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This is the third of three articles that quantify the high added value of frequent satellite microwave observations of the atmosphere (with a “refresh” time on the order of 1 min) to capture the dynamics of weather systems. Recent advances in small-satellite and microwave miniaturization, such as the “Temporal Experiment for Storms and Tropical systems” (TempEST) millimeter-wave radiometer developed at the Jet Propulsion Laboratory, are paving the way for the design of convoys of spaceborne radars that can directly observe the evolution of severe weather at very fine temporal scales. The analyses presented here are to establish the relation between passive microwave observations and their change in time and the underlying cloud variables and processes and to evaluate the sensitivity to the different physical and instrument parameters. In this third part, simulations are used to demonstrate and quantify the direct sensitivity of a time sequence of mm-wave radiances to the vertical structure of the vertical updrafts in convective storms. It is demonstrated that the brightness temperatures from a pair of low-Earth-orbit (LEO) radiometers maintaining a separation of 1 or 2 min can indeed be used to retrieve the column-maximum magnitude of the vertical wind as well as the height of the maximum. While such passive retrievals do not provide the vertical detail that a pair of radars would produce, the radiometers have a vast swath and therefore enable the observation of an entire storm. The application is therefore quite different from the case of a convoy of radars: rather than compiling radar statistics over multiple years, the radiometer-convoy measurements can be used to analyze every storm that is observed. [ABSTRACT FROM AUTHOR]

Radar sounders (RSs) are gaining importance in planetary missions thanks to their unique capability of providing direct measurements of subsurface (SS) structures. To support their design and data interpretation, several electromagnetic (e.m.) simulation techniques have been developed with enhanced capabilities for emulating the RS acquisition process. However, the raw simulated radargrams obtained from e.m. simulators are difficult to interpret and analyze without a focusing operation, which results in an underestimation of the RS detection performance. While frequency methods for range and azimuth compression of real RS data are well-established, their use on simulated data is not addressed in the literature and requires major modifications. This article presents a novel method that implements azimuth compression using unfocused and focused processing on simulated raw data. The proposed method is based on an adaptation of the range-Doppler algorithm to the case of raw data generated by a coherent RS simulator. The method is demonstrated in three case studies to show the similarity between simulated and real data processing: 1) simple geometries; 2) a simulated SHAllow RADar (SHARAD) radargram compared with the real data product; and 3) a real application scenario for supporting the design of a new RS instrument. The results indicate higher fidelity of the focused simulated data with the real data product and the target structure, confirming the usefulness of the proposed approach in obtaining realistic processing of simulated radargrams. [ABSTRACT FROM AUTHOR]

Spaceborne radar sounders are high frequency (HF)/very high frequency (VHF) nadir-looking sensors devoted to subsurface investigations. Their data interpretation can be severely hindered by off-nadir surface clutter. Recent literature showed that the clutter suppression capabilities of this class of systems can be greatly enhanced by deploying an array of orbiting sensors in formation flight synthesizing a narrow radar antenna beam. In this article, we assess the capability of distributed radar sounding to discriminate clutter from subsurface returns by exploiting direction of arrival (DOA) estimation techniques. This is achieved by first outlining an approach for designing and evaluating the distributed radar sounder DOA estimation performance as function of the radar system parameters (e.g., intersensor distance) and external noise factors such as ionospheric scintillations. Then, the theory is complemented by radar simulations of several acquisitions over Greenland assuming a variety of subsurface geometries. The simulations confirm that clutter discrimination through DOA estimation is a viable approach to further improve the array capability in disambiguation of subsurface echoes from surface ones. [ABSTRACT FROM AUTHOR]

Weather radar echo extrapolation, which predicts future echoes based on historical observations, is one of the complicated spatial–temporal sequence prediction tasks and plays a prominent role in severe convection and precipitation nowcasting. However, existing extrapolation methods mainly focus on a defective echo-motion extrapolation paradigm based on finite observational dynamics, neglecting that the actual echo sequence has a more complicated evolution process that contains both nonlinear motions and the lifecycle from initiation to decay, resulting in poor prediction precision and limited application ability. To complement this paradigm, we propose to incorporate a novel long-term evolution regularity memory (LERM) module into the network, which can memorize long-term echo-evolution regularities during training and be recalled for guiding extrapolation. Moreover, to resolve the blurry prediction problem and improve forecast accuracy, we also adopt a coarse–fine hierarchical extrapolation strategy and compositive loss function. We separate the extrapolation task into coarse and fine two levels which can reduce the downsampling loss and retain echo fine details. Except for the average reconstruction loss, we additionally employ adversarial loss and perceptual similarity loss to further improve the visual quality. Experimental results from two real radar echo datasets demonstrate the effectiveness of our methodology and show that it can accurately extrapolate the echo evolution while ensuring the echo details are realistic enough, even for the long term. Our method can further be improved in the future by integrating multimodal radar variables or introducing certain domain prior knowledge of physical mechanisms. It can also be applied to other spatial–temporal sequence prediction tasks, such as the prediction of satellite cloud images and wind field figures. [ABSTRACT FROM AUTHOR]

For a space-based radar system with hybrid baseline, the problem of clutter angle-Doppler spectral broadening poses a significant challenge to clutter cancellation in the terrain fluctuant observation scene. To improve the robustness of clutter suppression, this article proposed a homogeneous sample selection method based on a novel concept of generalized spatial spectrum density function (GSSDF). This method can be summarized as three crucial steps. First, the GSSDF was constructed by the prior information of digital elevation model (DEM) data, radar system parameters, and the backscattering model. Then, the angle of deflection (AOD) and the equivalent bandwidth (EBW) of GSSDF were adopted to measure the diffusion of clutter spectral. Subsequently, the sample selection criterion was established by a new threshold detection strategy, but the key here is that an appropriate detection threshold of the AOD and EBW can be determined by the characteristic of filter response. To summarize, this approach ensures that the training samples sharing similar clutter properties can be selected to estimate clutter covariance matrix (CCM), thereby enhances clutter suppression capability. Finally, the experimental results demonstrated that the proposed method can obtain better clutter suppression performance than the other contrast methods. [ABSTRACT FROM AUTHOR]

Multiple-input and multiple-output synthetic aperture radar (MIMO SAR) is a very potential technique for high-resolution and wide-swath (HRWS) imaging due to the fact that it can provide more degrees of freedom. In this article, an innovative MIMO-HRWS SAR imaging system is introduced with segmented phase coding (SPC) waveforms. However, it is well-known that the echo separation issue is the most technical challenge for MIMO SAR. It has shown that the separation of orthogonal echoes from cross correlation interference can be achieved by scan-on-receive (SCORE). However, this method often requires large computing resources on board, especially for null-steering process. For this, we propose a two-stage spaceborne–ground echo separation scheme. This scheme is mainly divided into two steps: the first step is a real-time SCORE beamforming process on board to reduce the downlink data volume, and the second step is a bandpass–null-steering process to suppress the interfering echoes on the ground. Thus, this scheme allows for the low computing resources on board and sufficient suppression of the interference simultaneously. Following this scheme, the MIMO-HRWS SAR system enables its number of equivalent phase centers nearly double that of the azimuth multichannel SAR system, leading to higher resolution and wider swath imaging. Finally, detailed simulation experiments for the MIMO-HRWS SAR imaging system are performed to verify the practicability and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

