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Advanced Applications of Remote Sensing in Monitoring Marine Environment

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 19479

Special Issue Editors


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Guest Editor
Institute of Marine Environmental Science and Technology & Department of Earth Science, National Taiwan Normal University, Taipei 106, Taiwan
Interests: remote sensing of oceanic environment; physical oceanography; typhoon-ocean Interaction

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Guest Editor
1. School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
2. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
Interests: coastal and lake remote sensing; coastal ocean dynamics; marine remote sensing physics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing in a marine environment is the use of sensors to retrieve remote, non-contact observations of the ocean to obtain images or data related to certain oceanic phenomena or processes. In recent years, advancements in remote sensing technology have enabled data collection with much higher spatial and temporal resolutions from either passive or active sensors. These new applications offer new opportunities and incredible new insights and interpretations for practical implementation in certain oceanic phenomena/processes. This Special Issue invites the most up-to-date applications on the hot topic of “Remote Sensing for Marine Environment Monitoring”, especially new observations, analytical methods, data, and modeling that can significantly improve our understanding of marine environmental sciences.

Prof. Dr. Zhe-Wen Zheng
Prof. Dr. Jiayi Pan
Guest Editors

Manuscript Submission Information

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Keywords

  • ocean remote sensing
  • ocean environment
  • environment monitoring
  • remote sensing techniques
  • satellite data

Published Papers (15 papers)

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20 pages, 7258 KiB  
Article
Impacts of Marine Heatwave Events on Three Distinct Upwelling Systems and Their Implications for Marine Ecosystems in the Northwestern South China Sea
by Sihai Liu, Qibin Lao, Xin Zhou, Guangzhe Jin, Chunqing Chen and Fajin Chen
Remote Sens. 2024, 16(1), 131; https://doi.org/10.3390/rs16010131 - 28 Dec 2023
Cited by 1 | Viewed by 713
Abstract
Under global warming, the frequency and intensity of marine heatwaves are increasing. However, the inhibition of atmospheric-forcing marine heatwaves (AMHW) on upwelling and their impacts on marine ecosystems remain poorly understood. To address this issue, the satellite sea surface temperature and reanalysis data [...] Read more.
Under global warming, the frequency and intensity of marine heatwaves are increasing. However, the inhibition of atmospheric-forcing marine heatwaves (AMHW) on upwelling and their impacts on marine ecosystems remain poorly understood. To address this issue, the satellite sea surface temperature and reanalysis data during 1998–2021 were analyzed in three distinct upwelling systems, in the northwestern South China Sea. The results showed that the coastal tide-induced upwelling in the west (W) of Hainan Island is primarily suppressed by enhanced stratification during the AMHW events, since the coastal tide-induced upwelling is insensitive to wind weakening. Contrarily, the wind-driven upwelling in the east (E) and northeast (NE) of Hainan Island are jointly regulated by wind and stratification during the AMHW. Specifically, the AMHW events have a stronger inhibitory effect on the upwelling and phytoplankton growth in the NE than that in the E. The causes could be the following: (1) the background upwelling in the NE region is stronger than in the E; thus, the NE region has a higher susceptibility to the wind weakening; (2) the wind-driven upwelling begins to be suppressed by AMHW when the high-pressure system is aligned with the coastline of the upwelling. In the NE region, the location of the high-pressure center during the occurrence of AMHW is positioned in closer proximity to the upwelling area. Moreover, the inhibitory effect of wind weakening and stratification enhancing on upwelling changes with the development of the AMHW. Before and during the mature phase of AMHW, stratification and wind jointly inhibit upwelling and phytoplankton growth, while a shift to stratification-dominated (>85%) occurs during the decline phase. This study suggests that MHW has a great impact on the upwelling ecosystem, especially the wind-driven upwelling, which should be given high attention under global warming (with increasing MHW events in the future). Full article
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17 pages, 2706 KiB  
Article
Monitoring Total Suspended Solids and Chlorophyll-a Concentrations in Turbid Waters: A Case Study of the Pearl River Estuary and Coast Using Machine Learning
by Jiaxin Liu, Zhongfeng Qiu, Jiajun Feng, Ka Po Wong, Jin Yeu Tsou, Yu Wang and Yuanzhi Zhang
Remote Sens. 2023, 15(23), 5559; https://doi.org/10.3390/rs15235559 - 29 Nov 2023
Cited by 1 | Viewed by 858
Abstract
Total suspended solids (TSS) and chlorophyll-a (Chl-a) are critical water quality parameters. Focusing on the Pearl River Estuary and its coastal waters, this study compared the performance of XGBoost- and BPNN-based algorithms in estimating TSS and Chl-a levels. The XGBoost-based algorithm demonstrated better [...] Read more.
