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Applications of Remote Sensing in Oceanography: Prospects and Challenges II

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 11137

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Prof. Dr. Changming Dong

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Oceanic Modeling and Observation Laboratory, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: oceanic eddy; front; sea surface wind; wave; current; altimeter; turbulence
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Prof. Dr. Chunyan Li

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Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
Interests: physical oceanography; transport processes; sediment transport; flushing of bays; coastal and estuarine circulations; innovative observations; modeling of coastal ocean processes; weather induced oceanographic and estuarine response and impact to the coast; storm surges; cold front induced oceanic and coastal processes; arctic estuarine dynamics
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Prof. Dr. Jingsong Yang

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State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
Interests: oceanographic techniques; remote sensing; atmospheric boundary layer; backscatter
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Dr. Guoqi Han

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Institute of Ocean Sciences, Fisheries and Oceans Canada, Sidney, BC V8L 5T5, Canada
Interests: ocean circulation; numerical modeling; satellite oceanography; ocean climate variability and change
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Special Issue Information

Dear Colleagues,

Remote sensing provides several advantages over in situ measurements, such as the ability to provide measurements of the ocean over a large spatial range at high temporal resolutions, but for a significantly reduced cost. Indeed, with the development and launch of a new generation of satellite platforms and their sensors, exciting avenues in the marine sciences are being opened at a rapid pace. Nevertheless, numerous challenges and unsolved problems related to the development of remote sensing applications within oceanography remain. This Special Issue seeks to explore the current state of the art, future developments, and open questions of the applications of remote sensing in oceanography. Authors are encouraged to provide submissions covering all aspects of ocean remote sensing, ranging from observation techniques used in high-frequency coastal radar or satellite studies to new applications of artificial intelligence methods for pre- and postprocessing remotely sensed data. Review articles are also welcomed, on the condition that they are authoritative. Submissions that contribute towards the United Nations’ Decade of Ocean Science for Sustainable Development (2021–2030), Sustainability Development Goals, Small Island Developing States, and those that consider the Blue Economy are especially welcomed. Articles related to the following topics are invited for submission:

Innovative or improved methods and algorithms of remote sensing for oceanography applications.
Observations using drone/unmanned aircraft system (UAS) imagery, high-frequency coastal radar, airborne LiDAR, satellites.
Applications of artificial intelligence for pre- and post-processing remotely sensed data.
Advances in big data management including the exploitation of cloud platforms for marine studies.
Climate and anthropogenic influence on marine systems.
Marine pollution and water quality monitoring.
Detection and monitoring of ocean circulations, wave properties, ocean temperatures, and sea ice mapping.
Coastal applications such as tracking shoreline changes, nearshore topography mapping, erosion assessment, sediment transport.
Integration of remoting sensing data with in situ measurements or numerical ocean modeling for data assimilation.
Research works related to the Surface Water and Ocean Topography (SWOT) mission.

Prof. Dr. Changming Dong
Prof. Dr. Chunyan Li
Prof. Dr. Jingsong Yang
Dr. Guoqi Han
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 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

SWOT
artificial intelligence
big remotely sensed data
sustainability
climate change
ocean numerical modeling
data assimilation

Published Papers (10 papers)

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15 pages, 3076 KiB

Open AccessArticle

Evaluation and Refinement of Chlorophyll-a Algorithms for High-Biomass Blooms in San Francisco Bay (USA)

by Raphael M. Kudela, David B. Senn, Emily T. Richardson, Keith Bouma-Gregson, Brian A. Bergamaschi and Lawrence Sim

