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Applications of Remote Sensing in Oceanography: Prospects and Challenges
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Applications of Remote Sensing in Oceanography: Prospects and Challenges

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

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 53053

Special Issue Editors

Prof. Dr. Changming Dong

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chunyan Li

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jingsong Yang

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals
Dr. Guoqi Han

E-Mail Website
Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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.

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

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

Published Papers (27 papers)

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16 pages, 21460 KiB  
Article
Statistical Characteristics of the Multiscale SST Fractal Structure over the Kuroshio Extension Region Using VIIRS Data
by Kai Yu, Changming Dong, Jin Wang, Xuhua Cheng and Yi Yu
Remote Sens. 2023, 15(4), 881; https://doi.org/10.3390/rs15040881 - 05 Feb 2023
Viewed by 1214
Abstract
The ocean behaves as a typical multiscale fractal structure, whose dynamic and thermal variabilities extend over a wide range of spatial scales, r, spanning from 10−3 to 107 m. Studying the statistical characteristics of multiscale fractal structures is crucial to [...] Read more.
The ocean behaves as a typical multiscale fractal structure, whose dynamic and thermal variabilities extend over a wide range of spatial scales, r, spanning from 10−3 to 107 m. Studying the statistical characteristics of multiscale fractal structures is crucial to understanding the interactions and energy cascade processes between different spatial scales. Remote sensing data are one of the best choices for revealing these statistical characteristics. This work analyzes the multiscale (1–1000 km) fractal structures of sea surface temperature (SST) from the Level-2+ (L2P) satellite orbit Visible Infrared Imaging Radiometer Suite (VIIRS) products over the Kuroshio Extension (KE) region (145°E–160°W, 20°N–50°N), using a conventional method (second-order structure function, D(r)) and a newly developed statistical method (spatial variance, V(r)). The results show that both the power-law distribution slopes of D(r) and V(r) are close to 2/3, which is equivalent to the −5/3 wavenumber spectrum. V(r) is found to be more robust when depicting the fractal structure and variance density, V’(r), compared with D(r). V’(r) is slightly larger at the mesoscale (50–150 km) than at the large scale (higher than 150 km) and is much smaller than that at the submesoscale (smaller than 50 km). Additionally, V’(r) has an indiscernible diurnal variation but remarkable seasonal and latitudinal variations. For the seasonal variability, the maximum V’(r) appears in winter at the large scale and mesoscale, and gradually shifts towards spring at the submesoscale, which implies that a forward energy cascade process may occur during this period. The maximum of the latitude-dependent V’(r) appears around 40°N for all the scales. It is consistent with the latitude of the strongest background SST gradient, indicating that the background SST front is the main source of the strong SST multiscale spatial variabilities over the KE region. This work benefits the application of other high-resolution remote sensing data in this research field, including the forthcoming Surface Water Ocean Topography (SWOT) satellite product. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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15 pages, 3838 KiB  
Article
Whitecap Fraction Parameterization and Understanding with Deep Neural Network
by Shuyi Zhou, Fanghua Xu and Ruizi Shi
Remote Sens. 2023, 15(1), 241; https://doi.org/10.3390/rs15010241 - 31 Dec 2022
Cited by 1 | Viewed by 1477
Abstract
Accurate calculation of the whitecap fraction is of great importance for the estimation of air-sea momentum flux, heat flux and sea-salt aerosol flux in Earth system models. Past whitecap fraction parameterizations were mostly power functions of wind speed, lacking consideration of other factors, [...] Read more.
Accurate calculation of the whitecap fraction is of great importance for the estimation of air-sea momentum flux, heat flux and sea-salt aerosol flux in Earth system models. Past whitecap fraction parameterizations were mostly power functions of wind speed, lacking consideration of other factors, while the single wind speed dependence makes it difficult to explain the variability of the whitecap fraction. In this work, we constructed a novel multivariate whitecap fraction parameterization using a deep neural network, which is diagnosed and interpreted. Compared with a recent developed parameterization by Albert and coworkers, the new parameterization can reduce the computational error of the whitecap fraction by about 15%, and it can better characterize the variability of the whitecap fraction, which provides a reference for the uncertainty study of sea-salt aerosol estimation. Through a permutation test, we ranked the importance of different input variables and revealed the indispensable role of variables such as significant wave height, sea surface temperature, etc., in the whitecap fraction parameterization. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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19 pages, 5366 KiB  
Article
Corrections of Mesoscale Eddies and Kuroshio Extension Surface Velocities Derived from Satellite Altimeters
by Yuhan Cao, Changming Dong, Zehao Qiu, Brandon J. Bethel, Haiyun Shi, Haibin Lü and Yinhe Cheng
Remote Sens. 2023, 15(1), 184; https://doi.org/10.3390/rs15010184 - 29 Dec 2022
Cited by 1 | Viewed by 1234
Abstract
Oceanic datasets derived from satellite altimeters are of great significance to physical oceanography and ocean dynamics research and the protection of marine environmental resources. Ageostrophic velocity induced by centrifugal force is not considered in altimeter products. This study introduces an iterative method to [...] Read more.
Oceanic datasets derived from satellite altimeters are of great significance to physical oceanography and ocean dynamics research and the protection of marine environmental resources. Ageostrophic velocity induced by centrifugal force is not considered in altimeter products. This study introduces an iterative method to perform cyclogeostrophic corrections of mesoscale eddies’ surface velocities derived from satellite altimeters. The corrected eddy velocity field and geostrophic velocity field were compared by combining eddy detection and mathematical statistics methods. The results show that eddies with small curvature radii, high roundness, or Rossby number larger than 0.1 illustrate that cyclogeostrophic correction is required. The cyclogeostrophic velocity is greater (less) than the geostrophic velocity in anticyclonic (cyclonic) eddies. Additionally, the iterative method is applied to cyclogeostrophic-corrected multi-year (1998–2012) Kuroshio surface velocities. The effect of cyclogeostrophic correction is significant for the Kuroshio Extension region, where the maximum relative difference of velocities with and without correction is about 10% and the eddy kinetic energy is 20%. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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18 pages, 4429 KiB  
Article
Improve the Accuracy in Numerical Modeling of Suspended Sediment Concentrations in the Hangzhou Bay by Assimilating Remote Sensing Data Utilizing Combined Techniques of Adjoint Data Assimilation and the Penalty Function Method
by Wenrui Chen, Daosheng Wang, Xiujuan Liu, Jun Cheng and Jicai Zhang
Remote Sens. 2023, 15(1), 148; https://doi.org/10.3390/rs15010148 - 27 Dec 2022
Viewed by 1053
Abstract
Suspended sediment dynamics play an important role in controlling nearshore and estuarine geomorphology and the associated ecological environments. Modeling the transport of suspended sediment is a complicated and challenging research topic. The goal of this study is to improve the accuracy of modeling [...] Read more.