Total suspended solids (TSS) and chlorophyll-a (Chl-a) are critical water quality parameters. Focusing on the Pearl River Estuary and its coastal waters, this study compared the performance of XGBoost- and BPNN-based algorithms in estimating TSS and Chl-a levels. The XGBoost-based algorithm demonstrated better performance and was then used to estimate TSS and Chl-a in the Pearl River Estuary and coastal waters from 2000 to 2021. According to our results, TSS and Chl-a were relatively high mainly in the northwest and low in the southeast. Furthermore, values were high in spring and summer and low in fall and winter, with high values emerging near the estuary of the Pearl River. In summer, a band zone with high Chl-a was observed from south of Yamen to south of Hong Kong. In terms of trends, TSS and Chl-a concentrations in the area around the Hong Kong–Zhuhai–Macao Bridge tended to decrease from 2000 to 2021. As the construction of the bridge began, changes in water flow caused by the bridge piers and artificial islands were influenced, the change in the rate of TSS in the west area of the bridge was greater than 0, and the TSS in the upstream area of the west side changed from decreasing to increasing trends. Concerning Chl-a concentrations, the change in the rate in the downstream area of the west side of the bridge was greater than 0. The study may provide a helpful example for similar estuarine and coastal waters in other coastal areas. Full article
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30 pages, 11936 KiB  
Article
The Potential of Multibeam Sonars as 3D Turbidity and SPM Monitoring Tool in the North Sea
by Nore Praet, Tim Collart, Anouk Ollevier, Marc Roche, Koen Degrendele, Maarten De Rijcke, Peter Urban and Thomas Vandorpe
Remote Sens. 2023, 15(20), 4918; https://doi.org/10.3390/rs15204918 - 11 Oct 2023
Viewed by 1254
Abstract
Monitoring turbidity is essential for sustainable coastal management because an increase in turbidity leading to diminishing water clarity has a detrimental ecological impact. Turbidity in coastal waters is strongly dependent on the concentration and physical properties of particles in the water column. In [...] Read more.
Monitoring turbidity is essential for sustainable coastal management because an increase in turbidity leading to diminishing water clarity has a detrimental ecological impact. Turbidity in coastal waters is strongly dependent on the concentration and physical properties of particles in the water column. In the Belgian part of the North Sea, turbidity and suspended particulate matter (SPM) concentrations have been monitored for decades by satellite remote sensing, but this technique only focuses on the surface layer of the water column. Within the water column, turbidity and SPM concentrations are measured in stations or transects with a suite of optical and acoustic sensors. However, the dynamic nature of SPM variability in coastal areas and the recent construction of offshore windmill parks and dredging and dumping activities justifies the need to monitor natural and human-induced SPM variability in 3D instead. A possible solution lies in modern multibeam echosounders (MBES), which, in addition to seafloor bathymetry data, are also able to deliver acoustic backscatter data from the water column. This study investigates the potential of MBES as a 3D turbidity and SPM monitoring tool. For this purpose, a novel empirical approach is developed, in which 3D MBES water column and in-situ optical sensor datasets were collected during ship transects to yield an empirical relation using linear regression modeling. This relationship was then used to predict SPM volume concentrations from the 3D acoustic measurements, which were further converted to SPM mass concentrations using calculated densities. Our results show that these converted mean mass concentrations at the Kwinte and Westdiep swale areas are within the limits of the reported yearly averages. Moreover, they are in the same order of magnitude as the measured mass concentrations from Niskin water samples during each campaign. While there is still need for further improvement of acquisition and processing workflows, this study presents a promising approach for converting MBES water column data to turbidity and SPM measurements. This opens possibilities for improving future monitoring tools, both in scientific and industrial sectors. Full article
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20 pages, 5591 KiB  
Article
Prediction of Sea Surface Chlorophyll-a Concentrations Based on Deep Learning and Time-Series Remote Sensing Data
by Lulu Yao, Xiaopeng Wang, Jiahua Zhang, Xiang Yu, Shichao Zhang and Qiang Li
Remote Sens. 2023, 15(18), 4486; https://doi.org/10.3390/rs15184486 - 12 Sep 2023
Cited by 3 | Viewed by 1427
Abstract
Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for effective management and early warning of marine ecological systems. However, previous studies primarily focused on chlorophyll-a inversion and reconstruction, while methods for predicting Chl-a concentrations remain limited. To address this issue, [...] Read more.