This article presents a new method to retrieve dynamic information from spaceborne Doppler radar observations. The method is based on a complex convolution Doppler resampling (CConDoR) formulation, which links the spaceborne pulse-pair (PP) correlation measurements to high-resolution PP products that are not affected by the spacecraft motion. The CConDoR formalism allows to easily simulate Doppler products via convolution products. The CConDoR representation enables also retrievals by deconvolution. This approach is illustrated using a Wiener deconvolution algorithm. Results are shown for simulations of the radar of the Earth Cloud Aerosol Radiation Explorer (EarthCARE; developed by the European and Japanese space agencies). The proposed corrections improve the accuracy of the measured mean velocities (by correcting for nonuniform beam-filling (NUBF) biases) and spectral widths (by correcting for the spectral broadening), which to the best of the authors’ knowledge is novel. [ABSTRACT FROM AUTHOR]

Geolocation is one of the key synthetic aperture radar (SAR) image processing procedures in SAR-based remote-sensing applications. The conventional SAR imagery geolocation model is based on the range-Doppler (RD) equation, which is derived from the Fresnel approximation of SAR. Currently, there is no other rigorous geolocation model for modern high-resolution SAR; however, this approximation may not be fully applicable for various modern SAR imaging modes and algorithms. This study aims to develop a novel SAR image geolocation model based on the SAR range history (RH) equation, which is derived from the basic SAR backscattered signal property. Moreover, a new geometric calibration model is presented for RH-based locations. Experiments based on real spaceborne SAR data are presented as examples, and the location accuracy of RH and RD location models are compared. The results show that the location accuracies of the RH and RD models are consistent under the existing SAR processing framework, but those for the RH model demonstrate better performance in the azimuth direction. In theory, the proposed RH model can be used for SAR image products generated from complex modes and non-Fresnel-approximation-based imaging algorithms, making it suitable for future SAR systems. [ABSTRACT FROM AUTHOR]

Currently, the spaceborne synthetic aperture radar (SAR) system transmits a great deal of data to the ground processing station and generates massive images daily, only a tiny fraction of which contains radio frequency interference (RFI). However, most of the existing RFI detection methods are based on the prior conditions in which the known image contains interference. In fact, it is difficult to learn whether SAR images contain RFIs without prescreening, so it is of great significance to the rapid and real-time screening and detection of RFI in SAR images. This article proposes a method to screen and detect RFI from massive SAR images simultaneously. 1) We construct an approximate RFI-free background image by using the preprocessed time-series SAR images acquired in the past. 2) We generate difference images based on the image change detection method and analyze them by using an adaptive threshold, then calculate the entropy of all difference images to complete the preliminary screening of the RFI-containing). According to the preliminary results, we remove the RFI-containing parts; after reconstructing the background, we repeat step 2 with the images to be detected and obtain the final screening and detection results. Massive experimental results based on Sentinel-1 images validate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

For the design of present and next-generation spaceborne SAR missions, constantly increasing data rates are being demanded, which impose stringent requirements in terms of onboard memory and downlink capacity. In this scenario, the efficient quantization of SAR raw data is of primary importance since the utilized compression rate is directly related to the volume of data to be stored and transmitted to the ground, and at the same time, it affects the resulting SAR imaging performance. In this article, we introduce the performance-optimized block-adaptive quantization (PO-BAQ), a novel approach for SAR raw data compression that aims at optimizing the resource allocation and, at the same time, the quality of the resulting SAR and InSAR products. This goal is achieved by exploiting the a priori knowledge of the local SAR backscatter statistics, which allows for the generation of high-resolution bitrate maps that can be employed to fulfill a predefined performance requirement. Analyses of experimental TanDEM-X interferometric data are presented, which demonstrates the potential of the proposed method as a helpful tool for performance budget definition and data rate optimization of present and future SAR missions. [ABSTRACT FROM AUTHOR]

Image fusion effectively solves the trade-off among spatial resolution, temporal resolution, and spectral resolution of remote sensing sensors. However, most of existing methods focus on the fusion of two of the spatial, temporal, and spectral metrics of remote sensing images. The few spatial–temporal–spectral fusion (STSF) methods available are mainly for fusing Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images, which are not suitable for the characteristics of the spaceborne hyperspectral images (HSIs) with low temporal resolution, such as Hyperion, ZY-1 02D, and PRISMA. For this purpose, we proposed a novel generalized linear spectral mixing model for STSF (GLMM-STSF). In the method, the GLMM is introduced into the STSF problem, and the temporal variations of images at different times are transferred to the endmember and abundance matrix variations of images for estimation. To the best of our knowledge, for the first time, the STSF task of remote sensing images is handled from the perspective of spectral unmixing. Compared with the existing STSF fusion methods, our method targets the task of fusing spaceborne HSI with low temporal and spatial resolutions with multispectral image (MSI) featured by high temporal and spatial resolutions. Taking the STSF of ZY-1 02D hyperspectral and Sentinel-2 multispectral real datasets as an example, comparisons with related state-of-the-art methods demonstrate that our proposed method achieves superior fusion performance. [ABSTRACT FROM AUTHOR]

A large time-bandwidth product bistatic radar offers several advantages in high-resolution target detection and motion parameter estimation, but the scale factor and inner-pulse Doppler will also be introduced in the radar echoes when detecting high-speed targets. Under this condition, the conventional matched filter will cause a certain energy loss and a nonnegligible shift of the range center, which is called mismatch effect. Considering the special geometries of the bistatic radar, we first establish the precise echo signal (PES) model to describe the radar echoes for high-speed targets in a space–air-based bistatic radar system. By analyzing the mismatch effect within the pulse and the migration between pulses, we have obtained the mathematic relationship between the scale factor, inner-pulse Doppler shift, range, equivalent velocity, and acceleration. Following that, we perceive that the parameters of the matched filter are related to the target motion parameters, i.e., a priori unknown, which inspires us to propose an iterative coherent integration method to achieve the intrapulse and interpulse integration. A PES model matched filter with initial parameters is defined and a roughly motion parameter estimation is acquired by the PES model-based Keystone transform (PES-KT) and inner-pulse chirp Fourier transform (CFT). The estimated parameters are used for matched filter construction and coherent integration. This process is performed over multiple iterations to provide an accurate motion parameter result. In the end, a target detection experiment is given to show the effectiveness of the proposed method using space–air-based bistatic radar. [ABSTRACT FROM AUTHOR]

Due to the minimum antenna area constraints of synthetic aperture radar (SAR), high-resolution and wide-swath (HRWS) imaging is difficult to be achieved using classical modes, such as scan, spotlight, and so on. Range ambiguity is one of the main technical challenges limiting HRWS imaging in spaceborne SAR. Inspired by the cocktail party effect, an advanced scheme based on blind source separation (BSS) is put forward to suppress the range ambiguity of spaceborne SAR in this article. In the scheme, multiple subantennas are used to collect multiple echo datasets, which are given different patterns. In this way, the echo signal of the desired region and that of the ambiguous region are weighted using different coefficients. During postprocessing, a detailed flow with the BSS algorithm is proposed to separate the desired echoes from those datasets. To verify the scheme, point target and scene simulations based on GF-3 are carried out. The results indicate that the range ambiguity can be reduced by more than 15 dB using the proposed scheme, the processing flow is effective, and the scheme has a good performance without increasing the complexity of the system design under the current SAR system conditions. The scheme has the potential to be adopted in future spaceborne SAR missions. [ABSTRACT FROM AUTHOR]

