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Remote Sensing Applications in Ocean Observation (Third Edition)
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Remote Sensing Applications in Ocean Observation (Third Edition)

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 5709

Special Issue Editor

Prof. Dr. Chung-Ru Ho

E-Mail Website
Guest Editor
Department of Marine Environmental Informatics, National Taiwan Ocean University, Keelung 202301, Taiwan
Interests: remote sensing; physical oceanography; global change; satellite oceanography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

It has been nearly half a century since the launch of artificial satellites to observe the ocean, and the observed data have been widely used in ocean, climate change, and other related research. The development of drones and coastal sensors in recent years has also been used to observe marine phenomena. In addition, with the rapid growth of computing speed, various artificial intelligence algorithms have also emerged. These technologies have been applied to the processing of 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 also welcomes the application of artificial intelligence technology in the analysis of ocean remote sensing data.

Prof. Dr. Chung-Ru Ho
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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 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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Related Special Issue

Published Papers (6 papers)

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Research

17 pages, 7985 KiB  
Article
The Influences of Environmental Factors on the Microwave Scattering Coefficient from the Sea Surface
by Yitong Jiang, Yanmin Zhang, Yunhua Wang, Fanwei Su and Daozhong Sun
Remote Sens. 2025, 17(8), 1405; https://doi.org/10.3390/rs17081405 - 15 Apr 2025
Viewed by 215
Abstract
The relationship between the microwave scattering coefficient from the sea surface and wind field has been extensively studied. Nevertheless, recent research on air–sea coupling has shown that sea–air temperature difference (SATD) also significantly affects the scattering coefficient. Therefore, to reveal the [...] Read more.
The relationship between the microwave scattering coefficient from the sea surface and wind field has been extensively studied. Nevertheless, recent research on air–sea coupling has shown that sea–air temperature difference (SATD) also significantly affects the scattering coefficient. Therefore, to reveal the influence of different environmental parameters, such as salinity, sea surface temperature (SST), and SATD on the scattering coefficient, a theoretical analysis has been carried out firstly. Meanwhile, the coupling coefficient between a scattering coefficient anomaly (SCA) and sea–air temperature difference anomaly (SATDA) over four typical sea regions is compared with that between an SCA and sea surface temperature anomaly (SSTA) by using the nearly 7–year data of the ECMWF, AMSR-E, and QSCAT. The results demonstrate that SCA is more sensitive to SATDA than SSTA. The values of ksatda between the SATDA and SCA exhibit seasonal variation, being higher in summer and lower in winter. Specifically, ksatda can reach a maximum of 0.62 in summer and drops to 0.2 in winter. Furthermore, the effects of the regional monthly mean sea surface temperature (RMMSST), regional monthly mean air temperature (RMMAT), regional monthly mean sea–air temperature difference (RMMSATD), and regional monthly mean wind speed (RMMWS) on ksatda are also discussed in detail. It is found that the RMMSATD is a crucial factor influencing ksatda. And the negative correlation coefficient between the RMMSATD and ksatda is −0.81. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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21 pages, 7818 KiB  
Article
BathyFormer: A Transformer-Based Deep Learning Method to Map Nearshore Bathymetry with High-Resolution Multispectral Satellite Imagery
by Zhonghui Lv, Julie Herman, Ethan Brewer, Karinna Nunez and Dan Runfola
Remote Sens. 2025, 17(7), 1195; https://doi.org/10.3390/rs17071195 - 27 Mar 2025
Viewed by 487
Abstract
Accurate mapping of nearshore bathymetry is essential for coastal management, navigation, and environmental monitoring. Traditional bathymetric mapping methods such as sonar surveys and LiDAR are often time-consuming and costly. This paper introduces BathyFormer, a novel vision transformer- and encoder-based deep learning model designed [...] Read more.
