Super-resolution (SR) techniques are commonly used to obtain high-resolution (HR) sea surface temperature (SST) data. However, existing SST SR methods require collocated optical and thermal infrared images as inputs, making them completely unusable at night. This letter presented a novel SST SR method that uses two thermal infrared sensors (TIRSs) by the residual channel attention network (RCAN). The process is tested by 1 km-resolution Sentiel-3/SLSTR and Terra/MODIS data to obtain 250 m SST data. Evaluation results indicate that this method outperforms the scheme that uses visible light data as input in terms of better metrics in root mean square error (RMSE) of$0.12~^{\circ }$C, structural similarity index measure (SSIM) of 0.784, and peak signal-to-noise ratio (PSNR) of 22.35 dB. The case studies also demonstrate its capability for night-time applications.

The C-band Advanced Scatterometer (ASCAT) has the advantages of good spatial-temporal coverage and low sensitivity to nonextreme rainfall. While the perceived wind speed underestimation issues of ASCAT sea surface wind (SSW) retrievals can be mitigated using appropriate high wind speed scalings, the low spatial resolution in ASCAT remains a challenge, which implicitly leads to the blurring effect in tropical cyclone (TC) inner-core regions. To overcome this issue, the 2-D variational (2DVAR) analysis method is modified from 12.5 to 1.8 km grid size, where the latter allows super-resolution (SR) spatial structure functions, empirically trained on synthetic aperture radar (SAR) data, to enhance TC structure retrievals of ASCAT. The method first employs triple collocation analysis to estimate observation and background errors under different TC categories. After that, the relevant spatial parameters during the data assimilation process are determined and linked to TC features. These analyses contribute to constructing SAR-learned structure functions, complementing ASCAT-observed TC characteristics, and then achieving TC vortex reconstruction and wind field SR. Validation studies demonstrate that the SR products possess the correct small-scale properties of TC inner-core structures, such as radius of maximum wind (RMW), TC asymmetry, and wind variability. Notably, the proposed SR approach can achieve a significant reduction in error standard deviations (SDs) of ($l,t$) wind components (by 37% and 33%, respectively) when compared to spatial interpolated results. The encouraging results suggest the feasibility of the method in enhancing the abundant but lower resolution scatterometer winds, potentially contributing to future advancements in TC advisories.

Deep learning-based road extraction technologies can swiftly identify road information in complex environments, playing a crucial role in advancing urban intelligence and achieving sustainable development goals. However, the current road extraction models exhibit significant omissions when confronted with scenarios involving occluded roads and densely distributed road networks. To address the issue of partial road extraction, this paper introduces a novel method named SSCNet. This approach addresses potential causes of road extraction failures by leveraging synchronous stratification learning and cross-level feature fusion to fully utilize information across various layers. The model incorporates a Dynamic Road Detail Matcher to extract a wealth of road detail information from shallow feature maps, a Cross Contextual Adaptive Attention to capture multi-scale contextual information and interact with shallow feature maps, and a Multi-Scale Global Information Integrator to consolidate global information from different levels of feature maps, enhancing the model’s understanding of road integrity. The model has been extensively tested on the public datasets DeepGlobe, Massachusetts, SpaceNet, and RoadTracer, showing significant improvements in F1 score and IoU compared to current state-of-the-art models.

In this study, a model-based neural network approach is proposed to retrieve ancillary parameters related to oil pollutants at sea. The proposed methodology consists of two pillars. First, an electromagnetic scattering model is used to generate radar backscatter for slick-free and slick-covered sea surface at variance of incidence angle, faction of water into the oil and oil thickness. Then, these radar backscatter values are combined to generated a metric adopted fro the retrieval process, namely the damping ratio. Second, an artificial neural network is first trained on the simulated damping ratio DR and then applied to actual synthetic aperture radar imagery to retrieve oil thickness and seawater volume fraction. Results, obtained processing synthetic aperture radar scenes collected during the Deep Water Horizon oil accident by the L-band uninhabited aerial vehicle synthetic aperture radar (National Aeronautics and Space Administration - Jet Propulsion Laboratory), show the soundness of the proposed methodology.

This study proposed an improved Doppler radar imaging model (IDopRIM) to address the overestimation of the original DopRIM and produce better accuracy for investigating the current-wave interaction effect on ocean surface current (OSC) retrieval. The IDopRIM’s performance, with the root-mean-square (rms) errors mostly under 0.3 m/s, is validated against the empirical model and actual measurements. It shows a notable improvement over the original model in which the maximum deviation could be up to about 1.5 m/s at moderate-to-high wind speeds and upwind direction for HH polarization. Furthermore, this study uses both numerical simulations and real synthetic aperture radar (SAR) imagery to assess the impact of current-wave interaction on OSC retrieval, especially in scenarios involving ocean internal waves (IWs), using the Doppler centroid anomaly (DCA) method incorporated with the proposed IDopRIM. The results underscore the significance of incorporating current-wave interactions in OSC retrieval for IW conditions, revealing that neglecting these interactions can result in relative errors of over 30% in certain cases.

In this study, the potential of electromagnetic scattering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial intelligence (AI)-based approach. The backscattering coefficient of a slick-covered sea surface is predicted using the advanced integral equation model augmented with the model of local balance (MLB), an effective dielectric constant model, and a composite medium model to include the effect of an oil slick. Damping ratios (DRs), predicted for different oil parameters (namely, the oil thickness and seawater volume fraction), are used to train and test a four-layer neural network. Once successfully tested, the neural network is applied to an uninhabited aerial vehicle synthetic aperture radar (UAVSAR) image collected during the DeepWater Horizon (DWH) oil spill accident to retrieve the oil slick thickness and volume fraction of seawater in the oil layer. The inversion results show that the thicker (i.e., 2–4 mm) emulsions are located in the south and west of the slick and they are surrounded by thinner (i.e., < 1 mm) oil films. In addition, the seawater volume fraction in the oil slick is found to be about 20%–30%. Results are contrasted with optical data and previous studies of the same accidental oil spill, showing qualitatively good agreement.

