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Microwave Remote Sensing on Ocean Observation
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Microwave Remote Sensing on Ocean Observation

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 2917

Special Issue Editors

Dr. Yanlei Du

E-Mail Website
Guest Editor
National Key Laboratory of Microwave Imaging, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: microwave ocean remote sensing; computational electromagnetics; microwave scattering and emission from ocean surface; retrieval of ocean dynamic parameters; maritime target detection
Prof. Dr. Ali Khenchaf

E-Mail Website
Guest Editor
Lab-STICC, UMR CNRS 6285, ENSTA Bretagne, 29806 Brest, France
Interests: Computer Science; Engineering; earth and planetary sciences; remote sensing, electromagnetic radar cross section, sea clutter, radar, propagation and em scattering; illumination radar using polarization states of photons in atmosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Microwave remote sensing technology, renowned for its capabilities of all-time, all-weather and large-scale observation, has greatly promoted the development of marine sciences since the 1970s. Particularly, global-scale remote sensing inversions of ocean dynamic parameters, such as sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), wind vectors, etc., have largely enriched the observational data obtained. These advancements have overcome the limitations of scattered and expensive traditional ocean data measurements for oceanography and global climate studies. This progress has also led to the rapid evolution of several research areas, including forward modeling based on electromagnetic scattering theory, applications of oceanic parameter retrievals and maritime target detections, and the system design of microwave remote sensing instruments for ocean observations, among others. In recent decades, emergent techniques such as deep learning and artificial intelligence have further advanced the developments of microwave remote sensing in ocean observation, resulting in a positive trend of higher precision, finer resolution, larger coverage, and broader applications.

This Special Issue aims to provide a comprehensive platform for researchers to share their latest studies and innovations on ocean observations using microwave remote sensing technology. This Special Issue’s scope aligns with the journal’s focus on microwave ocean remote sensing, including the relevant fundamental theories and practical applications.

We welcome original research articles, technical notes, and reviews related to the following topics: forward modeling of ocean microwave scattering and emission; retrievals of ocean dynamic parameters using active, passive, or GNSS reflectometry observations; detections of marine dynamic phenomena and artificial targets from SAR/PolSAR images; emergent concepts of forward modeling and parameter inversion through artificial intelligence; simulation and modeling of sea clutters for radar target detection; and other technical innovations in microwave ocean remote sensing.

Dr. Yanlei Du
Prof. Dr. Ali Khenchaf
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • ocean dynamic parameter retrieval
  • detection of marine dynamic phenomena and artificial targets
  • advanced forward modeling of ocean scattering and emission
  • data fusion and reconstruction of multi-source ocean products
  • deep learning/artificial intelligence in ocean remote sensing
  • scatterometer/radiometer/SAR/GNSS-reflectometry
  • scale effect analysis
  • Polarization theory and Polarimetric SAR applications in ocean remote sensing

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Published Papers (4 papers)

