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25 pages, 6613 KB

Open AccessArticle

Satellite-Based Assessment of Marine Environmental Indicators and Their Variability in the South Pacific Island Regions: A National-Scale Perspective

by Qunfei Hu, Teng Li, Yan Bai, Xianqiang He, Xueqian Chen, Liangyu Chen, Xiaochen Huang, Meng Huang and Difeng Wang

Remote Sens. 2026, 18(1), 165; https://doi.org/10.3390/rs18010165 - 4 Jan 2026

Viewed by 716

Abstract

The marine environment in the South Pacific Island Countries (SPICs) is sensitive and vulnerable to climate change. While large-scale changes in this region are well-documented, national-scale analyses that address management needs remain limited. This study evaluated the performance of satellite-derived datasets—including sea surface [...] Read more.

The marine environment in the South Pacific Island Countries (SPICs) is sensitive and vulnerable to climate change. While large-scale changes in this region are well-documented, national-scale analyses that address management needs remain limited. This study evaluated the performance of satellite-derived datasets—including sea surface temperature (SST), sea surface salinity (SSS), Secchi disk depth (SDD), chlorophyll-a (Chl-a), net primary production (NPP), and sea level anomaly (SLA)—against in situ observations, and analyzed their spatial and temporal variability across 12 national Exclusive Economic Zones (EEZs) during 1998–2023. Validation results presented that current satellite datasets could provide applicable information for EEZ-scale analyses. In the past decades, the SPICs experienced a general increase in SST and SLA, accompanied by marked within-EEZ heterogeneity in Chl-a and NPP variations, with Papua New Guinea exhibiting the largest within-EEZ inter-annual variability. In addition to monitoring, satellite data would help to constrain the uncertainty of CMIP6 results in the SPICs, subject to the accuracy of specific products. By 2100, Nauru might experience the most vulnerable EEZ, while the marine environment in the French Polynesian EEZ can keep relatively stable among all 12 EEZs. Meanwhile, CMIP6 projections in the Southeastern EEZs are more sensitive to satellite-based constraints, showing pronounced adjustments. Our results demonstrate the potential of combining validated satellite data with CMIP6 models to provide national-scale decision support for climate adaptation and marine resource management in the SPICs. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))

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22 pages, 2957 KB

Open AccessArticle

High-Resolution Retrieval of Radial Ocean Current Velocity from SAR Strip-Map Imagery

by Jian Wang, Tao Lai and Xiaoqing Wang

Remote Sens. 2025, 17(24), 3987; https://doi.org/10.3390/rs17243987 - 10 Dec 2025

Viewed by 743

Abstract

The retrieval of radial ocean surface current from Synthetic Aperture Radar (SAR) data is important for ocean current research and effective ocean remote sensing. Existing algorithms, primarily based on the Average Cross-Correlation Coefficient (ACCC) method, suffer from drawbacks, including low Doppler frequency-shift estimation [...] Read more.

The retrieval of radial ocean surface current from Synthetic Aperture Radar (SAR) data is important for ocean current research and effective ocean remote sensing. Existing algorithms, primarily based on the Average Cross-Correlation Coefficient (ACCC) method, suffer from drawbacks, including low Doppler frequency-shift estimation accuracy and susceptibility to azimuth ambiguity, hindering accurate measurements. To address these limitations, this paper proposes a method for high-resolution radial current velocity estimation. This approach employs Maximum A Posteriori (MAP) estimation based on signal modeling of the local Doppler power spectrum. This method achieves better Doppler frequency shift estimation accuracy than ACCC and effectively mitigates the azimuth ambiguity, substantially enhancing the precision of radial ocean surface velocity estimation. The algorithm was validated using raw Sentinel-1 Strip-map mode real data and HYCOM data acquired over the Seychelles Islands on 23 April 2023, and the central Indian Ocean (south of the equator) on 20 May 2023. Compared with the Sentinel-1 Level 2 ocean Surface Radial Velocity (RVL) product, the method demonstrates the improvements in both spatial resolution and retrieval accuracy. Specifically, the quantitative comparison with HYCOM data showed a reduction in Root Mean Square Error (RMSE) of up to 34.3% and an improvement in Mean Absolute Error (MAE) of up to 32.1%. Moreover, its ability to suppress the azimuth Doppler ambiguity is demonstrated in the real-data experiment. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))

