Radar Mode Research Articles

Unified Classification Framework for Multipolarization and Dual-Frequency SAR

For synthetic aperture radar (SAR), multi-polarization and multi-frequency modes greatly enrich the acquired earth resource information and have been widely applied in remote sensing fields. In this paper, we compare the classification capabilities of multi-polarization and dual-frequency SAR. To meet the objective of selecting consistent and complete polarimetric information, a unified classification framework is proposed. In the framework, covariance matrices are used directly as inputs instead of polarimetric indicators. Additionally, the Wishart mixture model (WMM) is utilized to characterize the statistical distribution of polarimetric SAR data. Besides, the data log-likelihood function is utilized to mitigate the influence of the initial values on the expectation-maximization (EM) algorithm. Then, among the combinations of four sample-to-subclass distances and two schemes for obtaining sample-to-class distances, the one with the best classification performance is selected as the default for this framework. In the experiments, the classification capabilities of full polarization (FP), compact polarization (CP), and dual polarization (DP) modes are first compared through the proposed classification framework. Then, we compare the classification capabilities of dual-frequency SAR in FP, CP, and DP modes. For PolSAR system design, it is necessary to strike a balance between demand indexes (classification performance, coverage width, and so on) and cost (such as budget, weight, and so on). The comparison results provide a reference for the optimization of polarization modes and frequency bands of the existing multi-polarization and dual-frequency SAR payloads and the design of future SAR systems.

A Novel Approach for Radar Passive Jamming Based on Multiphase Coding Rapid Modulation

Electromagnetic controlled surface (ECS) which can regulate the amplitude and phase of electromagnetic wave reflection characteristics has attracted extensive attention in recent years because of its radar stealth effect by reducing the radar cross section (RCS). However, radar detection cannot be avoided only by stealth in the energy field gradually. It is a novel pulse Doppler radar jamming mode proposed in this paper to utilize the self-correlation of radar-transmitted waveforms to result in signal processing and radar detection failure through the rapid time-domain variation of phase coding ECS. In principle, phase modulation alters the intra-pulse characteristics of the original signal and fundamentally interferes with the processing of the radar signal. In this paper, random and periodic phase coding sequences are proposed according to different forms of radar jamming. And through the derivation formula and simulation experiment, the jamming effect under multi-phase modulation is verified. Among them, the target energy is dispersed to the surroundings to form a wide envelope through random coding modulation, which leads to noise barrage jamming. While we use periodic sequence coding modulation, the target is shifted in the range-Doppler domain, causing misplaced coherence on the radar echo and deceptive jamming to the radar. Moreover, the technology can also be widely used in synthetic aperture radar (SAR) imaging, microwave measurement and other remote sensing fields. To further confirm the accuracy and efficiency of the proposed method, multi-parameter contrast experiments and parameter sensitivity analyses are conducted.

Modeling the Effects of Oscillator Phase Noise and Synchronization on Multistatic SAR Tomography

Recent results have highlighted the potential ability of bi- and multistatic synthetic aperture radar (SAR) tomographers to measure vegetation structure and surface topography. However, the quality of SAR tomographic measurements with multiple platforms is impacted by the phase instability in each platform’s oscillator. The phase noise, if uncompensated, may lead to degradation in the SAR data products such as increased sidelobe levels, reduced peak amplitude of the impulse response, and a low frequency phase modulation, among others. In this work, we model and examine the effects of oscillator phase noise on tomographic SAR signals for spaceborne missions flying in formation. A synchronization process is also adopted to help mitigate oscillator phase errors by measuring and predicting relative phase offsets at prescribed temporal intervals. A simulation tool was developed to examine the point target response as seen by realistic satellite constellations in low Earth orbit using different quality oscillators, radar configurations, and synchronization configurations. A first analysis of a multi-platform tomographic SAR mission suggests that a system without a dedicated physical link with minimal effects on the point target response may be achievable using current oscillators. Our analysis also shows that phase noise has differing effects on multistatic radar modes. Tomograms formed with a system operating in SIMO mode are the most affected by oscillator phase noise error, followed by MIMO, with negligible effects on the SAR-SISO mode. These trade studies and the simulation tool can be used to help inform the design of future multistatic radar missions.

