Space-based Radar Research Articles

A discrete side-lobe clutter recognition method based on sliding filter response loss for space-based radar

Different from ground-based or airborne early warning radar, space-based radar (SBR) possesses large coverage capability. As a result, several discrete strong scatter points from the antenna side-lobe shares the same feature with the real targets in range-Doppler domain, which leads to false alarms when conducting constant false alarm rate (CFAR) detection process, and the detection performance with regard to SBR deteriorates seriously. In this paper, a discrete side-lobe clutter recognition method based on sliding filter response loss is proposed for space-based radar. Firstly, considering both the echo inhomogeneity and the limited degrees of freedom (DOFs) after dimension-reduced space-time adaptive processing (STAP), the sliding window design strategy is employed to segment range cells for the observation scene. Then, the images related to different range segments are registered after clutter suppression, in this way, the candidate target parameters, including the position information and the amplitude information are counted. On this basis, the reliable recognition scheme between the real target and the discrete side-lobe clutter can be realized by comparing these filter response losses. Compared with recent works, experimental results based on real measured data show that the proposed method significantly improves the fault-tolerant discrimination ability, which possesses high robustness in algorithm performance as well as good prospect in engineering application.

Dynamics and attitude control of space-based synthetic aperture radar

Abstract This work tackles the problem of attitude control of a space-based synthetic aperture radar with a deployable reflector antenna, representing a lightly damped uncertain vibratory system with highly nonlinear dynamics. A control strategy based on two identifiable in-orbit vector parameters is proposed to make the robust controller less conservative. The first parameter is used in the feedforward loop to achieve a trade-off between the energy efficiency of maneuvers and the amplitudes of the oscillatory response. The feedback loop utilizes the second parameter to accurately handle the controller-structure interactions by adaptive notch filters. The notch filters are included in the augmented plant at the design stage to guarantee closed-loop robustness against disturbances, unmodeled dynamics, and parametric uncertainty. The system’s robustness and specified requirements are confirmed by formal criteria and numerical simulations using a realistic model of the flexible spacecraft.

Mapping lava flow from the Kilauea eruption of 2018 in the east rift zone using space-based synthetic aperture radar

ABSTRACT Lava flows from volcanoes can significantly destroy infrastructure, although they rarely threaten human life. Mapping lava flows is essential to managing the volcanic hazard during eruptions and understanding significant changes in geomorphology after eruptions. In 2018, the eruption of Kilauea, Hawaii’s youngest and most active volcano, destroyed more than 700 homes, farmlands, and roads. Active lava flow tracking is crucial for providing timely warnings to evacuate residents and protect infrastructure. We constructed a map of the lava flow in the East Rift Zone (ERZ) during the Kilauea volcanic eruption in 2018 using remotely sensed satellite synthetic aperture radar observations. We processed 23 C-band Sentinel-1 A/B interferometric wide mode acquisitions captured from May 1 to 17 September 2018 and applied a coherent change detection (CCD) technique to detect minute changes in the target structure. A time series of coherence maps provided detailed spatial information on the movement of the lava flow fields in the ERZ. The overall lava flow map had an area of approximately 33.3 km2 and was verified with a lava thickness map provided by the United States Geological Survey that was generated by two digital elevation models using airborne light detection and ranging observations. Excellent agreement (>90%) among the lava flow observations suggests that the CCD technique is effective in monitoring lava flows during a volcanic eruption.

Range-Ambiguous Clutter Suppression for Space-Based Early Warning Radar Using Vertical FDA and Horizontal EPC