Suspended sediment dynamics play an important role in controlling nearshore and estuarine geomorphology and the associated ecological environments. Modeling the transport of suspended sediment is a complicated and challenging research topic. The goal of this study is to improve the accuracy of modeling the suspended sediment concentrations (SSCs) with newly developed techniques. Based on a three-dimensional suspended cohesive sediment transport model, the transport of suspended sediment and SSCs are simulated by assimilating SSCs retrieved from the Geostationary Ocean Color Imager (GOCI) with the adjoint data assimilation in the Hangzhou Bay, a typical strong tidal estuary along the coast of the East China Sea. To improve the effect of the data assimilation, the penalty function method, in which the reasonable constraints of the estimated model parameters are added to the cost function as penalty terms, will be introduced for the first time into the adjoint data assimilation in the SSCs modeling. In twin experiments, the prescribed spatially varying settling velocity is estimated by assimilating the synthetic SSC observations, and the results show that the penalty function method can further improve the effect of data assimilation and parameter estimation, regardless of synthetic SSC observations being contaminated by random artificial errors. In practical experiments, the spatially varying settling velocity is firstly estimated by assimilating the actual GOCI-retrieved SSCs. The results demonstrate that the simulated results can be improved by the adjoint data assimilation, and the penalty function method can additionally reduce the mean absolute error (MAE) between the independent check observations and the corresponding simulated SSCs from 1.44 × 10−1 kg/m3 to 1.30 × 10−1 kg/m3. To pursue greater simulation accuracy, the spatially varying settling velocity, resuspension rate, critical shear stress and initial conditions are simultaneously estimated by assimilating the actual GOCI-retrieved SSCs to simulate the SSCs in the Hangzhou Bay. When the adjoint data assimilation and the penalty function method are simultaneously used, the MAE between the independent check observations and the corresponding simulated SSCs is just 9.90 × 10−2 kg/m3, which is substantially less than that when only the settling velocity is estimated. The MAE is also considerably less than that when the four model parameters are estimated to be without using the penalty function method. This study indicates that the adjoint data assimilation can effectively improve the SSC simulation accuracy, and the penalty function method can limit the variation range of the estimated model parameters to further improve the effect of data assimilation and parameter estimation. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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18 pages, 3694 KiB  
Article
Improvement of Typhoon Intensity Forecasting by Using a Novel Spatio-Temporal Deep Learning Model
by Shuailong Jiang, Hanjie Fan and Chunzai Wang
Remote Sens. 2022, 14(20), 5205; https://doi.org/10.3390/rs14205205 - 18 Oct 2022
Cited by 8 | Viewed by 2126
Abstract
Typhoons can cause massive casualties and economic damage, and accurately predicting typhoon intensity has always been a hot topic both in theory and practice. In consideration with the spatial and temporal complexity of typhoons, machine learning methods have recently been applied in typhoon [...] Read more.
Typhoons can cause massive casualties and economic damage, and accurately predicting typhoon intensity has always been a hot topic both in theory and practice. In consideration with the spatial and temporal complexity of typhoons, machine learning methods have recently been applied in typhoon forecasting. In this paper, we attempt to improve typhoon intensity forecasting by treating it as a spatio-temporal problem in the deep learning field. In particular, we propose a novel typhoon intensity forecasting model named the Typhoon Intensity Spatio-temporal Prediction Network (TITP-Net). The proposed model takes multidimensional environmental variables and physical factors of typhoons into account and fully extracts the information from the datasets by capturing spatio-temporal dependencies with a spatial attention module, which includes two-dimensional and three-dimensional convolutional operations. A series of experiments with a comprehensive framework by using TITP-Net are conducted. The MAEs of the forecasts with 18, 24, 36 and 48 h lead time obtain a significant improvement by 7.02%, 6.53%, 6.25% and 5.37% compared with some existing deep learning models and dynamical models from official agencies. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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17 pages, 8655 KiB  
Article
Shallow Sea Topography Detection from Multi-Source SAR Satellites: A Case Study of Dazhou Island in China
by Longyu Huang, Junmin Meng, Chenqing Fan, Jie Zhang and Jingsong Yang
Remote Sens. 2022, 14(20), 5184; https://doi.org/10.3390/rs14205184 - 17 Oct 2022
Cited by 1 | Viewed by 1964
Abstract
Accurate measurement of underwater topography in the coastal zone is essential for human marine activities, and the synthetic aperture radar (SAR) presents a completely new solution. However, underwater topography detection using a single SAR image is vulnerable to the interference of sea state [...] Read more.
Accurate measurement of underwater topography in the coastal zone is essential for human marine activities, and the synthetic aperture radar (SAR) presents a completely new solution. However, underwater topography detection using a single SAR image is vulnerable to the interference of sea state and sensor noise, which reduces the detection accuracy. A new underwater topography detection method based on multi-source SAR (MSSTD) was proposed in this study to improve the detection precision. GF-3, Sentinel-1, ALOS PALSAR, and ENVISAT ASAR data were used to verify the sea area of Dazhou Island. The detection result was in good agreement with the chart data (MAE of 2.9 m and correlation coefficient of 0.93), and the detection accuracy was improved over that of a single SAR image. GF-3 image with 3 m spatial resolution performed best in bathymetry among the four SAR images. Additionally, the resolution of the SAR image had greater influence on bathymetry compared with polarization and radar band. The ability of MSSTD has been proved in our work. Collaborative multi-source satellite observation is a feasible and effective scheme in marine research, but its application potential in underwater topography detection still requires further exploration. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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22 pages, 5745 KiB  
Article
Reconstruction of Monthly Surface Nutrient Concentrations in the Yellow and Bohai Seas from 2003–2019 Using Machine Learning
by Hao Liu, Lei Lin, Yujue Wang, Libin Du, Shengli Wang, Peng Zhou, Yang Yu, Xiang Gong and Xiushan Lu
Remote Sens. 2022, 14(19), 5021; https://doi.org/10.3390/rs14195021 - 09 Oct 2022
Cited by 1 | Viewed by 1594
Abstract
Monitoring the spatiotemporal variability of nutrient concentrations in shelf seas is important for understanding marine primary productivity and ecological problems. However, long time-series and high spatial-resolution nutrient concentration data are difficult to obtain using only on ship-based measurements. In this study, we developed [...] Read more.