In this study, the Artificial Neural Network technique is used to retrieve quantitative parameters of marine oil spill on Synthetic Aperture Radar imagery. In fact, while Synthetic Aperture Radar has been widely exploited to obtain morphological features of sea oil spills as extent and shape, its potential to extract oil thickness information has been underexplored. Hence, an artificial neural network is proposed that have been trained and tested using the damping ratio predicted by a microwave scattering model consisting of Advanced Integral Equation Method in combination with the local balance damping model and a layered-medium dielectric model. Experiments are performed on a L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar image collected during the DeepWater Horizon oil spill accident. The inversion results show that the central area of the slick is the thicker part of the oil emulsion (2 – 4 mm thickness), surrounded by thinner oil film whose thickness is lower than 1 mm.

This article proposed a graphical and statistical features-based broad learning system (GSF-BLS) to detect tropical cyclogenesis with the cross-calibrated multiplatform version 2.0 (CCMP V2.0) wind products. The framework of the proposed model is composed of three modules: the data preprocessing module, the feature extraction module, and the basis broad learning system (BLS). At the stage of data preprocessing, we use the CCMP V2.0 data to match the best tracks and the global tropical cloud cluster (TCC) tracks to obtain the developed and undeveloped samples. At the feature extraction stage, a convolution module with pretrained weights is used to extract the graphical features (GFs). Meanwhile, the statistical features (SFs) are calculated based on the divided subregions of each sample. Thus, the combination of these GFs and SFs forms the input vectors. Then, the training time of GSF-BLS on CPU is only 1/20th of that of deep learning models, showing its simplicity and efficiency in model training. The overall accuracy, probability of detection (POD), and false alarm rate (FAR) on the testing set are 89.46%, 86.78%, and 8.31%, respectively. More importantly, the incremental learning ability of GSF-BLS makes it superior to most deep learning models in model updating, which can avoid the computational burden caused by retraining. Finally, the case study results show that GSF-BLS can predict tropical cyclogenesis in 52 of 70 cases in advance, and the average lead times are 13.54 h. Therefore, the experimental results demonstrate that GSF-BLS is a promising tropical cyclogenesis detection model.

Empirical mode decomposition (EMD) is a commonly used method for retrieving internal wave (IW) parameters. However, the EMD-based method often suffers from the issue of mode mixing in practical retrievals. In this paper, a robust IW parameters retrieval method based on the improved complete ensemble EMD with adaptive noise (ICEEMDAN) is proposed. The method is tested on an ERS-1 SAR image which clearly captured an IW over South China Sea. Also, the typical EMD method is adopted for comparison. The experimental results indicate that the proposed method can effectively address the issue of mode mixing, and obtain satisfying accuracy of the retrieved IW amplitudes and half-wave widths.

This paper numerically investigates the spatial ergodicity of radar scattering from randomly rough ocean surface. Based on the accurate full-wave multi-level steepest decent-sparse matrix canonical grid (MLSD-SMCG) method and Monte Carlo simulation, we simulate the L-band normalized radar scattering coefficients from one-dimensional (I-D) rough ocean surfaces with different radar illumination sizes. According to the simulation results, it is found that: For the scattering angle less than 85°, the normalized bistatic radar scattering coefficient from ocean surface with radar illumination size no less than $16\lambda$ has good spatial ergodicity. Also, the emissivities from ocean surfaces with sizes exceeding $64\lambda$ manifest spatial ergodicity if the measurement accuracy of emissivity is less than the order of magnitude of 10 −4 .

Ocean front can influent the distribution of sea material and it has an impact on underwater acoustic communication. Thus, the detection of ocean front is very important. In this paper, the simple linear iterative clustering (SLIC) superpixel segmentation approach is applied to ocean front detection in SAR images, which can suppress the influence of coherent speckle noise and extract the fuzzy boundary from high-resolution SAR images. The results show that this method can extract ocean front with one pixel width and the extraction of ocean front is in good agreement with human visual interpretation. This method provides a new idea to extract ocean fronts from SAR images.

This study develops a deep learning (DL) model to classify the sea ice and open water from synthetic aperture radar (SAR) images. We use the U-Net, a well-known fully convolutional network (FCN) for pixel-level segmentation, as the model backbone. We employ a DL-based feature extracting model, ResNet-34, as the encoder of the U-Net. To achieve high accuracy classifications, we integrate the dual-attention mechanism into the original U-Net to improve the feature representations, forming a dual-attention U-Net model (DAU-Net). The SAR images are obtained from Sentinel-1A. The dual-polarized information and the incident angle of SAR images are model inputs. We used 15 dual-polarized images acquired near the Bering Sea to train the model and employ the other three images to test the model. Experiments show that the DAU-Net could achieve pixel-level classification; the dual-attention mechanism can improve the classification accuracy. Compared with the original U-Net, DAU-Net improves the intersection over union (IoU) by 7.48.% points, 0.96.% points, and 0.83.% points on three test images. Compared with the recently published model DenseNetFCN, the three improvement IoU values of DAU-Net are 3.04.% points, 2.53.% points, and 2.26.% points, respectively.

In this study, the bistatic radar scattering coefficients related to an oil-covered sea surface are predicted by modeling both the oil damping effect on surface roughness–through the advanced integral equation method–and the oil modification on the dielectric properties of the scattering surface. The bistatic scattering is analyzed in the whole upper scattering space under different radar frequencies, incidence angles, wind speeds, and oil thicknesses. Numerical predictions show that the scattering energy of an oil-covered sea surface is generally higher in the forward scattering zone than that in the backward one. In addition, the oil damping effect is the main mechanism ruling the scattering behavior in the backward region. The information related to the bistatic scattering geometry is also explored to retrieve oil thickness, representing one of the key parameters for radar-based marine oil pollution observation. A new index is proposed to quantify the sensitivity of bistatic scattering coefficients to oil thickness in different cases: single-polarization features, dual co-polarization features, namely, the polarization ratio (PR) and the normalized polarization difference index (NPDI), and dual-angular scattering features. Numerical results show that the bistatic scattering coefficients result in an enhanced sensitivity to oil thickness with respect to the monostatic case. The single HH-polarized scattering coefficients show better oil thickness sensitivity in the backward region, while the VV-polarized ones are more sensitive to oil thickness in the forward region. The combination of dual-polarized scattering coefficients significantly improves the oil thickness sensitivity compared to single-polarization radar observations, especially in the forward region. PR outperforms NPDI, even though the latter can suppress the effect of wind speed. The combination of dual-angular observations can significantly increase its sensitivity of oil thickness in the backward region but at the expense of reduced sensitivity in the forward region.