The range ambiguous clutter in space-based early warning radar is more severe compared with that in airborne radar due to the faster movement velocity. Meanwhile, the clutter angle-Doppler characteristics vary with range owing to Earth’s rotation. As a result, the mainbeam clutter returned from different ambiguous range occupies most of the Doppler spectrum, which leads to the degradation of the traditional space-time adaptive processing (STAP) performance in canceling clutter. Though frequency diverse array (FDA) based on multiple-input multiple-output can provide the ability to identify the range ambiguous clutter, it cannot perform well for space-based early warning radar because of the finite available vertical elements. In this paper, a novel STAP method, which utilizes the element-pulse coding (EPC) and FDA technique to mitigate the range ambiguous clutter, is proposed. Firstly, EPC is used to pre-whiten the most range ambiguous clutter via coding and decoding in horizontal elements and coherent pulses. Then the interval in the vertical frequency of the residual range-ambiguous clutter is expanded by FDA, and thus the targets and clutter from the vertical mainlobe are easily extracted using a spatial filter with a few vertical elements. Finally, the extracted clutter can be effectively suppressed through the traditional STAP method. Simulation results are provided to demonstrate the performance of the proposed method.

FY-3G Satellite Instruments and Precipitation Products: First Report of China’s Fengyun Rainfall Mission In-Orbit

Precipitation is one of the most important parameters in Earth system but is hard to measure. China began to develop satellites dedicated to precipitation measurements in the second generation of the FengYun polar-orbiting meteorological satellite program (FY-3). The first of total 2 rainfall missions scheduled, FY-3G, was successfully launched on 16 April 2023 and became the world’s third satellite to measure precipitation with space-borne radar after the tropical rainfall measuring mission in 1997 and global precipitation measurement core observatory in 2014. In this manuscript, we illustrate the platform of FY-3G and instruments mounted in great detail, with additional information about ground segments, designed sensor-based products, and retrieval of geophysical parameters. During the 4 months after launch, the specifications of the platform and instruments are under inspection as calibration and validation are carefully conducted. The first images captured by FY-3G are encouraging, and initial results show a strong capability for providing insights into all kinds of precipitation phenomena. The important work of data processing, such as data assimilation, data fusion between space-based and ground-based radar, and that between polar and geostationary satellites, as well as future applications in weather modification, has been prepared in advance. As a pioneer of China’s rainfall missions, FY-3G greatly improves our ability to provide global precipitation measurements, understand Earth’s water and energy cycle, and forecast extreme events for the benefit of society.

Achievable parameters of the X-band synthetic aperture radar based on small satellite

Space-based synthetic aperture radar systems are actively used for remote sensing of the Earth and have significant capabilities. Advances in microelectronics have made it possible to create and launch into orbit compact radars based on small satellites. Analysis of the capabilities of such radars is an actual problem. In this paper the issues of simulation and estimation of the antenna area, noise equivalent sigma zero, spatial and radiometric resolution of space-based synthetic aperture radars (SAR) operating on the basis of small spacecraft are considered. The necessary mathematical apparatus is given and the above values are calculated for the X-band SAR operating in strip mode at maximum resolution. The influence of the main technical parameters of space SAR on dimensions of antenna, on sensitivity, spatial and radiometric resolution is analyzed.

Research on Multi-Domain Dimensionality Reduction Joint Adaptive Processing Method for Range Ambiguous Clutter of FDA-Phase-MIMO Space-Based Early Warning Radar

The ground and sea clutter received by space-based early warning radar (SBEWR) has severely range ambiguous characteristics due to its platform location, and the non-stationary factor caused by Earth’s rotation makes the received clutter at different range ambiguous positions seriously broaden in the Doppler dimension. The complex clutter suppression performance of SBEWR obtained by traditional method is degraded significantly. To solve this problem and achieve better clutter suppression performance, a novel multi-domain adaptive processing method for clutter suppression is proposed in this paper. The proposed method introduced a range related signal processing domain based on conventional space–time domain by using frequency diverse array phase multiple-input multiple-output (FDA-Phase-MIMO) radar. In addition, a novel multi-domain joint dimensionality reduction structure was designed. The novel multi-domain joint adaptive processing using the proposed dimensionality reduction structure could not only obtain great clutter suppression performance of SBEWR, but also minimize the requirement of the number of selected auxiliary channels. Simulation examples show the effectiveness of the proposed method.

