Special Issue "Ocean Radar"

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 October 2018).

Special Issue Editors

Dr. Weimin Huang
Website
Guest Editor
Department of Electrical and Computer Engineering, Memorial University, St. John’s, A1B 3X5 NL, Canada
Interests: ocean remote sensing; high frequency surface-wave radar (HFSWR); microwave nautical radar; ocean observation using global navigation satellite systems (GNSS)
Special Issues and Collections in MDPI journals
Dr. Björn Lund
Website
Guest Editor
RSMAS-OCE, University of Miami, 4600 Rickenbacker Causeway, Miami, 33149 FL, USA
Interests: marine X-band radar; ocean remote sensing; air-sea-ice interaction processes; physical oceanography
Dr. Biyang Wen
Website
Guest Editor
School of Electronic Information, Wuhan University, Wuhan, 430072, Hubei, China
Interests: radar signal processing; ocean remote sensing; high frequency surface-wave radar (HFSWR); antenna design

Special Issue Information

Dear Colleagues,

Oceans cover more than 70% of the surface of the Earth. They play an extremely important role in affecting climate on a global scale, providing survival resources and environments to numerous species, and are the basis of human marine transportation and exploration. Thus, it is a worldwide and permanent common task to understand and monitor oceans. Radar is one of the most useful tools for obtaining ocean information using the technologies of remote sensing. The most widely accepted ocean sensing radars include but are not limited to the high-frequency (HF) surface wave and sky wave radars, microwave nautical radar, and laser radar (LIDAR). These “ocean radars” are able to provide sea surface information such as wind, wave, current, hard target, and bathymetry with different spatial and temporal resolutions. Though many successful applications have been reported for each kind ocean radar, there are still plenty of questions that deserve to be explored. The objective of this Special Issue is to provide a forum for ocean radar researchers to present their recent advances in the field. Possible topics for this Special Issue include, but are not limited to the investigation of:

  • Surface and internal waves
  • Sea surface winds and currents
  • Coastal bathymetry
  • Sea ice and iceberg
  • Small vessels
  • Oil spill
  • Sea surface scattering

using HF surface wave and sky wave radars, microwave nautical radar, and LIDAR.

Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.

Dr. Weimin Huang
Dr. Björn Lund
Dr. Biyang Wen
Guest Editors

Manuscript Submission Information

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Published Papers (13 papers)

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Editorial

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Open AccessEditorial
Editorial for Special Issue “Ocean Radar”
Remote Sens. 2019, 11(7), 834; https://doi.org/10.3390/rs11070834 - 08 Apr 2019
Abstract
This Special Issue hosts papers related to ocean radars including the high-frequency (HF) surface wave and sky wave radars, X-, L-, K-band marine radars, airborne scatterometers, and altimeter. The topics covered by these papers include sea surface wind, wave and current measurements, new [...] Read more.
This Special Issue hosts papers related to ocean radars including the high-frequency (HF) surface wave and sky wave radars, X-, L-, K-band marine radars, airborne scatterometers, and altimeter. The topics covered by these papers include sea surface wind, wave and current measurements, new methodologies and quality control schemes for improving the estimation results, clutter and interference classification and detection, and optimal design as well as calibration of the sensors for better performance. Although different problems are tackled in each paper, their ultimate purposes are the same, i.e., to improve the capacity and accuracy of these radars in ocean monitoring. Full article
(This article belongs to the Special Issue Ocean Radar)

