Remote Sensing

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7 pages, 174 KiB

Open AccessEditorial

Remote Sensing Applications in Ocean Observation (Second Edition)

by Chung-Ru Ho

Remote Sens. 2025, 17(7), 1153; https://doi.org/10.3390/rs17071153 - 25 Mar 2025

Viewed by 472

Abstract

The articles presented in this Special Issue epitomize the convergence of cutting-edge sensor technologies, innovative data processing techniques, and advanced algorithmic approaches in ocean remote sensing. Through studies ranging from sensor calibration and data fusion to the application of deep learning and transformer [...] Read more.

The articles presented in this Special Issue epitomize the convergence of cutting-edge sensor technologies, innovative data processing techniques, and advanced algorithmic approaches in ocean remote sensing. Through studies ranging from sensor calibration and data fusion to the application of deep learning and transformer models, the research showcased here pushes the boundaries of what can be achieved in ocean observation. A recurring theme among these contributions is the importance of integrating data from multiple sources and employing state-of-the-art computational methods. Deep learning and the transformer architecture highlight a paradigm shift in remote sensing data analysis. These advanced techniques help extract complex features from high-dimensional datasets and can process large amounts of data quickly and automatically. Furthermore, research focusing on spatiotemporal dynamics and environmental monitoring highlights the critical role of remote sensing in addressing global challenges. By capturing the dynamic interactions between atmospheric, oceanic, and terrestrial processes, these studies provide important insights into the drivers of climate and environmental change. This information is valuable for developing predictive models and informing policy decisions related to climate change mitigation and adaptation. Full article

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

Research

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32 pages, 16677 KiB

Open AccessArticle

Effect of the One-to-Many Relationship between the Depth and Spectral Profile on Shallow Water Depth Inversion Based on Sentinel-2 Data

by Erhui Huang, Benqing Chen, Kai Luo and Shuhan Chen

Remote Sens. 2024, 16(10), 1759; https://doi.org/10.3390/rs16101759 - 15 May 2024

Cited by 1 | Viewed by 1420

Abstract

In shallow water, Sentinel-2 multispectral imagery has only four visible bands and limited quantization levels, which easily leads to the occurrence of the same spectral profile but different depth (SSPBDD) phenomenon, resulting in a one-to-many relationship between water depth and spectral profile. Investigating [...] Read more.

In shallow water, Sentinel-2 multispectral imagery has only four visible bands and limited quantization levels, which easily leads to the occurrence of the same spectral profile but different depth (SSPBDD) phenomenon, resulting in a one-to-many relationship between water depth and spectral profile. Investigating the impact of this relationship on water depth inversion models is the main objective of this paper. The Stumpf model and three machine learning models (Random Forest, Support Vector Machine, and Mixture Density Network) are employed, and the performance of these models is analysed based on the spatial distribution of the training dataset and the input information composition of these models. The results show that the root mean square errors (RMSEs) of the depth inversion of Random Forest and Support Vector Machine are significantly affected by the spatial distribution of the training dataset, while minimal effects are observed for the Stumpf model and the Mixture Density Network model. The SSPBDD phenomenon is widespread in Sentinel-2 images at all depths, particularly between 5 m and 15 m, with most of the depth maximum difference of the SSPBDD pixels ranging from 0 to 5 m. The SSPBDDs phenomenon can significantly reduce the inversion accuracy of any model. The number and the depth maximum difference of the SSPBDDs pixels are the main influencing factors. However, by increasing the visible spectral information and the spatial neighbourhood information in the input layer of machine learning models, the inversion accuracy and stability of the models can be improved to a certain extent. Among the models, the Mixture Density Network achieves the best inversion accuracy and stability. Full article

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

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19 pages, 6517 KiB

Open AccessArticle

Concept of Spaceborne Ocean Microwave Dual-Function Integrated Sensor for Wind and Wave Measurement

by Hang Li, Wenkang Liu, Guangcai Sun, Changhong Chen, Mengdao Xing, Zhenhua Zhang and Jie Zhang

Remote Sens. 2024, 16(8), 1472; https://doi.org/10.3390/rs16081472 - 21 Apr 2024

Cited by 1 | Viewed by 1273

Abstract

Dedicated to synchronously acquiring large-area, high-precision, and multi-scale ocean wind and wave information, a novel concept of a spaceborne ocean microwave dual-function integrated sensor is proposed in this paper. It integrates the functions of a scatterometer and SAR by sharing a single phased-array [...] Read more.