Ship Classification in Synthetic Aperture Radar Images Based on Multiple Classifiers Ensemble Learning and Automatic Identification System Data Transfer Learning

With the continuous development of earth observation technology, space-based synthetic aperture radar (SAR) has become an important source of information for maritime surveillance, and ship classification in SAR images has also become a hot research direction in the field of maritime ship monitoring. In recent years, the remote sensing community has proposed several solutions to the problem of ship object classification in SAR images. However, it is difficult to obtain an adequate amount of labeled SAR samples for training classifiers, which limits the application of machine learning, particularly deep learning methods, in SAR image ship object classification. In contrast, as a real-time automatic tracking system for monitoring ships at sea, a ship automatic identification system (AIS) can provide a large amount of relatively easy-to-obtain labeled ship samples. Therefore, to solve the problem of SAR image ship classification and improve the classification performance of learning models with limited samples, we proposed a SAR image ship classification method based on multiple classifiers ensemble learning (MCEL) and AIS data transfer learning. The core idea of our method is to transfer the MCEL model trained on AIS data to SAR image ship classification, which mainly includes three steps: first, we use the acquired global space-based AIS data to build a dataset for ship object classification models training; then, the ensemble learning model is constructed by combining multiple base classifiers; and finally, the trained classification model is transferred to SAR images for ship type prediction. Experiments show that the proposed method achieves a classification accuracy of 85.00% for the SAR ship classification, which is better than the performance of each base classifier. This proves that AIS data transfer learning can effectively solve the problem of SAR ship classification with limited samples, and has important application value in maritime surveillance.

Ice water path retrievals from Meteosat-9 using quantile regression neural networks

Abstract. The relationship between geostationary radiances and ice water path (IWP) is complex, and traditional retrieval approaches are not optimal. This work applies machine learning to improve the IWP retrieval from Meteosat-9 observations, with a focus on low latitudes, training the models against retrievals based on CloudSat. Advantages of machine learning include avoiding explicit physical assumptions on the data, an efficient use of information from all channels, and easily leveraging spatial information. Thermal infrared (IR) retrievals are used as input to achieve a performance independent of the solar angle. They are compared with retrievals including solar reflectances as well as a subset of IR channels for compatibility with historical sensors. The retrievals are accomplished with quantile regression neural networks. This network type provides case-specific uncertainty estimates, compatible with non-Gaussian errors, and is flexible enough to be applied to different network architectures. Spatial information is incorporated into the network through a convolutional neural network (CNN) architecture. This choice outperforms architectures that only work pixelwise. In fact, the CNN shows a good retrieval performance by using only IR channels. This makes it possible to compute diurnal cycles, a problem that CloudSat cannot resolve due to its limited temporal and spatial sampling. These retrievals compare favourably with IWP retrievals in CLAAS, a dataset based on a traditional approach. These results highlight the possibilities to overcome limitations from physics-based approaches using machine learning while providing efficient, probabilistic IWP retrieval methods. Moreover, they suggest this first work can be extended to higher latitudes as well as that geostationary data can be considered as a complement to the upcoming Ice Cloud Imager mission, for example, to bridge the gap in temporal sampling with respect to space-based radars.

Improved Dimension-Reduced Structures of 3D-STAP on Nonstationary Clutter Suppression for Space-Based Early Warning Radar

By introducing degrees of freedom (DOFs) in elevation, the elevation-azimuth-Doppler three-dimensional space–time adaptive processing (3D-STAP) methods have better performance when suppressing the nonstationary clutter caused by the Earth’s rotation in space-based early warning radar (SBEWR). However, the 3D-STAP methods use much more auxiliary beams, leading to greater demands on the training samples and heavier computational burdens than the conventional STAP methods. To solve this problem, the ideas of sum–difference beams, generalized multiple beams and Doppler-domain localization are applied here, and three improved dimension-reduced structures of 3D-STAP are proposed in this article. After analyzing the characteristics and distribution of nonstationary clutter for SBEWR, we find that the demands for auxiliary beams are different in elevation, azimuth and Doppler dimension. In addition, the suggestion to choose the number of auxiliary beams in each dimension is given. Simulation experiments are conducted to verify the analysis and evaluate the performance of the proposed methods. The simulation results show that the proposed 3D-STAP methods have better performance and lower computational burdens than typical 3D-STAP methods.