Oils floating on the sea surface can be observed as “dark” patches on radar images since the backscattered signals from the contaminated area are reduced in two dominant ways. First, oil slicks could damp short gravity and capillary waves on the sea surface responsible for backscattering energy. Second, the oil-covered sea surface permittivity decreases significantly if the oil film is sufficiently thick or mixed with seawater, i.e., oil emulsion. In this article, the geometry of the oil-covered sea surface is accounted for by the damping of sea waves, which is described by the model of local balance (MLB) combined with the sea wave spectrum. The radar backscattering is predicted by the advanced integral equation method (AIEM) model. The reflection coefficients are calculated based on a layered-medium model to analyze the impact of oil thickness and emulsions on the radar scattering. Numerical simulations demonstrate that: 1) the sensitivity to oil thickness and water content of the oil spills increases when the radar frequency increases; 2) the backscattering signals exhibit a nonlinear behavior with respect to oil thickness; and 3) high wind speed can generally narrow the difference between the radar backscattering from the clean and oil-covered sea surface, while the incidence angle has little effect. Numerical simulations are then compared with the multifrequency synthetic aperture radar observations acquired during the Gulf of Mexico Deepwater Horizon (DWH) oil spill accident and the 2011 Norwegian Clean Seas Association for Operating Companies (NOFO) oil-on-water exercise. Comparison results show that it is possible to estimate the oil thickness at reasonably good accuracy.

The C-band synthetic aperture radar (SAR) observation is one of the most popular sources for high-resolution tropical cyclone (TC) wind speed estimation. The scarcity of high wind speed data with good quality restricts the inversion accuracy of high wind speed. It is a challenge to obtain a high-precision wind speed inversion model with sparse data points. In this paper, the TC wind speed is successfully estimated from the dual-polarization SAR signal. Firstly, the training dataset with a total of 327 data points is formed using the Sentinel-1A EW/IW mode images and their temporal-spatial matched Stepped Frequency Microwave Radiometer (SFMR) measurements. Then, a novel nonparametric TC wind speed estimation model (hereafter NWSE model) is proposed with this dataset by using the Bayesian nonparametric general regression method. The wind speed is interpreted as a function of the cross-polarized normalized radar cross-sections (VH-NRCS) and incident angle for the NWSE model. Moreover, the wind speed obtained from the co-polarized signal is used to improve the accuracy of NWSE model under low wind speed. Finally, the validation results show the excellent overall consistency between the model retrieved wind speed and the collocated SFMR and SMAP measurements. Specifically, considering all the wind speeds, the overall root-mean-square error and absolute bias of NWSE model are 2.85 m/s and 2.26 m/s compared with the SMAP wind speed. When considering the wind speeds larger than 30 m/s, the RMSE and bias of NWSE model are 3.75 m/s and 2.78 m/s, respectively.

The wind direction asymmetries of the fully polarimetric ocean emissivity at L-band are studied using a semi-theoretical approach. The model is tuned and validated against Aquarius and SMAP observations, which incorporates the two-scale polarimetric ocean emission model, an improved directional wave spectrum, the AG13 foam emissivity model and a novel foam coverage model. Comparisons of model simulations to satellite data show that the overall accuracy of model could reach 0.2 K. The average standard deviations of the differences of polarimetric brightness temperature between observations and model are less than about 0.16 K for wind speeds up to 15 m/s. The negative and positive upwind-crosswind (UC) asymmetries of L-band ocean emission and the transition of the phase signatures in terms of wind speed are well illustrated for all polarizations by the proposed model. The directional variations of L-band brightness temperature for the third Stokes parameter show less sensitivity to wind speed with the increase of observation angles.

The effects of ocean spectrum truncation on the sea clutter distribution properties in numerical simulations are studied using a recently developed full-wave method, i.e., the multilevel steepest decent - sparse matrix canonical grid (MLSD-SMCG) method and the KHCC03 spectrum. Two types of ocean surface profiles are generated for Monte Carlo simulations based on the full and truncated spectra at the wind speed of 10 m/s. The surface profiles generated by the truncated spectrum have lengths about 1/6 of those using full spectrum. 1000 realizations are conducted for each type of profiles. The simulations are illustrated at L-band (1.4 GHz) and the incidence angle is 40°. For the simulated far-field scattering fields and normalized radar cross sections (NRCS), we use the K-distribution model to fit the probability density functions (PDF) of the amplitude and backscatter of clutters. It is found that spectrum truncation has non-negligible effects on the distribution characteristics of sea clutter in the numerical simulations, particularly for the amplitude distributions. The fitted PDF indicates that the simulated sea clutter using truncated spectrum has more small values compared with that using full spectrum.

Emulsified oil slicks can not only damp short gravity and capillary waves on the sea surface, but also reduce the permittivity of the contaminated area. This paper simulates the backscattering coefficients of emulsion oil covered sea surfaces based on AIEM, with the damping model described by model of local balance (MLB). The sea surface covered by emulsion oil with finite thickness is modeled as a layered-medium to calculate the composite reflection coefficients. The simulation results of oil-covered sea surface are compared to those of clean sea surfaces and discussed in terms of incidence angles and frequencies of EM waves, oil thickness and wind speeds.

The effects of sea surface temperature (SST) on ocean radar backscattering are investigated under both the neutral and nonneutral atmospheric conditions for the applications of wind retrieval. The impact factors are parameterized as functions of SST. The SST effects on the variations in ocean scattering and wind retrieval are evaluated using an analytic model which combines the KHCC03 spectrum and the second-order small slope approximation (SSA-II) model. Under the neutral condition, we present the following new insights at three commonly used bands: 1) the seawater permittivity accounts for a dominant effect of SST at the L-band. The SST effects induce a wind underestimation of 0.3 m/s over cold seawater and a wind overestimation of 0.24 m/s over warm seawater at the L-band and a wind speed of 8 m/s. The seawater viscosity plays a significant role in the SST effects on ocean scattering at the C- and Ku-bands, while its variation induced by SST has insignificant effects on L-band scattering; 2) for the C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds; and 3) both the dielectric and dynamic factors play significant roles in the SST effects on ocean scattering at the Ku-band. Under the nonneutral condition, the simulation results show that the air–sea interaction governs the SST effects on ocean scattering and wind velocity variations. Other than the air–sea interaction, the wind retrieval errors induced by other SST-related factors are negligible at the L- and C-bands.