Nowadays, Sentinel-1 (S-1) synthetic aperture radars (SARs) operating in wave mode and the real aperture radar (RAR) called Surface Waves Investigation and Monitoring (SWIM) onboard the China-France Oceanography SATellite (CFOSAT) are the only two kinds of spaceborne radars providing directional ocean wave information globally. To quantify the absolute uncertainties in the swell wave heights of a specific wave system (Hss) observed from these two spaceborne sensors, a triple colocation error model is exploited via WaveWatch III (WW3) wave model hindcasts for the first time. After implementing spatiotemporal collocation, cross-assigning swell partitions, and rejecting suspicious data, a database of the optimal-matched Hss triplets (S-1, SWIM, and WW3) is determined over a one-year period (June 2020–June 2021). Qualitatively, traditional dual intercomparisons indicate the inconsistency between the Hss from both SAR and RAR radars in terms of systematic biases. Furthermore, the triple collocated error analysis quantitatively reveals that, at a global scale, SWIM onboard CFOSAT has the least uncertainty in Hss [~0.2-m root mean square error (RMSE) and ~11% scatter index (SI)] compared with S-1 SAR (0.35–0.50-m RMSE and 17%–26% SI depending on incidence modes) and WW3, under the assumption that the random errors of the three data sources are independent, indicating that the newly launched SWIM instrument is an invaluable resource of directional wave information for the scientific community. The results are discussed with respect to regional error characteristics along with a feasible explanation of error sources. The findings could be helpful for better understanding and synergistically exploiting the Hss datasets from these two spaceborne radars. [ABSTRACT FROM AUTHOR]

Polarization information can greatly improve the ability of detection, parameter retrieval, and classification for entomological radar. In recent, the instantaneous fully polarimetric measurement technique is used in entomological radar, so as to relax the restriction of invariant orientation during observation compared to the typical vertical-looking radar with linear-polarized rotation configuration. However, the fully polarimetric measurement is based on a multichannels’ radar system, and the imbalance and crosstalk between channels, namely, polarimetric errors, will severely affect the measurement of the polarimetric scattering matrix (PSM). Thus, polarimetric calibration is essential to obtain the accurate polarization information of targets. In contrast to inefficient and manual calibration methods, a data-driven polarimetric calibration method is proposed based on the assumption of reciprocity and bilateral symmetry of insects in this article. The polarimetric errors are elaborately decomposed into two components, which could be estimated by reciprocity and bilateral symmetry, respectively. In addition, because the proposed method is data-driven, the calibration process could be automatically executed to compensate for the temporal-variant polarimetric errors induced by temperature change and, thus, suitable for the long-term operation of entomological radar. Simulations and experiments are carried out to evaluate the performance of the proposed calibration method. Results show that it could achieve high accuracy when the number of insects used as calibrators is large enough. The proposed method has been applied in Ku-band high-resolution fully polarimetric entomological radars for cross-border migratory insect observation in Yunnan province, China. [ABSTRACT FROM AUTHOR]

Unsupervised methods play an essential role in polarimetric synthetic aperture radar (PolSAR) image classification, where labeled data are difficult to obtain. However, there is still a large gap between existing unsupervised learning methods and supervised learning methods. Without the semantic constraints of labeled data, pixels within the same category are often misclassified into different categories, leaving the output to be messy. To address the previous issue, this article proposes a fully unsupervised pipeline for training convolutional neural networks (CNNs). The pipeline combines low-level superpixels and high-level CNN semantic features for high-quality pseudolabel generation. It effectively eliminates the misclassified pixels by voting within the superpixel blob while preserving the sharpness of edges. With the training process of the model, the quality of the generated labels is getting improved. Experiments on airborne [experimental airborne SAR system from Germany (ESAR)/airborne synthetic aperture radar from America (AIRSAR)] and spaceborne (RadarSat2) PolSAR images prove the effectiveness of the proposed method (measured with overall accuracy (OA), average accuracy (AA), and Kappa metrics). Our method outperforms the previous unsupervised methods (H/alpha-Wishart, SM-Wishart, FDD-H, DEC, and VQC-CAE) with a large margin and even has comparable performance to the supervised CNN model [fully CNN (FCN)]. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting is an important task, which can be used in numerous applications. The key challenge of the task lies in radar echo map prediction. Previous studies leverage the convolutional recurrent neural network (ConvRNN) to address the problem. However, the approaches are built upon mean square losses, and the results tend to have inaccurate appearances, shapes, and positions for predictions. To alleviate this problem, we explore the idea of adversarial regularization and systematically compare four types of generative adversarial networks (GANs), which are the combinations of GAN/Wasserstein GAN (WGAN) and its multiscale version. Extensive experiments on a real-world radar dataset and four typical meteorological examples are conducted. The results validate the effectiveness of adversarial regularization. The developed models show superior performances over the existing prediction approaches in the majority of circumstances. Moreover, we find that the WGAN regularization often delivers better results than the GAN regularization due to its robustness, and the multiscale WGAN, in general, performs the best among all the methods. To reproduce the results, we release the source code at https://github.com/luochuyao/MultiScaleGAN and the test system at http://39.97.217.145:80/. [ABSTRACT FROM AUTHOR]

The total mean square slope (mss) of large-scale (in comparison with the scale of radar wavelengths) ocean waves can describe the roughness of the ocean surface. This important parameter has found many applications and can be determined by the dependence of large-scale mss on azimuthal angles. The Surface Waves Investigation and Monitoring instrument onboard the China France Oceanography Satellite (CFOSAT) can provide a 2-D normalized backscattering radar cross section (NRCS) for 2-D global ocean-surface wave detection. In this article, the 2-D NRCS is used to calculate the total large-scale mss via two methods. The first method is constructed from regression, while the second scheme is derived from a three-solution method. Finally, the total large-scale mss is calculated using these methods, and the map of the distribution of the global total mss is presented for the first time. Our results show that the two sources of total large-scale mss obtained are consistent with each other. CFOSAT can provide a 2-D total large-scale mss map and be used as a new data resource for global applications. [ABSTRACT FROM AUTHOR]

Exploring polarimetric diversity of synthetic aperture radar (SAR) data is directly applicable to conventional monostatic cases. For this, the most used convention is the backscatter alignment. While establishing important advantages for the monostatic case (possibility to have equal values on the cross-polarimetric channels), it has been proven to introduce some difficulties for the bistatic case. This appears in relation to the so-called conjugate similarity operation, when (mathematically) asymmetric scattering matrices occur. In this article, we propose the detailed algorithm which provides a solution to the conjugate similarity operation, in the case of general scattering matrices. The proposed algorithm is based on the real representation (RR) matrix transformation. Furthermore, we investigate the characterization of canonical bistatic scatterers (three elementary targets). Raw bistatic polarimetric signals are obtained using simulations with a computationally electromagnetic (EM) software, capable of complete EM analysis. The eigenvalue classification illustrates the potential of additional information brought using the proposed RR scattering matrix (RRSM). The presence of complex eigenvalues is investigated in relation to the bistatic angle and one nonreciprocity parameter. [ABSTRACT FROM AUTHOR]