Impacts of active satellite sensors' low-level cloud detection limitations on cloud radiative forcing in the Arctic

Abstract. Previous studies revealed that satellites sensors with the best detection capability identify 25 %–40 % and 0 %–25 % fewer clouds below 0.5 and between 0.5–1.0 km, respectively, over the Arctic. Quantifying the impacts of cloud detection limitations on the radiation flux are critical especially over the Arctic Ocean considering the dramatic changes in Arctic sea ice. In this study, the proxies of the space-based radar, CloudSat, and lidar, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), cloud masks are derived based on simulated radar reflectivity with QuickBeam and cloud optical thickness using retrieved cloud properties from surface-based radar and lidar during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment. Limitations in low-level cloud detection by the space-based active sensors, and the impact of these limitations on the radiation fluxes at the surface and the top of the atmosphere (TOA), are estimated with radiative transfer model Streamer. The results show that the combined CloudSat and CALIPSO product generally detects all clouds above 1 km, while detecting 25 % (9 %) fewer in absolute values below 600 m (600 m to 1 km) than surface observations. These detection limitations lead to uncertainties in the monthly mean cloud radiative forcing (CRF), with maximum absolute monthly mean values of 2.5 and 3.4 Wm−2 at the surface and TOA, respectively. Cloud information from only CALIPSO or CloudSat lead to larger cloud detection differences compared to the surface observations and larger CRF uncertainties with absolute monthly means larger than 10.0 Wm−2 at the surface and TOA. The uncertainties for individual cases are larger – up to 30 Wm−2. These uncertainties need to be considered when radiation flux products from CloudSat and CALIPSO are used in climate and weather studies.

Clutter Mitigation in Space-based Early Warning Radar Using a Convolutional Neural Network

Clutter Mitigation in Space-based Early Warning Radar Using a Convolutional Neural Network

Оптико-микроволновая диагностика восстановления леса после пожаров

Рассмотрена возможность обнаружения лесного подроста с помощью поляриметрических разложений и анализа временны´х рядов радиолокационных изображений ALOS-2 PALSAR-2 и Sentinel-1. Представленные результаты показывают принципиальную возможность уверенно определять рост лесных насаждений. The problem of large-scale assessment of forest restoration after artificial deforestation and wildfires is relevant in connection with climate change and the corresponding desire of the world community for further low-carbon development. One of the promising methods aimed at solving this problem is remote sensing of the Earth using space-based synthetic aperture radars (SAR). The paper proposes a comprehensive approach to assessing the dynamics of forest plantings growth using time series analysis of ALOS-2 PALSAR-2 and Sentinel-1 radars data, as well as Sentinel-2A/B optical sensors data. It is shown that with the help of model-based decomposition (Freeman-Durden decomposition) of fully polarimetric data in L-band (ALOS-2 PALSAR-2), the increasing of volume scattering component and the corresponding decrease in the surface component can confidently identify the growth of young forest. However, the data of ALOS-2 PALSAR-2 with dual polarization are not able to separate forest undergrowth from other types of vegetation over a seven-year observation interval. This is also true for the C-band. Thus, the polarimetric Cloud- Pottier decomposition of Sentinel-1 data allowing only separation for the areas with vegetation from the treeless ones. Time series analysis of radiometrically corrected radar backscattering at vertical co-polarization in this band, imaged in the winter period of time, allows reliable determining of the dynamics for the growth of forest plantations. The use of freely available Sentinel-2A/B multispectral sensors data makes it possible to further divide the identified undergrowth by species composition and exclude classification errors of radar data in treeless areas, which show an increasing of volume backscattering component on model-based polarimetric decompositions.