Electromagnetic scattering and emission from 1-D rough surfaces with multiple elevations are studied using full-wave simulations. Both the root-mean-square (rms) heights and the surface length are large compared to the wavelength. A novel multilevel steepest decent-sparse matrix canonical grid (MLSD-SMCG) method is proposed to address limitations in the original SMCG. The uniform Nystrom method and neighborhood impedance boundary condition (NIBC) are also incorporated in solving the dual surface integral equations (SIEs) of the method of moments (MoM). Simulation results are illustrated at L-band for soil and ocean surfaces. The surface rms heights and lengths are up to 1.43 and 243.8 m corresponding to 6 and 1024 wavelengths at 1.26 GHz, respectively. For ocean surfaces, the wind speeds up to 20 m/s are considered, and the entire spectrum is included to capture all relevant surface length scales. Numerical results indicate the proposed approach is computationally efficient and accurate. Energy conservation checks in simulations are at $10^{-4}$ for ocean scattering and emission. Also, the effects of wind-driven roughness on ocean emissivity are further investigated using the proposed approach in terms of wind speed and observation angle for both polarizations.

This article presents a new index, polarization-conversion ratio (PCR) to characterize depolarized bistatic scattering from rough surfaces with dielectric inhomogeneity and spatial anisotropy. We then investigate the dependence of PCR on both surface and radar parameters. Numerical results show that the distribution of PCR on the scattering plane varies with the polarization state of the incident wave and incident angle. The PCR clusters more in the cross-plane for horizontally polarized incidence. However, for vertically polarized incidence, the PCR disperses as “triangular shape” on the whole scattering plane with a sharp valley occurring in the incident plane. The following points can be drawn: 1) the inhomogeneity effectively enhances the PCR in the cross-plane; 2) the effect of anisotropy on the PCR is relatively weak, because the scattering is less affected by correlation length; 3) the impacts of surface rms height on the PCR are negative on the whole scattering plane; and 4) as the background permittivity increases, at the horizontally polarized incidence, the PCR is enhanced in the backward and forward regions, while at vertically polarized incidence, it is enhanced in the incident plane and the forward region. As is demonstrated, the PCR is an effective measure of the sensitivity of depolarization, making it potentially useful as a new reliable index for surface parameter inversion.

Estimating wind speed with parametric models is one of the important methods for the tropical cyclone prediction and risk assessment. In this study, high spatial resolution cross-polarized synthetic aperture radar (SAR) observations are used to investigate the asymmetric structure of hurricanes. Then, a modified asymmetric hurricane parametric (MAHP) model composed of tangential wind profile model and asymmetric distribution mode is proposed to reconstruct the asymmetric wind speed distribution of hurricanes. Compared to other existing models, the new model has less parameters but can better fit to SAR and other observations.

To achieve the demand of earth observation with high spatiotemporal resolution and large scale, a novel optical imaging system is planning to equip on Chinese next generation geo-stationary earth orbit (GEO) satellite. The primary mirror of camera is made of a set of thin films and adopts the diffraction imaging mechanism. A series of ground experiments have been carried out using a thin-film camera with 80 mm aperture for the technical verification. The inherent chromatic aberration due to diffraction imaging appears in the obtained data. To address the issue, a color restoration algorithm by matching, tailoring and linearly stretching the histograms is proposed in this paper. Experimental results show the proposed approach has good performances in color restoration of the diffractive optical images. The effectiveness and robustness of the algorithm are also assessed with various color deviation indexes.

The effects of sea surface temperature (SST) on ocean radar backscattering and wind retrieval are investigated using the second-order small slope approximation (SSA-II) model. Impact factors are parameterized and built into an SST-enhanced KHCC03 spectrum. By employing the Monin-Obukhov similarity theory (MOST), the air-sea interaction is considered in the analyses. Under the neutral condition, the SST effects on wind retrieval cannot be neglected at L-band and the seawater permittivity and air density are the dominant factors. For C-band, the SST-induced wind retrieval error can be neglected at a medium wind speed due to the neutralization of the effects of various factors on surface roughness. Yet, the SST effects are not negligible at low and high wind speeds. At Ku-band, the SST-related factors besides the air density and seawater viscosity can also significantly affect the ocean backscattering.

Typhoon (TC) is one of the most powerful and destructive natural disasters. The analysis and determination of TC intensity is of great importance for disaster prevention [1] - [3] . Satellite remote sensing has become an effective means of monitoring TCs based on its high temporal and spatial resolution and large coverage. It is possible to estimate TC intensity using these satellite measurements when direct measurements are not available [4] . Microwave observations from polar-orbiting satellites can play a crucial role in revealing convective organization and eyewall structure that would otherwise be obscured by cloud tops [5] . Passive microwave sensors, such as SSM/I, TRMM/TMI, have been used to estimate TC intensity [6] - [10] . Besides, scatterometer also has allowed for continuous observation of ocean surface vector winds. Thus, scatterometer is a potential alternative for monitoring TCs. However, scatterometer measured wind speed range 2-24m/s, which make it very difficult to directly obtain the intensity of TCs [11] .

Synthetic aperture radar (SAR) has emerged as a new tool for tropical cyclone (TC) monitoring by providing information on the location of TC centers. However, SAR does not usually cover the entire TC domain due to its limited swath width. In this paper, we develop a procedure to identify the location of the center of a TC when an SAR image only covers the rain band portion of the TC but not the eye. The algorithm is based on both an image processing procedure and the available knowledge of the inherent rain-band structure of a TC. The three-step algorithm includes: 1) applying a Canny edge detector to find the curves associated with rain bands; 2) defining two filter criteria to select the spiral curves that resemble the estimation based on a TC rain-band model; 3) searching for the optimal matching solution using the particle swarm optimization algorithm. Numerical experiments with images without TC eye information show that the proposed method can effectively locate the centers of TCs. We compare the experimental results with the best track data to indicate the accuracy. Then, we compare the inflow angle model and the logarithmic spiral model and find that the inflow angle model is more accurate for TC center identification.