Earth-based repeat-pass synthetic aperture radar (SAR) interferometry (InSAR) is a powerful tool to obtain the high-precision lunar terrain and deformation information, due to the short revisit period as well as the all-weather, long-time, and large-coverage characteristics of radar lunar observation. In this article, the baseline formation mechanism is investigated. Theoretical analyses show that the interferometric baselines mainly come from the lunar libration; however, the lunar libration may also result in a significant change of baselines and further lead to serious geometric decorrelation. To address this problem and ensure the Earth-based repeat-pass InSAR of the Moon, the precise relative motion model between the radar and the lunar target, considering lunar libration, is established, and the subradar point (SRP) wagging phenomenon is analyzed. Then, to evaluate the effects of SRP wagging on InSAR coherence, a geometric decorrelation model based on azimuth angle and incident angle is proposed. Based on that, the spatial–temporal baseline for Earth-based repeat-pass InSAR of the Moon is analyzed based on numerical simulation. Results show that the temporal baseline has obvious periodicity with a cycle of about 27 days. When the temporal baseline is an integral multiple of 27, the spatial baseline usually has the minimum value and the geometric decorrelation effect is relatively slight. Moreover, using the geometric decorrelation criteria, we examine the effects of radar frequency, position, and target location on the quantity of viable interferometric pairs, which has guiding significance for the selection of radar system parameters, radar position, and lunar target area. [ABSTRACT FROM AUTHOR]

Global navigation satellite system (GNSS)-based passive bistatic radar (PBR) has many advantages benefiting from the bistatic configuration and the excellent properties of the GNSS signals. In this article, the possibility of moving target indication (MTI) using a GNSS-based PBR is analyzed and illustrated by experiments. The power budget is first evaluated to investigate the capabilities and limitations of the GNSS-based PBR. Due to the low power density of GNSS signals, long-time integration is required to achieve long-range surveillance. In our previous work, a Radon Fourier transform (RFT)-based long-time integration algorithm has been proposed for GNSS-based PBR. However, the RFT-based methods are computationally inefficient. In this article, a novel MTI algorithm based on high frame rate image sequence (HFRIS) is introduced to the GNSS-based PBR. Benefiting from the avoiding of the iterative multidimensional parameter searching, it has a higher computational efficiency than the RFT-based methods. At the same time, the possibility of multistatic positioning for moving targets using the GNSS-based PBR is analyzed. It is shown that the absolute position of the target in three dimensions could be determined if at least three satellites are utilized to measure the range delay of the target return. To confirm our analysis, experiments are conducted which detects the airplanes using the GPS signals as illuminators of opportunity. The collected data are processed by both the RFT-based method and the HFRIS-based method. Both methods have successfully detected the airplane target at a distance of about 5 km and the HFRIS-based method shows a far better performance in terms of processing efficiency. The multistatic positioning experiment is also conducted and the estimated position of the target is well consistent with the values acquired by the flight record, which shows the potential of the GNSS-based PBR on multistatic operations. [ABSTRACT FROM AUTHOR]

In this article, we investigate the potential of using single-pass InSAR model-based approaches to retrieve dry snow parameters. Two InSAR scattering models of dry snow are considered: the dense-medium random volume over ground (RVoG) model and the simple variant of the full penetration (FP) model. A quasi-crystalline approximation (QCA)-based extinction analysis confirms the negligible extinction dependence of the InSAR observables at L/C/X-band for fresh dry snow. The FP models the low-frequency (L/C/X-band) InSAR phase as a single constraint of snow depth and density, which can be supplemented by an extra observation (e.g., InSAR coherence or in situ depth/density). The single-pass InSAR models and inversion approaches were validated using X-band InSAR data collected from a tower-based three-frequency (X/Ku-low/Ku-high) fully polarimetric TomoSAR system, where a multi-frequency polarimetric InSAR analysis and ground-to-volume ratio-based snow condition analysis were conducted. We also analyzed the sensitivity and error propagation of the single-pass InSAR phase and coherence in measuring dry snow depth/density. It was found that the X-band HH-pol FP-modeled single-pass InSAR phase along with RVoG-modeled coherence or in situ depth is capable of measuring snow water equivalent (SWE) with a 23–26 mm uncertainty (13–15%) and a 20–26 mm bias (12–15%) for dry snow SWE of 0.2 m, and with an optimal perpendicular baseline on the order of a tenth of the snow depth (0.8 m) at our test site. This single-pass InSAR approach with the FP model is potentially useful and thus needs further investigation for large-scale dry snow retrieval with a wide range of snow conditions using ground-based/airborne/spaceborne low-frequency (L/C/X-band) InSAR observations. [ABSTRACT FROM AUTHOR]

A new algorithm is proposed, which estimates two parameters of the particle size distribution (PSD) at each range bin from the global precipitation measurement’s (GPM’s) dual-frequency precipitation radar (DPR) data. The equation that expresses the relationship of the PSD parameters between adjacent range bins is derived. By including the attenuation effect within the bin in the discretized equation, the new algorithm alleviates the double-solution problem when attenuation within the bin is sufficiently large. The stability of the solutions to the equation depends on the value of the mean diameter $D_{m}$ and its gradient with respect to the range in the case of liquid precipitation. There is a critical diameter above which the backward processing of the equation provides a stable or moderately diverging solution, unlike the forward processing that often gives unstable solutions. To provide a set of initial conditions without using the surface reference technique (SRT) in the backward processing, an initialization method using the Hitschfeld–Bordan (HB) attenuation correction method is proposed and tested. The proposed algorithm may provide a tool for investigating the assumptions used in various algorithms. [ABSTRACT FROM AUTHOR]

This article outlines the archetype of a novel SpaceBorne Radar (SBR) in the Ka-band for space situational awareness (SSA) based on a code-division multiplexing (CDM) multiple-input–multiple-output (MIMO) payload transceiver. Considering small-size hypervelocity debris, the functional architecture of the fully polarimetric SBR is described, including key comparisons with previous works based on single-input–multiple-output (SIMO) configurations. SBR operations are clarified via timing hierarchies in surveillance mode, the complex data hypercube structure, and the low pulse repetition frequency (L-PRF) range and range rate search (RRRS) entailing a track while simultaneous search (TWSS) contacts collection strategy. Ancillary details on the SBR functional architecture provide paramount insights to ponder critical MIMO aspects and pave the way for key research and development efforts. Finally, numerical results provide a proof of concept for the signal processor upstream the constant false alarm rate-like (CFAR-like) detection block. [ABSTRACT FROM AUTHOR]