Cooperative Electromagnetic Data Annotation via Low-Rank Matrix Completion

Electromagnetic data annotation is one of the most important steps in many signal processing applications, e.g., radar signal deinterleaving and radar mode analysis. This work considers cooperative electromagnetic data annotation from multiple reconnaissance receivers/platforms. By exploiting the inherent correlation of the electromagnetic signal, as well as the correlation of the observations from multiple receivers, a low-rank matrix recovery formulation is proposed for the cooperative annotation problem. Specifically, considering the measured parameters of the same emitter should be roughly the same at different platforms, the cooperative annotation is modeled as a low-rank matrix recovery problem, which is solved iteratively either by the rank minimization method or the maximum-rank decomposition method. A comparison of the two methods, with the traditional annotation method on both the synthetic and real data, is given. Numerical experiments show that the proposed methods can effectively recover missing annotations and correct annotation errors.

Evaluation of Sentinel-6 Altimetry Data over Ocean

The Sentinel-6 Michael Freilich (S6-MF) satellite was launched on 21st November 2020. Poseidon-4, the main payload onboard S6-MF, is the first synthetic aperture radar (SAR) altimeter operating in an interleaved open burst mode. In this study, the sea surface height (SSH), significant wave height (SWH) and wind speed observations from the Poseidon-4 Level 2 altimetry products from November 2021 to October 2022 are assessed. The assessment contains synthetic aperture radar mode (SARM) as well as low-resolution mode (LRM) data. The SSH assessment is conducted using range noise, sea level anomaly (SLA) spectral analysis and crossover analysis, whereas the SWH and wind speed assessments are performed against NDBC buoy data and other satellite altimetry missions. The performance of the Sentinel-6 altimetry data is compared to those of Sentinel-3A/B and Jason-3 altimetry data. The 20 Hz range noise is 3.07 cm for SARM and 6.40 cm for LRM when SWH is 2 m. The standard deviation (STD) of SSH differences at crossovers is 3.76 cm for SARM and 4.27 cm for LRM. Compared against the NDBC measurements, the Sentinel-6 SWH measurements have a root-mean-square error (RMSE) of 0.361 m for SARM and an RMSE of 0.225 m for LRM. The Sentinel-6 wind speed measurements show an RMSE of 1.216 m/s for SARM and an RMSE of 1.323 m/s for LRM. We also present the impacts of ocean waves on parameter retrievals from Sentinel-6 SARM data. The Sentinel-6 SARM data are sensitive to wave period and direction as well as vertical velocity. It should be paid attention to in the future.

SINR- and MI-Based Double-Robust Waveform Design.

Owing to cognitive radar breaking the open-loop receiving-transmitting mode of traditional radar, adaptive waveform design for cognitive radar has become a central issue in radar system research. In this paper, the method of radar transmitted waveform design in the presence of clutter is studied. Since exact characterizations of the target and clutter spectra are uncommon in practice, a single-robust transmitted waveform design method is introduced to solve the problem of the imprecise target spectrum or the imprecise clutter spectrum. Furthermore, considering that radar cannot simultaneously obtain precise target and clutter spectra, a novel double-robust transmitted waveform design method is proposed. In this method, the signal-to-interference-plus-noise ratio and mutual information are used as the objective functions, and the optimization models for the double-robust waveform are established under the transmitted energy constraint. The Lagrange multiplier method was used to solve the optimal double-robust transmitted waveform. The simulation results show that the double-robust transmitted waveform can maximize SINR and MI in the worst case; the performance of SINR and MI will degrade if other transmitted waveforms are employed in the radar system.