Wind wave spectrum describes the quasi-periodic nature of the ocean surface oscillations and plays an indispensable role in the study of microwave electromagnetic scattering from sea surface. A reliable spectrum model suitable for radar cross section (RCS) predictions at different radar frequencies is desired. This paper evaluated the performances of five common spectrum models (i.e., Fung spectrum, Durden-Vesecky spectrum, Apel spectrum, Elfouhaily spectrum, and the newest version of Hwang spectrum, H18) on the normalized radar backscattering cross section (NRBCS) simulations based on advanced integral equation model (AIEM) at L-, C-, X-, and Ku-bands versus incidence angle, wind direction, and wind speed by comparing with the model and measured data for validation. These results indicate no single wave spectrum of them is satisfying for all the four radar frequencies, e.g., Apel and H18 spectra are better for L- and C-bands, Apel spectrum for X-band, and Elfouhaily and H18 spectra for Ku-band. Given this, three average composite spectrum models are constructed using different spectral models (i.e., all five spectra, Apel + Elfouhaily + H18, and Apel + H18) to simulate NRBCSs, similar to that of the individual spectrum model. It is concluded that the combination of Apel and H18 spectra overall performs best among the individual one and other composited spectra in like-polarized NRBCSs versus incidence angles, wind directions, and wind speeds, for wind speed greater than 30 m/s where the combination of the five spectra work well at Ku-band.

In this study, polarimetric synthetic aperture radar (PoISAR)-based classification algorithms are considered to investigate the role played by polarimetric information in the classification process of coastal areas that call for heterogeneous scattering properties. Hence, a multi-frequency PolSAR dataset collected over the study area of the Yellow River delta (China) is exploited to point out benefits and limitations that characterize well-known unsupervised classification schemes. Experimental results show the potential and the drawbacks of the exploitation of multi-frequency and multi-polarization SAR measurements for challenging coastal area classification.

Typhoon is one of the most powerful and destructive natural disasters. Accurate forecasting of Typhoon track and intensity is very important to disaster prevention and reduction. Satellite observations can effectively compensate for the shortcomings of traditional methods of sea surface measurement and provide all-weather observation over the sea surface, which is of great significance to improve the numerical prediction of strong convective weather over ocean. The spaceborne radar observes the backscattering caused by the sea surface roughness, and then, the sea surface wind can be retrieved. The Synthetic Aperture Radar (SAR) is an important data source for sea surface monitoring. A variety of meteorological hydrological elements can be retrieved by SAR observation, and it has been used in data assimilation in recent years [1]. SAR imagery is also used to monitor strength and structure of typhoons [2]. The accuracy of sea surface winds retrieved from SAR has been found to be comparable to that of scatterometer data [3], and these wind fields can be used with a data assimilation system to provide the initial conditions for the numerical weather prediction (NWP) model [4].

A semi-empirical model is proposed by merging an improved directional spectrum into the advanced integral equation method (AIEM) in this paper. By establishing a new angular spreading function (ASF), this improved directional spectrum provides a better description of wave directionality, especially over wavenumber range from short-gravity waves to capillary waves. Based on this scattering model, the features of L-band ocean surface backscatter are studied. A unique negative upwind-crosswind (NUC) asymmetry of L-band ocean backscatter over a low wind speed range that was recently observed is interpreted and simulated first. The model is validated against Aquarius/SAC-D observations at the L band and the geophysical model functions (GMFs) at higher frequency band. The simulation results have good agreements with observations and the GMFs for different incidence angles, azimuth angles and wind speeds.

The most common polarimetric decompositions can only be computed for fully polarimetric data at the cost of reduced spatial resolution and areal coverage. In this paper, we analyze the potential of dual-pol SAR imagery for the monitoring of bivalve beds on intertidal flats. The normalized Kennaugh elements, which can be calculated from dual-pol data of any wavelength, are applied to deduce polarimetric information from HH and VV polarized TerraSAR-X imagery. The real (K3) and imaginary (K7) parts of the inter-channel correlations are demonstrated to be indicators for bivalve (oyster and mussel) beds in a test site in the German Wadden Sea. Our results show that K3 and K7 can be used to detect bivalve beds with high accuracy.

Recently the bistatic radar systems have seen increasing attention and development for the advantages in remote sensing of ocean surfaces. With the considerable merits of spatial diversity, bistatic systems supplement the retrieval of ocean physical parameters with conventional monostatic systems. For instance, an emerging bistatic radar technique, global navigation satellite signal reflectometry (GNSS-R), has been developed and utilized in retrieval of high wind speed. Moreover, bistatic phenomena such as the Brewster effect can reveal target properties that are not revealed clearly in monostatic scattering [1]. Therefore, for better application of ocean microwave remote sensing, understanding of bistatic scattering from the ocean surface is crucial and meaningful.

Two satellite synthetic aperture radar (SAR) observations of the fetch in the Bohai Sea of China are presented. The sea surface winds derived from SAR data indicated a high wind of 15-16 m/s that occurred in the fetch zone. The winds are shown to have immediate direct mechanical forcing impacts on the significant wave heights (Hs) of ocean surface gravity waves. Buoy measurements and numerical wave modeling results show that the Hs increased to a maximum of 3.5 m in the semienclosed sea, 3 h after the passage of the fetch winds, and the high Hs in the sea was sustained for a total of 6 h. The Weather Research and Forecasting (WRF) model implemented in our modeling simulation captured the wind field responsible for the evolution of the fetch event. The model-simulated surface horizontal winds agree with the SAR-derived winds. In addition, the vertical wind distribution reveals that the fetch wind field reached the 800 hPa level, and the event lasted less than one day. This study demonstrates the synergy of using SAR imagery and the WRF model as effective tools to investigate the lateral and vertical structures of coastal wind.