Accurate mapping of nearshore bathymetry is essential for coastal management, navigation, and environmental monitoring. Traditional bathymetric mapping methods such as sonar surveys and LiDAR are often time-consuming and costly. This paper introduces BathyFormer, a novel vision transformer- and encoder-based deep learning model designed to estimate nearshore bathymetry from high-resolution multispectral satellite imagery. This methodology involves training the BathyFormer model on a dataset comprising satellite images and corresponding bathymetric data obtained from the Continuously Updated Digital Elevation Model (CUDEM). The model learns to predict water depths by analyzing the spectral signatures and spatial patterns present in the multispectral imagery. Validation of the estimated bathymetry maps using independent hydrographic survey data produces a root mean squared error (RMSE) ranging from 0.55 to 0.73 m at depths of 2 to 5 m across three different locations within the Chesapeake Bay, which were independent of the training set. This approach shows significant promise for large-scale, cost-effective shallow water nearshore bathymetric mapping, providing a valuable tool for coastal scientists, marine planners, and environmental managers. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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16 pages, 12210 KiB  
Article
Analysis of the Influence of Different Reference Models on Recovering Gravity Anomalies from Satellite Altimetry
by Yu Han, Fangjun Qin, Hongwei Wei, Fengshun Zhu and Leiyuan Qian
Remote Sens. 2024, 16(20), 3758; https://doi.org/10.3390/rs16203758 - 10 Oct 2024
Viewed by 1027
Abstract
A satellite altimetry mission can measure high-precision sea surface height (SSH) to recover a marine gravity field. The reference gravity field model plays an important role in this recovery. In this paper, reference gravity field models with different degrees are used to analyze [...] Read more.
A satellite altimetry mission can measure high-precision sea surface height (SSH) to recover a marine gravity field. The reference gravity field model plays an important role in this recovery. In this paper, reference gravity field models with different degrees are used to analyze their effects on the accuracy of recovering gravity anomalies using the inverse Vening Meinesz (IVM) method. We evaluate the specific performance of different reference gravity field models using CryoSat-2 and HY-2A under different marine bathymetry conditions. For the assessments using 1-mGal-accuracy shipborne gravity anomalies and the DTU17 model based on the inverse Stokes principle, the results show that CryoSat-2 and HY-2A using XGM2019e_2159 obtains the highest inversion accuracy when marine bathymetry is less than 2000 m. Compared with the EGM2008 model, the accuracy of CryoSat-2 and HY-2A is improved by 0.6747 mGal and 0.6165 mGal, respectively. A weighted fusion method that incorporates multiple reference models is proposed to improve the accuracy of recovering gravity anomalies using altimetry satellites in shallow water. The experiments show that the weighted fusion method using different reference models can improve the accuracy of recovering gravity anomalies in shallow water. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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17 pages, 11732 KiB  
Article
Two-Dimensional Legendre Polynomial Method for Internal Tide Signal Extraction
by Yunfei Zhang, Cheng Luo, Haibo Chen, Wei Cui and Xianqing Lv
Remote Sens. 2024, 16(18), 3447; https://doi.org/10.3390/rs16183447 - 17 Sep 2024
Viewed by 1046
Abstract
This study employs the two-dimensional Legendre polynomial fitting (2-D LPF) method to fit M2 tidal harmonic constants from satellite altimetry data within the region of 53°E–131°E, 34°S–6°N, extracting internal tide signals acting on the sea surface. The M2 tidal harmonic constants are derived [...] Read more.
This study employs the two-dimensional Legendre polynomial fitting (2-D LPF) method to fit M2 tidal harmonic constants from satellite altimetry data within the region of 53°E–131°E, 34°S–6°N, extracting internal tide signals acting on the sea surface. The M2 tidal harmonic constants are derived from the sea surface height (SSH) data of the TOPEX/Poseidon (T/P), Jason-1, Jason-2, and Jason-3 satellites via t-tide analysis. We validate the 2-D LPF method against the 300 km moving average (300 km smooth) method and the one-dimensional Legendre polynomial fitting (1-D LPF) method. Through cross-validation across 42 orbits, the optimal polynomial orders are determined to be seven for 1-D LPF, and eight and seven for the longitudinal and latitudinal directions in 2-D LPF, respectively. The 2-D LPF method demonstrated superior spatial continuity and smoothness of internal tide signals. Further single-orbit correlation analysis confirmed generally higher correlation with topographic and density perturbations (correlation coefficients: 0.502, 0.620, 0.245; 0.420, 0.273, −0.101), underscoring its accuracy. Overall, the 2-D LPF method can use all regional data points, overcoming the limitations of single-orbit approaches and proving its effectiveness in extracting internal tide signals acting on the sea surface. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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21 pages, 968 KiB  