Robust Bayesian attention belief network for radar work mode recognition

Understanding and analyzing radar work modes play a key role in electronic support measure system. Many classifiers, for example those based on convolutional neural network (CNN) and recurrent neural network (RNN), are available for recognizing radar work modes as well as emitter types from their waveform parameters. However, the performance of these methods may suffer significantly when confronting different types of signal degradation, e.g., measurement error, lost pulse and spurious pulse. To tackle this issue, we in this paper develop a Bayesian attention belief network (BABNet) based on Bayesian neural networks in which the probability distribution over weights can help to enhance the model robustness for corrupted data. In particular, we adopt pre-trained CNN as the Bayesian inference prior. This not only accelerates the convergence speed, but also avoids the training process getting stuck in bad local minima. Meanwhile, instead of using RNNs which are difficult to be implemented in parallel, the combination of padding operation and attention module in the proposed BABNet enables CNN, as the backbone, to process sequential data with variable length. Extensive experiments are conducted to demonstrate the recognition capability and robustness of the BABNet in different environments.

A Joint Design of Radar Sensing, Wireless Power Transfer, and Communication Based on Reconfigurable Software Defined Radio

This paper proposes a compact three-mode base station capable of performing radar sensing, communication, and wireless power transfer (WPT) in collaboration with indoor sensor networks. With regard to the wireless sensor node, the base station transmits two-tone signals in the downlink to support its operation and provides two-way communication. The sensor node sends uplink information through backscattering using the third order intermodulation (IM3) product of the rectification. In the radar mode, a single-tone continuous wave (CW) is used to monitor if there is a moving target in the static environment. If a speed is detected, the transmit signal to the node is stopped, while the single-tone CW excitation will continue until the speed of the target is zero, and then the base station transmits a stepped frequency continuous wave (SFCW) signal to measure the distance of the target. The repeat between the two radar waveforms continues until the target is undetectable within the detection range. The software defined radio PlutoSDR is adopted as the base station. The system can wirelessly supply power and bi-directionally communicate with a CO2 sensor node 2 m away. It gives a range resolution of 2.5 cm and a minimum detectable speed of 0.25 m/s in the radar mode.

A novel radar operating mode identification approach based on variational relevance vector machine with chaotic gravitational search optimization

Radar operating mode identification is one of the top priorities in the electronic countermeasure field for threat prediction. However, the inference speed and accuracy of existing identification methods are still not satisfactory for threat detection, and some of them need extra prior knowledge to extract features. This paper introduces an identification approach for radar operating mode to solve these problems. The variational relevance vector machine (VRVM) is presented as the feature extractor and the basic unit of identification. An improved chaotic gravity search algorithm (CGSA) is developed to increase the search breadth and optimize the hyperparameters of VRVM. Threshold-one-versus-one (TOVO) is further proposed to screen the identification results to form the final ensemble method called CGSA–VRVM–TOVO. Experimental results indicate the CGSA–VRVM–TOVO achieves 99.44% accuracy without prior knowledge. Compared with XGBoost, Random Forest, and LightGBM, our approach is 1.94%, 0.55%, and 1.11% higher in accuracy, respectively, and twice as fast as the fastest method of them all.

Numerical modeling and data signal analysis of GPR array based on dual-field domain-decomposition time-domain finite element method

Traditional ground-penetrating radar (GPR) systems typically use a single transceiver antenna unit, which is challenging to achieve high-fidelity, ultra-narrow pulse transmission. It leads to problems such as limited detection resolution and insufficient data acquisition. This paper proposes a multi-polarization GPR array that uses multiple antenna elements to work synergistically. An electromagnetic field solving algorithm that enables parallel computation is introduced the time-domain finite element method of dual-field decomposition to achieve efficient and wideband simulation of the electromagnetic characteristics of large arrays. Each antenna element of the array system is treated as a subdomain during the solution process, and the electric and magnetic field values for each subdomain are calculated separately. Adjacent antenna elements are explicitly connected by equivalent surface currents on the interface of the subdomains. Through the method, numerically simulates the antenna array, establishes the spatial multi-polarization input signal, and analyzes the polarization response of the ground-penetrating radar (GPR) electromagnetic signal under the antenna array structure to the target body. The research results demonstrate that under the ground-penetrating radar array observation mode, the target's multi-polarization signal information can accurately and effectively reflect it's corresponding position and morphological relationship. The multi-polarized input signal can better compress the width of the wavelet and improve the range resolution of the target body. AAt the same time, the antenna array excites pulse signals with the same center frequency to be superimposed in the equivalent amplitude and same phase on space. The formed plane beam signal increases the radiation energy, improves the signal-to-noise ratio of the data, and eliminates the flicker caused by the radar cross-section changing with the observation angle, and weakens the influence of the discontinuity of the profile data in the horizontal direction caused by the antenna's unstable pattern and strengthens the data quality.