Monitoring the spatiotemporal variability of nutrient concentrations in shelf seas is important for understanding marine primary productivity and ecological problems. However, long time-series and high spatial-resolution nutrient concentration data are difficult to obtain using only on ship-based measurements. In this study, we developed a machine-learning approach to reconstruct monthly sea-surface dissolved inorganic nitrogen (DIN), dissolved inorganic phosphorus (DIP), and dissolved silicate (DSi) concentrations in the Yellow and Bohai seas from 2003–2019. A large amount of in situ measured data were first used to train the machine-learning model and derive a reliable model with input of environmental data (including sea-surface temperature, salinity, chlorophyll-a, and Kd490) and output of DIN, DIP, and DSi concentrations. Then, longitudinal (2003–2019) monthly satellite remote-sensing environmental data were input into the model to reconstruct the surface nutrient concentrations. The results showed that the nutrient concentrations in nearshore (water depth < 40 m) and offshore (water depth > 40 m) waters had opposite seasonal variabilities; the highest (lowest) in summer in nearshore (offshore) waters and the lowest (highest) in winter in nearshore (offshore) waters. However, the DIN:DIP and DIN:DSi in most regions were consistently higher in spring and summer than in autumn and winter, and generally exceeded the Redfield ratio. From 2003–2019, DIN showed an increasing trend in nearshore waters (average 0.14 μmol/L/y), while DSi showed a slight increasing trend in the Changjiang River Estuary (0.06 μmol/L/y) but a decreasing trend in the Yellow River Estuary (–0.03 μmol/L/y), and DIP exhibited no significant trend. Furthermore, surface nutrient concentrations were sensitive to changes in sea-surface temperature and salinity, with distinct responses between nearshore and offshore waters. We believe that our novel machine learning method can be applied to other shelf seas based on sufficient observational data to reconstruct a long time-series and high spatial resolution sea-surface nutrient concentrations. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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17 pages, 7252 KiB  
Article
An Improved Retrieval Method for Porphyra Cultivation Area Based on Suspended Sediment Concentration
by Yinhe Cheng, Yue Sun, Lin Peng, Yijun He and Mengling Zha
Remote Sens. 2022, 14(17), 4338; https://doi.org/10.3390/rs14174338 - 01 Sep 2022
Cited by 4 | Viewed by 2051
Abstract
The rapid expansion of Porphyra farming in China lends considerable urgency to developing a satellite remote sensing retrieval method to monitor its cultivation, in order to promote sustainable economic development and protective utilization of ecosystem-oriented marine natural resources. For medium-resolution satellite imagery such [...] Read more.
The rapid expansion of Porphyra farming in China lends considerable urgency to developing a satellite remote sensing retrieval method to monitor its cultivation, in order to promote sustainable economic development and protective utilization of ecosystem-oriented marine natural resources. For medium-resolution satellite imagery such as HY-1C images, pixel-by-pixel techniques are appropriate; however, many factors affect the retrieval accuracy of the Porphyra cultivation area. In coastal regions, Porphyra and suspended sediment radiate a similar spectrum, which inevitably causes errors in the identification of the Porphyra. To improve the overall retrieval accuracy of Porphyra cultivation area from medium-resolution HY-1C images, we considered suspended sediment concentration (SSC) as an independent variable and constructed a new model in conjunction with high-resolution Sentinel-2 satellite images using a linear regression method in Haizhou Bay, China. A comparative analysis was performed with a traditional random forest classification algorithm and pixel-based dichotomy model in different SSC seawater. The results showed that the new model expressed the best ability to supervise Porphyra cultivation area, and its overall relative error and root mean square deviation, whether in area or in validation sample points, were the lowest among the models. The experiment was performed by removing the SSC variable while using the same processes as in the new model, and the results indicate that the SSC played an important role in new model, which is suitable to be applied to coastal seawater containing more suspended sediment, as in the HY-1C coastal zone image. The application of the new model on temporal change in the retrieved results was indirectly verified as effective. This study provides an effective method to exactly extract Porphyra cultivation area in the coastal sea using medium-resolution HY-1C satellite imagery. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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18 pages, 7116 KiB  
Article
Tropical Cyclone Wind Field Reconstruction and Validation Using Measurements from SFMR and SMAP Radiometer
by Xiaohui Li, Jingsong Yang, Guoqi Han, Lin Ren, Gang Zheng, Peng Chen and Han Zhang
Remote Sens. 2022, 14(16), 3929; https://doi.org/10.3390/rs14163929 - 13 Aug 2022
Cited by 9 | Viewed by 1946
Abstract
Accurate information on tropical cyclone position, intensity, and structure is critical for storm surge prediction. Atmospheric reanalysis datasets can provide gridded, full coverage, long-term and multi-parameter atmospheric fields for the research on the impact of tropical cyclones on the upper ocean, which effectively [...] Read more.
Accurate information on tropical cyclone position, intensity, and structure is critical for storm surge prediction. Atmospheric reanalysis datasets can provide gridded, full coverage, long-term and multi-parameter atmospheric fields for the research on the impact of tropical cyclones on the upper ocean, which effectively makes up for the uneven temporal and spatial distribution of satellite remote sensing and in situ data. However, the reanalysis data cannot accurately describe characteristic parameters of tropical cyclones, especially in high wind conditions. In this paper, the performance of the tropical cyclone representation in ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation) is investigated and analyzed with respect to IBTrACS (International Best Track Archive for Climate Stewardship) during the period 2018–2020. Comparisons demonstrate that ERA5 winds significantly underestimate the maximum wind speed during the tropical cyclones (>30 m/s) compared to those provided by IBTrACS. An effective wind reconstruction method is examined to enhance tropical cyclone intensity representation in reanalysis data in 94 cases of 31 tropical cyclones 2018–2020. The reconstructed wind speeds are in good agreement with the SFMR (Stepped Frequency Microwave Radiometer) measured data and SMAP (Soil Moisture Active Passive) L-band radiometer remotely sensed measurements. The proposed wind reconstruction method can effectively improve the accuracy of the tropical cyclone representation in ERA5, and will benefit from the establishment of remote sensing satellite retrieval model and the forcing fields of the ocean model. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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16 pages, 4637 KiB  
Article
Improved ENSO and PDO Prediction Skill Resulting from Finer Parameterization Schemes in a CGCM
by Yuxing Yang, Xiaokai Hu, Guanghong Liao, Qian Cao, Sijie Chen, Hui Gao and Xiaowei Wei
Remote Sens. 2022, 14(14), 3363; https://doi.org/10.3390/rs14143363 - 13 Jul 2022
Cited by 2 | Viewed by 1514
Abstract
Coupled general circulation models (CGCMs), as tools of predicting climate variability, are constantly being improved due to their immense value in a host of theoretical and practical, real-world problems. Consequently, four new parameterization schemes are introduced in the First Institute of Oceanography Earth [...] Read more.