Dedicated to synchronously acquiring large-area, high-precision, and multi-scale ocean wind and wave information, a novel concept of a spaceborne ocean microwave dual-function integrated sensor is proposed in this paper. It integrates the functions of a scatterometer and SAR by sharing a single phased-array antenna. An overview of the scientific requirements and motivations for the sensor are outlined firstly. In order to fulfill the observation requirements of both the functions, the constraints on the system parameters such as frequency, antenna size, and incidence angle are analyzed. Then, the selection principles of these parameters are discussed within the limitations of antenna area, bandwidth, available time, and cost. Additionally, the constraints on the time sequence of transmitting and receiving pulses are derived to ensure that there is no conflict when the two functions operate simultaneously. Subsequently, a method for jointly designing the pulse repetition frequency (PRF) of both the functions is introduced, along with zebra maps to verify its effectiveness. At the end of the paper, the system and performance parameters of the sensor are given for further insight into it. Full article

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

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26 pages, 21642 KiB

Open AccessArticle

Studying the Internal Wave Generation Mechanism in the Northern South China Sea Using Numerical Simulation, Synthetic Aperture Radar, and In Situ Measurements

by Kan Zeng, Ruyin Lyu, Hengyu Li, Rongqing Suo, Tao Du and Mingxia He

Remote Sens. 2024, 16(8), 1440; https://doi.org/10.3390/rs16081440 - 18 Apr 2024

Cited by 1 | Viewed by 1790

Abstract

The internal waves in the South China Sea are highly correlated with the tidal currents in the Luzon Strait, which makes it possible to establish an internal wave prediction model based on internal wave kinematics. However, the kinematic model requires the input of [...] Read more.

The internal waves in the South China Sea are highly correlated with the tidal currents in the Luzon Strait, which makes it possible to establish an internal wave prediction model based on internal wave kinematics. However, the kinematic model requires the input of the exact location and time of the initial internal wave for which the generation mechanism of internal waves in the northern South China Sea must be well understood. By analyzing the internal wave field in the northern South China Sea (SCS) simulated using the MIT General Circulation Model (MITgcm) and observations from satellite synthetic aperture radar (SAR) and mooring temperature–salinity–depth (TSD) chains, the source regions and propagation initiation times of internal waves are identified for three typical tidal phases, i.e., the diurnal-tide-dominated phase (DTP), transition tide phase (TTP), and semidiurnal-tide-dominated phase (STP). The generation procedures of Type A and Type B internal waves are discussed in detail with those data. The present study reveals that Type A and Type B waves are generated at the eastern and western ridges, respectively, and both commence their westward propagation at the peak of the eastward tidal flow. The dynamics of lee waves and the resonance effect with double ridges constitute the generation mechanisms of internal waves in the northern SCS. Combined with varying configurations of tidal conditions, topography, and stratification, the generation procedures of Type A and Type B waves in the DTP, TTP, and STP are elucidated with the generation mechanism in a unified and self-consistent way. In short, during DTP, weaker A waves alternate with weaker B waves each day; during TTP, strong A waves and strong B waves appear alternately every day; and there are two weak A waves per day during the STP. The generation mechanism can help in developing future empirical models for generating internal waves using tidal currents, topography, and stratification without requiring complex fluid dynamics calculations. Full article

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

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20 pages, 13421 KiB

Open AccessArticle

Modulations of the South China Sea Ocean Circulation by the Summer Monsoon Intraseasonal Oscillation Inferred from Satellite Observations

by Zhiyuan Hu, Keiwei Lyu and Jianyu Hu

Remote Sens. 2024, 16(7), 1195; https://doi.org/10.3390/rs16071195 - 29 Mar 2024

Viewed by 1418

Abstract

The South China Sea (SCS) displays remarkable responses and feedback to the summer monsoon intraseasonal oscillation (ISO). This study investigates how the SCS summer ocean circulation responds to the monsoon ISO based on weekly satellite data. In summer, the largest amplitudes for intraseasonal [...] Read more.