Deep-Learning-Based Feature Extraction Approach for Significant Wave Height Prediction in SAR Mode Altimeter Data

Predicting sea wave parameters such as significant wave height (SWH) has recently been identified as a critical requirement for maritime security and economy. Earth observation satellite missions have resulted in a massive rise in marine data volume and dimensionality. Deep learning technologies have proven their capabilities to process large amounts of data, draw useful insights, and assist in environmental decision making. In this study, a new deep-learning-based hybrid feature selection approach is proposed for SWH prediction using satellite Synthetic Aperture Radar (SAR) mode altimeter data. The introduced approach integrates the power of autoencoder deep neural networks in mapping input features into representative latent-space features with the feature selection power of the principal component analysis (PCA) algorithm to create significant features from altimeter observations. Several hybrid feature sets were generated using the proposed approach and utilized for modeling SWH using Gaussian Process Regression (GPR) and Neural Network Regression (NNR). SAR mode altimeter data from the Sentinel-3A mission calibrated by in situ buoy data was used for training and evaluating the SWH models. The significance of the autoencoder-based feature sets in improving the prediction performance of SWH models is investigated against original, traditionally selected, and hybrid features. The autoencoder–PCA hybrid feature set generated by the proposed approach recorded the lowest average RMSE values of 0.11069 for GPR models, which outperforms the state-of-the-art results. The findings of this study reveal the superiority of the autoencoder deep learning network in generating latent features that aid in improving the prediction performance of SWH models over traditional feature extraction methods.

A 60-GHz Hybrid FMCW-Doppler Radar for Vibration Detection With a Robust I/Q Calibration Method

In the field of noncontact sensing, both the continuous-wave (CW) Doppler radar and the frequency-modulated CW (FMCW) radar have their own superiorities. A combination of these two radar modes would be a suitable choice to accommodate various application scenarios. This article presents a 60-GHz hybrid FMCW-Doppler radar transceiver fabricated in a 65-nm CMOS process. An on-chip frequency quadrupler driven by a 15-GHz external signal source is utilized for the 60-GHz hybrid waveform generation. Based on the hybrid operating modes, a robust I/Q imbalance calibration method is also proposed. Specifically, in the Doppler radar mode, the imbalance factors are extracted by evaluating the quadrature beat signals during the FMCW period, and then the interferometry signals are calibrated to improve the detection performance. Unlike the trajectory-based ellipse-fitting method, the accuracy of the estimated imbalance factor is independent of the target displacement. Compared with the test-signal-based imbalance factors extraction ahead of the target detection, the real-time extraction during the target detection is more efficient and can also mitigate the estimation errors caused by environmental variations. With the proposed hybrid radar system and the I/Q calibration method, an 80-mm linear displacement is recovered with a 0.016-mm root mean square error (RMSE), and a periodic vibration with a 1- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> amplitude is detected with a 16.4-dB signal-to-noise ratio (SNR). Multiple-target detection is also performed in the FMCW radar mode, and the vibration trajectories are recovered accurately.

A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency.