Accurate prediction of future chlorophyll-a (Chl-a) concentrations is of great importance for effective management and early warning of marine ecological systems. However, previous studies primarily focused on chlorophyll-a inversion and reconstruction, while methods for predicting Chl-a concentrations remain limited. To address this issue, we adopted four deep learning approaches, including Convolutional LSTM Network (ConvLSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM), Eidetic 3D LSTM (E3D-LSTM), and Self-Attention ConvLSTM (SA-ConvLSTM) models, to predict Chl-a over the Yellow Sea and Bohai Sea (YBS) in China. Furthermore, 14 environmental variables obtained from the remote sensing data of Moderate-resolution Imaging Spectroradiometer (MODIS) and ECMWF Reanalysis v5 (ERA5) were utilized to predict the Chl-a concentrations in the study area. The results showed that all four models performed satisfactorily in predicting Chl-a concentrations in the YBS, with SA-ConvLSTM exhibiting a closer approximation to true values. Furthermore, we analyzed the impact of the Self-Attention Memory Module (SAM) on the prediction results. Compared to the ConvLSTM model, the SA-ConvLSTM model integrated with the SAM module better captured subtle large-scale variations within the study area. The SA-ConvLSTM model exhibited the highest prediction accuracy, and the one-month Pearson correlation coefficient reached 0.887. Our study provides an available approach for anticipating Chl-a concentrations over a large area of sea. Full article
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17 pages, 10462 KiB  
Article
Improved Global Navigation Satellite System–Multipath Reflectometry (GNSS-MR) Tide Variation Monitoring Using Variational Mode Decomposition Enhancement
by Di Yang, Wei Feng, Dingfa Huang and Jianfeng Li
Remote Sens. 2023, 15(17), 4331; https://doi.org/10.3390/rs15174331 - 02 Sep 2023
Viewed by 778
Abstract
Accuracy and resolution are the two primary challenges that impose limitations on the practical implementation of classical tide-level remote sensing. To improve the accuracy and applicability and increase the temporal resolution of the inversion point near the shore area, the influence of coastal [...] Read more.
Accuracy and resolution are the two primary challenges that impose limitations on the practical implementation of classical tide-level remote sensing. To improve the accuracy and applicability and increase the temporal resolution of the inversion point near the shore area, the influence of coastal reflection signals in the signal-to-noise ratio (SNR) residual sequence should be weakened significantly. This contribution proposes an anti-interference GNSS Multipath Reflectometry (GNSS-MR) algorithm called VMD_SNR, which is enhanced using variational mode decomposition (VMD). Compared with wavelet decomposition and empirical mode decomposition (EMD) methods, VMD_SNR exhibits superior capabilities in reducing the interference caused by noisy signals. The measurements of ground-based GNSS stations are used to verify the performance improvement in the VMD_SNR algorithm. The results show that the proposed algorithm is better than the wavelet decomposition method and EMD method in terms of accuracy and stability in the shore area, where the effective number is higher than 99% of the total number, and the accuracy is better than 13.80 cm. Moreover, the accuracy improvement is more significant in the high-elevation range, which is 30.16% higher than the wavelet decomposition method and 38.34% higher than the EMD method. Full article
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16 pages, 4047 KiB  
Article
Remote Sensing Estimates of Particulate Organic Carbon Sources in the Zhanjiang Bay Using Sentinel-2 Data and Carbon Isotopes
by Guo Yu, Yafeng Zhong, Sihai Liu, Qibin Lao, Chunqing Chen, Dongyang Fu and Fajin Chen
Remote Sens. 2023, 15(15), 3768; https://doi.org/10.3390/rs15153768 - 28 Jul 2023
Cited by 1 | Viewed by 867
Abstract
The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our [...] Read more.