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26 pages, 9668 KB  
Article
Sea Surface Wind Speed Retrieval with a Dual-Branch Feature-Fusion Network Using GaoFen-3 Series SAR Data
by Xing Li, Xiao-Ming Li, Yongzheng Ren, Ke Wu and Chunbo Li
Remote Sens. 2026, 18(7), 971; https://doi.org/10.3390/rs18070971 - 24 Mar 2026
Viewed by 111
Abstract
To address the suboptimal radiometric calibration accuracy observed in specific beam codes of the GaoFen-3 (GF-3) series satellite for sea surface wind speed (SSWS) retrieval, this study introduces a calibration constant correction method based on the geophysical model function (GMF). This approach enables [...] Read more.
To address the suboptimal radiometric calibration accuracy observed in specific beam codes of the GaoFen-3 (GF-3) series satellite for sea surface wind speed (SSWS) retrieval, this study introduces a calibration constant correction method based on the geophysical model function (GMF). This approach enables high-precision SSWS retrieval from GF-3B data. Conventional SAR-based SSWS retrieval models typically rely on pointwise mapping relationships, which overlook the spatial characteristics inherent in dynamic sea surface wind fields. To overcome this limitation, this study proposes an attention-guided dual-branch feature-fusion network (ADBFF-NET). The first branch, implemented as a backpropagation neural network (BPNN), learns nonlinear mappings between the normalized radar cross-section (NRCS, σ0), incidence angle, azimuth look direction, and wind vectors (speed and direction). The second branch, designed as a residual convolutional neural network, extracts spatial features of wind fields. An attention mechanism fuses the outputs of both branches, thereby enhancing retrieval accuracy. Experiments conducted with GF-3 series satellite data were validated against the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5), Advanced Scatterometer (ASCAT) wind fields, and altimeter-derived wind speeds. The results indicate that the SSWS retrieved from GF-3B SAR data using the corrected calibration constants achieve a root mean square error (RMSE) of 1 m/s against ERA5 wind speeds, representing an approximately 40% reduction compared with the RMSE obtained using the original calibration constant. Furthermore, compared to ERA5 and ASCAT data, the RMSE of the wind speeds retrieved by the ADBFF-NET model reaches 1.17 m/s and 1.03 m/s, respectively. Full article
(This article belongs to the Special Issue Microwave Remote Sensing on Ocean Observation)
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19 pages, 10269 KB  
Article
Semantic Segmentation of Typical Oceanic and Atmospheric Phenomena in SAR Images Based on Modified Segformer
by Quankun Li, Xue Bai, Lizhen Hu, Liangsheng Li, Yaohui Bao, Xupu Geng and Xiao-Hai Yan
Remote Sens. 2026, 18(1), 113; https://doi.org/10.3390/rs18010113 - 28 Dec 2025
Cited by 1 | Viewed by 606
Abstract
Synthetic Aperture Radar (SAR) images of the sea surface reveal a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is essential for understanding ocean dynamics and ocean–atmosphere interactions. This study selected 2383 Sentinel-1 Wave (WV) mode images and 2628 [...] Read more.
Synthetic Aperture Radar (SAR) images of the sea surface reveal a variety of oceanic and atmospheric phenomena. Automatically detecting and identifying these phenomena is essential for understanding ocean dynamics and ocean–atmosphere interactions. This study selected 2383 Sentinel-1 Wave (WV) mode images and 2628 Interferometric Wide swath (IW) mode sub-images to construct a semantic segmentation dataset covering 12 typical oceanic and atmospheric phenomena, with a balanced distribution of approximately 400 sub-images per category, culminating in a comprehensive dataset of 5011 samples. The images in this dataset have a resolution of 100 m and dimensions of 256 × 256 pixels. We propose Segformer-OcnP model based on Segformer for the semantic segmentation of these multiple oceanic and atmospheric phenomena. Experimental results demonstrate that Segformer-OcnP outperforms classic CNN-based models (U-Net, DeepLabV3+) and mainstream Transformer-based models (SETR, the original Segformer), achieving 80.98% mDice, 70.32% mIoU, and 86.77% Overall Accuracy, verifying its superior segmentation performance. Full article
(This article belongs to the Special Issue Microwave Remote Sensing on Ocean Observation)
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23 pages, 41707 KB  
Article
Ship Detection in SAR Images Using Sparse R-CNN with Wavelet Deformable Convolution and Attention Mechanism
by Zhiqiang Zeng, Zongsi Chen, Junjun Yin and Huiping Lin
Remote Sens. 2025, 17(23), 3794; https://doi.org/10.3390/rs17233794 - 22 Nov 2025
Viewed by 1045
Abstract
This paper proposes a synthetic aperture radar (SAR) ship detection method based on wavelet-domain deformable convolution (WDC) and multi-head attention, built upon the Sparse R-CNN framework. First, a wavelet-domain convolution module is introduced to enhance the modeling of ship targets with diverse scales [...] Read more.
This paper proposes a synthetic aperture radar (SAR) ship detection method based on wavelet-domain deformable convolution (WDC) and multi-head attention, built upon the Sparse R-CNN framework. First, a wavelet-domain convolution module is introduced to enhance the modeling of ship targets with diverse scales and shapes while incorporating frequency-domain information. Deformable convolution adaptively adjusts sampling locations, overcoming the limitations of traditional convolution in capturing target edges and blurred boundaries. Next, a position encoding module is employed to normalize candidate bounding box coordinates and integrate them into region-of-interest features. By providing spatial context, position encoding strengthens spatial perception and enables the subsequent multi-head attention mechanism to more effectively capture associations between targets and candidate regions, thereby improving localization accuracy under arbitrary spatial distributions. Furthermore, the original dynamic head is replaced with a multi-head attention mechanism. Through position-encoded multi-head attention, the model more accurately emphasizes regions with spatial and semantic correlations to the target, enhancing both focus and discrimination for sparse targets. Extensive experiments conducted on two benchmark datasets (SSDD and HRSID) demonstrate the effectiveness and superiority of the proposed method. Overall, the method significantly improves the detection of sparse, multi-scale, and randomly distributed ship targets in SAR images. Full article
(This article belongs to the Special Issue Microwave Remote Sensing on Ocean Observation)
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15 pages, 3959 KB  
Technical Note
Airborne SAR Imaging Algorithm for Ocean Waves Oriented to Sea Spike Suppression
by Yawei Zhao, Yongsheng Xu, Yanlei Du and Jinsong Chong
Remote Sens. 2026, 18(3), 397; https://doi.org/10.3390/rs18030397 - 24 Jan 2026
Viewed by 417
Abstract
Synthetic aperture radar (SAR) is widely used in the field of ocean remote sensing. However, SAR images are usually affected by sea spikes, which appear as strong echo and azimuth defocus characteristics. The texture features of ocean waves in SAR images are submerged [...] Read more.
Synthetic aperture radar (SAR) is widely used in the field of ocean remote sensing. However, SAR images are usually affected by sea spikes, which appear as strong echo and azimuth defocus characteristics. The texture features of ocean waves in SAR images are submerged by sea spikes, making them weak or even invisible. This seriously affects the further applications of SAR technology in ocean remote sensing. To address this issue, an airborne SAR imaging algorithm for ocean waves oriented to sea spike suppression is proposed in this paper. The non-stationary characteristics of sea spikes are taken into account in the proposed algorithm. The SAR echo data is transformed into the time–frequency domain by short-time Fourier transform (STFT). And the echo signals of sea spikes are suppressed in the time–frequency domain. Then, the ocean waves are imaged in focus by applying focus settings. In order to verify the effectiveness of the proposed algorithm, airborne SAR data was processed using the proposed algorithm, including SAR data with completely invisible waves and other data with weakly visible waves under sea spike influence. Through analyzing the ocean wave spectrum and imaging quality, it is confirmed that the proposed algorithm can significantly suppress sea spikes and improve the texture features of ocean waves in SAR images. Full article
(This article belongs to the Special Issue Microwave Remote Sensing on Ocean Observation)
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