Coupled general circulation models (CGCMs), as tools of predicting climate variability, are constantly being improved due to their immense value in a host of theoretical and practical, real-world problems. Consequently, four new parameterization schemes are introduced in the First Institute of Oceanography Earth System Model (FIO-ESM), and a new climate prediction System (CPS) is built up based on modified and original FIO-ESM. Here, turbulence from the sea surface to deep ocean were fully described, and seasonal forecasts of El Niño-Southern Oscillation (ENSO) and year-to-year prediction of Pacific Decadal Oscillation (PDO) were made with both the modified and original FIO-ESM-CPS. The results illustrate that the anomaly correlation coefficient (ACC) of the Niño 3.4 index significantly increased, and the root mean square error (RMSE) significantly decreased, respectively, in the modified FIO-ESM-CPS as compared to the original. The RMSE is improved by over 20% at 4- and 5-month lead times. Over longer leads, and in the modified FIO-ESM-CPS, forecast ENSO amplitudes are far closer to observations than the original CGCM, which significantly overestimates amplitudes. PDO prediction skill is also improved in the modified FIO-ESM-CPS with ACC improving by 36% at the 4-year lead time and RMSE decreasing by 21% at the 3-year lead time. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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20 pages, 9986 KiB  
Article
Seasonal Variability in Chlorophyll and Air-Sea CO2 Flux in the Sri Lanka Dome: Hydrodynamic Implications
by Wentao Ma, Yuntao Wang, Yan Bai, Xiaolin Ma, Yi Yu, Zhiwei Zhang and Jingyuan Xi
Remote Sens. 2022, 14(14), 3239; https://doi.org/10.3390/rs14143239 - 06 Jul 2022
Cited by 1 | Viewed by 2185
Abstract
Multiple upwelling systems develop in the Indian Ocean during the summer monsoon. The Sri Lanka dome (SLD), which occurs in the open ocean off the east coast of Sri Lanka from June to September, is distinct from those near the coast. The SLD [...] Read more.
Multiple upwelling systems develop in the Indian Ocean during the summer monsoon. The Sri Lanka dome (SLD), which occurs in the open ocean off the east coast of Sri Lanka from June to September, is distinct from those near the coast. The SLD is characterized by uplifted thermocline and increased chlorophyll concentration. Mechanisms of the upwelling and its biogeochemical response are not well understood. Here, we explored the dynamics of the chlorophyll and sea-to-air CO2 flux in the SLD using ocean color and altimetry remote sensing data, together with other reanalysis products. We found that the occurrence of high chlorophyll concentration and sea-to-air CO2 flux happens along the pathway of the southwest monsoon current (SMC). The annual cycle of chlorophyll in the SLD has a one-month lag relative to that in the southern coast of Sri Lanka. The positive wind stress curl that forms in the SLD during the summer does not fully explain the seasonal chlorophyll maximum. Transport of the SMC, eddy activity, and associated frontal processes also play an important role in regulating the variability in chlorophyll. In the SLD, upwelled subsurface water has excess dissolved inorganic carbon (DIC) relative to the conventional Redfield ratio between DIC and nutrients; thus, upwelling and sub-mesoscale processes determine this region to be a net carbon source to the atmosphere. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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19 pages, 4984 KiB  
Article
Synchronous Assimilation of Tidal Current-Related Data Obtained Using Coastal Acoustic Tomography and High-Frequency Radar in the Xiangshan Bay, China
by Ze-Nan Zhu, Xiao-Hua Zhu, Weibing Guan, Chuanzheng Zhang, Minmo Chen, Zhao-Jun Liu, Min Wang, Hua Zheng, Juntian Chen, Longhao Dai, Zhenyi Cao, Qi Chen and Arata Kaneko
Remote Sens. 2022, 14(13), 3235; https://doi.org/10.3390/rs14133235 - 05 Jul 2022
Cited by 2 | Viewed by 2057
Abstract
To accurately reconstruct large-area three-dimensional current fields in coastal regions, simultaneous observations with ten coastal acoustic tomography (CAT) stations and two high-frequency radar (HFR) stations were performed in the Xiangshan Bay (XSB) on 4–5 December 2020. The section-averaged velocity that was observed by [...] Read more.
To accurately reconstruct large-area three-dimensional current fields in coastal regions, simultaneous observations with ten coastal acoustic tomography (CAT) stations and two high-frequency radar (HFR) stations were performed in the Xiangshan Bay (XSB) on 4–5 December 2020. The section-averaged velocity that was observed by CAT and the radial velocity that was observed by HFR were, for the first time, synchronously assimilated into a three-dimensional barotropic ocean model. Compared with acoustic Doppler current profile data, the velocities of the model assimilating both CAT and HFR data had the highest accuracy according to root mean square differences (RMSDs), ranging from 0.05 to 0.08 m/s for all the vertical layers. For the models individually assimilating CAT and HFR, the values in the vertical layers ranged from 0.07 to 0.12 m/s and 0.08 to 0.13 m/s, respectively. A harmonic analysis of the model grid data showed that the spatial mean amplitudes of the M2, M4, and residual currents were 0.66, 0.14, and 0.09 m/s, respectively. Furthermore, the standing wave characteristics of the M2 current and M4 associated-oscillation in the inner XSB, mouth of the Xiangshan fjord, were better captured by the model assimilating both CAT and HFR. Our study demonstrates the advances in three-dimensional tidal current analysis using a model that assimilates both CAT and HFR data, especially in regions with complex coastal geography. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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19 pages, 7372 KiB  
Article
A Two-Dimensional Variational Scheme for Merging Multiple Satellite Altimetry Data and Eddy Analysis
by Xingliang Jiang, Lei Liu, Zhijin Li, Lingxiao Liu, Kenny T. C. Lim Kam Sian and Changming Dong
Remote Sens. 2022, 14(13), 3026; https://doi.org/10.3390/rs14133026 - 24 Jun 2022
Cited by 2 | Viewed by 1862
Abstract
With the increasing number of satellite altimeters in orbit, the effective resolution of merged multiple satellite altimetry data can be improved. We implement a two-dimensional variational (2-DVar) method to merge multiple satellite altimetry data and produce a daily gridded absolute dynamic topography (ADT) [...] Read more.