Sediments on exposed intertidal flats are very dynamic and perform vital ecosystem functions. This paper proposes a new classification scheme for mud and sand flats on intertidal flats using fully polarimetric synthetic aperture radar (SAR) data. Freeman-Durden (FD) and Cloude-Pottier (CP) polarimetric decomposition components as well as double bounce eigenvalue relative difference (DERD) are introduced into the feature sets instead of the original intensity polarimetric channels. Classification is carried out using the random forest (RF) theory, and the results are evaluated using confusion matrices, kappa coefficients, and RF variable importance indices. Three study sites with different environmental conditions are chosen to demonstrate the effectiveness of the proposed classification chain. To further assess the performance of the proposed feature set, we set different feature combinations and process with the same processing chain. Results show that the DERD parameter can detail the sediment mappings on exposed intertidal flats and is a useful SAR feature to distinguish mud and sand flats efficiently. The combined FD and CP components have the ability to describe the polarimetric characteristics of sediments more correctly than the commonly used original intensity channels. Meanwhile, the RF theory shows great potential in distinguishing sediments in intertidal zones accurately and time efficiently.

Theoretical modeling plays a significant role as forward and inverse problem in active and passive microwave remote sensing. Understanding the validity and limitations of the models is essential for model refinements and, perhaps more importantly, model applications. Motivated by these, this paper presents a comprehensive analysis of the scattering, both backscattering and bistatic scattering, and emission of rough soil surface predicted by the advanced integral equation model (AIEM), a well-established theoretical model. Numerically simulated data, covering a wide range of surface parameters, and in situ measurement data set of well-characterized bare soil surfaces were used to evaluate the performance of AIEM in predicting the scattering coefficient and microwave emissivity over a wide range of geometric parameters and ground surface conditions. The results show that the AIEM predictions are generally in good consistency with both numerical simulations and experiment measurements in terms of angular, frequency, and polarization dependences, except for some deviations in a few cases (e.g., at large incident angles and dry soil conditions). Extensive comparison confirms the effectiveness and practicability of AIEM for both scattering and emission of rough soil surface. Possible explanations for the discrepancy between the model prediction and data are given, together with suggestions for model usage and refinements.

A hybrid backscattering model is built to provide a consistent description for C-band VH- and VV-polarized normalized radar cross sections (NRCSs). Ocean surface coand cross-polarized NRCS are both treated as a sum of Bragg and non-Bragg scattering components. To better understand the synthetic aperture radar (SAR) observed NRCS signals under high-wind conditions, five C-band RADARSAT-2 dual-polarization SAR hurricane images and the collocated wind vectors measured by the airborne stepped-frequency microwave radiometer (SFMR) are collected. Based on the match-up data, we add a non-Bragg term in the composite Bragg theory to explain the discrepancy between the measurements in the cross-polarization channel and the existing theory results. The non-Bragg scattering to Bragg scattering ratio (Br) is found to be a constant. We build the hybrid backscattering model with Br and establish a relationship between the cross-polarization NRCS and the radar incidence angle under different wind conditions. The NRCS dependence on incidence angle is simulated by the hybrid backscattering model. Finally, a C-band Cross-Polarization Coupled-Parameters Ocean (C-3PO) model is developed to retrieve hurricane winds using VH-polarized ScanSAR by including the radar incidence angle. The collocated SAR and SFMR data sets are separated into two parts: data set-A, for hybrid backscattering model derivation and C-3PO model coefficients tuning, and data set-B, for hurricane wind validation. C-3PO model validation results show that the model is suitable for ocean surface wind mapping from RADARSAT-2 cross-polarization ScanSAR images. The retrieval has a rootmean-square error less than 3 m/s for wind speed up to 40 m/s.

This paper introduces a new eddy detection method based on object segmentation strategies. It incorporates the advantages of the Okubo-Weiss (OW) method, and improves the algorithm robustness by using three different segmentation methods. First, by intersecting the OW mask with the positive and negative SLA masks, two separate initial eddy candidate segment masks for anticyclonic and cyclonic eddies are produced. Then the number of extrema inside the individual segments is calculated. If the number is two/greater than two, a histogram threshold/watershed method is applied to further divide the segments into subsegments. If the number is one, the segment is tested by predefined eddy criteria. If the criteria are not fulfilled, the eddy boundary is repeatedly shrunk by one pixel inward until the segment meets all the criteria. In this case it is saved as an eddy. A segment is discarded if the number of extrema is zero. The eddy detection results of this method, of the OW method and of a geometric eddy detection method are displayed and analyzed statistically. The seasonal cycle of eddy number from the three eddy detection methods is discussed in comparison to the EKE.

In this study, an atmospheric phenomenon, land breeze system, was observed on one image mode high resolution SAR image and one middle resolution optical image. WRF model was implemented with actual meteorological conditions as inputs to successfully simulate this process with some understandable discrepancies. The overpass time of ASAR and MODIS are at 10:35 AM and 11:10 AM local time in Hainan. Long time in-situ observations show that the land breeze process is converting to sea process between 10:00 AM and 12:00 AM. Thus, the satellite observed land breeze front is at its ending stage. That is one possible reason that land breeze front on ASAR image is clearer than on MODIS image. It also explained why the cloud line in MODIS image cannot maintain its shape and started to dissipate. The MODIS image also shows that the land breeze dissipation process is not spatial uniform along the whole land-sea breeze front.

The wind energy production estimates are very important to a wind power project. The remote sensing technique has been widely used to retrieve the offshore wind speed and direction which could be used to calculate the wind energy of potential wind farm. However, almost all existing methods focus on wind power density, and the directional wind energy distributions are rarely studied. Before using remote sensing data to estimate directional wind energy, the uncertainties inherent in application of current remote sensing methodologies should be quantified. In this study, those uncertainties are discussed and analyzed.

