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Remote Sensing Applications in Ocean Observation—4th Edition

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 893

Special Issue Editor

Special Issue Information

Dear Colleagues,

It has been nearly half a century since the launch of artificial satellites for ocean observation, and the resulting data have been widely used in ocean, climate change, and other related research. Drones and coastal sensors developed in recent years have also been used to observe marine phenomena. In addition, with rapidly increasing computing speeds, various artificial intelligence algorithms have also emerged. These technologies have been used to process remote sensing images and data. Therefore, this Special Issue welcomes research on the application of remote sensing data from spaceborne, airborne, or ground sensors in ocean observation and the application of artificial intelligence technology to the analysis of ocean remote sensing data.

Prof. Dr. Chung-Ru Ho
Guest Editor

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Keywords

  • ocean remote sensing
  • internal waves
  • eddies
  • oil spills
  • algal blooms
  • sea ices
  • rogue waves
  • upwelling
  • bathymetry
  • air–sea interaction
  • marine debris
  • AI in ocean remote sensing

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Published Papers (1 paper)

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Research

19 pages, 4739 KB  
Article
Retrieval of Significant Wave Height in Coastal Seas of China from GaoFen-3 Satellites Based on Deep Learning
by Fengjia Sun, Xing Li, Xiao-Ming Li, Yongzheng Ren and Ke Wu
Remote Sens. 2026, 18(6), 966; https://doi.org/10.3390/rs18060966 - 23 Mar 2026
Viewed by 563
Abstract
The acquisition of significant wave height (SWH) in coastal seas is significantly important to human activities. The Gaofen-3 (GF-3) satellites, comprising GF-3, GF-3B and GF-3C, are independently developed operational SAR of China, capable of providing high-precision, high-resolution, multi-polarization coastal ocean wave observations. In [...] Read more.
The acquisition of significant wave height (SWH) in coastal seas is significantly important to human activities. The Gaofen-3 (GF-3) satellites, comprising GF-3, GF-3B and GF-3C, are independently developed operational SAR of China, capable of providing high-precision, high-resolution, multi-polarization coastal ocean wave observations. In order to obtain SWH in coastal seas, the retrieval of SWH using Quad-Polarization Stripmap (QPS) mode data from GF-3 satellites based on the deep learning method is implemented in this study. Furthermore, to obtain more SWH data, the polarization ratio model was applied to the Fine Stripmap (FS) mode data and Ultra Fine Stripmap (UFS) mode data to extend the model application. Comparisons with ECMWF Reanalysis v5 (ERA5) wave heights show that the QPS mode SWH retrieval achieves a root mean square error (RMSE) of 0.33 m. For the FS mode, the RMSE is 0.44 m (vs. ERA5) and 0.52 m (vs. altimeter). For the UFS mode, the RMSE is 0.39 m (vs. ERA5). Evaluation results indicate the feasibility of the proposed method for coastal SWH retrieval. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation—4th Edition)
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