With the increasing number of satellite altimeters in orbit, the effective resolution of merged multiple satellite altimetry data can be improved. We implement a two-dimensional variational (2-DVar) method to merge multiple satellite altimetry data and produce a daily gridded absolute dynamic topography (ADT) dataset with a grid size of 0.08 degrees. We conduct an observing system simulation experiment (OSSE), and the results show that the merged ADT dataset has an effective resolution of about 210 km. Compared with an independent sea surface temperature (SST) data, fine-scale structures can also be observed in the geostrophic flow of the new dataset. A relationship between effective resolution and the radius of a detected eddy is established and used for eddy analysis in the East China Sea (ECS) region. We observe that eddies in the open ocean are more numerous, have larger radii and live longer than those in other areas. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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16 pages, 6683 KiB  
Article
Summer Marine Heatwaves in the Kuroshio-Oyashio Extension Region
by Yanzhen Du, Ming Feng, Zhenhua Xu, Baoshu Yin and Alistair J. Hobday
Remote Sens. 2022, 14(13), 2980; https://doi.org/10.3390/rs14132980 - 22 Jun 2022
Cited by 5 | Viewed by 2199
Abstract
During 1982–2021, the highest sea surface temperature (SST) variability over the North Pacific was in the Kuroshio-Oyashio Extension (KOE) region, with more intense marine heatwaves (MHWs), especially during summertime. In this study, we explored the evolution and driving factors of the strongest summer [...] Read more.
During 1982–2021, the highest sea surface temperature (SST) variability over the North Pacific was in the Kuroshio-Oyashio Extension (KOE) region, with more intense marine heatwaves (MHWs), especially during summertime. In this study, we explored the evolution and driving factors of the strongest summer MHWs based on their cumulative intensity using satellite observations and reanalyzed model results. Strong summer MHWs in 1999, 2008, 2012, and 2016 were initiated and peaked around summer. The more recent summer MHW events in 2018, 2020, and 2021 appeared to be associated with intermittent MHW events in the previous winter that extended to boreal summer. Based on a mixed layer temperature budget analysis from March to their peaks in summer, MHWs in 1999, 2008, 2012, and 2016 were primarily driven by the air-sea heat flux anomalies, with anomalous shortwave radiation due to reduced cloud cover being the dominant factor. Summer MHWs in 2018, 2020, and 2021 were mainly contributed by the ocean memory of winter warming. The northward shift of the Kuroshio Extension axis, the northward intrusion of the anticyclonic eddies, and the decadal warming trend may contribute to the positive sea surface height anomalies and increased upper ocean heat content in the KOE to increase winter SST and precondition the summer MHWs. Understanding MHW variability and the underlying mechanisms will help manage the marine ecosystem of the KOE region, as well as predict climate change impacts. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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16 pages, 4379 KiB  
Article
Impacts of Climate Change on a Coastal Wetland from Model Simulation Combining Satellite and Gauge Observations: A Case Study of Jiangsu, China
by Jihai Dong, Changming Dong and Kai Yu
Remote Sens. 2022, 14(10), 2473; https://doi.org/10.3390/rs14102473 - 20 May 2022
Cited by 1 | Viewed by 1987
Abstract
Coastal wetlands are affected by both natural processes and human activities. In the present study, the impacts of natural processes on wetland area variations along the Jiangsu coast in the East China Sea were investigated using a long-term high-resolution numerical model (55 years [...] Read more.
Coastal wetlands are affected by both natural processes and human activities. In the present study, the impacts of natural processes on wetland area variations along the Jiangsu coast in the East China Sea were investigated using a long-term high-resolution numerical model (55 years from 1955 to 2010) validated by satellite and gauge observations. In our 55-year simulation, a 1.33 km2 yr−1 decreasing trend of wetland area as a result of global warming was identified. It was found that the wetland area varied depending on the following temporal scales: intra-seasonal, seasonal, interannual, and decadal. Tides and steric sea level changes are responsible for the intra-seasonal and seasonal wetland variations, respectively. The long-term variations are attributable to the El Niño-Southern Oscillation and Pacific Decadal Oscillation. These basin-scale phenomena cause changes in the local wind patterns along the Jiangsu coast and impact the wetland variation on long-term scales. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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19 pages, 8320 KiB  
Article
Aircraft and Satellite Observations of Vortex Evolution and Surface Wind Asymmetry of Concentric Eyewalls in Hurricane Irma
by Han Hua, Biao Zhang, Guosheng Zhang, William Perrie, Changlin Chen and Yuanben Li
Remote Sens. 2022, 14(9), 2158; https://doi.org/10.3390/rs14092158 - 30 Apr 2022
Viewed by 1579
Abstract
We compare the vortex evolutions of eyewall replacement cycles (ERCs) between the sea-surface and the free-atmosphere levels and investigate the asymmetric structure of concentric eyewalls (CEs) by examining a combination of aircraft observations and surface wind fields derived from C-band spaceborne synthetic aperture [...] Read more.
We compare the vortex evolutions of eyewall replacement cycles (ERCs) between the sea-surface and the free-atmosphere levels and investigate the asymmetric structure of concentric eyewalls (CEs) by examining a combination of aircraft observations and surface wind fields derived from C-band spaceborne synthetic aperture radar (SAR) images during Hurricane Irma (2017) from 4 September 2017 to 8 September 2017. A total of 116 radial wind profiles measured by an aircraft were collected and showed that ERCs occur at both the sea-surface and the free-atmosphere levels. The outer eyewall was shown to form at the free atmospheric level (~3 km) with a narrow structure at the sea-surface level and an outward tilt with height in the cross-section. In our study, four ERC events were determined from wind profile parameters fitted by a modified Rankine vortex model, which was validated by 328 radial legs collected from six hurricanes. The outer eyewall did not replace the inner eyewall at the sea-surface level in the third ERC, due to the maintenance of a short duration and intense original eyewall. Additionally, Irma’s intensity weakened during the fourth ERC rather than re-intensified, because of the generation of a third wind maximum outside the secondary eyewall. Comparisons of five SAR-derived surface wind fields in Irma and another two hurricane cases illustrated that the location of the secondary eyewall generation is a key point in the interpretation of anomaly intensity changes in the fourth ERC. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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18 pages, 7867 KiB  
Article
Impacts of Climate Oscillation on Offshore Wind Resources in China Seas
by Qing Xu, Yizhi Li, Yongcun Cheng, Xiaomin Ye and Zenghai Zhang
Remote Sens. 2022, 14(8), 1879; https://doi.org/10.3390/rs14081879 - 14 Apr 2022
Cited by 4 | Viewed by 1619
Abstract
The long-term stability and sustainability of offshore wind energy resources are very important for wind energy exploration. In this study, the Cyclostationary Empirical Orthogonal Function (CSEOF) method, which can determine the time varying spatial distributions and long-term fluctuations in the cyclostationary geophysical process, [...] Read more.