Research

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Open AccessArticle
Interoperability of Direction-Finding and Beam-Forming High-Frequency Radar Systems: An Example from the Australian High-Frequency Ocean Radar Network
Remote Sens. 2019, 11(3), 291; https://doi.org/10.3390/rs11030291 - 01 Feb 2019
Cited by 6
Abstract
Direction-finding SeaSonde (4.463 MHz; 5.2625 MHz) and phased-array WEllen RAdar WERA (9.33 MHz; 13.5 MHz) High-frequency radar (HFR) systems are routinely operated in Australia for scientific research, operational modeling, coastal monitoring, fisheries, and other applications. Coverage of WERA and SeaSonde HFRs in Western [...] Read more.
Direction-finding SeaSonde (4.463 MHz; 5.2625 MHz) and phased-array WEllen RAdar WERA (9.33 MHz; 13.5 MHz) High-frequency radar (HFR) systems are routinely operated in Australia for scientific research, operational modeling, coastal monitoring, fisheries, and other applications. Coverage of WERA and SeaSonde HFRs in Western Australia overlap. Comparisons with subsurface currents show that both HFR types agree well with current meter records. Correlation (R), root-mean-squares differences (RMSDs), and mean bias (bias) for hourly-averaged radial currents range between R = (−0.03, 0.78), RMSD = (9.2, 30.3) cm/s, and bias = (−5.2, 5.2) cm/s for WERAs; and R = (0.1, 0.76), RMSD = (17.4, 33.6) cm/s, bias = (0.03, 0.36) cm/s for SeaSonde HFRs. Pointing errors (θ) are in the range θ = (1°, 21°) for SeaSonde HFRs, and θ = (3°, 8°) for WERA HFRs. For WERA HFR current components, comparison metrics are RU = (−0.12, 0.86), RMSDU = (12.3, 15.7) cm/s, biasU = (−5.1, −0.5) cm/s; and, RV = (0.61, 0.86), RMSDV = (15.4, 21.1) cm/s, and biasV = (−0.5, 9.6) cm/s for the zonal (u) and the meridional (v) components. Magnitude and phase angle for the vector correlation are ρ = (0.58, 0.86), φ = (−10°, 28°). Good match was found in a direct comparison of SeaSonde and WERA HFR currents in their overlap (ρ = (0.19, 0.59), φ = (−4°, +54°)). Comparison metrics at the mooring slightly decrease when SeaSonde HFR radials are combined with WERA HFR: scalar (vector) correlations for RU, V, (ρ) are in the range RU = (−0.20, 0.83), RV = (0.39, 0.79), ρ = (0.47, 0.72). When directly compared over the same grid, however, vectors from WERA HFR radials and vectors from merged SeaSonde–WERA show RU (RV) exceeding 0.9 (0.7) within the HFR grid. Despite the intrinsic differences between the two types of radars used here, findings show that different HFR genres can be successfully merged, thus increasing current mapping capability of the existing HFR networks, and minimising operational downtime, however at a likely cost of slightly decreased data quality. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
On the Optimal Design of Doppler Scatterometers
Remote Sens. 2018, 10(11), 1765; https://doi.org/10.3390/rs10111765 - 08 Nov 2018
Cited by 7
Abstract
Pencil-beam Doppler scatterometers are a promising remote sensing tool for measuring ocean vector winds and currents from space. While several point designs exist in the literature, these designs have been constrained by the hardware they inherited, and the design is sub-optimal. Here, guidelines [...] Read more.
Pencil-beam Doppler scatterometers are a promising remote sensing tool for measuring ocean vector winds and currents from space. While several point designs exist in the literature, these designs have been constrained by the hardware they inherited, and the design is sub-optimal. Here, guidelines to optimize the design of these instruments starting from the basic sensitivity equations are presented. Unlike conventional scatterometers or pencil-beam imagers, appropriate sampling of the Doppler spectrum and optimizing the radial velocity error lead naturally to a design that incorporates a pulse-to-pulse separation and pulse length that vary with scan angle. Including this variation can improve radial velocity performance significantly and the optimal selection of system timing and bandwidth is derived. Following this, optimization of the performance based on frequency, incidence angle, antenna length, and spatial sampling strategy are considered. It is shown that antenna length influences the performance most strongly, while the errors depend only on the square root of the transmit power. Finally, a set of example designs and associated performance are presented. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Deep Learning-Based Automatic Clutter/Interference Detection for HFSWR
Remote Sens. 2018, 10(10), 1517; https://doi.org/10.3390/rs10101517 - 21 Sep 2018
Cited by 7
Abstract
High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected [...] Read more.