The South China Sea (SCS) displays remarkable responses and feedback to the summer monsoon intraseasonal oscillation (ISO). This study investigates how the SCS summer ocean circulation responds to the monsoon ISO based on weekly satellite data. In summer, the largest amplitudes for intraseasonal (30–90 days) sea surface height variations in the SCS occur around the northeastward offshore current off southeast Vietnam between a north–south eddy dipole. Our results show that such strong intraseasonal sea surface height variations are mainly caused by the alternate enhancement of the two eddies of the eddy dipole. Specifically, in response to the intraseasonal intensification of southwesterly winds, the northern cyclonic eddy of the eddy dipole strengthens within 1–2 weeks, and its southern boundary tends to be more southerly. Afterwards, as the wind-driven southern anticyclonic gyre spins up, the southern anticyclonic eddy gradually intensifies and expands its northern boundary northward, while the northern cyclonic eddy weakens and retreats northward. Besides the local wind forcing, westward propagations of the eastern boundary-originated sea surface height anomalies, which exhibit latitude-dependent features that are consistent with the linear Rossby wave theory, play an important role in ocean dynamical adjustments to the monsoon ISO, especially in the southern SCS. Case studies further confirm our findings and indicate that understanding this wind-driven process makes the ocean more predictable on short-term timescales. Full article

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

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19 pages, 6147 KiB

Open AccessArticle

Exploring Spatial and Temporal Dynamics of Red Sea Air Quality through Multivariate Analysis, Trajectories, and Satellite Observations

by Bijoy Mitra, Al-Ekram Elahee Hridoy, Khaled Mahmud, Mohammed Sakib Uddin, Abu Talha, Nayan Das, Sajib Kumar Nath, Md Shafiullah, Syed Masiur Rahman and Muhammad Muhitur Rahman

Remote Sens. 2024, 16(2), 381; https://doi.org/10.3390/rs16020381 - 18 Jan 2024

Cited by 2 | Viewed by 2302

Abstract

The Red Sea, a significant ecoregion and vital marine transportation route, has experienced a consistent rise in air pollution in recent years. Hence, it is imperative to assess the spatial and temporal distribution of air quality parameters across the Red Sea and identify [...] Read more.

The Red Sea, a significant ecoregion and vital marine transportation route, has experienced a consistent rise in air pollution in recent years. Hence, it is imperative to assess the spatial and temporal distribution of air quality parameters across the Red Sea and identify temporal trends. This study concentrates on utilizing multiple satellite observations to gather diverse meteorological data and vertical tropospheric columns of aerosols and trace gases, encompassing carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃). Furthermore, the study employs the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze the backward trajectory of air mass movement, aiding in the identification of significant sources of air pollutants. A principal component analysis (PCA) with varimax rotation is applied to explore the relationship and co-variance between the aerosol index (AI), trace gas concentrations, and meteorological data. The investigation reveals seasonal and regional patterns in the tropospheric columns of trace gases and AI over the Red Sea. The correlation analysis indicates medium-to-low positive correlations (0.2 < r < 0.6) between air pollutants (NO₂, SO₂, and O₃) and meteorological parameters, while negative correlations (−0.3 < r < −0.7) are observed between O₃, aerosol index, and wind speed. The results from the HYSPLIT model unveil long-range trajectory patterns. Despite inherent limitations in satellite observations compared to in situ measurements, this study provides an encompassing view of air pollution across the Red Sea, offering valuable insights for future researchers and policymakers. Full article

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

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19 pages, 8473 KiB

Open AccessArticle

Assessing and Improving the Accuracy of Visible Infrared Imaging Radiometer Suite Ocean Color Products in Environments with High Solar Zenith Angles

by Hao Li, Xianqiang He, Palanisamy Shanmugam, Yan Bai, Difeng Wang, Teng Li and Fang Gong

Remote Sens. 2024, 16(2), 339; https://doi.org/10.3390/rs16020339 - 15 Jan 2024

Cited by 4 | Viewed by 1544

Abstract

Utilizing in situ measurement data to assess satellite-derived long-term ocean color products under different observational conditions is crucial for ensuring data quality and integrity. In this study, we conducted an extensive evaluation and analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing [...] Read more.

