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The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. The Leica CityMapper-2 hybrid sensor has been used for aerial surveys as it can capture precise and high-resolution multiple data (imagery and LiDAR). The surveying activities are completed, and quality checks are in progress. This paper concerns assessing aerial LiDAR data of a significant part of the metropolitan area, particularly evaluating the accuracy, precision, and congruency between strips and the point density estimation. The analysis has been conducted by exploiting a ground control network of GNSS and terrestrial LiDAR measurements created explicitly for this purpose. The vertical component has an accuracy root mean square error (RMSE) of around 5 cm, and a horizontal component of around 12 cm. Meanwhile, the precision RMSE ranges from 2 to 8 cm. These values are suitable for generating products such as DSM/DTM. [ABSTRACT FROM AUTHOR]

Bogotá, the largest urban center and capital city of Colombia, is located within the Bogotá savanna, which originated as a lake in the central part of the Colombian Eastern Cordillera. Over time, the lake transformed into a gently undulating plain with horizontally deposited sediments that formed around five million years ago. Over the last few decades, the region has undergone significant population growth and rapid urban development, largely driven by migration from rural areas. This development has substantially impacted the subsidence observed in the city, primarily due to the extraction of groundwater. A previous study by the Servicio Geológico Colombiano (SGC) utilized data from GNSS stations and synthetic aperture radar interferometry (InSAR) with TerraSAR-X SAR between 2011 and 2017 to identify a subsidence pattern in the central region of Bogotá. The purpose of the study was to evaluate the risks and potential disasters associated with the subsidence phenomenon. Our study investigates both the subsidence in Bogotá, previously studied, as well as the rural savanna area, which is currently undergoing significant residential and industrial development. We utilized multi-temporal InSAR (MT-InSAR) techniques with Sentinel-1 SAR images from 2014 to 2021. The analysis results indicate that the outer regions of the city display the most significant subsidence, extending from the center to the north. The subsidence velocities in these areas reach approximately 5–6 cm/year. [ABSTRACT FROM AUTHOR]

River water surface extent can be extracted from optical and radar satellite images; this is useful for estimating streamflow from space. The radiation characteristics of open water from the visible and microwave bands are different and provide independent information. In this study, for the purpose of improving streamflow estimation from space for data-sparse regions, a method that combines satellite optical and radar images data for streamflow estimation using a machine learning technique was proposed. The method was demonstratedthrough a case study in the river segment upstream of the Ganzi gauging station on the Yalong River, China. Utilizing the support vector regression (SVR) model, the feasibility of different combinations of water surface area derived from Sentinel-1 synthetic aperture radar images (AREA_SAR), modified normalized difference water index derived from Landsat 8 images (MNDWI), and reflectance ratios between NIR and SWIR channels derived from MODIS images (RNIR/RSWIR) for streamflow estimation were evaluated through three experiments. In Experiment I, three models using AREA_SAR (Model 1), MNDWI (Model 2), and a combination of AREA_SAR and MNDWI (Model 3) were built; the mean relative error (MRE) and mean absolute error (MAE) of streamflow estimates corresponding to the SVR model using both AREA_SAR and MNDWI (Model 3) were 0.19 and 31.6 m3/s for the testing dataset, respectively, and were lower than two models using AREA_SAR (Model 1) or MNDWI (Model 2) solely as inputs. In Experiment II, three models with AREA_SAR (Model 4), RNIR/RSWIR (Model 5), and a combination of AREA_SAR and RNIR/RSWIR (Model 6) as inputs were developed; the MRE and MAE for the model using AREA_SAR and RNIR/RSWIR (Model 6) were 0.25 and 56.5 m3/s, respectively, which outperformed the two models treating AREA_SAR (Model 4) or MNDWI (Model 5) as single types of inputs. In Experiment III, three models using AREA_SAR (Model 7), MNDWI, and RNIR/RSWIR (Model 8) and the combination of AREA_SAR, MNDWI and RNIR/RSWIR (Model 9) were built; combining all three types of satellite observations (Model 9) exhibited the highest accuracy, for which the MRE and MAE were 0.18 and 18.4 m3/s, respectively. The results of all three experiments demonstrated that integrating optical and microwave observations could improve the accuracy of streamflow estimates using a data-driven model; the proposed method has great potential for near-real-time estimations of flood magnitude or to reconstruct past variations in streamflow using historical satellite images in data-sparse regions. [ABSTRACT FROM AUTHOR]