Classifying different types of land cover in coastal zones using synthetic aperture radar (SAR) imagery is a challenge due to the fact that many types of coastal zone have similar backscattering characteristics. In this letter, we propose an unsupervised method based on a three-channel joint sparse representation (SR) classification with fully polarimetric SAR (PolSAR) data. The proposed method utilizes both texture and polarimetric feature information extracted from the HH, HV, and VV channels of a SAR image. The texture features are extracted by applying a wavelet transform to a SAR image, and then sparsely represented based on the correlation among the three channels. The polarimetric features, i.e., the scattering entropy and scattering angle from the H/α model, are also sparsely represented. A joint SR algorithm using both texture and polarimetric features is constructed to establish target dictionaries. An orthogonal matching pursuit algorithm is then used to calculate sparse coefficients. Hybrid coefficients are inputted to the kernel support vector machine for a fully PolSAR image classification. We applied the proposed algorithm to an Advanced Land Observing Satellite-2 L-band SAR image acquired in the Yellow River Delta, China. The classified land types are validated against the official survey map. The algorithm performs well in distinguishing six coastal land-use types. A comparison study is also conducted to show that proposed algorithm outperforms two commonly used classification methods.

This letter investigates the bistatic radar response of soil moisture and surface roughness of bare soil surfaces at L-band using the advanced integral equation model (AIEM). It focuses on the use of bistatic geometries away from the specular region. To better explore the potential of bistatic scattering for soil moisture sensing, both polarized and angular scattering coefficients, and their combinations, are evaluated using a defined sensitivity index. The results show that sensitivity is enhanced in a bistatic mode compared with the monostatic case. Using a combination of dual polarized and angular data suppresses an undesired impact of the surface correlation function. Moreover, the forward region is preferred to soil moisture sensing regardless of the surface correlation function. Among the combinations investigated, the dual angular observation reduces the influence of surface roughness, preserves a good sensitivity to soil moisture response, and thus seems to be a good candidate for soil moisture sensing in bistatic configuration.

Two cases of mode-2 internal solitary waves (ISWs) induced by an anticyclonic eddy (AE) were clearly present in two synthetic aperture radar images acquired in the South China Sea (SCS) in April 2001. Similar ISWs were repeatedly observed in the same area one month later in May, but in that case, the ISW patterns indicated that they were regular mode-1 ISWs. To confirm that those ISWs in April are of mode-2 type, we analyze the in situ and other remote sensing data and propose two possible mode-2 ISW generation processes: 1) an eddy-induced change of the water stratification, which results in favorable hydrographic conditions for internal wave generation, and 2) the resonance between mode-1 internal tides and AE excites mode-2 internal tides, and the mode-2 internal tides disintegrate into mode-2 ISWs. The sea level anomaly and sea surface temperature data in April 2001 show that an AE existed in this area in April. Argo profile data within AEs from 1990 to 2014 in the SCS are used to show how an AE affects the vertical water properties and creates favorable conditions for mode-2 ISWs, and these conditions did exist in the study area in April 2001. The observed mode-2 ISW cases provide the first evidence of the existence of eddy-induced mode-2 ISWs in the SCS.

In this paper, we present the synthetic aperture radar (SAR) observation of the detailed sea surface wind patterns associated with fetch in the Bohai Sea, China. We then implemented the WRF model to simulate the entire processes of this weather event. WRF model results show the dynamics and evolution of this event.

Mode 2 internal wave (IW) signatures are observed by the RADARSAT-1 synthetic aperture radar (SAR) in the Northeast South China Sea on April 07, 2001. In this study, we prove that the conditions of the study area are favorable for the generation and propagation of mode-2 waves to appear in SAR imagery. The generation mechanism of this mode 2 IW is mode-1 IW evolves into mode-2 convex IW packets when shoaling.

Synthetic aperture radar (SAR) images reveal the surface imprints of atmospheric occluded fronts. An occluded front is characterized as a low-wind zone located between and within two zones of higher winds blowing in the opposite directions on the left and right sides of the occluded front. A group of four SAR images reveal that the width of an individual occluded frontal zone and the wind magnitudes outside fronts vary greatly from case to case. In this paper, we performed a case study to analyze an occluded front observed by an Environmental Satellite (Envisat) Advanced SAR and ASCAT scatterometer along the west coast of Canada on November 24, 2011. The two-way interactive, triply nested grid (9-3-1 km) Weather Research and Forecasting (WRF) model was utilized to simulate the evolution of the occluded front. The occluded front moved toward the east during a 24-h model simulation, and the movement between 18:00 and 21:00 UTC matched the occluded front positions derived from the concurrently collected surface weather maps; from the National Oceanic and Atmospheric National Weather Service archives. The WRF-simulated low-wind zone associated with the occluded front and ocean surface wind speed match well with the SAR and scatterometer wind retrievals. High wind outside the front zone became weaker during the front evolution, whereas the width of the occluded frontal zone was contracted laterally. Analysis of the WRF model derived potential temperature field suggests that the occlusion process occurred below the 800-mb level. The structure of the occluded front studied here not only follows the conventional conceptual model and also supports the findings of a novel wrap-up conceptual model for an atmospheric frontal occlusion process.

In this paper, an algorithm for estimating shallow-water depth from hyperspectral data is proposed. This methodology is based on the different responses of shallow-water reflectance on depth and substrate type. Two parameters-similarity coefficient and Pearson correlation coefficient-are introduced to describe the different types of responses, and a linear logarithm ratio model is established. Using Hyperion data over the coastal regions of O'Ahu Island and Saint Thomas Island, the retrieved bathymetry is compared with the airborne LIDAR data. The validation results show that the proposed method has good performance, and the root mean square error is less than 1.5 m over shallow water (shallower than 20 m).

Tropical cyclones generate powerful wind, torrential rainfall, high waves and damaging storm surge that affect coastal communities. Tracking and predicting cyclones is one of the most important tasks for meteorologists. In this study, we compare the hurricane/typhoon eye locations at the sea level observed by spaceborne Synthetic Aperture Radar (SAR) and its counterpart at the cloud level by the simultaneous infrared imagery. The vertical eye tilt at these two heights is compared with 850-200hPa vertical wind shear from SHIPS data. Five case studies show that the displacements vary from 10 to 22 km, with tilt direction oriented from downshear-left to downshear to downshear-right. These results are consistent with former studies.

