Sentinel-2 and ICESat-2 datasets using machine learning

Frontiers in Earth Science

Earth Science

Congshuang Xie¹  Zhenhua Zhang²  Peng Chen²  Delu Pan² 

[1] School of Oceangray, Shanghai Jiao Tong University, Shanghai, China;State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China;State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou, China;Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China;

关键词: satellite-derived bathymetry;    ICESat-2;    sentinel-2;    DBSCAN;    atmospheric correction;   

DOI  :  10.3389/feart.2023.1111817

 received in 2022-11-30, accepted in 2023-03-22,  发布年份 2023

来源: Frontiers

【 摘 要 】

Most satellite-derived bathymetry (SDB) methods developed thus far from passive remote sensing data have required in situ water depth, thus limiting their utility in areas with no in situ data. Recently, new Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) observations have shown great potential in providing high-precision a priori water depth benefits from range-resolved lidar. In this study, we propose a combined active and passive remote sensing SDB method using only satellite data. An adaptive ellipse DBSCAN (AE-DBSCAN) algorithm is introduced to derive a priori bathymetric data from ICESat-2 data to automatically adapt to the terrain change complexity, and then we use these a priori bathymetric data in Sentinel-2 images to help build a model between remote sensing reflectance (Rrs) and water depth. Three machine learning (ML) methods are then employed, and the performances compared with conventional empirical SDB models are presented. After that, the results using different Sentinel-2 Rrs band combinations and the effects with and without atmospheric correction on ML-based SDB are discussed. The results showed that our AE-DBSCAN method performs better than the standard DBSCAN method, and the ML-based SDB can achieve an overall RMSE of less than 1.5 m in St. Thomas better than the traditional SDB method. They also indicate that ML-based SDB can obtain a relatively high-precision water depth without atmospheric correction, which helps to improve processing efficiency by avoiding a complex atmospheric correction process.

【 授权许可】

Unknown   
Copyright © 2023 Xie, Chen, Zhang and Pan.