Remote Sens. 2024, 16(6), 1103; https://doi.org/10.3390/rs16061103 - 21 Mar 2024

Viewed by 603

Abstract

A massive bloom of the raphidophyte Heterosigma akashiwo occurred in summer 2022 in San Francisco Bay, causing widespread ecological impacts including events of low dissolved oxygen and mass fish kills. The rapidly evolving bloom required equally rapid management response, leading to the use of near-real-time image analysis of chlorophyll from the Ocean and Land Colour Instrument (OLCI) aboard Sentinel-3. Standard algorithms failed to adequately capture the bloom, signifying a need to refine a two-band algorithm developed for coastal and inland waters that relates the red-edge part of the remote sensing reflectance spectrum to chlorophyll. While the bloom was the initial motivation for optimizing this algorithm, an extensive dataset of in-water validation measurements from both bloom and non-bloom periods was used to evaluate performance over a range of concentrations and community composition. The modified red-edge algorithm with a simplified atmospheric correction scheme outperformed existing standard products across diverse conditions, and given the modest computational requirements, was found suitable for operational use and near-real-time product generation. The final version of the algorithm successfully minimizes error for non-bloom periods when chlorophyll a is typically <30 mg m⁻³, while also capturing bloom periods of >100 mg m⁻³ chlorophyll a. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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23 pages, 12059 KiB

Open AccessArticle

A Novel Rain Identification and Rain Intensity Classification Method for the CFOSAT Scatterometer

by Meixuan Quan, Jie Zhang and Rui Zhang

Remote Sens. 2024, 16(5), 887; https://doi.org/10.3390/rs16050887 - 02 Mar 2024

Viewed by 451

Abstract

The China–France oceanography satellite scatterometer (CSCAT) is a rotating fan-beam scanning observation scatterometer operating in the Ku-band, and its product quality is affected by rain contamination. The multiple azimuthal NRCS measurements provided by CSCAT L2A, the retrieved wind speed and wind direction provided by CSCAT L2B, as well as the rain data provided by GPM, are used to construct a new rain identification and rain intensity classification model for CSCAT. The EXtreme Gradient Boosting (XGBoost) model, optimized by the Dung Beetle Optimizer (DBO) algorithm, is developed and evaluated. The performance of the DBO-XGBoost exceeds that of the CSCAT rain flag in terms of rain identification ability. Also, compared with XGBoost without parameter optimization, K-nearest Neighbor with K = 5 (KNN5) and K-nearest Neighbor with K = 3 (KNN3), the performance of DBO-XGBoost is better. Its rain identification achieves an accuracy of about 90% and a precision of about 80%, which enhances the quality control of rain. DBO-XGBoost has also shown good results in the classification of rain intensity. This ability is not available in traditional rain flags. In the global regional and local regional tests, most of the accuracy and precision in rain intensity classification have reached more than 80%. This technology makes full use of the rich observed information of CSCAT, realizes rain identification, and can also classify the rain intensity so as to further evaluate the degree of rain contamination of CSCAT products. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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0 pages, 12596 KiB

Open AccessArticle

Multi-Timescale Characteristics of Southwestern Australia Nearshore Surface Current and Its Response to ENSO Revealed by High-Frequency Radar

by Hongfei Gu and Yadan Mao

Remote Sens. 2024, 16(1), 209; https://doi.org/10.3390/rs16010209 - 04 Jan 2024

Viewed by 863

Abstract

The surface currents in coastal areas are closely related to the ecological environment and human activities, and are influenced by both local and remote factors of different timescales, resulting in complex genesis and multi-timescale characteristics. In this research, 9-year-long, hourly high-frequency radar (HFR) surface current observations are utilized together with satellite remote sensing reanalysis products and mooring data, and based on the Empirical Orthogonal Function (EOF) and correlation analysis, we revealed the multi-timescale characteristics of the surface currents in Fremantle Sea (32°S), Southwestern Australia, and explored the corresponding driving factors as well as the impact of El Niño-Southern Oscillation (ENSO) on the nearshore currents. Results show that the currents on the slope are dominated by the southward Leeuwin Current (LC), and the currents within the shelf are dominated by winds, which are subject to obvious diurnal and seasonal variations. The strong bathymetry variation there, from a wide shelf in the north to a narrow shelf in this study region, also plays an important role, resulting in the frequent occurrence of nearshore eddies. In addition, the near-zonal winds south of 30°S in winter contribute to the interannual variability of the Leeuwin Current at Fremantle, especially in 2011, when the onshore shelf circulation is particularly strong because of the climatic factors, together with the wind-driven offshore circulation, which results in significant and long-lasting eddies. The southward Leeuwin Current along Southwestern Australia shows a strong response to interannual climatic variability. During La Niña years, the equatorial thermal anomalies generate the westward anomalies in winds and equatorial currents, which in turn strengthen the Leeuwin Current and trigger the cross-shelf current as well as downwelling within the shelf at Fremantle, whereas during El Niño years, the climate anomalies and the response of coastal currents are opposite. This paper provides insights into the multi-timescale nature of coastal surface currents and the relative importance of different driving mechanisms. It also demonstrates the potential of HFR to reveal the response of nearshore currents to climate anomalies when combined with other multivariate data. Meanwhile, the methodology adopted in this research is applicable to other coastal regions with long-term available HFR observations. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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17 pages, 10123 KiB