Echo Doppler Frequency Compensation Analysis of Space-based Radars

Based on the geometric model between space-based radar (SBR) and the Earth, Earth’s rotation adds an additional component to the echo Doppler frequency. The result is as follows, when the orbit inclination is fixed, the crab magnitude has an offset effect on the clutter Doppler frequency, and the fluctuation of the offset value is very small, so the echo data can be uniformly compensated. However, the effect of the crab angle on the clutter Doppler frequency is two orders of magnitude larger than that of the crab magnitude and changes with time, so real-time compensation is needed. The maximum compensation interval is also related to the radar pulse repetition time and the number of accumulated pulses. Compensation interval needs to be less than 6s under a typical scene. The above conclusions lay the foundation for effective detection of SBR.

Multichannel Signal Modeling and AMTI Performance Analysis for Distributed Space-Based Radar Systems

Due to the limited size, carrying capacity, power-aperture product, and high hardware cost of satellite platform, the traditional single-platform spaceborne radar system encounters the problems of poor target minimum detectable velocity (MDV) performance, considerably deteriorating the moving target detection performance. To improve the air moving target indication (AMTI) performance, especially for a weak target, distributed space-based radar system (DSBR) becomes a good candidate due to the longer along-track baseline (ATB) and spatial power synthesis. However, due to the sparse configuration of radar baseline distribution, the detection performance of air moving targets (AMTs) will be restricted by many practical factors in an actual DSBR system. In this paper, multi-channel signal models of an observed moving target and ground clutter are accurately established in a DSBR framework, where the error influences of cross-track baseline (CTB), terrain fluctuation, and channel inconsistency response are considered. Then, the influence of the non-ideal factors, including the channel noise, long-intersatellite ATB, long-intersatellite CTB, synchronization errors, and interchannel amplitude and phase inconsistency errors, on the AMTI performance is analyzed term by term. The simulation results provide the useful guidance for the system design of a DSBR with the AMTI tasks.

Land uplift linked to managed aquifer recharge in the Perth Basin, Australia

While the link between groundwater extraction and land subsidence is well documented, observations of land uplift associated with groundwater replenishment are less so. In the Perth Basin, Western Australia, a programme of managed aquifer recharge (MAR) commenced in August 2017 and is designed to sustain levels of hydraulic head in aquifers valuable for extraction. Space-based TerraSAR-X satellite radar measurements were used to capture the first 3.5 years of MAR, providing an insight into the evolution of ground uplift in the Perth Basin that is spatially and temporally related to the MAR injection volumes and the injection-induced changes in hydraulic head. Significantly, the X-band InSAR has spatial coverage around the single injection point, and the time series begins prior to the start of the injection, rather than a generalised study of ground surface and aquifer change from multiple groundwater recharge contributions. This enables the observed ground uplift to be correlated with the time of initial injection, pause, then resumption with increased volumes. The X-band InSAR identified maximum displacements of up to 20±3 mm in the vicinity of the injection bores, but which subside when injection is paused. The spread of displacements from the injection site extends over 14 km southwards with the dispersion pattern identifying linear boundaries that sharply delineate displacements in the north-west and north-east. The extent of the region impacted by ground uplift is likely linked to the distribution of extraction bores and heterogeneities in the subsurface geology, including a persistent linear feature that has not yet been considered in hydrogeological models of the region. This article focusses on the immediate surface response to the MAR injection, and identifying the constraining physical features for the injected recharge, thus providing an additional insight into the challenging and complex Perth Basin. It also demonstrates the millimetric accuracy possible from X-band radar satellites that permits MAR volumes to be managed to avoid infrastructure damage that may undermine public confidence in the MAR program.

SAR-Based Flood Monitoring for Flatland with Frequently Fluctuating Water Surfaces: Proposal for the Normalized Backscatter Amplitude Difference Index (NoBADI)