High-frequency surface wave radar (HFSWR) plays an important role in wide area monitoring of the marine target and the sea state. However, the detection ability of HFSWR is severely limited by the strong clutter and the interference, which are difficult to be detected due to many factors such as random occurrence and complex distribution characteristics. Hence the automatic detection of the clutter and interference is an important step towards extracting them. In this paper, an automatic clutter and interference detection method based on deep learning is proposed to improve the performance of HFSWR. Conventionally, the Range-Doppler (RD) spectrum image processing method requires the target feature extraction including feature design and preselection, which is not only complicated and time-consuming, but the quality of the designed features is bound up with the performance of the algorithm. By analyzing the features of the target, the clutter and the interference in RD spectrum images, a lightweight deep convolutional learning network is established based on a faster region-based convolutional neural networks (Faster R-CNN). By using effective feature extraction combined with a classifier, the clutter and the interference can be automatically detected. Due to the end-to-end architecture and the numerous convolutional features, the deep learning-based method can avoid the difficulty and absence of uniform standard inherent in handcrafted feature design and preselection. Field experimental results show that the Faster R-CNN based method can automatically detect the clutter and interference with decent performance and classify them with high accuracy. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Optimization of Airborne Antenna Geometry for Ocean Surface Scatterometric Measurements
Remote Sens. 2018, 10(10), 1501; https://doi.org/10.3390/rs10101501 - 20 Sep 2018
Cited by 3
Abstract
We consider different antenna configurations, ranging from simple X-configuration to multi-beam star geometries, for airborne scatterometric measurements of the wind vector near the ocean surface. For all geometries, track-stabilized antenna configurations, as well as horizontal transmitter and receiver polarizations, are considered. The wind [...] Read more.
We consider different antenna configurations, ranging from simple X-configuration to multi-beam star geometries, for airborne scatterometric measurements of the wind vector near the ocean surface. For all geometries, track-stabilized antenna configurations, as well as horizontal transmitter and receiver polarizations, are considered. The wind vector retrieval algorithm is generalized here for an arbitrary star geometry antenna configuration and tested using the Ku-Band geophysical model function. Using Monte Carlo simulations for the fixed total measurement time, we show explicitly that the relative wind speed estimation accuracy barely depends on the chosen antenna geometry, while the maximum wind direction retrieval error reduces moderately with increasing angular resolution, although at the cost of increased retrieval algorithm computational complexity, thus, limiting online analysis options with onboard equipment. Remarkably, the simplest X-configuration, while the simplest in terms of hardware implementation and computational time, appears an outlier, yielding considerably higher maximum retrieval errors when compared to all other configurations. We believe that our results are useful for the optimization of both hardware and software design for modern airborne scatterometric measurement systems based on tunable antenna arrays especially, those requiring online data processing. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Wind Direction Inversion from Narrow-Beam HF Radar Backscatter Signals in Low and High Wind Conditions at Different Radar Frequencies
Remote Sens. 2018, 10(9), 1480; https://doi.org/10.3390/rs10091480 - 16 Sep 2018
Cited by 4
Abstract
Land-based, high-frequency (HF) surface wave radar has the unique capability of monitoring coastal surface parameters, such as current, waves, and wind, up to 200 km off the coast. The Doppler spectrum of the backscattered radar signal is characterized by two strong peaks that [...] Read more.
Land-based, high-frequency (HF) surface wave radar has the unique capability of monitoring coastal surface parameters, such as current, waves, and wind, up to 200 km off the coast. The Doppler spectrum of the backscattered radar signal is characterized by two strong peaks that are caused by the Bragg-resonant scattering from the ocean surface. The wavelength of Bragg resonant waves is exactly half the radio wavelength (grazing incidence), and these waves are located at the higher frequency part of the wave spectral distribution. When HF radar operates at higher frequencies, the resonant waves are relatively shorter waves, which are more sensitive to a change in wind direction, and they rapidly respond to local wind excitation and