Open AccessArticle

Analysis of the Differences in Internal Solitary Wave Characteristics Retrieved from Synthetic Aperture Radar Images under Different Background Environments in the Northern South China Sea

by Pai Peng, Jieshuo Xie, Hui Du, Shaodong Wang, Pu Xuan, Guanjing Wang, Gang Wei and Shuqun Cai

Remote Sens. 2023, 15(14), 3624; https://doi.org/10.3390/rs15143624 - 20 Jul 2023

Cited by 2 | Viewed by 933

Abstract

Two internal solitary waves (ISWs) with very long fronts observed by synthetic aperture radar (SAR) images in the northern South China Sea (NSCS) are comparatively analyzed based on oceanic reanalysis data and the Korteweg–de Vries (KdV) theory. The differences in the environmental parameters, wave half-width, and amplitude of the two ISW fronts in the two distinct oceanic environments are studied. In the presence of a weak westward surface current of approximately 0.05 m/s, the values of the linear wave speed increase by up to 0.056 m/s, and the retrieved ISW amplitudes decrease by up to 14 m. On the contrary, for another background oceanic environment considering a relatively strong eastward surface current of approximately 0.2 m/s, there are decreases of up to 0.17 m/s in the linear wave speed and increases of up to 32 m in the retrieved amplitudes. However, the results retrieved from both the SAR observations commonly show that the ISW amplitudes along the fronts reach their maximums at roughly 21°N and decrease toward the southern and northern sides, in spite of their distinct background environments. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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34 pages, 14089 KiB

Open AccessArticle

Assessment and Projections of Marine Heatwaves in the Northwest Pacific Based on CMIP6 Models

by Jingyuan Xue, Haixia Shan, Jun-Hong Liang and Changming Dong

Remote Sens. 2023, 15(12), 2957; https://doi.org/10.3390/rs15122957 - 06 Jun 2023

Cited by 1 | Viewed by 1346

Abstract

To assess the abilities of global climate models (GCMs) on simulating the spatiotemporal distribution of marine heatwaves (MHWs), GCMs from the Coupled Model Intercomparison Program in Phase 6 (CMIP6) were evaluated from a historical period between 1985 and 2014 in the Northwest Pacific Ocean using a dataset that synthesizes remote sensing data. MHW simulation capabilities were assessed using Rank Score (RS) and Comprehensive Rating (MR) metrics that include both spatial and temporal scoring metrics. It was found that most CMIP6 models overestimate cumulative intensity, while mean and maximum intensities, in addition to the duration, were underestimated in the historical period. Possible future changes in MHWs were also examined based on the rank-based weighting ensembles under four shared socioeconomic pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). MHWs were identified using both a fixed 30-year baseline and a 30-year sliding baseline. In all scenarios, all MHWs metrics except frequency will have an increasing trend for the fixed baseline method. The frequency of MHWs will decrease after the 2050s. Days will first increase and then stabilize under various scenarios. MHWs will take place for more than 300 days by the end of the 21st century for the SSP5-8.5 scenario. The cumulative intensity in the SSP5-8.5 scenario is roughly six times higher than that in the SSP1-2.6 scenario by the end of the 21st century. A fixed baseline will result in near-permanent MHWs at the end of the 21st century. There will be no permanent MHWs at the end of the 21st century. Using the 30-year shifting baseline to define the MHWs can improve future MHW projections by capturing the spatiotemporal variability features of the MHWs. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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19 pages, 4274 KiB

Open AccessArticle

Characteristics of Internal Solitary Waves in the Timor Sea Observed by SAR Satellite

by Yunxiang Zhang, Mei Hong, Yongchui Zhang, Xiaojiang Zhang, Jiehua Cai, Tengfei Xu and Zilong Guo