Utilizing in situ measurement data to assess satellite-derived long-term ocean color products under different observational conditions is crucial for ensuring data quality and integrity. In this study, we conducted an extensive evaluation and analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) remote sensing reflectance (Rrs) products using long-term OC-CCI in situ data from 2012 to 2021. Our research findings indicate that, well beyond its designed operational lifespan, the root mean square difference accuracy of VIIRS Rrs products across most spectral bands remains superior to 0.002 (sr⁻¹). However, VIIRS Rrs products in shorter wavelength bands (e.g., at 412 nm) have exhibited significantly lower accuracy and a long-term bias in recent years. The annual precision of VIIRS Rrs products demonstrated a declining trend, particularly in coastal or eutrophic waters. This degradation in accuracy highlights the imperative for continuous monitoring of VIIRS performance and further advancements in the atmospheric correction algorithm, especially to address satellite records at high solar zenith angles (SZAs) and observation zenith angles (OZAs). Our analysis indicates that, in observation environments with high SZAs (greater than 70°), the accuracy of VIIRS Rrs products has declined by nearly 50% compared to typical solar zenith angle observation conditions. To address the challenge of declining accuracy under large observation geometries, we introduced the neural network atmospheric correction model (NN-V). Developed based on meticulously curated VIIRS products, the NN-V model exhibits outstanding performance in handling VIIRS data in conditions of extensive observation geometries. During the winter season in high-latitude marine regions, the NN-V model demonstrates a remarkable enhancement in ocean color product coverage, achieving an increase of nearly 20 times compared to traditional methods. Full article

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

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25 pages, 9398 KiB

Open AccessArticle

Variations and Depth of Formation of Submesoscale Eddy Structures in Satellite Ocean Color Data in the Southwestern Region of the Peter the Great Bay

by Nadezhda A. Lipinskaya, Pavel A. Salyuk and Irina A. Golik

Remote Sens. 2023, 15(23), 5600; https://doi.org/10.3390/rs15235600 - 1 Dec 2023

Cited by 2 | Viewed by 1694

Abstract

The aim of this study was to develop methods for determining the most significant contrasts in satellite ocean color data arising in the presence of a submesoscale eddy structure, as well as to determine the corresponding depths of the upper layer of the [...] Read more.

The aim of this study was to develop methods for determining the most significant contrasts in satellite ocean color data arising in the presence of a submesoscale eddy structure, as well as to determine the corresponding depths of the upper layer of the sea where these contrasts are formed. The research was carried out on the example of the chain of submesoscale eddies identified in the Tumen River water transport area in the Japan/East Sea. MODIS Aqua/Terra satellite data of the remotely sensed reflectance (Rrs) and Rrs band ratio at various wavelengths, chlorophyll-a concentration, and, for comparison, sea surface temperature (sst) were analyzed. Additionally, the results of ship surveys in September 2009 were used to study the influence of eddy vertical structure on the obtained remote characteristics. The best characteristic for detecting the studied eddies in satellite ocean color data was the MODIS chlor_a standard product, which is an estimate of chlorophyll-a concentration obtained by a combination of the three-band reflectance difference algorithm (CI) for low concentrations and the band-ratio algorithm (OCx) for high concentrations. At the same time, the weakest contrasts were in sst data due to similar water heating inside and outside the eddies. The best eddy contrast-to-noise ratio according to Rrs spectra is achieved at 547 nm in the spectral region of seawater with maximum transparency and low relative errors of measurements. The Rrs at 678 nm and associated products may be a significant characteristic for eddy detection if there are many phytoplankton in the eddy waters. The maximum depth of the remotely sensed contrast formation of the considered eddy vertical structure was ~6 m, which was significantly less than the maximum spectral penetration depth of solar radiation for remote sensing, which was in the 14–17 m range. The results obtained can be used to determine the characteristics that provide the best contrast for detecting eddy structures in remotely sensed reflectance data and to improve the interpretation of remote spectral ocean color data in the areas of eddies activity. Full article