Space-based synthetic aperture radar (SAR) is a powerful tool for monitoring flood conditions over large areas without the influence of clouds and daylight. Permanent water surfaces can be excluded by comparing SAR images with pre-flood images, but fluctuating water surfaces, such as those found in flat wetlands, introduce uncertainty into flood mapping results. In order to reduce this uncertainty, a simple method called Normalized Backscatter Amplitude Difference Index (NoBADI) is proposed in this study. The NoBADI is calculated from a post-flood SAR image of backscatter amplitude and multiple images on non-flooding conditions. Preliminary analysis conducted in the US state of Florida, which was affected by Hurricane Irma in September 2017, shows that surfaces frequently covered by water (more than 20% of available data) have been successfully excluded by means of C-/L-band SAR (HH, HV, VV, and VH polarizations). Although a simple comparison of pre-flood and post-flood images is greatly affected by the spatial distribution of the water surface in the pre-flood image, the NoBADI method reduces the uncertainty of the reference water surface. This advantage will contribute in making quicker decisions during crisis management.

Analysis of the Deformation Behavior and Sinkhole Risk in Kerdabad, Iran Using the PS-InSAR Method

The unexpected collapse of land surface due to subsidence is one of the most significant geohazards that threatens human life and infrastructure. Kabudrahang and Famenin are two Iranian plains experiencing several sinkholes due to the characteristics of the underground soil layers and extreme groundwater depletion. In this study, space-based Synthetic Aperture Radar images are used to investigate the ground displacement behavior to examine the feasibility of Sentinel-1 data in detecting precursory deformation proceeding before the sinkhole formation. The selected sinkhole occurred in August 2018 in the vicinity of Kerdabad village in Hamedan province with a 40 m diameter and depth of ~40 m. Time series of the European constellation Sentinel-1 data, spanning from January 2015 to August 2018, is analyzed, and the results revealed a 3 cm annual subsidence (–3 cm/year) along with the line-of-sight direction. Time-series analysis demonstrated that the driving mechanism of the sinkhole formation had a gradual process. Displacement of persistent scatterers (PSs) near the cave area had an acceleration by approaching the sinkhole formation date. In contrast, other areas that are far from the cave area show linear subsidence behavior over time. Additionally, the one-kilometer deformation profile over the cave area indicates a high subsidence rate precisely at the location where the sinkhole was formed later on 20 August 2018.

A comprehensive five-year evaluation of IMERG Late Run precipitation estimates over the Netherlands

AbstractApplications like drought monitoring and forecasting can profit from the global and near real-time availability of satellite-based precipitation estimates once their related uncertainties and challenges are identified and treated. To this end, this study evaluates the IMERG V06B Late Run precipitation product from the Global Precipitation Measurement mission (GPM), a multi-satellite product that combines space-based radar, passive microwave (PMW), and infrared (IR) data into gridded precipitation estimates. The evaluation is performed on the spatiotemporal resolution of IMERG (0.1° × 0.1°, 30 min) over the Netherlands over a five-year period. A gauge-adjusted radar precipitation product from the Royal Netherlands Meteorological Institute (KNMI) is used as reference, against which IMERG shows a large positive bias. To find the origin of this systematic overestimation, the data is divided into seasons, rainfall intensity ranges, echo top height (ETH) ranges, and categories based on the relative contributions of IR, morphing, and PMW data to the IMERG estimates. Furthermore, the specific radiometer is identified for each PMW-based estimate. IMERG’s detection performance improves with higher ETH and rainfall intensity, but the associated error and relative bias increase as well. Severe overestimation occurs during low-intensity rainfall events and is especially linked to PMW observations. All individual PMW instruments show the same pattern: overestimation of low-intensity events and underestimation of high-intensity events. IMERG misses a large fraction of shallow rainfall events, which is amplified when IR data is included. Space-based retrieval of shallow and low-intensity precipitation events should improve before IMERG can be implemented over the middle and high-latitudes.

Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning

Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning.The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over three years (2016–2019), achieving large reductions in false alarms (77%) and missed detections (82%) relative to GPM Ku-PR precipitation products. The spatial distribution of predicted precipitation classes within the GPM overpass reflects the complex interactions between storms and topography that determine orographic precipitation regimes. For each GPM pixel, the local precipitation class informs on the vertical structure of rainfall microphysics aiming to capture low-level processes missed in GPM DPR reflectivity profiles contaminated by ground-clutter (i.e., the radar blind-zone).