Remote Sens. 2023, 15(11), 2878; https://doi.org/10.3390/rs15112878 - 01 Jun 2023

Cited by 2 | Viewed by 1050

Abstract

Internal solitary waves (ISWs) with features such as large amplitude, short period, and fast speed have great influence on underwater thermohaline structure, nutrient transport, and acoustic signal propagation. The characteristics of ISWs in hotspot areas have been revealed by satellite images combined with mooring observation. However, the ISWs in the Timor Sea, which is located in the outflow of the ITF, have not been studied yet and the characteristics are unrevealed. In this study, by employing the Synthetic Aperture Radar (SAR) images taken by the Sentinel-1 satellite from 2017 to 2022, the temporal and spatial distribution characteristics of ISWs in the Timor Sea are analyzed. The results show that most of ISWs appear in Bonaparte basin and its vicinity. The average wavelength of the ISWs is 248 m, and most of the wave lengths are less than 400 m. The peak line of ISWs is longer in deeper water. The underwater structures of two typical ISWs are reconstructed based on the Korteweg–de Vries (KdV) equation combined with mooring observation. This shows that, compared with the two-layer model, the continuous layered model is more suitable for reconstructing the underwater structures of ISWs. Further analysis shows that both the rough topography and the spring-neap tides contribute to the generation of ISWs in the Timor Sea. This study fills a gap in knowledge of ISWs in regional seas, such as the Timor Sea. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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16 pages, 4785 KiB

Open AccessArticle

Analysis of Seasonal and Long-Term Variations in the Surface and Vertical Structures of the Lofoten Vortex

by Yu Liu, Jing Meng, Jianhui Wang, Guoqing Han, Xiayan Lin, Junming Chen and Qiyan Ji

Remote Sens. 2023, 15(7), 1903; https://doi.org/10.3390/rs15071903 - 02 Apr 2023

Viewed by 1193

Abstract

The Lofoten Vortex (LV) is a quasi-permanent anticyclonic eddy with the characteristic of periodic regeneration in the Lofoten Basin (LB), which is one of the major areas of deep vertical mixing in the Nordic Sea. Our analysis of the LV contributes to our understanding of the variations in convective mixing in the LB. Based on drifter data and satellite altimeter data, the climatological results show that the LV has the sea surface characteristics of relative stability in terms of its spatial position and significant seasonal variations in its physical characteristics. Combined with the temperature and salinity data of Argo profiles, the vertical structures of the LV are presented here in terms of their spatial distribution and monthly variations. The wavelet analysis of the satellite sea surface temperature (SST) data shows that the period of SST anomaly (SSTA) in the LV sea area is 8–16 years. In the stage marked by a decreasing (increasing) trend of SSTA, the vertical mixing is strengthened (weakened). Current vertical mixing is clearly revealed by the Argo profiles, and the SSTA shows a significant impact of cooling. However, against a background of warming and freshening, this vertical mixing will be greatly weakened in the next increasing trending stage of the SSTA. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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16 pages, 8967 KiB

Open AccessArticle

Remote Sensing Analysis of Typhoon-Induced Storm Surges and Sea Surface Cooling in Chinese Coastal Waters

by Xiaohui Li, Guoqi Han, Jingsong Yang and Caixia Wang

Remote Sens. 2023, 15(7), 1844; https://doi.org/10.3390/rs15071844 - 30 Mar 2023

Cited by 1 | Viewed by 1568

Abstract

Inthis study, remote sensing measurements were utilized to examine the characteristics of storm surges and sea surface cooling in Chinese coastal waters caused by typhoons. Altimetric data from satellite altimeters were used to determine the magnitude, cross-shelf decaying scale, and propagating speed of storm surges from typhoons. The results were in agreement with estimates obtained from a theoretical model and tide gauge data, showing that the two storm surges propagated as continental shelf waves along the southeastern coast of China. The sea surface cooling, driven by Typhoons 1319Usagi and 1323Fitow, was analyzed using the remote sensing sea surface temperature product, named the global 1 km sea surface temperature (G1SST) dataset, revealing a considerable decrease in the temperature, with the largest decrease reaching 4.5 °C after the passage of 1319Usagi, in line with buoy estimates of 4.6 °C. It was found that 1323Fitow and 1324Danas jointly impacted the southeastern coast of China, resulting in a significant temperature drop of 4.0 °C. Our study shows that incorporating remotely sensed measurements into the study of oceanic responses to typhoons has significant benefits and complements the traditional tide gauge network and buoy data. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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19 pages, 8162 KiB