a change in wind direction. When the radar operates at lower frequencies, the corresponding resonant waves are relatively longer and take longer time to respond to a change in wind direction due to the progress of wave growth from short waves to long waves. For the wind inversion from HF radar backscatter signals, the accuracy of wind measurement is also relevant to radar frequency. In this paper, a pattern-fitting method for extracting wind direction by estimating the wave spreading parameter is presented, and a comparison of the pattern-fitting method and a conventional method is given as well, which concludes that the pattern-fitting method presents better results than the conventional method. In order to analyze the wind direction inversion from radar backscatter signals under different wind conditions and at different radar frequencies, two radar experiments accomplished in Norway and Italy are introduced, and the results of wind direction inversion are presented. In the two experiments, the radar worked at 27.68 MHz and 12 MHz, respectively, and the wind conditions at the sea surface were quite different. In the experiment in Norway, 67.4% of the wind records were higher than 5 m/s, while, in the experiment in Italy, only 18.9% of the wind records were higher than 5 m/s. All these factors affect the accuracy of wind direction inversion. The paper analyzes the radar data and draws a conclusion on the influencing factor of wind direction inversion. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Improving Data Quality for the Australian High Frequency Ocean Radar Network through Real-Time and Delayed-Mode Quality-Control Procedures
Remote Sens. 2018, 10(9), 1476; https://doi.org/10.3390/rs10091476 - 16 Sep 2018
Cited by 12
Abstract
Quality-control procedures and their impact on data quality are described for the High-Frequency Ocean Radar (HFR) network in Australia, in particular for the commercial phased-array (WERA) HFR type. Threshold-based quality-control procedures were used to obtain radial velocity and signal-to-noise ratio (SNR), however, values [...] Read more.
Quality-control procedures and their impact on data quality are described for the High-Frequency Ocean Radar (HFR) network in Australia, in particular for the commercial phased-array (WERA) HFR type. Threshold-based quality-control procedures were used to obtain radial velocity and signal-to-noise ratio (SNR), however, values were set through quantitative analyses with independent measurements available within the HFR coverage, when available, or from long-term data statistics. An artifact removal procedure was also applied to the spatial distribution of SNR for the first-order Bragg peaks, under the assumption the SNR is a valid proxy for radial velocity quality and that SNR decays with range from the receiver. The proposed iterative procedure was specially designed to remove anomalous observations associated with strong SNR peaks caused by the 50 Hz sources. The procedure iteratively fits a polynomial along the radial beam (1-D case) or a surface (2-D case) to the SNR associated with the radial velocity. Observations that exceed a detection threshold were then identified and flagged. After removing suspect data, new iterations were run with updated detection thresholds until no additional spikes were found or a maximum number of iterations was reached. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Ocean Wave Measurement Using Short-Range K-Band Narrow Beam Continuous Wave Radar
Remote Sens. 2018, 10(8), 1242; https://doi.org/10.3390/rs10081242 - 07 Aug 2018
Cited by 2
Abstract
We describe a technique to measure ocean wave period, height and direction. The technique is based on the characteristics of transmission and backscattering of short-range K-band narrow beam continuous wave radar at the sea surface. The short-range K-band radar transmits and receives continuous [...] Read more.
We describe a technique to measure ocean wave period, height and direction. The technique is based on the characteristics of transmission and backscattering of short-range K-band narrow beam continuous wave radar at the sea surface. The short-range K-band radar transmits and receives continuous signals close to the sea surface at a low-grazing angle. By sensing the motions of a dominant facet at the sea surface that strongly scatters signals back and is located directly in front of the radar, the wave orbital velocity can be measured from the Doppler shift of the received radar signal. The period, height and direction of ocean wave are determined from the relationships among wave orbital velocity, ocean wave characteristics and the Doppler shift. Numerical simulations were performed to validate that the dominant facet exists and ocean waves are measured by sensing its motion. Validation experiments were conducted in a wave tank to verify the feasibility of the proposed ocean wave measurement method. The results of simulations and experiments demonstrate the effectiveness of the short-range K-band narrow beam continuous wave radar for the measurement of ocean waves. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Real-Time Tsunami Detection with Oceanographic Radar Based on Virtual Tsunami Observation Experiments