Article
Classification of Small Targets on Sea Surface Based on Improved Residual Fusion Network and Complex Time–Frequency Spectra
by Shuwen Xu, Xiaoqing Niu, Hongtao Ru and Xiaolong Chen
Remote Sens. 2024, 16(18), 3387; https://doi.org/10.3390/rs16183387 - 12 Sep 2024
Viewed by 951
Abstract
To address the problem that conventional neural networks trained on radar echo data cannot handle the phase of the echoes, resulting in insufficient information utilization and limited performance in detection and classification, we extend neural networks from the real-valued neural networks to the [...] Read more.
To address the problem that conventional neural networks trained on radar echo data cannot handle the phase of the echoes, resulting in insufficient information utilization and limited performance in detection and classification, we extend neural networks from the real-valued neural networks to the complex-valued neural networks, presenting a novel algorithm for classifying small sea surface targets. The proposed algorithm leverages an improved residual fusion network and complex time–frequency spectra. Specifically, we augment the Deep Residual Network-50 (ResNet50) with a spatial pyramid pooling (SPP) module to fuse feature maps from different receptive fields. Additionally, we enhance the feature extraction and fusion capabilities by replacing the conventional residual block layer with a multi-branch residual fusion (MBRF) module. Furthermore, we construct a complex time–frequency spectrum dataset based on radar echo data from four different types of sea surface targets. We employ a complex-valued improved residual fusion network for learning and training, ultimately yielding the result of small target classification. By incorporating both the real and imaginary parts of the echoes, the proposed complex-valued improved residual fusion network has the potential to extract more comprehensive features and enhance classification performance. Experimental results demonstrate that the proposed method achieves superior classification performance across various evaluation metrics. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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11 pages, 2354 KiB  
Article
Influence of Abnormal Eddies on Seasonal Variations in Sonic Layer Depth in the South China Sea
by Xintong Liu, Chunhua Qiu, Tianlin Wang, Huabin Mao and Peng Xiao
Remote Sens. 2024, 16(15), 2845; https://doi.org/10.3390/rs16152845 - 2 Aug 2024
Viewed by 1267
Abstract
Sonic layer depth (SLD) is crucial in ocean acoustics research and profoundly influences sound propagation and Sonar detection. Carrying 90% of oceanic kinetic energy, mesoscale eddies significantly impact the propagation of acoustic energy in the ocean. Recent studies classified mesoscale eddies into normal [...] Read more.
Sonic layer depth (SLD) is crucial in ocean acoustics research and profoundly influences sound propagation and Sonar detection. Carrying 90% of oceanic kinetic energy, mesoscale eddies significantly impact the propagation of acoustic energy in the ocean. Recent studies classified mesoscale eddies into normal eddies (warm anticyclonic and cold cyclonic eddies) and abnormal eddies (cold anticyclonic and warm cyclonic eddies). However, the influence of mesoscale eddies, especially abnormal eddies, on SLD remains unclear. Based on satellite altimeter and reanalysis data, we explored the influence of mesoscale eddies on seasonal variations in SLD in the South China Sea. We found that the vertical structures of temperature anomalies within the eddies had a significant impact on the sound speed field. A positive correlation between sonic layer depth anomaly (SLDA) and eddy intensity (absolute value of relative vorticity) was investigated. The SLDA showed significant seasonal variations: during summer (winter), the proportion of negative (positive) SLDA increased. Normal eddies (abnormal eddies) had a more pronounced effect during summer and autumn (spring and winter). Based on mixed-layer heat budget analysis, it was found that the seasonal variation in SLD was primarily induced by air–sea heat fluxes. However, for abnormal eddies, the horizontal advection and vertical convective terms modulated the variations in the SLDA. This study provides additional theoretical support for mesoscale eddy–acoustic coupling models and advances our understanding of the impact of mesoscale eddies on sound propagation. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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