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Recent Trends and Advances in Microwave Sea Remote Sensing

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 25 September 2024 | Viewed by 8574

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Special Issue Editors

Dr. Yunhua Wang

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Guest Editor

1. Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
2. Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
Interests: asymptotic and numerical simulations of electromagnetic wave scattering; ocean microwave remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Quanan Zheng

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Guest Editor

Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
Interests: ocean remote sensing (including physics, data interpretation, applications, and laboratory simulation); ocean surface processes (including wind friction, wave spectra, skin layer physics, and surfactant effects); upper ocean dynamics (including internal wave dynamics and ocean–atmospheric coupling); meso-scale ocean dynamics; solitary waves in the atmosphere and ocean; space shuttle photographic applications

Special Issue Information

Dear Colleagues,

Over the last 40 years, microwave remote sensing has played an increasingly important role in ocean observations, such as ocean wave inversion, internal wave observation, and sea surface wind field measurements. In this regard, a wealth of research has been conducted and a large amount of data has been obtained. Microwave ocean remote sensing has shown high potential with the rapid development of microwave sensors such as SAR, altimeters, scatterometers, and radiometers, in addition to the advanced interferometric radar altimeter, synthetic aperture radar altimeter, and wave spectrometer.

This Special Issue aims to highlight recent trends and advances in microwave ocean remote sensing. Topics of interest include, but are not limited to:

Observations of oceanic phenomena;
Ocean element inversion;
Error analysis;
Simulation analysis and algorithms;
Instrumentation;
Methodologies for data processing.

Dr. Yunhua Wang
Dr. Quanan Zheng
Guest Editors

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Research

16 pages, 9055 KiB

Open AccessArticle

X-Band Radar System to Detect Bathymetric Changes at River Mouths during Storm Surges: A Case Study of the Arno River

by Francesco Raffa, Ines Alberico and Francesco Serafino

Sensors 2022, 22(23), 9415; https://doi.org/10.3390/s22239415 - 02 Dec 2022

Cited by 2 | Viewed by 1665

Abstract

Storm surges are natural events that influence the dispersion of sediment along coasts, leading to sudden morphological changes in the seabed. From this perspective, we focused our study on the analysis of measurements from a mobile X-band radar system to survey the sea state and the changes in the seabed depth during storm surges. This analysis was supported by additional information from Sentinel 2 satellite images, the Gorgona wave buoy, the San Giovanni alla Vena hydrometric station, and an echosounder survey. The survey period was from 26 to 28 February and 3 March 2020. During these days, the simultaneous occurrence of a storm surge and flooding of the Arno River was monitored. The analysis of the marine X-band radar mobile images determined the formation and dismantling of seabed shapes. An elongated shoal and a bar-like shape are visible on the right side of the Arno River in the radar image of 26 February and at the Arno mouth on that of 28 February, respectively. The radar image of 3 March shows, near the mouth of the Arno, a delta shape probably due to the deposition of sediment favoured by the interaction between the river flow and storm waves. X-band coastal radar is a detection system that improves the effectiveness and reliability of coastal monitoring because it has a high temporal and spatial resolution. It can be considered a valuable warning system to monitor the sea-bed depth changes in strategic sites, such as harbour areas, during sea storms. Moreover, this system, together with a satellite observing system, is a valid tool for shedding light on the environmental drivers that reshape coastal areas. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Microwave Sea Remote Sensing)

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18 pages, 10852 KiB

Open AccessArticle

Study on the Elimination Method of Wind Field Influence in Retrieving a Sea Surface Current Field

by Xinzhe Yuan, Jian Wang, Bing Han and Xiaoqing Wang

Sensors 2022, 22(22), 8781; https://doi.org/10.3390/s22228781 - 14 Nov 2022

Viewed by 1108

Abstract

An along-the-track interferometric synthetic aperture radar (ATI-SAR) system can estimate the radial velocity of a moving target on the ground and on a sea surface current. This acquires the interference phase by combining two composite SAR images obtained by two antennas spatially separated along the direction of movement of the platform. The key to retrieving the sea surface current is to remove the interference of sea surface waves, wind-generated current, and Bragg phase velocity in the interference Doppler velocity. Previous methods removed the surface waves, Bragg phase velocity, and other interferences based on externally-assisted wind fields (e.g., ECMWF), using the M4S or other models. However, the wind fields obtained from ECMWF and other external information are often average results of a large temporal and spatial scale, while the images obtained from SAR are high-resolution images of sea surface transients, which are quite different in time and space. This paper takes the SAR image data of the Gaofen-3 satellite as the research object and employs an SAR-based wind field retrieval method to obtain an SAR-observed transient wind field. Combined with the CDOP model, the interference of Doppler velocities, such as the sea surface wave, wind-generated current, and Bragg wave phase velocity, was calculated and subtracted from the Doppler velocity, to obtain the sea surface velocity result. Then, the current field measured by the shore-based HF radar was compared with that obtained by correcting the ATI Doppler velocity based on the SAR retrieved wind field and the ECMWF wind field. The comparison of results indicated that the wind field correction result based on the SAR retrieved wind field was closer to the current field measured by the shore-based HF radar than the wind field correction result based on the ECMWF wind field. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Microwave Sea Remote Sensing)