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21 pages, 10076 KB

Open AccessArticle

Intercomparison, Fusion and Application of FY-3E/WindRAD and HY-2B/SCA Ocean Surface Wind Products for Tropical Cyclone Monitoring

by Zonghao Qian, Wei Yu, Wei Guo, Lina Bai and Xiaoqin Lu

Remote Sens. 2025, 17(23), 3809; https://doi.org/10.3390/rs17233809 - 24 Nov 2025

Viewed by 848

Abstract

Ocean surface wind vector (OWV) is a key variable for ocean remote sensing and tropical cyclone (TC) monitoring. This study presents the first comprehensive intercomparison of Ku-band OWV products from FY-3E/WindRAD and HY-2B/SCA scatterometers using full-year data from 2022 (583,805 spatiotemporal collocations), with [...] Read more.

Ocean surface wind vector (OWV) is a key variable for ocean remote sensing and tropical cyclone (TC) monitoring. This study presents the first comprehensive intercomparison of Ku-band OWV products from FY-3E/WindRAD and HY-2B/SCA scatterometers using full-year data from 2022 (583,805 spatiotemporal collocations), with both sensors sampling the morning–evening local-time sector in sun-synchronous orbits. Results indicate strong agreement in wind speed (R = 0.95; mean bias −0.47 m/s; RMSE 1.30 m/s) and wind direction (mean bias 0.22°; std 28.13°) for wind speeds ≥ 3.4 m/s (Beaufort scale B3 and above), with the highest consistency across Beaufort scale 3–8 (B3–B8); however, at wind speeds greater than 20.8 m/s (B9) the bias increases. A fusion leveraging FY-3E’s fine resolution and HY-2B’s wide coverage is implemented and applied to Super Typhoon Hinnamnor (2022), enhancing the spatial coverage and structural detail of TC winds. Quadrant 34 kt wind radii (R34) are estimated from the fused wind fields and evaluated against the best-track data from the Joint Typhoon Warning Center (JTWC), showing close agreement during compact, symmetric TC stages but larger differences during structural reorganization. Overall, the findings confirm inter-satellite consistency for the two Chinese scatterometers and demonstrate the practical value of a multi-source fusion approach that benefits TC monitoring, wind radii estimation, and marine weather services. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))

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18 pages, 55646 KB

Open AccessArticle

Physics-Constrained Deterministic Sea Wave Reconstruction Methodology Based on X-Band Coherent Radar

by Jingjun Li, Can Zhao, Xuewen Ma, Jihao Fan, Guangbiao Wang, Limin Huang and Yukang Li

Remote Sens. 2025, 17(17), 3004; https://doi.org/10.3390/rs17173004 - 29 Aug 2025

Viewed by 1124

Abstract

Deterministic sea wave reconstruction techniques are critical for enhancing maritime safety and disaster warnings. Coherent radar remote sensing captures sea surface velocity information to enable more precise wave reconstruction. Existing difference matrix methods address rank-deficient systems through artificial boundary processing, which distorts local [...] Read more.

Deterministic sea wave reconstruction techniques are critical for enhancing maritime safety and disaster warnings. Coherent radar remote sensing captures sea surface velocity information to enable more precise wave reconstruction. Existing difference matrix methods address rank-deficient systems through artificial boundary processing, which distorts local hydrodynamic characteristics and propagates errors to global features, thereby limiting the accuracy and stability of reconstructions. To resolve this limitation, this study proposes a physics-constrained deterministic wave reconstruction methodology. We introduce the Data-Anchored Projection model for the differential matrix, extracting hydrodynamic constraints directly from radar backscatter data. This approach achieves stable solutions for rank-deficient systems without artificial boundaries. The model’s performance was rigorously validated through both simulated and real-sea experiments. The simulation results demonstrate a minimum 13% accuracy improvement over conventional methods and high stability under various sea states and at different range resolutions. In a real-sea trial under sea states 3 to 5, reconstruction errors remained below 10%, with consistent stability observed across varying sea states. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))

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20 pages, 11125 KB

Open AccessArticle

Application of a Bicubic Quasi-Uniform B-Spline Surface Fitting Method for Characterizing Mesoscale Eddies in the Atlantic Ocean

by Chunzheng Kong, Shengyi Jiao, Xuefeng Cao and Xianqing Lv

Remote Sens. 2025, 17(15), 2744; https://doi.org/10.3390/rs17152744 - 7 Aug 2025

Cited by 3 | Viewed by 790

Abstract

The direct fitting of sea level anomaly (SLA) using satellite along-track data provides a critical approach for monitoring mesoscale ocean dynamics. While bicubic quasi-uniform B-spline surface fitting has demonstrated feasibility in localized sea areas, its applicability to basin-scale regions remains underexplored. This study [...] Read more.