Multifrequency (MF) polarization synthetic aperture radar (PolSAR) systems can obtain more abundant and continuous earth resource information than single-frequency ones and have been widely used in the remote sensing community. However, there are relatively a few researches for the fine MF PolSAR image classification, which is an important part of remote sensing image interpretation. The main focus currently is on the single-frequency part. Therefore, for dual-frequency PolSAR image classification, this article proposes the dual-frequency attention fusion network (DFAF-Net). It is based on frequency-aware attention and adaptive feature fusion to improve classification performance. First, the dual-frequency PolSAR data is input into the joint feature extraction (JFE) module to obtain the joint feature representation. Meanwhile, two frequency-aware attention block (FAB) modules with the same structure are constructed, which, respectively, generate frequency-specific attention masks based on the guidance of different frequency data. Subsequently, these masks are used to weigh the joint features to highlight the description of different frequency-aware importance. The activated frequency-aware features can fully mine and utilize the complementary information provided by different frequencies, thereby enhancing the discrimination of similar landcover categories. Finally, the adaptive feature fusion block (AFFB) module is utilized to adaptively aggregate different frequency-aware features multiple times, which can effectively eliminate information differences. The obtained fusion features are more compact within classes and separable between classes, thereby effectively improving the classification performance. Experiments on three measured spaceborne and airborne dual-frequency PolSAR datasets verify that DFAF-Net can better perceive frequency characteristics and fully mine the complementary. Therefore, the classification accuracy is effectively enhanced, and the inaccuracy of single-frequency classification can be eliminated. Meanwhile, due to the introduction of the attention module, the classification performance of the proposed DFAF-Net is more competitive than the related deep learning networks. Quantitatively, the overall accuracy of DFAF-Net on the three datasets is respectively 98.30%, 97.42%, and 99.45%, which is better than other methods. [ABSTRACT FROM AUTHOR]

The highly correlated spectral features and the limited training samples pose challenges in hyperspectral image classification. In this article, to tackle the issues of end-to-end feature learning and transfer learning with limited labeled samples, we propose a unified multiscale learning (UML) framework, which is based on a fully convolutional network. A multiscale spatial-channel attention mechanism and a multiscale shuffle block are proposed in the UML framework to improve the problem of land-cover map distortion. The contextual information and the spectral feature are enhanced before the last classification layer based on three strategies in this work: 1) the channel shuffle operation, which was employed to learn the more effective spectral characteristics by disordering the channels of the feature map; 2) multiscale block, which considered the contextual information in multiple ranges; and 3) spatiospectral attention, which enhanced the expression of the important characteristic among all pixels. Three hyperspectral datasets, including two airborne hyperspectral images and one spaceborne hyperspectral image, were used to demonstrate the performance of the UML framework in both classification and transfer learning. The experimental results confirmed that the proposed method outperforms most of the state-of-the-art hyperspectral image classification methods. The source code is released at https://github.com/Hyper-NN/UML. [ABSTRACT FROM AUTHOR]

When an early warning radar installed in a spaceborne platform works in a down-looking mode to detect a low-altitude flying target, the severely broadened main-lobe clutter cannot be ignored, which will cause the deterioration of the moving target detection capability. To deal with this problem, a space–time adaptive processing (STAP) technique is proposed for effective clutter suppression based on the spatial–temporal 2-D joint filtering. However, the full-dimensional optimal STAP encounters the challenges of high computational complexity and large training sample requirement. Therefore, the dimension-reduced STAP technique becomes necessary. This article proposes a novel dimension-reduced STAP algorithm based on spatial–temporal 2-D sliding window processing. First, several sets of spatial–temporal data are obtained by using spatial–temporal 2-D sliding window. Then, for each set of data, the 2-D discrete Fourier transform is performed to transform the echo data into the angle-Doppler domain. Finally, jointly adaptive processing is performed to realize the clutter suppression. Compared with the conventional STAP algorithms, the improvements of this method over the existing methods are: 1) the proposed method requires fewer training samples due to the 2-D localization processing and 2) the proposed method can obtain the better clutter suppression performance with lower computational complexity. The feasibility and effectiveness of the proposed algorithm are verified by both simulated and real-measured multichannel surveillance radar data. [ABSTRACT FROM AUTHOR]

Due to the small size of foreign object debris (FOD) and varied complex weather conditions, the detection of FOD on airport runways is a great challenge. Radar is an important method for detecting FOD targets. However, almost all the existing systems are based on real apertures, which have disadvantages such as low azimuth resolution and susceptibility to rain interference. Here, an innovative FOD detection radar system based on arc-scanning synthetic aperture radar (AS-SAR) technology, the AS-SAR based FOD detection system (AS-FODR), achieves omnidirectional coverage with a very high azimuth resolution and the suppression of flicker clutter, such as rain drops in severe weather. According to the radar imaging simulation of a scene under rainy conditions and the information processing analysis of field experiments, a prototype system was built, and an efficient data process flow was proposed. In short, a one centimeter FOD target was detected on the runway more than 250 m away, proving that the use of AS-FODR is feasible and effective. [ABSTRACT FROM AUTHOR]

A quantum-state-based technique for change detection and analysis is presented. The method establishes a framework for change detection based on quantum states’ entanglement concept. The approach is applicable to quantum sensing (QS) (i.e., low field intensity, photon pairs) but is contingent to feasibility of quantum implementations and measurements. The main objective in this work is to develop a new change detection technique for classical radar/synthetic aperture radar (SAR) remote sensing that is inspired by quantum illumination (QI) and exploits the underlying characteristics and mathematical analogy between quantum-states and radar scatter-field-states. The described application of QI concept to develop a quantum signal processing (QSP) algorithm for classical radar/SAR data exploitations introduces a new change detection approach for SAR multi-pol exploitations. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (NNs) have proven their efficiency for image processing and are routinely used for image classification. In this article, we use them to convert radar measurements into water distance and ultimately into water levels of inland waterbodies. The measurements used are the successive echoes of the spaceborne radar altimeter signal on a waterbody, the radargram. We show that by using forward modeling with an accurate altimetry simulator, we can generate a sufficient amount of radargrams and train a deep NN accurately enough to obtain water level series from radargrams in a hydrology context. The method is validated at selected waterbodies by comparing these water level time series with in situ measurements on rivers whose width varied between 50 m and 4 km. The correlation of these time series with in situ data was over 0.95 with root mean square error (RMSE) between 26 and 43 cm. The results were also more robust than the Offset Center of Gravity (OCOG)/Ice-1 retracker time series of the same data. The validation shows that this automatic method performs generally as well as a carefully tuned manual method for removing outliers from the ranges provided by the state of the art classical retrackers used by the spatial hydrology community. This new tool is a big step toward a generic, global, and automated method to retrieve inland water levels from altimetry measurements. This goal is especially important in the context of continuously declining number of in situ measurements and of utmost importance for adequate water resources management at the global scale. [ABSTRACT FROM AUTHOR]

One of the challenges of the low-frequency spaceborne synthetic aperture radar (SAR) is that propagation through the ionosphere will introduce nonnegligible errors in the final SAR product. In the low-frequency bistatic SAR (BiSAR) system, the ionosphere will degrade the imaging performance and cause nonnegligible phase errors in the single-pass SAR interferometry application, which results in undesired errors of digital elevation model (DEM). In this article, a method that embedded into the focusing procedure is proposed, which aims to estimate and remove ionospheric effects on L-band spaceborne BiSAR system. First, the impacts of ionospheric effects on the BiSAR system are demonstrated, including the deterioration of imaging performance and the geometric distortion. Then, a method is proposed to correct ionospheric effects. Afterward, the simulations, including point targets and distributed targets, are carried out to verify the effectiveness of the proposed method. The imaging results and the DEM reconstruction results show that the proposed method can effectively estimate and remove ionospheric effects on spaceborne BiSAR systems. [ABSTRACT FROM AUTHOR]