The source information of coastal particulate organic carbon (POC) with high spatial and temporal resolution is of great significance for the study of marine carbon cycles and marine biogeochemical processes. Over the past decade, satellite ocean color remote sensing has greatly improved our understanding of the spatiotemporal dynamics of ocean particulate organic carbon concentrations. However, due to the complexity of coastal POC sources, remote sensing methods for coastal POC sources have not yet been established. With an attempt to fill the gap, this study developed an algorithm for retrieving coastal POC sources using remote sensing and geochemical isotope technology. The isotope end-member mixing model was used to calculate the proportion of POC sources, and the response relationship between POC source information and in situ remote sensing reflectance (Rrs) was established to develop a retrieval algorithm for POC sources with the following four bands: (Rrs(443)/Rrs(492)) × (Rrs(704)/Rrs(665)). The results showed that the four-band algorithm performed well with R2, mean absolute percentage error (MAPE) and root mean square error (RMSE) values of 0.78, 33.57% and 13.74%, respectively. Validation against in situ data showed that the four-band algorithm derived calculated the proportion of marine POC accurately, with an MAPE and RMSE of 27.49% and 13.58%, respectively. The accuracy of the algorithm was verified based on the Sentinel-2 data, with an MAPE and RMSE of 28.02% and 15.72%, respectively. Additionally, we found that the proportion of marine POC sources was higher outside the Zhanjiang Bay than inside it using in situ survey data, which was consistent with the retrieved results. Influencing factors of POC sources may be due to the occurrence of phytoplankton blooms outside the bay and the impact of terrestrial inputs inside the bay. Remote sensing in combination with carbon isotopes provides important technical assistance in comprehending the biogeochemical process of POC and uncovering spatiotemporal variations in POC sources and their underlying causes. Full article
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14 pages, 24765 KiB  
Communication
Sea Surface Chlorophyll-a Concentration Retrieval from HY-1C Satellite Data Based on Residual Network
by Guiying Yang, Xiaomin Ye, Qing Xu, Xiaobin Yin and Siyang Xu
Remote Sens. 2023, 15(14), 3696; https://doi.org/10.3390/rs15143696 - 24 Jul 2023
Cited by 2 | Viewed by 1192
Abstract
A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (Rrs) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September [...] Read more.
A residual network (ResNet) model was proposed for estimating Chl-a concentrations in global oceans from the remote sensing reflectance (Rrs) observed by the Chinese ocean color and temperature scanner (COCTS) onboard the HY-1C satellite. A total of 52 images from September 2018 to September 2019 were collected, and the label data were from the multi-task Ocean Color-Climate Change Initiative (OC-CCI) daily products. The results of feature selection and sensitivity experiments show that the logarithmic values of Rrs565 and Rrs520/Rrs443, Rrs565/Rrs490, Rrs520/Rrs490, Rrs490/Rrs443, and Rrs670/Rrs565 are the optimal input parameters for the model. Compared with the classical empirical OC4 algorithm and other machine learning models, including the artificial neural network (ANN), deep neural network (DNN), and random forest (RF), the ResNet retrievals are in better agreement with the OC-CCI Chl-a products. The root-mean-square error (RMSE), unbiased percentage difference (UPD), and correlation coefficient (logarithmic, R(log)) are 0.13 mg/m3, 17.31%, and 0.97, respectively. The performance of the ResNet model was also evaluated against in situ measurements from the Aerosol Robotic Network-Ocean Color (AERONET-OC) and field survey observations in the East and South China Seas. Compared with DNN, ANN, RF, and OC4 models, the UPD is reduced by 5.9%, 0.7%, 6.8%, and 6.3%, respectively. Full article
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25 pages, 19291 KiB  
Article
Uncertainty of CYGNSS-Derived Heat Flux Variations at Diurnal to Seasonal Time Scales over the Tropical Oceans
by Jinsong Lin, Yanfeng Wang, Haidong Pan, Zexun Wei and Tengfei Xu
Remote Sens. 2023, 15(12), 3161; https://doi.org/10.3390/rs15123161 - 17 Jun 2023
Cited by 2 | Viewed by 880
Abstract
Air–sea heat flux is one of the most important factors that affects ocean circulation, weather, and climate. Satellite remote sensing could serve as an important supplement to the sparse in situ observations for heat flux estimations. In this study, we analyze the uncertainty [...] Read more.