In this study, a comparison of wind field measurements from HY-2A Scatterometer and U.S. National Data Buoy Center (NDBC)'s moored buoys is performed. These comparisons were made in Pacific Ocean and Atlantic Ocean over one month period of August 2012. The SCAT wind speed retrieval agreed well with the buoy measurements, with mean differences of -0.74 m/s and standard deviations of 1.52 m/s. The results indicate that SCAT-derived ocean surface wind speeds are as accurate as other scatterometer, such as Quikscat and ASCAT. However, the wind direction retrieval still has some problems need to be investigated in the future.

In this paper, we performed a comparison of wind speed from synthetic aperture radar (SAR), scatterometer, moored buoys and numerical model. These comparisons were made in near U.S. coast regions. The results indicate that SAR-derived ocean surface wind speeds are as accurate as the scatterometer and model wind products.

In this study, we analyze 83 synthetic aperture radar (SAR) images including 73 from RADARSAT-1 and 10 from ENVISAT that contain tropical cyclone eye information. We also obtain ancillary tropical cyclone intensity information from NOAA National Hurricane Center and Japan Meteorological Agency. Based on this information, we generate tropical cyclone morphology statistics. We found that majority of the hurricanes are in wavenumber 1 and 2 category. Marine atmospheric boundary layer rolls can also be extracted from SAR image.

The relationship between ocean surface dark/bright pattern of internal wave in SAR images and sea surface wind field is investigated. Two cases of satellite SAR images have ISWs are analyzed, and SAR signature of ISWs under different current and wind conditions are simulated. It is shown that the sequence of brighter and darker stripes of ISWs signatures in SAR images are relative to surface current. The wind direction and wind speed differences can affect the shape and brightness of ISWs stripes, but cannot change the sequence of the stripes.

Marine atmospheric boundary phenomena, i.e., atmospheric gravity waves (AGW), vortex streets and boundary rolls, modulate the surface wind field and will leave imprints visible on synthetic aperture radar (SAR) images. In this case study, we present an ENVISAT SAR observation of AGW offshore of the mountain Laoshan along the yellow sea coast of China. The Weather Research and Forecasting (WRF) weather model is used to simulate the development of this group of AGW. The model simulation and SAR observation agree reasonable well.

SAR-derived wind measurements are in the process of being implemented for operational production within NOAA's National Environmental Satellite, Data, and Information Service. For C-band ENVISAT and RADARSAT-1/2 data, the CMOD5 algorithm is being used; for ALOS data, a special L-band wind algorithm is employed. Comparisons of both C-band and L-band winds with ASCAT scatterometer wind measurements show biases of 0.58 m/s or less and standard deviations of 1.31 m/s or less. SAR wind vectors will be stored in a NetCDF4-formatted file and made available in a number of product formats via the NOAA CoastWatch program.

High spatial resolutions synthetic aperture radar (SAR) retrieved ocean surface wind field under actual conditions can be affected by several ocean surface features, such as artificial object, surface oil slicks and air-sea boundary layer stabilities etc. In this paper we present several case studies of those impacts, and a semi-empirical model to correct the air-sea boundary layer stabilities. We demonstrate that the new model helps to improve the wind retrieval accuracy in the Gulf Stream north wall areas.

In this letter, we generate a temporal/spatial matchup data set between QuikSCAT scatterometer and RADARSAT-1 synthetic aperture radar (SAR) wind products in offshore waters along the U.S. West Coast. Analysis of the resulting three-year database shows that, in general, the wind products from both sensors have characteristics similar to those reported in the literature. Then, we perform an error analysis in the space domain and find that there is significant discrepancy between the two wind products as the matchup points move closer to the coast. The root-mean-square error (rmse) and standard deviation (STD) between the two data sets increases markedly for points matched within about 100 km of the coastline. Beyond 100 km, the rmse, STD, and systematic bias become small and stable. In addition, an empirical relationship between QuikSCAT and SAR winds in coastal region is proposed. Thus, the bias and errors should be taken into account if the standard operational QuikSCAT wind products are used for forcing models in the coastal ocean.

In this paper, we perform a comparison of wind speed measurements from the ENVISAT Advanced Synthetic Aperture Radar (ASAR), the MetOp-A Advanced Scatterometer (ASCAT), the U.S. National Data Buoy Center's moored buoys, and the U.S. Navy Operational Global Atmospheric Prediction System (NOGAPS) model. These comparisons were made in near U.S. coast regions over a 17-month period from March 2009 to July 2010. The ASAR wind speed retrieval agreed well with the scatterometer and model estimates, with mean differences ranging from -0.69 to 0.85 m/s and standard deviations between 1.16 and 1.77 m/s, depending upon the ASAR beam mode type. The results indicate that ASAR-derived ocean surface wind speeds are as accurate as the ASCAT and NOGAPS wind products. Comparisons between ASCAT winds and synthetic aperture radar (SAR) winds averaged at different spatial resolutions show very little change. This demonstrates that it is suitable that the scatterometer wind retrieval geophysical model function, i.e., CMOD5, is used for SAR wind retrieval. The impact of C-band VV polarization SAR calibration error on wind retrieval is also discussed.

Synthetic aperture radar (SAR) observes the large-scale ocean surface wind field. With SAR instruments, we can actively monitor phenomena in the coastal ocean and marine atmospheric boundary layer at very high spatial resolution (on the order of tens of meters) in all weather conditions day and night. SAR observations are particularly useful in coastal regions where clouds are usually present, causing observation problems for visible and infrared sensors. SAR sensors onboard the RADARSAT-1/2, ENVISAT, ALOS, and other satellites can provide swath coverage of about 100 to 450 km, wide enough to cover oceanic and atmospheric meso-scale features. SAR has long been used to monitor the ocean surface wind field, vessel locations, oil spills, sea state, and sea ice at NOAA. In this paper, we present several case studies.

An atmospheric correction algorithm for ocean color data with multiple viewing and polarization information is proposed. The correction is based on using the directional and polarized data for 865 nm and 665 nm to estimate the properties of aerosols over the ocean. The aerosol models used in atmospheric correction consist of bimodal size distributions. And, a best-fit optical thickness is grossly determined using the generated look up table of the upwelling radiation. Moreover, Validation of the improved algorithm with the standard POLDER atmospheric correction algorithm is given.