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19 pages, 17799 KiB

Open AccessArticle

Retrieving the Motion of Beaufort Sea Ice Using Brightness Temperature Data from FY-3D Microwave Radiometer Imager

by Kun Ni, Haihua Chen, Lele Li and Xin Meng

Sensors 2022, 22(21), 8298; https://doi.org/10.3390/s22218298 - 29 Oct 2022

Cited by 1 | Viewed by 1143

Abstract

Sea ice is an important marine phenomenon in the Arctic region, and it is of great importance to study the motion of Arctic sea ice in the present day when its melting is accelerated by global warming. This study proposes a method to retrieve the motion of sea ice based on the maximum cross-correlation (MCC) and the successive correction method (SCM). The proposed method can apply different scales of search ranges to template matching according to the location of sea ice in the Arctic area. In addition, the data assimilation method can assign different weights to different data. We used 36.5 GHz and 89 GHz brightness temperature (T_b) data from the microwave radiometer imager (MWRI) aboard the Fengyun-3D (FY-3D) satellite, for the first time in the literature, to retrieve the sea ice motion in the Beaufort Sea from January to April 2019. The retrieved sea ice motion results were in good agreement with those obtained from the motion of the buoys. Compared with the data from the buoys, the root mean-squared error (RMSE) of the sea ice motion retrieved from FY-3D/MWRI

T_{b}

data was 1.1418 cm/s in the zonal direction and 1.0481 cm/s in the meridional direction, and the mean absolute error (MAE) between them was 0.7166 cm/s in the zonal direction and 0.6777 cm/s in the meridional direction. The RMSE between the sea ice motion obtained from the National Snow and Ice Data Center (NSIDC) and the motion of the buoys was 0.9515 cm/s in the zonal direction and 0.67003 cm/s in the meridional direction, and the MAE between them was 0.6576 cm/s in the zonal direction and 0.4922 cm/s in the meridional direction. The

RMSE

of daily average velocity from the FY-3D/MWRI results and NSIDC data product was 2.2726 cm/s in zonal and 1.9270 cm/s in meridional, and the MAE was 1.5103 cm/s in zonal and 1.1071 cm/s in zonal. The density of the merged data was higher than that obtained from a single polarization or frequency in this paper. The results indicate that FY-3D/MWRI T_b data can retrieve the sea ice motion successfully. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Microwave Sea Remote Sensing)

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15 pages, 5321 KiB

Open AccessArticle

Scaled Sea Surface Design and RCS Measurement Based on Rough Film Medium

by Chenyu Guo, Hongxia Ye, Yi Zhou, Yonggang Xu and Longxiang Wang

Sensors 2022, 22(16), 6290; https://doi.org/10.3390/s22166290 - 21 Aug 2022

Cited by 1 | Viewed by 1733

Abstract

The electromagnetic (EM) scattering characteristics of the rough sea surface is very important for target surveying and detection in a sea environment. This work proposes a scaled sea surface designing method based on a rough thin-film medium. For the prototype sea surface, the permittivity is calculated with the seawater temperature, salinity, and EM wave frequency according to the Debye model. The scale film material is mixed with carbon black and epoxy, whose volume ratio is optimized with the genetic algorithm through the existing electromagnetic parameter library. This method can overcome the previous difficulties of adjusting the same permittivity of the prototype sea water. According to the EM scaled theory, the scaled geometric sample is numerically generated with the D-V spectrum for the given wind speed, and is fabricated using 3D printing to keep the similar seawater shape. Then, the sample is sprayed with a layer of film material for EM scattering measurement. The simulated and measured radar cross-section (RCS) results show good consistency for the prototype seawater and scaled materials, which indicates the proposed scaled method is a more efficient method to get the seawater scattering characteristics. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Microwave Sea Remote Sensing)

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17 pages, 6041 KiB

Open AccessArticle

Estimation of the Mixed Layer Depth in the Indian Ocean from Surface Parameters: A Clustering-Neural Network Method

by Chen Gu, Jifeng Qi, Yizhi Zhao, Wenming Yin and Shanliang Zhu

Sensors 2022, 22(15), 5600; https://doi.org/10.3390/s22155600 - 26 Jul 2022

Cited by 6 | Viewed by 1827

Abstract

The effective estimation of mixed-layer depth (MLD) plays a significant role in the study of ocean dynamics and global climate change. However, the methods of estimating MLD still have limitations due to the sparse resolution of the observed data. In this study, a hybrid estimation method that combines the K-means clustering algorithm and an artificial neural network (ANN) model was developed using sea-surface parameter data in the Indian Ocean as a case study. The oceanic datasets from January 2012 to December 2019 were obtained via satellite observations, Argo in situ data, and reanalysis data. These datasets were unified to the same spatial and temporal resolution (1° × 1°, monthly). Based on the processed datasets, the K-means classifier was applied to divide the Indian Ocean into four regions with different characteristics. For ANN training and testing in each region, the gridded data of 84 months were used for training, and 12-month data were used for testing. The ANN results show that the optimized NN architecture comprises five input variables, one output variable, and four hidden layers, each of which has 40 neurons. Compared with the multiple linear regression model (MLR) with a root-mean-square error (RMSE) of 5.2248 m and the HYbrid-Coordinate Ocean Model (HYCOM) with an RMSE of 4.8422 m, the RMSE of the model proposed in this study was reduced by 27% and 22%, respectively. Three typical regions with high variability in their MLDs were selected to further evaluate the performance of the ANN model. Our results showed that the model could reveal the seasonal variation trend in each of the selected regions, but the estimation accuracy showed room for improvement. Furthermore, a correlation analysis between the MLD and input variables showed that the surface temperature and salinity were the main influencing factors of the model. The results of this study suggest that the pre-clustering ANN method proposed could be used to estimate and analyze the MLD in the Indian Ocean. Moreover, this method can be further expanded to estimate other internal parameters for typical ocean regions and to provide effective technical support for ocean researchers when studying the variability of these parameters. Full article

(This article belongs to the Special Issue Recent Trends and Advances in Microwave Sea Remote Sensing)

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