The long-term stability and sustainability of offshore wind energy resources are very important for wind energy exploration. In this study, the Cyclostationary Empirical Orthogonal Function (CSEOF) method, which can determine the time varying spatial distributions and long-term fluctuations in the cyclostationary geophysical process, was adopted to investigate the geographical and temporal variability of offshore wind resources in China Seas. The CSEOF analysis was performed on wind speeds at 70 m height above the sea surface from a validated combined Quick Scatterometer (QuikSCAT) and Advanced Scatterometer (ASCAT) wind product (2000–2016) with high spatial resolution of 12.5 km, and Climate Forecast System Reanalysis (CFSR) wind data (1979–2016) with a grid size of 0.5° × 0.5°. The decomposition results of the two datasets indicate that the first CSEOF mode represents the variability of wind annual cycle signal and contributes 77.7% and 76.5% to the wind energy variability, respectively. The principal component time series (PCTS) shows an interannual variability of annual wind cycle with a period of 3–4 years. The second mode accounts for 4.3% and 4.7% of total wind speed variability, respectively, and captures the spatiotemporal contribution of El Niño Southern Oscillation (ENSO) on regional wind energy variability. The correlations between the mode-2 PCTS of scatterometer or CFSR winds and the Southern Oscillation Index (SOI) are greater than 0.7, illustrating that ENSO has a significant impact on China’s offshore wind resources. Moreover, the mode-1 or mode-2 spatial pattern of CFSR winds is basically consistent with that of scatterometer data, but CFSR underestimates the temporal variability of annual wind speed cycle and the spatial changes of wind speed related to ENSO. Compared with reanalysis data, scatterometer winds always demonstrate a finer structure of wind energy variability due to their higher spatial resolution. For ENSO events with different intensities, the impact of ENSO on regional wind resources varies with time and space. In general, El Niño has reduced wind energy in most regions of China Seas except for the Bohai Sea and Beibu Bay, while La Niña has strengthened the winds in most areas except for the Bohai Sea and southern South China Sea. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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18 pages, 8188 KiB  
Article
Diagnostics of Coherent Eddy Transport in the South China Sea Based on Satellite Observations
by Tongya Liu, Yinghui He, Xiaoming Zhai and Xiaohui Liu
Remote Sens. 2022, 14(7), 1690; https://doi.org/10.3390/rs14071690 - 31 Mar 2022
Cited by 3 | Viewed by 1666
Abstract
The large discrepancy between Eulerian and Lagrangian work motivates us to examine the leakage of Eulerian eddies and quantify the contribution of coherent eddy transport in the South China Sea (SCS). In this study, Lagrangian particles with a resolution of 1/32° are advected [...] Read more.
The large discrepancy between Eulerian and Lagrangian work motivates us to examine the leakage of Eulerian eddies and quantify the contribution of coherent eddy transport in the South China Sea (SCS). In this study, Lagrangian particles with a resolution of 1/32° are advected by surface geostrophic currents derived from satellite observations spanning 23 years, and two types of methods are employed to identify sea surface height (SSH) eddies and Lagrangian coherent structures. SSH eddies are proven to be highly leaky during their lifetimes, with more than 80% of the original water leaking out of the eddy interior. As a result of zonal and meridional eddy propagation, the leaked water exhibits a spatial pattern of asymmetry relative to the eddy center. The degree of eddy leakage is found to be independent of several eddy parameters including the nonlinearity parameter U/c, which has been commonly used to assess eddy coherency. Finally, the Lagrangian coherent structures in the SCS are diagnosed and the associated coherent eddy diffusivity is calculated. It is found that coherent eddies contribute to less than 5% of the total eddy material transport in both zonal and meridional directions. These findings suggest that previous studies based on the Eulerian framework significantly overestimate the contribution of coherent eddy transport in the SCS. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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18 pages, 6019 KiB  
Article
Numerical Simulation and Observational Data Analysis of Mesoscale Eddy Effects on Surface Waves in the South China Sea
by Jin Wang, Brandon J. Bethel, Changming Dong, Chunhui Li and Yuhan Cao
Remote Sens. 2022, 14(6), 1463; https://doi.org/10.3390/rs14061463 - 18 Mar 2022
Cited by 6 | Viewed by 2940
Abstract
Surface current velocities of mesoscale eddies have a unique annular structure, which can inevitably influence surface wave properties and energy distribution. Sensitivity experiments of ideal mesoscale eddies on waves were carried out by the Simulating WAves Nearshore (SWAN) wave model to investigate these [...] Read more.
Surface current velocities of mesoscale eddies have a unique annular structure, which can inevitably influence surface wave properties and energy distribution. Sensitivity experiments of ideal mesoscale eddies on waves were carried out by the Simulating WAves Nearshore (SWAN) wave model to investigate these influences. In addition, China–France Oceanography SATellite Surface Wave Investigation and Monitoring (CFOSAT-SWIM) observational data of a large warm-cored eddy in the South China Sea (SCS) during the period of October–November 2019 were used to validate the influence of mesoscale eddies on waves. The results illustrated that mesoscale eddies can alter wave properties (wave height, period, and steepness) by 20–30%. Moreover, wave direction could also be modified by 30°–40°. The current effect on waves (CEW) was more noticeable with strong currents and weak winds, and was governed by wave age and the ratio of wave group velocity to current velocity. Wave spectra clearly indicated that current-induced variability in wave energy distribution happens on a spatial scale of 5–90 km (i.e., the sub- and mesoscales). Through comparing the difference of wave energy on both sides of an eddy perpendicular to the wave propagation direction in an eddy, a simple way to trace the footprints of waves on eddies was devised. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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16 pages, 8773 KiB  
Article
Wind Speed Retrieval Using Global Precipitation Measurement Dual-Frequency Precipitation Radar Ka-Band Data at Low Incidence Angles
by Chong Jiang, Lin Ren, Jingsong Yang, Qing Xu and Jinyuan Dai
Remote Sens. 2022, 14(6), 1454; https://doi.org/10.3390/rs14061454 - 18 Mar 2022
Viewed by 1778
Abstract
In this study, sea surface wind speed was retrieved using the Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) Ka-band data. In order to establish the Ka-band model at low incidence angles, the dependence of the DPR Ka-band normalized radar cross section (NRCS) [...] Read more.