Open AccessArticle

A Hybrid ENSO Prediction System Based on the FIO−CPS and XGBoost Algorithm

by Zhiyuan Kuang, Yajuan Song, Jie Wu, Qiuying Fu, Qi Shu, Fangli Qiao and Zhenya Song

Remote Sens. 2023, 15(7), 1728; https://doi.org/10.3390/rs15071728 - 23 Mar 2023

Cited by 1 | Viewed by 1588

Abstract

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for climate change research and disaster prevention and mitigation. In recent decades, the prediction skill for ENSO has improved significantly; however, accurate forecasting at a lead time of more than six months remains challenging. By using a machine learning method called eXtreme Gradient Boosting (XGBoost), we corrected the ENSO predicted results from the First Institute of Oceanography Climate Prediction System version 2.0 (FIO−CPS v2.0) based on the satellite remote sensing sea surface temperature data, and then developed a dynamic and statistical hybrid prediction model, named FIO−CPS−HY. The latest 15 years (2007–2021) of independent testing results showed that the average anomaly correlation coefficient (ACC) and root mean square error (RMSE) of the Niño3.4 index from FIO−CPS v2.0 to FIO−CPS−HY for 7− to 13−month lead times could be increased by 57.80% (from 0.40 to 0.63) and reduced by 24.79% (from 0.86 °C to 0.65 °C), respectively. The real−time predictions from FIO−CPS−HY indicated that the sea surface state of the Niño3.4 area would likely be in neutral conditions in 2023. Although FIO−CPS−HY still has some biases in real−time prediction, this study provides possible ideas and methods to enhance short−term climate prediction ability and shows the potential of integration between machine learning and numerical models in climate research and applications. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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14 pages, 4582 KiB

Open AccessTechnical Note

Enhancing the Assimilation of SWOT Simulated Observations Using a Multi-Scale 4DVAR Method in Regional Ocean Modeling System

by Chaojie Zhou, Wei Cui, Ruili Sun, Ying Huang and Zhanpeng Zhuang

Remote Sens. 2024, 16(5), 778; https://doi.org/10.3390/rs16050778 - 23 Feb 2024

Viewed by 565

Abstract

This paper presents an innovative approach to enhance the assimilation of high-resolution simulated observations, specifically targeting Surface Water Ocean Topography (SWOT) Ka-band Radar Interferometer Sea Surface Height (SSH) products, within the Regional Ocean Modeling System (ROMS). Responding to the demand for improved assimilation techniques, we developed a multi-scale Four-Dimensional Variational Data Assimilation (4DVAR) system, building upon validated fine-scale correction capabilities from prior studies. The multi-scale strategy was extended to the ROMS-4DVAR system, providing a comprehensive solution for assimilating high-resolution observations. Leveraging the Observing System Simulation Experiment (OSSE) framework, we conducted a twin experiment comprising a nature run and a free run case. Subsequently, synthetic SWOT SSH measurements were decomposed, considering the model configuration resolution. These components, derived from dense SSH observations, were integrated into a two-step 4DVAR assimilation scheme. The first cycle targets large-scale features for model field correction, and the updated analysis serves as the background for the second assimilation step, addressing fine-scale observation components. Comparisons with the primitive ROMS-4DVAR using a single-scale scheme highlight the superiority of the multi-scale strategy in reducing gaps between the model and the SSH observations. The Root Mean Squared Error (RMSE) is halved, and the Mean Absolute Percentage Error (MAPE) decreases from 2.237% to 0.93%. The two-step assimilation procedure ensures comprehensive multi-scale updates in the SSH field simulation, enhancing fine-scale features in the analysis fields. The quantification of three-dimensional-model dynamic fields further validates the efficiency and superiority of the multi-scale 4DVAR approach, offering a robust methodology for assimilating high-resolution observations within the ROMS. Full article

(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges II)

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