Joint low-rank and sparse representation for micro-Doppler effects removed inverse synthetic aperture radar imaging

In terms of the target with micromotion parts, the readability of inverse synthetic aperture radar (ISAR) image of the main body is influenced by micro-Doppler (m-D) effects. Some facts, such as radar working modes and electromagnetic environment, may lead to sparse aperture, which further induces high side lobes and makes the removal of m-D effects more difficult. To jointly eliminate m-D effects and the interference introduced by sparse aperture, a novel m-D effects removed ISAR imaging algorithm is proposed. In this technique, sparse ISAR imaging with the removal of m-D effects can be established as an optimization model of joint low-rank and sparse representation, which is a quadruple constraint optimization problem, including the low rank of the high-resolution range profile (HRRP) of the main body, the sparsity of the HRRP of micromotion parts, the sparsity of the imaging result of the main body, and the noise constraint of the echo signal. Furthermore, the matrix factorization method is theoretically presented to simplify the optimization process of the nuclear norm, and the alternating direction method of multipliers is utilized to solve all constructed models efficiently. Experimental results based on both simulated and measured data demonstrate the superiority of the proposed algorithm.

Exploring the potential of Sentinel-3 delay Doppler altimetry for enhanced detection of coastal currents along the Northwest Atlantic shelf

In this study, we evaluate Sentinel-3A satellite synthetic aperture radar (SAR) altimeter observations along the Northwest Atlantic coast, spanning the Nova Scotian Shelf, Gulf of Maine, and Mid-Atlantic Bight. Comparisons are made of altimeter sea surface height (SSH) measurements from three different altimeter data processing approaches: fully-focused synthetic aperture radar (FFSAR), un-focused SAR (UFSAR), and conventional low-resolution mode (LRM). Results show that fully-focused SAR data always outperform LRM data and are comparable or slightly better than the nominal un-focused SAR product. SSH measurement noise in both SAR-mode datasets is significantly reduced compared to LRM. FFSAR SSH 20-Hz noise levels, derived from 80-Hz FFSAR data, are lower than 20-Hz UFSAR SSH with 25% noise reduction offshore of 5 km, and 55–70% within 5 km of the coast. The offshore noise improvement is most likely due to the higher native along-track data posting rate (80 Hz for FFSAR, and 20 Hz for UFSAR), while the large coastal improvement indicates an apparent FFSAR data processing advantage approaching the coastlines. FFSAR-derived geostrophic ocean current estimates exhibit the lowest bias and noise when compared to in situ buoy-measured currents. Assessment at short spatial scales of 5–20 km reveals that Sentinel-3A SAR data provide sharper and more realistic measurement of small-scale sea surface slopes associated with expected nearshore coastal currents and small-scale gyre features that are much less well resolved in conventional altimetric LRM data.

Application of hash function for generation of modulation data in RadCom system

The challenge of current radiocommunication systems is the efficient use of the frequency spectrum. One of the rapidly evolving tools is a RadCom system, which combines the functionality of a radar sensor with a communication interface. RadCom systems are used in several fields, and the presented work is focused on their applications in the automotive industry. The goal was to design a modulation technique and generate modulation data, which will be suitable for radar determination of the distance and velocity of targets, but also enable their mutual communication. A model of the RadCom system, which simultaneously allows a simulation of detection and communication of several moving targets, has been developed. Herein, a method of generating pseudo-random data based on the MD5 hashing algorithm is described. The proposed method of radar signal processing enables unambiguous detection of moving targets and determination of the position and velocity of these targets. Principles of synchronization and coding of a radar and communication mode of the RadCom system are explained. A numerical verification of the proposed algorithms using simulation tools is discussed in the final part of the work. The proposed system solution achieves more than 70% probability that the decoded message will not contain erroneous bits.

Resolving the High‐Latitude Ionospheric Irregularity Spectra Using Multi‐Point Incoherent Scatter Radar Measurements

AbstractTo provide new insights into the relationship between geomagnetic conditions and plasma irregularity scale‐sizes, high‐latitude irregularity spectra are computed using a novel Incoherent Scatter Radar (ISR) technique. This new technique leverages: (a) the ability of phased array Advanced Modular ISR (AMISR) technology to collect volumetric measurements of plasma density, (b) the slow F‐region cross‐field plasma diffusion at scales greater than 10 km, and (c) the high dip angle of geomagnetic field lines at high‐latitudes. The resulting irregularity spectra are of a higher spatiotemporal resolution than what has been previously possible with ISRs. Spatial structures as small as 20 km are resolved in less than 2 minutes (depending on the radar mode). In this work, we focus on Resolute Bay ISR‐North (RISR‐N) observations operating in the “imaginglptight” mode. In addition to having an unprecedented view of the size and occurrence of irregularities as they traverse the polar cap, we find that as the F‐region plasma density decreases below approximately 2.5 × 1010 m−3 at 350 km altitude the spectral power shifts to scale‐sizes lower than 50 km. Additionally, near magnetic local noon, the spectral power shifts to scales greater than 50 km, and from 15 to 6 magnetic local time, the spectral power shifts to scales lower than 50 km. This reflects the role of photoionization dominating high‐latitude ionospheric structuring in the polar cap.