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

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18 pages, 7543 KiB

Open AccessArticle

Intra-Seasonal Variability of Sea Level on the Southwestern Bering Sea Shelf and Its Impact on the East Kamchatka and East Sakhalin Currents

by Andrey Andreev

Remote Sens. 2023, 15(20), 4984; https://doi.org/10.3390/rs15204984 - 16 Oct 2023

Viewed by 1436

Abstract

The East Kamchatka and East Sakhalin Currents (EKC and ESC) are the western boundary currents of the subarctic North Pacific and Okhotsk Sea. Variability in the EKC and ESC velocities could exert a substantial effect on ecosystems and fish stocks in the southwestern [...] Read more.

The East Kamchatka and East Sakhalin Currents (EKC and ESC) are the western boundary currents of the subarctic North Pacific and Okhotsk Sea. Variability in the EKC and ESC velocities could exert a substantial effect on ecosystems and fish stocks in the southwestern Bering Sea and Okhotsk Sea. Using satellite-derived data (sea surface heights, geostrophic current velocities, and sea surface temperatures, 2002–2020), we demonstrate that changes in zonal wind generate sea level variations on the shelf in the southwestern Bering Sea over a period of 18–29 days and with an amplitude of 5–20 cm. The ebb/flood events on the shelf lead to changes in the velocity, direction, and position of the EKC. The sea level anomalies propagate along the western Kamchatka, northern Kuril Islands and the northern and western Okhotsk Sea and result in the variability of geostrophic current velocities in the ESC zone. The strengthening (weakening) of ESC leads to an increase (a decrease) in SST in the southern part of the Okhotsk Sea by 1–3 °C. In the northwestern Okhotsk Sea, in addition to wind-induced variability, there are temporary changes in the geostrophic currents with a period of 14 days caused by fortnightly tides. Full article

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

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21 pages, 42086 KiB

Open AccessArticle

A Transformer Model for Coastline Prediction in Weitou Bay, China

by Zhihai Yang, Guangjun Wang, Lei Feng, Yuxian Wang, Guowei Wang and Sihai Liang

Remote Sens. 2023, 15(19), 4771; https://doi.org/10.3390/rs15194771 - 29 Sep 2023

Cited by 6 | Viewed by 1709

Abstract

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is [...] Read more.

The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management. Full article

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

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19 pages, 6240 KiB

Open AccessArticle

Detection of Macroalgal Bloom from Sentinel−1 Imagery

by Sree Juwel Kumar Chowdhury, Ahmed Harun-Al-Rashid, Chan-Su Yang and Dae-Woon Shin

Remote Sens. 2023, 15(19), 4764; https://doi.org/10.3390/rs15194764 - 28 Sep 2023

Cited by 2 | Viewed by 1872

Abstract

The macroalgal bloom (MAB) is caused by brown algae forming a floating mat. Most of its parts stay below the water surface, unlike green algae; thus, its backscatter value becomes weaker in the synthetic aperture radar (SAR) images, such as Sentinel−1, due to [...] Read more.

The macroalgal bloom (MAB) is caused by brown algae forming a floating mat. Most of its parts stay below the water surface, unlike green algae; thus, its backscatter value becomes weaker in the synthetic aperture radar (SAR) images, such as Sentinel−1, due to the dampening effect. Thus, brown algae patches appear to be thin strands in contrast to green algae and their detection by using a global threshold, which is challenging due to a similarity between the MAB patch and the ship’s sidelobe in the case of pixel value. Therefore, a novel approach is proposed to detect the MAB from the Sentinel−1 image by eliminating the ship’s sidelobe. An individually optimized threshold is applied to extract the MAB and the ships with sidelobes from the image. Then, parameters are adjusted based on the object’s area information and the ratio of length and width to filter out ships with sidelobes and clutter objects. With this method, an average detection accuracy of 82.2% is achieved by comparing it with the reference data. The proposed approach is simple and effective for detecting the thin MAB patch from the SAR image. Full article