Remote Sens. 2018, 10(7), 1126; https://doi.org/10.3390/rs10071126 - 17 Jul 2018
Cited by 3
Abstract
The tsunami generated by the 2011 Tohoku-Oki earthquake was the first time that the velocity fields of a tsunami were measured by using high-frequency oceanographic radar (HF radar) and since then, the development of HF radar systems for tsunami detection has progressed. Here, [...] Read more.
The tsunami generated by the 2011 Tohoku-Oki earthquake was the first time that the velocity fields of a tsunami were measured by using high-frequency oceanographic radar (HF radar) and since then, the development of HF radar systems for tsunami detection has progressed. Here, a real-time tsunami detection method was developed, based on virtual tsunami observation experiments proposed by Fuji et al. In the experiments, we used actual signals received in February 2014 by the Nagano Japan Radio Co., Ltd. radar system installed on the Mihama coast and simulated tsunami velocities induced by the Nankai Trough earthquake. The tsunami was detected based on the temporal change in the cross-correlation of radial velocities between two observation points. Performance of the method was statistically evaluated referring to Fuji and Hinata. Statistical analysis of the detection probability was performed using 590 scenarios. The maximum detection probability was 15% at 4 min after tsunami occurrence and increased to 80% at 7 min, which corresponds to 9 min before tsunami arrival at the coast. The 80% detection probability line located 3 km behind the tsunami wavefront proceeded to the coast as the tsunami propagated to the coast. To obtain a comprehensive understanding of the tsunami detection probability of the radar system, virtual tsunami observation experiments are required for other seasons in 2014, when the sea surface state was different from that in February, and for other earthquakes. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Quantifying and Reducing the DOA Estimation Error Resulting from Antenna Pattern Deviation for Direction-Finding HF Radar
Remote Sens. 2017, 9(12), 1285; https://doi.org/10.3390/rs9121285 - 11 Dec 2017
Cited by 10
Abstract
High-frequency (HF) radars are routinely used for remotely sensing ocean surface currents. However, the performance of the most widely used direction-finding HF radar is degraded due to the effect of the inevitable deviations of actual antenna pattern on the direction of arrival (DOA) [...] Read more.
High-frequency (HF) radars are routinely used for remotely sensing ocean surface currents. However, the performance of the most widely used direction-finding HF radar is degraded due to the effect of the inevitable deviations of actual antenna pattern on the direction of arrival (DOA) estimation. In this paper, we quantify the DOA estimation error resulting from the deviation of the actual antenna pattern from the ideal one. Theoretical analysis and field experiment results suggest that the ratio of the deviations for the two loops dominates the DOA estimation error. Thus, eliminating the effect of the antenna pattern deviations on DOA estimation error is transformed into eliminating the effect of this ratio. From this, a calibration method based on the time-averaged local spatial coverage rate (TLSCR) is proposed to reduce the effect of the antenna pattern deviations on current extraction, which uses the ideal antenna pattern to estimate the DOA of the echoes. To validate this proposed calibration method, we assess the radar-derived radial velocities by comparing with in situ observations. The comparison results indicate that the proposed TLSCR calibration method can effectively reduce the DOA estimation error and improve the performance of the direction-finding HF radar in current observation. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Validation of the Significant Wave Height Product of HY-2 Altimeter
Remote Sens. 2017, 9(10), 1016; https://doi.org/10.3390/rs9101016 - 30 Sep 2017
Cited by 5
Abstract
HY-2 was launched by China on August 2011, which has provided continuous wave height measurements to monitor ocean dynamic environments for more than 5 years. Before using these data, however, the measurements need to be validated. Based on the in situ buoy data [...] Read more.