In this study, we advance a new family of model-based decompositions adapted for dual-pol synthetic aperture radar data. These are formulated using the Stokes vector formalism, coupled to mappings from full quad-pol decomposition theory. A generalized model-based decomposition is developed, which allows separation of an arbitrary Stokes vector into partially polarized and polarized wave components. We employ the widely used random dipole cloud as a volume model but, in general, nondipole options can be used. The cross-polarized phase $\delta $ , and the $\alpha $ angle, which is a function of the ratio between wave components, measure the transformation of polarization state on reflection. We apply the decomposition to dual-pol data provided by Sentinel-1 (S1) covering different scenarios, such as agricultural, forest, urban, and glacial land-ice. We show that the polarized term of received polarization state is not usually the same as the transmitted one, and can therefore be used for key applications, e.g., classification and geo-physical parameter estimation. We show that, for vegetated terrain, depolarization is not the only influencing factor to S1 backscattered intensities and, in the case of vertical crops (e.g., rice), this allows the crop orientation effects to be decoupled from volume scattering in the canopy. We demonstrate that coherent dual-pol systems show strong phase signatures over glaciers, where the polarized contribution significantly affects the backscattered state, resulting in elliptical polarization on receive. This is a key result for S1, for which dual-pol phase analysis coupled to dense time series offer great opportunities for land-ice monitoring. [ABSTRACT FROM AUTHOR]

Conventional synthetic aperture radar (SAR) processing can be adapted to obtain 2-D high-resolution images of moving targets. The resulting images, however, suffer from layover effects, therefore making their interpretation more challenging. This problem can be dealt with by combining radar imaging with interferometry, which allows for 3-D target reconstruction. This article presents a 3-D imaging technique built upon the well-known global backprojection (GBP) algorithm, which has been adapted to focus moving targets rather than the stationary scene. The proposed algorithm is tested on real datasets acquired with a circular acquisition geometry. Using a nonlinear flight-path allows to monitor the target from multiple viewing angles, therefore overcoming the limitations that are common to stripmap SAR (e.g., shadowing effects, geometrically adverse cases, and so on). The obtained results, which were compared with independent ground measurements, demonstrate the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

An intrinsic challenge in the geophysical interpretation of low-frequency synthetic aperture radar (SAR) imagery of semitransparent media, such as ice sheets, is the position ambiguity of the scattering structures within the glacial volume. Commonly tackled by applying interferometric and tomographic techniques, their spaceborne implementation exhibits by orders higher complexity compared to missions relying on single SAR images, making them cost expensive or, in the context of planetary missions, even impossible due to limited navigation capability. Besides, even these sophisticated techniques are commonly biased due to inaccurate permittivity estimates, leading to geometric distortions up to several meters. We present a novel inversion procedure to estimate volume parameters of ice sheets, namely, the depth of the scattering layer within the glacial volume and the dielectric permittivity of the ice, based on single-image single-polarization SAR acquisitions. The information is inherent in the processed SAR data as phase errors on the azimuth signals resulting from uncompensated nonlinear propagation of the radar echoes through ice. We suggest a local map-drift autofocus approach to quantify and spatially resolve the phase errors and an inversion model to relate them to the penetration depth and permittivity. Testing the proposed technique using P-band SAR data acquired using DLR’s airborne sensor F-SAR during the ARCTIC15 campaign in Greenland shows promising results and good agreement with tomographic products of the analyzed test site. [ABSTRACT FROM AUTHOR]

Observations and analysis from the first airborne deployment of a G-band atmospheric radar, the Jet Propulsion Laboratory’s vapor in-cloud profiling radar (VIPR), are presented with a focus on ocean surface scatterometry and water vapor retrievals using the differential absorption radar (DAR) technique. We assess transceiver cross-calibration stability between the two DAR frequency channels using a combination of ground-based calibration measurements and airborne cloud observations that are validated by independent sensors. By comparing calibrated, unattenuated ocean surface backscatter measurements with existing models, we demonstrate the need for improvements in treating ocean surface scattering at G-band frequencies for future in-flight radar calibration applications. During science-oriented flights off the coast of California, we demonstrate partial column integrated water vapor (IWV) measurement capabilities with 0.3-mm precision in clear sky by performing a descent maneuver over an operational radiosonde (RS) launching site and comparing with the coincident in situ profiles to find a root mean square error (RMSE) of 0.5 mm. Furthermore, we present water vapor profiling and IWV measurements in the presence of clouds and precipitation when flying through a frontal weather system in the vicinity of Bodega Bay. By comparing with coincident RS profiles, we find an RMSE for in-cloud humidity profile retrievals of 1.1 ${\rm gm}^{-3}$ and precision of 0.6 ${\rm gm}^{-3}$ while the IWV retrievals reveal a bias from differential hydrometeor extinction of 5 mm. We provide a critical assessment of cloud-scattering-induced biases in the water vapor retrievals, as well as discuss a potential method for eliminating systematic error from the DAR measurements. [ABSTRACT FROM AUTHOR]

Radio spectrum bands allocated to spaceborne synthetic aperture radar (SAR) imagery are shared by multiple missions. In practical radio spectrum environments, these bands are also used by some ground radars, e.g., C-band weather radar. Due to this fact, radio frequency interference (RFI) may occur for a spaceborne SAR when its received signals contain the transmitted waveforms from another SAR or radar operating at the same frequency band. This particular class of RFI is usually linear-frequency-modulation (LFM) signals, which can cause bright radiometric artifacts in focused SAR images. Most existing signal processing approaches designed for addressing this problem belong to the class of preprocessing methods, which removes RFI in level-0 raw radar data before SAR focusing. In this article, we propose a postprocessing kernel—2-D SPECtral ANalysis (2-D SPECAN) filter, for removing the class of LFM RFI in level-1 SLC images. The filtering consists of three main steps: Step 1: focus LFM RFI artifacts in SLC images as point-like responses in the spectral domain via 2-D SPECAN; Step 2: perform 2-D notch filtering in the spectral domain to remove the most contribution of the RFI responses; and Step 3: transform the filtered spectrum back into the SLC image domain using the inverse operation of the 2-D SPECAN. For computation efficiency, we design a simplified processing flow and adopt a blockwise processing strategy. Experiments with several Sentinel-1 SLC images demonstrate that severe RFI artifacts in SLC images can be removed significantly by the proposed method. [ABSTRACT FROM AUTHOR]