Air–sea heat flux is one of the most important factors that affects ocean circulation, weather, and climate. Satellite remote sensing could serve as an important supplement to the sparse in situ observations for heat flux estimations. In this study, we analyze the uncertainty of the turbulent heat fluxes derived from wind speed measured by the Cyclone Global Navigation Satellite System (CYGNSS) over the global tropical oceans at different time scales. In terms of spatial distribution, there is large uncertainty (approximately 50 to 85 W·m−2 in the RMSE) near the equator in the western Pacific Ocean, the Arabian Sea, the Bay of Bengal, and near the Gulf of Guinea. The turbulent heat fluxes are in agreement with the buoys in representing the intraseasonal and seasonal variability, but more specific regional validations are needed for revealing the synoptic and sub-synoptic phenomena and the diurnal cycle. The uncertainty of the CYGNSS wind speed contributes approximately 50–57% to the uncertainty of the estimation of turbulent heat fluxes at the frequency band with a typical period of 3–7 days. In addition, the input sea surface temperature, rather than the wind speed, results in differences in the estimation of the monthly mean turbulent heat fluxes in the tropical Atlantic Ocean based on the COARE 3.5 algorithm. In conclusion, although the CYGNSS-derived turbulent heat fluxes are basically in good agreement with the in situ observations, our analysis highlights the importance of considering the limitations of these datasets, particularly in high wind speed conditions and for higher-frequency variations, including at synoptic, sub-synoptic, and diurnal time scales. Full article
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24 pages, 8835 KiB  
Article
Monitoring and Forecasting Green Tide in the Yellow Sea Using Satellite Imagery
by Shuwen Xu, Tan Yu, Jinmeng Xu, Xishan Pan, Weizeng Shao, Juncheng Zuo and Yang Yu
Remote Sens. 2023, 15(8), 2196; https://doi.org/10.3390/rs15082196 - 21 Apr 2023
Viewed by 1627
Abstract
This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The [...] Read more.
This paper proposes a semi-automatic green tide extraction method based on the NDVI to extract Yellow Sea green tides from 2008 to 2022 using remote sensing (RS) images from multiple satellites: GF-1, Landsat 5 TM, Landsat 8 OLI_TIRS, HJ-1A/B, HY-1C, and MODIS. The results of the accuracy assessment based on three indicators: Precision, Recall, and F1-score, showed that our extraction method can be applied to the images of most satellites and different environments. We traced the source of the Yellow Sea green tide to Jiangsu Subei shoal and the southeastern Yellow Sea and earliest advanced the tracing time to early April. The Gompertz and Logistic growth curve models were selected to predict and monitor the extent and duration of the Yellow Sea green tide, and uncertainty for the predicted growth curve was estimated. The prediction for 2022 was that its start and dissipation dates were expected to be June 1 and August 15, respectively, and the accumulative cover area was expected to be approximately 1190.90–1191.21 km2. Full article
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21 pages, 6033 KiB  
Article
Performance Evaluation of Mangrove Species Classification Based on Multi-Source Remote Sensing Data Using Extremely Randomized Trees in Fucheng Town, Leizhou City, Guangdong Province
by Xinzhe Wang, Linlin Tan and Jianchao Fan
Remote Sens. 2023, 15(5), 1386; https://doi.org/10.3390/rs15051386 - 01 Mar 2023
Cited by 8 | Viewed by 1902
Abstract
Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized [...] Read more.