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

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26 pages, 8387 KiB

Open AccessArticle

Spatiotemporal Variation of Anticyclonic Eddies in the South China Sea during 1993–2019

by Weian Shi and Jianyu Hu

Remote Sens. 2023, 15(19), 4720; https://doi.org/10.3390/rs15194720 - 27 Sep 2023

Cited by 1 | Viewed by 1500

Abstract

Based on the absolute dynamic topography data from the Copernicus Marine Environment Monitoring Service, this paper applies the Topographic Position Index to develop a new approach for mapping the anticyclonic eddies in the South China Sea (SCS). The results show that anticyclonic eddies [...] Read more.

Based on the absolute dynamic topography data from the Copernicus Marine Environment Monitoring Service, this paper applies the Topographic Position Index to develop a new approach for mapping the anticyclonic eddies in the South China Sea (SCS). The results show that anticyclonic eddies are active in the deep basin of SCS, and the five selected parameters (number or frequency, lifetime, kinetic energy, amplitude, and area or radius) of anticyclonic eddies have a similar temporal variation and a similar spatial distribution pattern. (1) As for monthly variations, anticyclonic eddies are active in late spring and most active in summer. (2) The El Niño–Southern Oscillation had a stronger impact on the inter-annual variations of anticyclonic eddies in the SCS before 2013, resulting in a significant transition of inter-annual variations of these five parameters in around 2004. After 2013, most of these five parameters had a minimum in 2015 and a maximum in 2017. (3) Analyses show that the eddy activities in the SCS are significantly influenced by the monsoon wind and the western boundary current like Kuroshio. Therefore, the areas southwest of Taiwan Island and east of Vietnam are the two areas where the anticyclonic eddies are most active, with much larger eddy kinetic energy and much higher eddy amplitude. Full article

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

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20 pages, 9468 KiB

Open AccessArticle

Spatiotemporal Characteristics and Volume Transport of Lagrangian Eddies in the Northwest Pacific

by Quanmu Yuan and Jianyu Hu

Remote Sens. 2023, 15(17), 4355; https://doi.org/10.3390/rs15174355 - 4 Sep 2023

Cited by 2 | Viewed by 1995

Abstract

Mesoscale eddies play a crucial role in the transport of mass, heat, salt and nutrients, exerting significant influence on ocean circulation patterns, biogeochemical processes and the global climate system. Based on Lagrangian-Averaged Vorticity Deviation (LAVD) method, this study applies 27 years (1993–2019) of [...] Read more.

Mesoscale eddies play a crucial role in the transport of mass, heat, salt and nutrients, exerting significant influence on ocean circulation patterns, biogeochemical processes and the global climate system. Based on Lagrangian-Averaged Vorticity Deviation (LAVD) method, this study applies 27 years (1993–2019) of geostrophic current velocity data to detect Rotationally Coherent Lagrangian Vortices (RCLVs) in the Northwest Pacific (NWP; 10°N–30°N, 115°E–155°E), with the spatiotemporal characteristics of Eulerian Sea Surface Height Eddies (SSH eddies) and RCLVs being compared. A higher number of SSH eddies and RCLVs can be observed in spring and winter, and their inter-annual variations are similar. SSH eddies show higher generation number and larger radius in the Subtropical Countercurrent region, while RCLVs occur more favorably in the ocean basin. The propagation speed distributions of both eddy types are nearly identical and decrease with increasing latitude. Due to the material coherent transport maintained by RCLVs within a finite time interval, the coherent cores of RCLVs are considerably smaller in scale as compared to those of SSH eddies. The average zonal transports induced by SSH eddies and RCLVs are estimated to be −0.82 Sv and −0.51 Sv (1 Sv = 10⁶ m³/s), respectively. For non-overlapping SSH eddies with RCLVs, approximately 80% of the water within the eddy leaks out during the eddy’s lifespan. In the case of overlapping SSH eddies, the ratio of coherent water inside the eddy decreases with increasing radius, and the leakage rate is around 58%. Finally, an examination of 36 shedding RCLVs events from the Kuroshio near the Luzon Strait, which induce an average zonal transport of −0.14 Sv, reveals that 54% of the water within the shedding RCLVs originates from the Kuroshio. Full article