Radio signals emitted by various sources, such as ground radars and broadcast/communication devices, can unintentionally cause radio frequency interference (RFI) to spaceborne synthetic aperture radar (SAR), degrading SAR image qualities to various degrees. Most existing methods tackle this problem by applying specially designed preprocessing steps to RFI-polluted level-0 SAR data before SAR focusing. However, such preprocessing is not widely used in spaceborne SAR, as there exist radiometric artifacts due to various RFI sources in the level-1 single-look complex (SLC) image products in many spaceborne SAR data, e.g., Sentinel-1 open data archives. To address this problem, in this article, we first propose a generic subspace model for characterizing a variety of RFI types, which reveals a low-dimensional structure of RFI subspace. Based on the proposed model, we next design a block subspace filter (BSF) for removing RFI artifacts in SLC SAR images directly. Experiments with ERS-2, ENVISAT/ASAR, Sentinel-1, and Gaofen-3 data are presented, and quantitative assessments based on numerical simulations are provided, which demonstrates the promising performance and application potentials of the proposed method. BSF is simple yet efficient and does not require performing preprocessing on level-0 raw data, which is helpful for users to obtain clean SAR images. MATLAB/Octave code implementation of BSF is available at https://github.com/huizhangyang/BSF. [ABSTRACT FROM AUTHOR]

In this article, the wideband ambiguity function (WBAF) of monostatic radar and that of bistatic radar, both operating with ultrawideband (UWB)-throb signals, are presented and used to calculate Cramér–Rao lower bounds (CRLBs) on the estimation accuracy of time delay and Doppler stretch in the case of monostatic radar and CRLBs on the estimation accuracy of receiver-to-target range and relative-velocity of a moving point target in the case of bistatic radar. Performance analysis is carried out to investigate the effects of the parameters that characterize the UWB-throb signal, i.e., pulse scaling factor, initial pulse repetition frequency (PRF), throb rate, and signal duration, and the parameters defining the bistatic-radar geometry, i.e., receiver-to-target range, baseline distance, and elevation angle, on the estimation accuracy of the monostatic-CRLBs and on that of the bistatic-CRLBs. Computer plots demonstrate that the estimation accuracy of the monostatic-CRLBs and the bistatic-CRLBs is influenced by the aforementioned radar parameters. It is also shown that target relative velocity has no major effects on the estimation performance of monostatic-CRLBs and bistatic-CRLBs. [ABSTRACT FROM AUTHOR]

The surface wave investigation and monitoring (SWIM) aboard the China-France Oceanography Satellite (CFOSAT), a pioneer conically scanning wave spectrometer, was successfully launched on October 29, 2018. Its innovative configuration composed of one nadir and five rotating near-nadir beams is designed to simultaneously observe the directional wave spectrum at a global scale. In this study, we systematically implement the spectral analysis of the radar backscattering with the periodogram technique to obtain the fluctuation spectrum for each azimuth direction. The 2-D fluctuation spectrum of the three spectral beams ($\theta = 6^\circ $ , 8°, and 10°) combines all the azimuth directions within one entire rotation of 360°. The case study demonstrates that the wave features (peak wavelength and direction) are roughly consistent between the estimated fluctuation spectrum and the collocated WaveWatch III wave slope spectrum. A marked up-to-downwave asymmetry of the fluctuation spectrum with larger spectral level in the upwave direction for all the three spectral beams is observed. A ratio is defined between the fluctuation spectrum within the [0°, 180°] sector relative to the [180°, 360°] sector. Statistics display that this ratio is greater than 1 when it denotes the up-to-downwave ratio and smaller than 1 for the down-to-upwave ratio. This observed spectrum asymmetry is linked to the asymmetric modulation from upwind to downwind. In addition, we employ such finding to help remove the 180° wave direction ambiguity from a practical point of view. Preliminary results of the direction ambiguity removal display a bias of 41.3°, 40.6°, and 36.7° for the beams. The 10° beam shows slightly better performance compared to the other two beams in terms of bias and standard deviation. This shall lay a strong basis for the operational implementation of such algorithm to resolve the direction ambiguity. [ABSTRACT FROM AUTHOR]

In this article, an effective clutter suppression algorithm is presented for the space-borne azimuth multiantenna high-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) system, which is based on the null-space technique. First, the echo of bistatic geosynchronous-low earth orbit (GEO-LEO) azimuth multiantenna HRWS SAR-ground moving target indication (GMTI) system is utilized to deduce the coarse-focused image of moving targets and clutter, where the Chirp Fourier transform (CFT) in azimuth is involved. Then, the matrix form can be utilized to describe the coarse-focused image of the multiantenna SAR system and the corresponding covariance matrix can be estimated. After that, the null-space is constructed using the covariance matrix corresponding to clutter, where at least a redundant channel freedom is required. Since the null-space vector is orthogonal to signal-space vector, it can be used to suppress the clutter. As an equivalent phase is brought by the slant velocity, the moving targets’ echo can be preserved during clutter suppression. Then, the optimization and suboptimization vectors for clutter suppression are introduced. It is worth noting that the proposed clutter suppression algorithm is robust for the antenna mismatch, that is, the antenna mismatch in phase and the corresponding position error. Finally, the theoretical investigations are validated using some simulation experiments, where the experiments for bistatic GEO-LEO and single-platform azimuth multiantenna HRWS SAR-GMTI system are both included. In addition, the real measured single-platform azimuth multiantenna HRWS SAR data experiments are also performed. [ABSTRACT FROM AUTHOR]

Geosynchronous spaceborne–airborne bistatic synthetic aperture radar (GEO-SA-BiSAR) consists of a GEO transmitter and airborne receiver, which has extensive application prospects in both civilian and military fields for its ability to generate high-resolution images of the ground target with frequent coverage and abundant scattering information. However, the Doppler bandwidth in this configuration exceeds the transmitted pulse repetition frequency (PRF), which leads to sub-Nyquist sampling. To solve this problem, a multireceiving technique has been applied to the receiver to increase the equivalent sampling rate and reconstruct an unambiguous image. In this article, we take a different approach to recover the unambiguous image for GEO-SA-BiSAR with fewer receiving channels. First, the accurate echo model is established based on the “non-stop-and-go” propagation delay model to lay the foundation of accurate imaging. After that, the GEO-SA-BiSAR imaging problem is modeled as a problem of joint sparse and low-rank matrices’ recovery. To reduce the computing time of the traditional recovery method, a modified alternating direction method of multipliers (M-ADMM) is proposed, where the computation and storage of the computational expensive observation matrix are avoided. Furthermore, an M-ADMM method with multiple receiving channels, which combines the recovery theory and multireceiving information, is also proposed to handle the severe sub-Nyquist sampling echo of GEO-SA-BiSAR. Simulation results reveal that the proposed method can recover the original image scene with high computational efficiency. Meanwhile, the number of receiving channels can be reduced compared with the multireceiving technique. [ABSTRACT FROM AUTHOR]

This article develops and examines methods for the production of real-world, very high-resolution imagery using a high-frequency drone-borne synthetic aperture radar (SAR) operating at short ranges. The significance of motion errors which lead to space-invariant/variant phase errors is discussed. Subsequently, an imaging algorithm capable of handling these errors is proposed and presented. The validity of the approach is tested through both simulation and experiment. We present novel short-range, fine-resolution imagery (less than 2 cm in cross-range) of an extended target area generated using a low-cost, drone-borne vehicular frequency-modulated continuous-wave (FMCW) radar operating at 77 GHz, without using a dedicated inertial navigation system (INS) or Global Positioning System (GPS). [ABSTRACT FROM AUTHOR]