Mangroves are an important source of blue carbon that grow in coastal areas. The study of mangrove species distribution is the basis of carbon storage research. In this study, we explored the potential of combining optical (Gaofen-1, Sentinel-2, and Landsat-9) and fully polarized synthetic aperture radar data from different periods (Gaofen-3) to distinguish mangrove species in the Fucheng town of Leizhou, Guangdong Province. The Gaofen-1 data were fused with Sentinel-2 and Landsat-9 satellite data, respectively. The new data after fusion had both high spatial and spectral resolution. The backscattering coefficient and polarization decomposition parameters of the fully polarized SAR data which could characterize the canopy structure of mangroves were extracted. Ten different feature combinations were designed by combining the two types of data. The extremely randomized trees algorithm (ERT) was used to classify the species, and the optimal feature subset was selected by the feature selection algorithm on the basis of the ERT, and the importance of the features was sorted. Studies show the following: (1) When controlling a single variable, the higher the spatial resolution of the multi-spectral data, the higher the interspecific classification accuracy. (2) The coupled Sentinel-2 and Landsat-9 data with a 2 m resolution will have higher classification accuracy than a single data source. (3) The selected feature subset contains all types of features in the optical data and the polarization decomposition features of the SAR data from different periods: multi-spectral band > texture feature > polarization decomposition parameter > vegetation index. Among the optimized feature combinations, the classification accuracy of mangrove species was the highest, the overall classification accuracy was 90.13%, and Kappa was 0.84, indicating that multi-source and SAR data from different periods coupling could improve the discrimination of mangrove species. (4) The ERT classification algorithm is suitable for the study of mangrove species classification, and the classification accuracy of extremely random trees in this paper is higher than that of random forest (RF), K-nearest neighbor (KNN), and Bayesian (Bayes). The results can provide technical guidance and data support for mangrove species monitoring based on multi-source satellite data. Full article
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21 pages, 2700 KiB  
Article
A Method for Long-Term Target Anti-Interference Tracking Combining Deep Learning and CKF for LARS Tracking and Capturing
by Tao Zou, Weilun Situ, Wenlin Yang, Weixiang Zeng and Yunting Wang
Remote Sens. 2023, 15(3), 748; https://doi.org/10.3390/rs15030748 - 28 Jan 2023
Cited by 4 | Viewed by 1574
Abstract
Autonomous underwater vehicles (AUV) recycling in an underwater environment is particularly challenging due to the continuous exploitation of marine resources. AUV recycling via visual technology is the primary method. However, the current visual technology is limited by harsh sea conditions and has problems, [...] Read more.
Autonomous underwater vehicles (AUV) recycling in an underwater environment is particularly challenging due to the continuous exploitation of marine resources. AUV recycling via visual technology is the primary method. However, the current visual technology is limited by harsh sea conditions and has problems, such as poor tracking and detection. To solve these problems, we propose a long-term target anti-interference tracking (LTAT) method, which integrates Siamese networks, You Only Look Once (YOLO) networks and online learning ideas. Meanwhile, we propose using the cubature Kalman filter (CKF) for optimization and prediction of the position. We constructed a launch and recovery system (LARS) tracking and capturing the AUV. The system consists of the following parts: First, images are acquired via binocular cameras. Next, the relative position between the AUV and the end of the LARS was estimated based on the pixel positions of the tracking AUV feature points and binocular camera data. Finally, using a discrete proportion integration differentiation (PID) method, the LARS is controlled to capture the moving AUV via a CKF-optimized position. To verify the feasibility of our proposed system, we used the robot operating system (ROS) platform and Gazebo software to simulate the system for experiments and visualization. The experiment demonstrates that in the tracking process when the AUV makes a sinusoidal motion with an amplitude of 0.2 m in the three-dimensional space and the relative distance between the AUV and LARS is no more than 1 m, the estimated position error of the AUV does not exceed 0.03 m. In the capturing process, the final capturing error is about 28 mm. Our results verify that our proposed system has high robustness and accuracy, providing the foundation for future AUV recycling research. Full article
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17 pages, 7550 KiB  
Article
Research on Self-Noise Suppression of Marine Acoustic Sensor Arrays
by Haoyu Tan, Guochang Liu, Haoxuan Li, Guojun Zhang, Jiangong Cui, Yuhua Yang, Changde He, Licheng Jia, Wendong Zhang and Renxin Wang
Remote Sens. 2022, 14(24), 6186; https://doi.org/10.3390/rs14246186 - 07 Dec 2022
Cited by 1 | Viewed by 1426
Abstract
Marine acoustic sensors can detect underwater acoustic information. The cilium micro-electro-mechanical system (MEMS) vector hydrophone (CVH) is the core component of the ocean noise measurement system. The performance of the CVH, especially its self-noise, has received widespread attention. In this paper, we propose [...] Read more.