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

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18 pages, 3011 KiB

Open AccessArticle

On-Orbit Calibration and Wet Tropospheric Correction of HY-2C Correction Microwave Radiometer

by Xiaomeng Zheng, Dehai Zhang, Jin Zhao and Maofei Jiang

Remote Sens. 2023, 15(14), 3633; https://doi.org/10.3390/rs15143633 - 21 Jul 2023

Viewed by 1503

Abstract

HY-2C is the third satellite in China’s ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (T_B) of CMR [...] Read more.

HY-2C is the third satellite in China’s ocean dynamic environment satellite series, and carries a correction microwave radiometer (CMR) to correct the wet tropospheric path delay for the aligned radar altimeter. To effectively use the brightness temperatures (T_B) of CMR to retrieve path delay, an on-orbit calibration effort is required. In this study, an antenna pattern correction (APC) method and a neural network method are used to perform an on-orbit calibration for CMR’s antenna temperatures and a model based on the Whale Optimization Algorithm (WOA), Levenberg–Marquardt (LM) algorithm, and Back-Propagation neural network (WOA–LM–BP) has been proposed to retrieve the wet tropospheric correction (WTC) of CMR. The on-orbit calibration results, compared with the simulated brightness temperatures calculated by the radiative transfer model (RTM), have shown that compared with the APC method, the neural network method can almost eliminate the latitude variation, and the total bias and standard deviation of the on-orbit calibrated T_B at all channels have obviously decreased. The retrieved WTC results also have shown that the retrieved WTC of CMR has a good agreement with the corresponding ones from the model-derived WTC and Jason-3. Full article

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

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28 pages, 7616 KiB

Open AccessArticle

Optimization of Airborne Scatterometer NRCS Semicircular Sampling for Sea Wind Retrieval

by Alexey Nekrasov, Alena Khachaturian and Colin Fidge

Remote Sens. 2023, 15(6), 1613; https://doi.org/10.3390/rs15061613 - 16 Mar 2023

Cited by 2 | Viewed by 1689

Abstract

Airborne scatterometer capability depends on not only the device’s technical characteristics but also the scheme used for surface observations. Typically, a rotating-beam scatterometer uses a circular scheme for sampling normalized radar cross-sections (NRCS) at wind measurements over the sea. Here, we investigate wind [...] Read more.

Airborne scatterometer capability depends on not only the device’s technical characteristics but also the scheme used for surface observations. Typically, a rotating-beam scatterometer uses a circular scheme for sampling normalized radar cross-sections (NRCS) at wind measurements over the sea. Here, we investigate wind retrieval using an updated semicircular scheme, providing the NRCS sampling at various combinations of incidence angles within the range 30° to 60°. The effectiveness of the wind retrieval using our semicircular sampling scheme was evaluated using Monte Carlo simulations, and we then developed corresponding wind algorithms that used a geophysical model function (GMF). As a result of the study, we found that a semicircular sampling scheme is well suited for wind retrieval over the sea using a rotating-beam scatterometer. We showed that a semicircular scheme can provide wind retrieval accuracies similar to those achievable with a conventional circular scheme, although the semicircular scheme requires approximately three times the number of NRCS samples integrated in each azimuth sector. Most importantly, however, the semicircular scheme enabled a maximum altitude for wind retrieval of twice the height possible with a circular scheme. In this study, we also demonstrate that the wind speed accuracy tends to increase with an increase in the incidence angle and similarly for the wind direction accuracy. Nonetheless, we then show that the simultaneous use of the NRCS sampling scheme at several incidence angles can increase the wind retrieval accuracy, especially when three or four incidence angles are used. The obtained results can be used to enhance airborne scatterometers and multimode radars operated in a scatterometer mode, including airborne high-altitude conical scanning radars, and can be applied to new remote sensing systems’ development. Full article