Ocean microplastic concentrations are known to vary significantly by location, with especially high levels in the North Atlantic and North Pacific gyres. Most direct measurements come from plankton net trawling made in these regions; concentrations in other regions have been estimated by microplastic transport models that depend on large-scale ocean circulation patterns. However, global measurements of microplastic distribution and its temporal variability are lacking. A new method is presented for detecting and imaging the global distribution of ocean microplastics from space. The method uses spaceborne bistatic radar measurements of ocean surface roughness and relies on an assumed reduction in responsiveness to wind-driven roughening caused by surfactants that act as tracers for microplastics near the surface. Annual mean microplastic distributions estimated by the radars are generally consistent with model predictions. The spaceborne observations are also able to detect temporal changes that are not resolved by the models. For example, seasonal dependencies are observed at mid-latitudes in both Northern and Southern Hemispheres, with lower concentrations noted in the winter months. Time lapse images at finer spatial and temporal scales reveal episodic bursts of microplastic tracers in the outflow from major river discharges into the sea. This new method will provide better monitoring of ocean microplastics and will support future model development and validation. [ABSTRACT FROM AUTHOR]

Considering that synthetic aperture radar (SAR) images obtained directly after signal processing are in the form of complex matrices, we propose a complex convolutional network for SAR target recognition. In this article, we give a brief introduction to complex convolutional networks and compare them with the real counterpart. A complex activation function is applied to analyze the influence of phase information in complex neural networks. Inspired by the theory of network visualization, a special kind of transfer learning based on the electromagnetic property from the attributed scattering center model is applied in our networks to modulate the first convolutional layer. The experiment shows a better performance in terms of classification accuracy compared to random weight initialization. [ABSTRACT FROM AUTHOR]

Fostered by the upcoming BIOMASS mission, this article explores long-term trends of P-band temporal decorrelation over a tropical forest due to a time series of 617 days acquired during the TropiScat-2 experiment. The interest in this unique time series is twofold. First, it provides consistent statistics to monitor the yearly evolution of temporal coherences according to specific time scales of the BIOMASS tomographic and interferometric phases. Second, it provides key insights to explore new processing approaches with the combination of data from different orbit directions (ascending/descending) and different mission cycles separated by about seven months according to the current acquisition plan. For the first time, this study shows that 18-day coherences (corresponding to the time interval between the first and last acquisitions of the BIOMASS tomographic processing) can vary significantly according to rainy and dry seasons (medians from 0.3 to 0.9). The extension to time intervals of up to 90 days within both seasons and over two consecutive years puts forward the key role of the typical sporadic rainfalls occurring during dry periods in tropical rainforests, with a stronger impact on temporal coherence evolution compared to the more reproducible rainy seasons. Furthermore, outstanding values significantly above zero have been obtained for the 7- and 14-month coherences (medians of 0.35 and 0.2, respectively), opening the way to new methods of change detection. Overall, this study highlights the role of P-band temporal decorrelation not only as a disturbance factor for coherent applications but also as a relevant indicator of forest changes. [ABSTRACT FROM AUTHOR]

An alternate transmitting mode (ATM) is an important synthetic aperture radar (SAR) imaging mode as it can provide rich waveform design degrees of freedom to improve the system performance, especially to mitigate range ambiguities. However, the azimuth ambiguity issue caused by the differences between the autocorrelation functions of transmitted waveforms is ignored in existing studies. In this article, a deep understanding of the ambiguities in the ATM allows a correct evaluation of the ambiguity-to-signal ratio and the design of quasi-orthogonal nonlinear frequency modulation (NLFM) waveforms optimized for ambiguity suppression. Moreover, a novel azimuth compensation method is developed to remove the azimuth ambiguities caused by waveform diversity. Finally, detailed simulation experiments are carried out to verify the theoretical analysis. [ABSTRACT FROM AUTHOR]

Marine X-band radar has been suggested to be capable of monitoring significant wave heights in both offshore and open sea areas. In contrast to studies on offshore radar, significant wave height estimations from coastal radar images, which exhibit complicated radar backscattering features, have received little attention. This study proposes a method for retrieving the significant wave height from coastal areas that are often influenced by nononshore winds. The square root of the signal-to-noise ratio in radar images has been widely applied to estimate the significant wave height. However, nononshore wind cases show a poor correlation between the square root of the signal-to-noise ratio and the in situ significant wave height. In addition, the spectral shapes from radar images in nononshore wind cases are very different from those in onshore wind cases. To improve the significant wave height estimations from coastal radar images, we implement an artificial neural network algorithm. After training and testing the algorithm, we confirm that the estimated significant wave heights are more reliable for both onshore and nononshore wind cases if the square root of the signal-to-noise ratio, power from nearshore radar subimages, and in situ wind components are included in the input layer of the neural network. [ABSTRACT FROM AUTHOR]

Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and the effects of spectral variability in hyperspectral imaging, the ability to accurately and effectively unmixing the complex HSI still remains limited. To this end, this article presents a novel blind HU model, called sparsity-enhanced convolutional decomposition (SeCoDe), by jointly capturing spatial–spectral information of HSI in a tensor-based fashion. SeCoDe benefits from two perspectives. On the one hand, the convolutional operation is employed in SeCoDe to locally model the spatial relation between the targeted pixel and its neighbors, which can be well explained by spectral bundles that are capable of addressing spectral variabilities effectively. It maintains, on the other hand, physically continuous spectral components by decomposing the HSI along with the spectral domain. With sparsity-enhanced regularization, an alternative optimization strategy with alternating direction method of multipliers (ADMM)-based optimization algorithm is devised for efficient model inference. Extensive experiments conducted on three different data sets demonstrate the superiority of the proposed SeCoDe compared to previous state-of-the-art methods. We will also release the code at https://github.com/danfenghong/IEEE%5fTGRS%5fSeCoDe to encourage the reproduction of the given results. [ABSTRACT FROM AUTHOR]

For the convenience of target feature extraction, classification, and recognition, it is necessary to research the imaging projection plane (IPP) of spaceborne hybrid synthetic aperture radar (SAR) and inverse SAR (ISAR) imaging results of ship target on the sea surface. In this article, a novel analysis approach of IPP is proposed, by which the expressions of IPPs can be derived under the condition of different rotation and ship’s bow directions. Then, the representation of radar image can be predicted in advance, i.e., top view, side view, or hybrid view of the target. Finally, the simulated and real measured data are processed to generate the radar images to verify the correctness of conclusions in this article. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) real-time imaging is especially important for disaster emergencies and real-time monitoring applications with highly desired real-time requirements. Therefore, the continuous improvement of real-time imaging efficiency is an important development trend. At present, traditional real-time imaging algorithms based on constant pulse repetition frequency (PRF) have low accuracy when processing spaceborne SAR data with nonlinear trajectory. For this problem, the existing methods usually introduce some complex signal processing steps, such as scaling or interpolation processing, to improve the accuracy of the real-time imaging, but this will reduce its efficiency. Therefore, this article proposes a new real-time imaging algorithm based on variable PRF for nonlinear trajectory spaceborne SAR. By introducing the variable PRF, the proposed algorithm is equivalent to complete the complex signal processing steps in the radar signal transmission stage, which can greatly improve the efficiency of real-time imaging. Simulation experiments verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]