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This document is an editorial for a special issue of the journal Remote Sensing. The issue focuses on hyperspectral remote sensing from spaceborne and low-altitude aerial/drone-based platforms. The editorial highlights the advancements in hyperspectral remote sensing imagery and the progress of novel image processing techniques. It also discusses the limitations and potential applications of spaceborne and aerial/drone-based hyperspectral sensors. The editorial provides a summary of the papers included in the special issue, which cover a range of topics such as wetland mapping, mineral exploration, water quality estimation, and land-use change analysis. The authors express their gratitude to the contributors and reviewers of the special issue and hope that the studies will provide valuable insights to remote sensing researchers. [Extracted from the article]

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products. [ABSTRACT FROM AUTHOR]

The WIVERN (WInd VElocity Radar Nephoscope) mission, currently under the Phase‐0 of the ESA Earth Explorer program, promises to provide new insight in the coupling between winds and microphysics by globally observing, for the first time, vertical profiles of horizontal winds in cloudy areas. The objective of this work is to explore the potential of the WIVERN conically scanning Doppler 94 GHz radar for filling the wind observation gap inside tropical cyclones (TCs). To this aim, realistic WIVERN notional observations of TCs are produced by combining the CloudSat 94 GHz radar reflectivity observations from 2007 to 2009 with ECMWF co‐located winds. Despite the short wavelength of the radar (3 mm), which causes strong attenuation in presence of large amount of liquid hydrometeors, the system can profile most of the TCs, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. The statistical analysis of the results shows that, (a) because of its lower sensitivity, a nadir pointing WIVERN would detect 75% of the clouds observed by CloudSat (45% of winds with 3 m s−1 accuracy, in comparison to CloudSat sampling of clouds), (b) but thanks to its scanning capability, WIVERN would actually provide 53 times more observations of clouds than CloudSat in TCs (30 times more observations of horizontal winds), (c) this corresponds to about 350 (200) million observations of clouds (accurate winds) every year. Such observations could be used to shed light on the physical processes underpinning the evolution of TCs and in data assimilation in order to improve numerical weather prediction. Plain Language Summary: Despite their huge impact on tropical weather, tropical cyclones are still not fully understood with uncertainties in the predictions of their evolution and trajectory. In order to provide more detailed observations of tropical cyclones, a ground‐breaking spaceborne observing system hosting a conically scanning Doppler high frequency radar that could provide information both on in‐cloud winds and cloud mass contents is proposed. Despite the fact that the observations from such radar are strongly attenuated in presence of heavy rain, the system can sample most of the tropical cyclones, particularly the cloudy areas above the freezing level and the precipitating stratiform regions. Our notional study, based on the climatology of tropical cyclones collected during 3 years by a spaceborne nadir pointing non‐Doppler radar, demonstrates that the newly proposed satellite would provide hundred millions observations of clouds and accurate winds in tropical cyclones every year. Such observations could be used to improve our understanding of the processes underpinning the evolution of tropical cyclones and hence, their forecast. Key Points: Attenuation makes a spaceborne 94 GHz radar blind for only 5% of the cloudy/precipitating part of tropical cyclones (TCs)Thanks to its scanning capability, WIVERN would provide about 50 times more observations in TCs than the CloudSat sampling of cloudsWIVERN wind uncertainty is dominated by pulse‐pair estimator noise (3 m/s at 1 km horizontal resolution and reflectivity exceeding −15 dBZ) [ABSTRACT FROM AUTHOR]

The main goal of the research is to present a methodological approach based on geotechnologies to acquire, process and analyse high-resolution 3D data to support georesources monitoring and geoheritage site preservation. The work is focused on the unmanned aerial vehicle (UAV) mapping technology with applications in a challenging framework, i.e. rock quarry and geomining heritage sites. The investigation interconnects several ground fields: engineering geosciences, rock engineering, sensing and aerial technology, and data processing systems for acquiring high-resolution imagery. The study couples two technologies: (1) robotic systems and (2) LiDAR-based mapping and GIS geovisualisation techniques. The approach presented herein shows the complementarity between techniques to improve the rock quarry's geotechnical assessment and geomining heritage site. It was also possible to operate 3D recording technologies to preserve and record ancient mines' cultural heritage and surroundings. The methodology shows a common ground for an integrated vision for rocky georesources monitoring by applying UAV mapping technologies. The final outputs allowed the image processing, geospatial analysis and 3D ground modelling of the 'Malaposta' gneiss quarry (Lourosa) and the Roman mine 'Fojo das Pombas' (Valongo), both sites in NW Portugal. [ABSTRACT FROM AUTHOR]