HY-2 was launched by China on August 2011, which has provided continuous wave height measurements to monitor ocean dynamic environments for more than 5 years. Before using these data, however, the measurements need to be validated. Based on the in situ buoy data from the National Data Buoy Center (NDBC) and the Jason-2 altimeter data, the HY-2 Ku-band significant wave height (SWH) measurements were validated. The comparisons showed that a linear regression with NDBC measurements can be used to improve the accuracy of the HY-2 SWH measurements. Compared with the NDBC SWH data, the validation results of the HY-2 SWH data show an RMS (root mean square) of 0.33 m, which is similar to that of the Jason-1 and Jason-2 data; the RMS of the HY-2 SWH is 0.30 m, which, corrected via linear regression, is similar to that of the corrected Jason-1 and Jason-2 data (0.27 m and 0.23 m, respectively). Therefore, the accuracy of the HY-2 SWH products is close to that of the Jason-1/2 SWH data. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
Use of Proper Orthogonal Decomposition for Extraction of Ocean Surface Wave Fields from X-Band Radar Measurements of the Sea Surface
Remote Sens. 2017, 9(9), 881; https://doi.org/10.3390/rs9090881 - 25 Aug 2017
Cited by 4
Abstract
Radar remote sensing of the sea surface for the extraction of ocean surface wave fields requires separating wave and non-wave contributions to the sea surface measurement. Conventional methods of extracting wave information from radar measurements of the sea surface rely on filtering the [...] Read more.
Radar remote sensing of the sea surface for the extraction of ocean surface wave fields requires separating wave and non-wave contributions to the sea surface measurement. Conventional methods of extracting wave information from radar measurements of the sea surface rely on filtering the wavenumber-frequency spectrum using the linear dispersion relationship for ocean surface waves. However, this technique has limitations, e.g., it isn’t suited for the inclusion of non-linear wave features. This study evaluates an alternative method called proper orthogonal decomposition (POD) for the extraction of ocean surface wave fields remotely sensed by marine radar. POD is an empirical and optimal linear method for representing non-linear processes. The method was applied to Doppler velocity data of the sea surface collected using two different radar systems during two different experiments that spanned a variety of environmental conditions. During both experiments, GPS mini-buoys simultaneously collected wave data. The POD method was used to generate phase-resolved wave orbital velocity maps that are statistically evaluated by comparing wave statistics computed from the buoy data to those obtained from these maps. The results show that leading POD modes contain energy associated with the peak wavelength(s) of the measured wave field, and consequently, wave contributions to the radar measurement of the sea surface can be separated based on modes. Wave statistics calculated from optimized POD reconstructions are comparable to those calculated from GPS wave buoys. The accuracy of the wave statistics generated from POD-reconstructed orbital velocity maps was not sensitive to the radar configuration or environmental conditions examined. Further research is needed to determine a rigorous method for selecting modes a priori. Full article
(This article belongs to the Special Issue Ocean Radar)
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Open AccessArticle
An Improved Spectrum Model for Sea Surface Radar Backscattering at L-Band
Remote Sens. 2017, 9(8), 776; https://doi.org/10.3390/rs9080776 - 29 Jul 2017
Cited by 9
Abstract
L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the [...] Read more.
L-band active microwave remote sensing is one of the most important technical methods of ocean environmental monitoring and dynamic parameter retrieval. Recently, a unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range was observed. To study the directional features of L-band ocean surface backscattering, a new directional spectrum model is proposed and built into the advanced integral equation method (AIEM). This spectrum combines Apel’s omnidirectional spectrum and an improved empirical angular spreading function (ASF). The coefficients in the ASF were determined by the fitting of radar observations so that it provides a better description of wave directionality, especially over wavenumber ranges from short-gravity waves to capillary waves. Based on the improved spectrum and the AIEM scattering model, L-band NUC asymmetry at low wind speeds and positive upwind-crosswind (PUC) asymmetry at higher wind speeds are simulated successfully. The model outputs are validated against Aquarius/SAC-D observations under different incidence angles, azimuth angles and wind speed conditions. Full article
(This article belongs to the Special Issue Ocean Radar)
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