Marine acoustic sensors can detect underwater acoustic information. The cilium micro-electro-mechanical system (MEMS) vector hydrophone (CVH) is the core component of the ocean noise measurement system. The performance of the CVH, especially its self-noise, has received widespread attention. In this paper, we propose a solution to improve the performance of the CVH using an array to detect environmental noise in a complex deep-water environment. We analyzed the self-noise source of the CVH and the noise suppression principle of the four-unit MEMS vector hydrophone (FUVH). In addition, we designed the pre-circuit of the FUVH, completed the cross-beam structure by the MEMS processing, and packaged a FUVH. Then, we tested the performance of a packaged FUVH. Finally, the experimental results show that the FUVH reduces the self-noise voltage power spectrum by 6 dB compared to the CVH structure. The FUVH achieves better linearity at low frequencies without reducing the bandwidth and sensitivity. In addition, it minimizes the equivalent self-noise levels by 5.18 and 5.14 dB in the X and Y channels, respectively. Full article
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12 pages, 3209 KiB  
Communication
Improved Understanding of Typhoon-Induced Immediate Chlorophyll-A Response Using Advanced Himawari Imager (AHI) Onboard Himawari-8
by Jia-Yi Lin, Hua Ho and Zhe-Wen Zheng
Remote Sens. 2022, 14(23), 6055; https://doi.org/10.3390/rs14236055 - 29 Nov 2022
Cited by 1 | Viewed by 1092
Abstract
The biological response triggered by a tropical cyclone (TC) passage has attracted much attention due to its possible impacts on regional oceanic, ecological environment, and regional climate balance. However, the detailed progress of TC-induced chlorophyll-a (Chl-a) responses (TICRs) remains unclear due to the [...] Read more.
The biological response triggered by a tropical cyclone (TC) passage has attracted much attention due to its possible impacts on regional oceanic, ecological environment, and regional climate balance. However, the detailed progress of TC-induced chlorophyll-a (Chl-a) responses (TICRs) remains unclear due to the inherent limitation of observations in ocean color with polar-orbiting satellites as used in previous studies. The appearance of the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite opens the opportunity of correcting all our understanding of TICRs due to its hyper temporal image acquisition capability. In this study, the more real relationship between Chl-a response and TC is further clarified. Results show an essentially different reacting progress of TICRs given by AHI/Himawari-8. It shows a much quicker response relative to previous understanding. Chl-a concentrations reached the highest value on the first day under the severe influences of typhoons. The averaged Chl-a response (0–3 days behind TC passage) observed by AHI is approximately three (2.95) times stronger than that observed by the Moderate Resolution Imaging Spectrometer onboard the National Aeronautics and Space Administration Terra/Aqua satellites. The spatial characteristics of TICRs by AHI show marked differences. Overall, the rapid and strong response sheds new light on the role of TICRs in influencing the regional oceanic environment, marine ecosystem, and local climate. Whole new estimations for the impacts of TICRs on the aforementioned issues are needed urgently. Full article
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26 pages, 4820 KiB  
Article
Variability of Chl a Concentration of Priority Marine Regions of the Northwest of Mexico
by Carlos Manuel Robles-Tamayo, Ricardo García-Morales, José Raúl Romo-León, Gudelia Figueroa-Preciado, María Cristina Peñalba-Garmendia and Luis Fernando Enríquez-Ocaña
Remote Sens. 2022, 14(19), 4891; https://doi.org/10.3390/rs14194891 - 30 Sep 2022
Cited by 3 | Viewed by 1794
Abstract
Priority Marine Regions (PMR) are important areas for biodiversity conservation in the Northwest Pacific Ocean in Mexico. The oceanographic dynamics of these regions are very important to understand their variability, generate analyses, and predict climate change trends by generating an adequate management of [...] Read more.