The direct fitting of sea level anomaly (SLA) using satellite along-track data provides a critical approach for monitoring mesoscale ocean dynamics. While bicubic quasi-uniform B-spline surface fitting has demonstrated feasibility in localized sea areas, its applicability to basin-scale regions remains underexplored. This study focuses on the northern Atlantic Ocean, employing B-spline surface fitting to derive SLA fields from satellite along-track data. The results show strong agreement with in situ measurements, yielding a mean absolute error (MAE) of 1.89 cm and a root mean square error (RMSE) of 3.02 cm. Comparative analysis against the Copernicus Marine Environment Monitoring Service (CMEMS) Level-4 gridded SSH data reveals nearly equivalent accuracy (MAE: 1.95 cm; RMSE: 3.06 cm). The relationship between the order of fitting and the spatial extent of the fitting domain is also examined. Furthermore, the influence of the coastline on the fitting results is investigated in detail. As the coastline area expanded, the MAE and RMSE for the entire region increased. But the maximum increase in MAE was only 1.20 cm, and the maximum increase in RMSE was only 2.49 cm. Notably, there was no upward trend in MAE and RMSE in the mesoscale vortex dense area, which highlights the advantage of B-spline’s local support. Geostrophic flow and vertical component of relative vorticity are computed from the satellite along-track SLA data, with results showing agreement with Level-4 gridded geostrophic flow and vertical component of relative vorticity data. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))

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17 pages, 7985 KB

Open AccessArticle

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 811

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

k_{s a t d a}

between the SATDA and SCA exhibit seasonal variation, being higher in summer and lower in winter. Specifically,

k_{s a t d a}

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

k_{s a t d a}

are also discussed in detail. It is found that the RMMSATD is a crucial factor influencing

k_{s a t d a}

. And the negative correlation coefficient between the RMMSATD and

k_{s a t d a}

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 KB

Open AccessArticle

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

Cited by 10 | Viewed by 2974

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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2074

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 KB

Open AccessArticle

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 1830

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 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1706

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 KB

Open AccessArticle

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

Cited by 1 | Viewed by 2526

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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Review

Jump to: Research

33 pages, 4428 KB

Open AccessReview

A Review of Artificial Intelligence and Remote Sensing for Marine Oil Spill Detection, Classification, and Thickness Estimation

by Shaokang Dong, Jiangfan Feng, Zhujun Gu, Kuan Yin and Ying Long

Remote Sens. 2025, 17(22), 3681; https://doi.org/10.3390/rs17223681 - 10 Nov 2025

Cited by 5 | Viewed by 3855

Abstract

Marine oil spill incidents are one of the major global marine pollution issues, which pose significant threats to ocean ecosystems. However, traditional monitoring methods often suffer from time delays, high costs, and limited real-time capability, making them inadequate for timely and large-scale oil [...] Read more.

Marine oil spill incidents are one of the major global marine pollution issues, which pose significant threats to ocean ecosystems. However, traditional monitoring methods often suffer from time delays, high costs, and limited real-time capability, making them inadequate for timely and large-scale oil spill detection. With the development of remote sensing (RS) technology and artificial intelligence (AI) methods, as well as the increasing frequency of marine oil spill accidents, plenty of AI-based methods using RS imagery have been proposed for more efficient and accurate oil spill monitoring. This review presents a comprehensive and systematic overview of recent progress in marine oil spill analysis using RS imagery, emphasizing the integration of AI methods across three key tasks: detection, classification, and thickness estimation. Specifically, we first introduce the main types of RS data and discuss the significance of publicly available datasets, which can facilitate method validation and model comparison. Second, we briefly review the application of RS imagery from different sensors in oil spill detection, highlighting the strengths of various spectral and polarimetric methods. Third, we summarize advances in oil spill classification, including AI-based methods that enable differentiation between mineral oil, biogenic films, and various emulsified oils. Fourth, we discuss emerging techniques for oil spill thickness estimation. Finally, we analyze the challenges of existing methods and future directions, including the need for real-time monitoring, the integration of multi-source RS data, and the development of robust models that can generalize across different environmental conditions. This review adopts a comprehensive perspective from both AI methods and RS technology, provides a systematic overview of recent advancements, identifies critical gaps in current methodologies, and serves as a valuable reference for researchers and practitioners working on oil spill monitoring. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))

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