In this study, sea surface wind speed was retrieved using the Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) Ka-band data. In order to establish the Ka-band model at low incidence angles, the dependence of the DPR Ka-band normalized radar cross section (NRCS) on the wind speed, incidence angle, sea surface temperature (SST), significant wave height (SWH), and sea surface current speed (CSPD) was analyzed first. We confirmed that the normalized radar cross section depends on the wind speed, incidence angle, and SST. Second, an empirical model at low incidence angles was established. This model links the Ka-band NRCS to the incidence angle, wind speed, and SST. Additionally, the wind speed was retrieved by the model and was validated via the GPM Microwave Imager (GMI) wind product. The validation yielded a root mean square error (RMSE) of 1.45 m/s and the RMSE was better at a lower incidence angle and a higher SST. This model may expand the use of GPM DPR data in enriching the sea surface wind speed data set. It is also helpful for other Ka-band spaceborne radars at low incidence angles to measure wind speed in the future. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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17 pages, 7582 KiB  
Article
A Comparative Study of the Landfall Precipitation by Tropical Cyclones ARB 01 (2002) and Luban (2018) near the Arabian Peninsula
by Yusheng Cui, Haibin Lü, Dawei Shi, Chuqi Xia and Changming Dong
Remote Sens. 2022, 14(5), 1194; https://doi.org/10.3390/rs14051194 - 28 Feb 2022
Cited by 2 | Viewed by 2156
Abstract
Considering the high risk of flooding during tropical cyclones (TCs), there is great practical significance in researching and predicting precipitation during TC landfalls. Using NECP FNL reanalysis data and GPM_MERGIR datasets, two TCs with similar trajectories, ARB 01 in 2002 and Luban in [...] Read more.
Considering the high risk of flooding during tropical cyclones (TCs), there is great practical significance in researching and predicting precipitation during TC landfalls. Using NECP FNL reanalysis data and GPM_MERGIR datasets, two TCs with similar trajectories, ARB 01 in 2002 and Luban in 2018, were analyzed. For ARB 01 and Luban, there are separate effects of wind shear at different heights on the development of vertical motion. Meridional wind shear affects the main deviation direction of vertical motion (downshear), while zonal wind shear mainly affects the deviation direction of vertical motion to the left or right of downshear. The divergent configuration of wind promotes the development of vertical motion. The influence of wind speed provided ideal conditions for ARB 01 to generate symmetric precipitation along its path when it made landfall. Additionally, more water vapor support was obtained from the southern Indian Ocean, which enabled ARB 01 to have a larger and broader average precipitation rate after landing. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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19 pages, 4364 KiB  
Article
Can Multi-Mission Altimeter Datasets Accurately Measure Long-Term Trends in Wave Height?
by Ian R. Young and Agustinus Ribal
Remote Sens. 2022, 14(4), 974; https://doi.org/10.3390/rs14040974 - 16 Feb 2022
Cited by 17 | Viewed by 2718
Abstract
A long-duration, multi-mission altimeter dataset is analyzed to determine its accuracy in determining long-term trends in significant wave height. Two calibration methods are investigated: “altimeter–buoy” calibration and “altimeter–altimeter” calibration. The “altimeter–altimeter” approach shows larger positive trends globally, but both approaches are subject to [...] Read more.
A long-duration, multi-mission altimeter dataset is analyzed to determine its accuracy in determining long-term trends in significant wave height. Two calibration methods are investigated: “altimeter–buoy” calibration and “altimeter–altimeter” calibration. The “altimeter–altimeter” approach shows larger positive trends globally, but both approaches are subject to temporal non-homogeneity between altimeter missions. This limits the accuracy of such datasets to approximately ±0.2 cm/year in determining trends in significant wave height. The sampling pattern of the altimeters is also investigated to determine if under-sampling impacts the ability of altimeters to measure trends for higher percentiles. It is concluded that, at the 99th percentile level, sampling issues result in a positive bias in values of trend. At lower percentiles (90th and mean), the sampling issues do not bias the trend estimates significantly. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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16 pages, 5814 KiB  
Technical Note
Improving the Reconstruction of Vertical Temperature Profiles on Account of Oceanic Front Impacts
by Xin Chen, Chen Wang, Huimin Li and Yijun He
Remote Sens. 2022, 14(19), 4821; https://doi.org/10.3390/rs14194821 - 27 Sep 2022
Viewed by 1446
Abstract
The application of remote sensing observations in estimating ocean sub-surface temperatures has been widely adopted. Machine learning-based methods in particular are gaining more and more interest. While there is promising relevant progress, most temperature profile reconstruction models are still built upon the gridded [...] Read more.
The application of remote sensing observations in estimating ocean sub-surface temperatures has been widely adopted. Machine learning-based methods in particular are gaining more and more interest. While there is promising relevant progress, most temperature profile reconstruction models are still built upon the gridded Argo data regardless of the impacts of mesoscale oceanic processes. As a follow-on to the previous study that demonstrates the influence of ocean fronts is negligible, we focus on the improvement of temperature profile reconstruction by introducing the sea surface temperature (SST) gradient into the neural network model. The model sensitivity assessments reveal that the normalization of the input variables achieves a higher estimation accuracy than the original scale. Five experiments are then designed to examine the model performances with or without the SST gradient input. Our results confirm that, for a given model configuration, the one with the input of the SST gradient has the lowest reconstruction bias in comparison to the in situ Argo measurements. Such improvement is particularly pronounced below 200 m depth. We also found that the non-linear activation functions and deeper network structures facilitate the performance of reconstruction models. Results of this work open new insights and challenges to refine the mapping of upper ocean temperature structures. While more relevant machine learning methods are worth further exploitation, how to better characterize the mesoscale oceanic processes from surface observations and bring them into the reconstruction models is the key and needs much attention. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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12 pages, 4259 KiB  
Technical Note
Performance of SMAP and SMOS Salinity Products under Tropical Cyclones in the Bay of Bengal
by Huabing Xu, Yucai Shan and Guangjun Xu
Remote Sens. 2022, 14(15), 3733; https://doi.org/10.3390/rs14153733 - 04 Aug 2022
Viewed by 1213
Abstract
To compare the accuracy of satellite salinity data of level-3 Soil Moisture Active Passive V4.0 (SSMAP) and debiased v5 CATDS level-3 Soil Moisture and Ocean Salinity (SSMOS) before and after tropical cyclones (TCs) in the Bay of Bengal (BoB), [...] Read more.