Priority Marine Regions (PMR) are important areas for biodiversity conservation in the Northwest Pacific Ocean in Mexico. The oceanographic dynamics of these regions are very important to understand their variability, generate analyses, and predict climate change trends by generating an adequate management of marine resources and their ecological characterization. Chlorophyll a (Chl a) is important to quantify phytoplankton biomass, consider the main basis of the trophic web in marine ecosystems, and determine the primary productivity levels and trends of change. The objective of this research is to analyze the oceanographic variability of 24 PMR through monthly 1-km satellite image resolution Chl a data from September 1997 to October 2018. A cluster analysis of Chl a data yielded 18 regions with clear seasonal variability in the Chl a concentration in the South-Californian Pacific (maximum values in spring-summer and minimum ones in autumn-winter) and Gulf of California (maximum values in winter-spring and minimum ones in summer-autumn). Significant differences (p < 0.05) were observed in Chl a concentration analyses for each one of the regions when climate patterns—El Niño/La Niña Southern Oscillation (ENSO) and normal events—were compared for all the seasons of the year (spring, summer, autumn, and winter). Full article
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14 pages, 4438 KiB  
Technical Note
Typhoon-Induced Extreme Sea Surface Temperature Drops in the Western North Pacific and the Impact of Extra Cooling Due to Precipitation
by Jia-Yi Lin, Hua Ho, Zhe-Wen Zheng, Yung-Cheng Tseng and Da-Guang Lu
Remote Sens. 2024, 16(1), 205; https://doi.org/10.3390/rs16010205 - 04 Jan 2024
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Abstract
Sea surface temperature (SST) responses have been perceived as crucial to consequential tropical cyclone (TC) intensity development. In addition to regular cooling responses, a few TCs could cause extreme SST drops (ESSTDs) (e.g., SST drops more than 6 °C) during their passage. Given [...] Read more.
Sea surface temperature (SST) responses have been perceived as crucial to consequential tropical cyclone (TC) intensity development. In addition to regular cooling responses, a few TCs could cause extreme SST drops (ESSTDs) (e.g., SST drops more than 6 °C) during their passage. Given the extreme temperature differences and the consequentially marked air–sea flux modulations, ESSTDs are intuitively supposed to play a serious role in modifying TC intensities. Nevertheless, the relationship between ESSTDs and consequential storm intensity changes remains unclear. In this study, satellite-observed microwave SST drops and the International Best Track Archive for Climate Stewardship TC data from 2001 to 2021 were used to elucidate the relationship between ESSTDs and the consequential TC intensity changes in the Western North Pacific typhoon season (July–October). Subsequently, the distributed characteristics of ESSTDs were systematically examined based on statistical analyses. Among them, Typhoon Kilo (2015) triggered an unexpected ESSTD behind its passage, according to existing theories. Numerical experiments based on the Regional Ocean Modeling System were carried out to explore the possible mechanisms that resulted in the ESSTD due to Kilo. The results indicate that heavy rainfall leads to additional SST cooling through the enhanced sensible heat flux leaving the surface layer in addition to the cooling from momentum-driven vertical mixing. This process enhanced the sensible heat flux leaving the sea surface since the temperature of the raindrops could be much colder than the SST in the tropical ocean, specifically under heavy rainfall and relatively less momentum entering the upper ocean during Kilo. Full article
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