Investigation of Stepped-Frequency Hybrid Modulation Radar for Respiration Monitoring, 2-D Localization, and Moving Target Tracking

This article presents a stepped-frequency hybrid modulation (SFHM) methodology, which is proposed to work in conjunction with a frequency scanning array to effectively perform respiration monitoring, 2-D localization for stationary human subjects, and target trajectory tracking for moving ones. Conventional frequency-shift keying (FSK) radar can accurately detect the absolute distance and small displacement but suffers from the problem of a maximum unambiguous range. In comparison, continuous-wave radar with a chirp signal has no range limitation but suffers from a limited displacement measurement accuracy. To solve the two problems, SFHM is proposed to make full use of the advantages of both FSK mode and chirp mode, collaboratively resolving each other&#x2019;s issues. In addition, the high flexibility of SFHM facilitates the implementation of a frequency scanning array, which provides a simple and cost-efficient means for fast beam steering. A prototype of the radar system is implemented, and a new signal separation algorithm was derived based on the SFHM theory. Various types of experiments confirm the ability of the radar to accurately provide the respiration and range information of stationary subjects and trajectory information of moving subjects.

High-Resolution Wide-Swath Ambiguous Synthetic Aperture Radar Modes for Ship Monitoring

This paper proposes two high-resolution, wide-swath synthetic aperture radar (SAR) acquisition modes for ship monitoring that tolerate ambiguities and do not require digital beamforming. Both modes, referred to as the low pulse repetition frequency (PRF) and the staggered (high PRF) ambiguous modes, make use of a wide elevation beam, which can be obtained by phase tapering. The first mode is a conventional stripmap mode with a PRF much lower than the nominal Doppler bandwidth, allowing for the imaging of a large swath, because the ships’ azimuth ambiguities can be recognized as they appear at known positions. The second mode exploits a continuous variation of the pulse repetition interval, with a mean PRF greater than the nominal Doppler bandwidth as the range ambiguities of the ships are smeared and are unlikely to determine false alarms. Both modes are thought to operate in open sea surveillance, monitoring Exclusive Economic Zones or international waters. Examples of implementation of both modes for TerraSAR-X show that ground swaths of 120 km or 240 km can be mapped with 2 m2 resolution, ensuring outstanding detection performance even for small ships. The importance of resolution over noise and ambiguity level was highlighted by a comparison with ScanSAR modes that image comparable swaths with better noise and ambiguity levels but coarser resolutions.

Automated Global Classification of Surface Layer Stratification Using High‐Resolution Sea Surface Roughness Measurements by Satellite Synthetic Aperture Radar

AbstractA three‐state global estimator of marine surface layer atmospheric stratification is demonstrated using more than 600,000 Sentinel‐1 synthetic aperture radar wave mode images at incidence angle ≈36.8°. Stratification is quantified using a bulk Richardson number, Ri, derived from collocated ERA5 surface analyses. The three stratification states are defined as unstable: Ri &lt; −0.012, near‐neutral: −0.012 &lt; Ri &lt; +0.001, and stable: Ri &gt; +0.001. These boundaries are identified by the characteristic boundary layer coherent structures that form in these regimes and modulate the surface roughness imaged by the radar. An automated machine learning algorithm identifies the coherent structures impressed on the images. Data from 2016 to 2019 are used to examine spatial and temporal variation in these state estimates in terms of expected wind and thermal forcing. This new satellite‐based approach for detecting air‐sea stratification has implications for weather modeling and air‐sea flux products.