To compare the accuracy of satellite salinity data of level-3 Soil Moisture Active Passive V4.0 (SSMAP) and debiased v5 CATDS level-3 Soil Moisture and Ocean Salinity (SSMOS) before and after tropical cyclones (TCs) in the Bay of Bengal (BoB), this study used the sea surface salinity of Argo (SArgo) to assess SSMAP and SSMOS before and after the passage of 10 TCs from 2015 to 2019. The results indicate that the SSMAP and SSMOS agreed well with SArgo before and after 10 TCs. It can be seen that the correlation between SSMAP and SArgo (before TCs: SSMAP = 0.95SArgo + 1.52, R2 = 0.83; after TCs: SSMAP = 0.87SArgo + 4.34, R2 = 0.79) was obviously higher than that of SSMOS and SArgo (before TCs: SSMOS = 0.68SArgo + 10.38, R2 = 0.62; after TCs: SSMOS = 0.88SArgo + 3.98, R2 = 0.58). The root mean square error (RMSE) was also significantly higher between SSMOS and SArgo (before TCs: 0.84 psu; after TCs: 0.78 psu) than between SSMAP and SArgo (before TCs: 0.58 psu; after TCs: 0.47 psu). In addition, this study compared SSMAP and SSMOS during two TCs that swept in nearshore and offshore waters, and the results show good agreement between SSMAP and SArgo in the nearshore and offshore waters of BoB. In the BoB, both SSMAP and SSMOS can retrieve sea surface salinity well, and SSMAP is overall better than SSMOS, but the SMOS salinity product can fill the gap of SMAP from 2010 to 2015. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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12 pages, 4912 KiB  
Technical Note
Coastal Acoustic Tomography of the Neko-Seto Channel with a Focus on the Generation of Nonlinear Tidal Currents—Revisiting the First Experiment
by Minmo Chen, Aruni Dinan Hanifa, Naokazu Taniguchi, Hidemi Mutsuda, Xiaohua Zhu, Zenan Zhu, Chuanzheng Zhang, Ju Lin and Arata Kaneko
Remote Sens. 2022, 14(7), 1699; https://doi.org/10.3390/rs14071699 - 31 Mar 2022
Cited by 4 | Viewed by 1807
Abstract
The first coastal acoustic tomography (CAT) experiment site of the Neko-Seto Channel was revisited to elucidate the propagation and generation characteristics of the M2 and M4 tidal currents with a second CAT experiment, which was conducted from 3–6 April 2018 (local [...] Read more.
The first coastal acoustic tomography (CAT) experiment site of the Neko-Seto Channel was revisited to elucidate the propagation and generation characteristics of the M2 and M4 tidal currents with a second CAT experiment, which was conducted from 3–6 April 2018 (local time). Two-dimensional flow fields of the M2 and M4 tidal currents and the residual current were reconstructed using a coast-fitting inversion model with the reciprocal travel-time data of five acoustic stations. The M2 tidal current is a progressive-type wave that propagates eastward at a speed of 0.7 ms−1, much slower than expected for free progressive tides in this region (19 ms−1). The M4 nonlinear current constructed an out-of-phase relationship between the western and eastern halves of the tomography domain, implying the generation of standing-type waves. Such nonlinear processes led to flood- and ebb-dominant tidal current asymmetries for the western and eastern halves of the model domain, respectively. The two-day mean residual currents constructed a northeastward current with a maximum speed of 0.3 ms−1 in the western half of the model domain and a clockwise rotation in the eastern half. The averaged inversion errors were 0.03 ms−1, significantly smaller than the amplitude of the aforementioned currents. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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12 pages, 6059 KiB  
Technical Note
Variability of Longshore Surface Current on the Shelf Edge and Continental Slope off the West Coast of Canada
by Guoqi Han and Nancy Chen
Remote Sens. 2022, 14(6), 1407; https://doi.org/10.3390/rs14061407 - 15 Mar 2022
Cited by 2 | Viewed by 1469
Abstract
The shelf-edge and continental slope current off the west coast of Canada has been monitored at a site off West Vancouver Island since 1985. However, observations at this site may not represent the characteristics of the shelf-edge and slope current off the entire [...] Read more.
The shelf-edge and continental slope current off the west coast of Canada has been monitored at a site off West Vancouver Island since 1985. However, observations at this site may not represent the characteristics of the shelf-edge and slope current off the entire west coast of Canada. Here, we use along-track satellite altimetry data over six transects to investigate the characteristics of the surface geostrophic currents over the shelf edge and continental slope off the west coast of Canada from 1992 to 2020. It is shown that along-track satellite altimetry is well suited for monitoring longshore and climatic variations of the near-surface shelf-edge and slope currents off the west coast of Canada. It is found that the surface current over the shelf edge and slope has different features from the south to the north. While the surface current is poleward in winter and equatorward in summer off South Vancouver Island, it is poleward year-round off the rest of the west coast of Canada. The seasonal current anomalies show longshore correlation significant at the 95% confidence level, except at the North Haida Gwaii transect. The first empirical orthogonal function mode of the seasonal current anomalies is correlated with the longshore wind anomalies both off South Vancouver Island and off Oregon. However, this first mode is not correlated with either the Niño 3.4 index or the Pacific Decadal Oscillation index, though they often show large episodic events during strong El Niño and La Niña years. Consistent with previous findings, the present study indicates that the surface currents over the shelf edge and continental slope off the west coast of Canada are related to regional and remote longshore wind forcing. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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16 pages, 1255 KiB  
Technical Note
Past, Present and Future Marine Microwave Satellite Missions in China
by Mingsen Lin and Yongjun Jia
Remote Sens. 2022, 14(6), 1330; https://doi.org/10.3390/rs14061330 - 09 Mar 2022
Cited by 13 | Viewed by 2990
Abstract
Over the past 60 years, China has made fruitful achievements in the field of ocean microwave remote sensing satellite technology. A long-term plan has now been formulated for the development of Chinese ocean satellites, as well as the construction of a constellation of [...] Read more.
Over the past 60 years, China has made fruitful achievements in the field of ocean microwave remote sensing satellite technology. A long-term plan has now been formulated for the development of Chinese ocean satellites, as well as the construction of a constellation of ocean dynamic environmental and ocean surveillance satellites. These will gradually form China’s ocean monitoring network from space, thereby playing important roles in future ocean resource and environmental monitoring, marine disaster prevention and reduction, and global climate change. In this review manuscript, the developmental history of ocean microwave satellites and the development status of oceanic microwave remote sensing satellites in China are reviewed. In addition, China’s achievements in the field of oceanic microwave remote sensing satellite technology are summarized, and the future development of China’s ocean microwave remote sensing satellite program is analysed. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges)
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