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

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18 pages, 7972 KiB

Open AccessArticle

Monitoring Green Tide in the Yellow Sea Using High-Resolution Imagery and Deep Learning

by Weitao Shang, Zhiqiang Gao, Meng Gao and Xiaopeng Jiang

Remote Sens. 2023, 15(4), 1101; https://doi.org/10.3390/rs15041101 - 17 Feb 2023

Cited by 7 | Viewed by 2735

Abstract

Green tide beaching events have occurred frequently in the Yellow Sea since 2007, causing a series of ecological and economic problems. Satellite imagery has been widely applied to monitor green tide outbreaks in open water. Traditional satellite sensors, however, are limited by coarse [...] Read more.

Green tide beaching events have occurred frequently in the Yellow Sea since 2007, causing a series of ecological and economic problems. Satellite imagery has been widely applied to monitor green tide outbreaks in open water. Traditional satellite sensors, however, are limited by coarse resolution or a low revisit rate, making it difficult to provide timely distribution of information about green tides in the nearshore. In this study, both PlanetScope Super Dove images and unmanned aerial vehicle (UAV) images are used to monitor green tide beaching events on the southern side of Shandong Peninsula, China. A deep learning model (VGGUnet) is used to extract the green tide features and quantify the green tide coverage area or biomass density. Compared with the U-net model, the VGGUnet model has a higher accuracy on the Super Dove and UAV images, with F1-scores of 0.93 and 0.92, respectively. The VGGUnet model is then applied to monitor the distribution of green tide on the beach and in the nearshore water; the results suggest that the VGGUnet model can accurately extract green tide features while discarding other confusing features. By using the Super Dove and UAV images, green tide beaching events can be accurately monitored and are consistent with field investigations. From the perspective of near real-time green tide monitoring, high-resolution imagery combined with deep learning is an effective approach. The findings pave the way for monitoring and tracking green tides in coastal zones, as well as assisting in the prevention and control of green tide disasters. Full article

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

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16 pages, 17425 KiB

Open AccessArticle

An Iterative Algorithm for Predicting Seafloor Topography from Gravity Anomalies

by Jinhai Yu, Bang An, Huan Xu, Zhongmiao Sun, Yuwei Tian and Qiuyu Wang

Remote Sens. 2023, 15(4), 1069; https://doi.org/10.3390/rs15041069 - 15 Feb 2023

Cited by 5 | Viewed by 2107

Abstract

As high-resolution global coverage cannot easily be achieved by direct bathymetry, the use of gravity data is an alternative method to predict seafloor topography. Currently, the commonly used algorithms for predicting seafloor topography are mainly based on the approximate linear relationship between topography [...] Read more.

As high-resolution global coverage cannot easily be achieved by direct bathymetry, the use of gravity data is an alternative method to predict seafloor topography. Currently, the commonly used algorithms for predicting seafloor topography are mainly based on the approximate linear relationship between topography and gravity anomaly. In actual application, it is also necessary to process the corresponding data according to some empirical methods, which can cause uncertainty in predicting topography. In this paper, we established analytical observation equations between the gravity anomaly and topography, and obtained the corresponding iterative solving method based on the least square method after linearizing the equations. Furthermore, the regularization method and piecewise bilinear interpolation function are introduced into the observation equations to effectively suppress the high-frequency effect of the boundary sea region and the low-frequency effect of the far sea region. Finally, the seafloor topography beneath a sea region (117.25°–118.25°E, 13.85°–14.85°N) in the South China Sea is predicted as an actual application, where gravity anomaly data of the study area with a resolution of 1′ × 1′ are from the DTU17 model. Comparing the prediction results with the data of ship soundings from the National Geophysical Data Center (NGDC), the root-mean-square (RMS) error and relative error can be up to 127.4 m and approximately 3.4%, respectively. Full article

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

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