Snow depth on sea ice is an Essential Climate Variable and a major source of uncertainty in satellite altimetry‐derived sea ice thickness. During winter of the MOSAiC Expedition, the "KuKa" dual‐frequency, fully polarized Ku‐ and Ka‐band radar was deployed in "stare" nadir‐looking mode to investigate the possibility of combining these two frequencies to retrieve snow depth. Three approaches were investigated: dual‐frequency, dual‐polarization and waveform shape, and compared to independent snow depth measurements. Novel dual‐polarization approaches yielded r2 values up to 0.77. Mean snow depths agreed within 1 cm, even for data sub‐banded to CryoSat‐2 SIRAL and SARAL AltiKa bandwidths. Snow depths from co‐polarized dual‐frequency approaches were at least a factor of four too small and had a r2 0.15 or lower. r2 for waveform shape techniques reached 0.72 but depths were underestimated. Snow depth retrievals using polarimetric information or waveform shape may therefore be possible from airborne/satellite radar altimeters. Plain Language Summary: Data collected using a surface‐based radar instrument on sea ice during the MOSAiC Arctic expedition were used to develop new techniques to estimate the depth of the overlying snow. We used different polarizations of the radiation to detect the depths of the upper and lower snow surfaces, and subtracted them to give snow depth. These depths agreed well with an independently collected snow depth data set. Estimates of snow depth using two different radar frequencies were less accurate, whilst using information of the shape of the returning pulse of radiation also showed a relationship with the independent snow depths, though not as strong as the polarization method. These results indicate that polarimetry (using a new satellite mission) and/or waveform shape (using existing missions) could be used to estimate snow depth on sea ice from airborne or satellite platforms. Key Points: Novel polarization‐based snow depth estimation techniques were developed using surface‐based Ku‐ and Ka‐band polarimetric radar altimeter dataThe dominant scattering surface was the air/snow and snow/ice interface in co‐ and cross‐polarized data, respectively, at both frequenciesRadar‐derived snow depths agreed with independent measurements, with r2 up to 0.77 and accuracy of 1 cm for best‐performing techniques [ABSTRACT FROM AUTHOR]

Conventional squinted sliding spotlight synthetic aperture radar (SAR) imaging suffers from substantial swath width reduction and complex processing requirements due to the continuous variation in the squint angle and the large range cell migration (RCM) throughout the data acquisition interval. A novel two-dimensional (2D) beam scanning mode for high-resolution wide swath (HRWS) imaging is proposed. The key to the novel imaging mode lies in the synchronous scanning of azimuth and range beams, allowing for a broader and more flexible imaging swath with a high geometric resolution. Azimuth beam scanning from fore to aft was used to improve the azimuth resolution, while range beam scanning was adopted to illuminate the oblique wide swath to avoid the large RCM and the serious swath width reduction. Compared with the conventional sliding spotlight mode, both the swath width and swath length could be extended. According to the echo model of this imaging mode, an echo signal preprocessing approach is proposed. The key points of this approach are range data extension and azimuth data upsampling. A designed system example with a resolution of 0.5 m, swath width of 60 km, and azimuth coverage length of 134 km is presented. Furthermore, a simulation experiment on point targets was carried out. Both the presented system example and imaging results of point targets validated the proposed imaging mode. [ABSTRACT FROM AUTHOR]

Backscatter communication (BC) systems are a promising technology for internet of things (IoT) applications that allow devices to transmit information by modulating ambient radio signals without the need for a dedicated power source. However, the security of BC systems is a critical concern due to the vulnerability of the wireless channel. This paper investigates the impact of side information (SI) on the secrecy performance of BC systems. SI mainly refers to the additional knowledge that is available to the communicating parties beyond transmitted data, which can be used to enhance reliability, efficiency, security, and quality of service in various communication systems. In particular, in this paper, by considering a non-causally known SI at the transmitter, we derive compact analytical expressions of average secrecy capacity (ASC) and secrecy outage probability (SOP) for the proposed system model to analyze how SI affects the secrecy performance of BC systems. Moreover, a Monte Carlo simulation validates the accuracy of our analytical results and reveals that considering such knowledge at the transmitter has constructive effects on the system performance and ensures reliable communication with higher rates than the conventional BC systems without SI, namely, lower SOP and higher ASC are achievable. [ABSTRACT FROM AUTHOR]

Aiming at SAR imaging for large coastal scenes, a comprehensive comparative study is performed based on Sentinel-1 raw data, SAR imaging simulation, and Google Maps. A parallel Range-Doppler (RD) algorithm is developed and applied to focus Sentinel-1 raw data for large coastal scenes, and the focused SAR image is compared with the multi-look-processed SAR image using SNAP 9.0.0 software, as well as the corresponding areas of Google Maps. A scheme is proposed to convert the LiDAR point cloud data of the coast into a 3D coastal area digital elevation model (DEM), and a tailored 3D model suitable for RaySAR simulator is obtained after statistical outlier removal (SOR) denoising and down-sampling processing. Comparison results show good agreements, which verify the effectiveness of the parallel RD algorithm as well as the backward ray-tracing-based RaySAR simulator, which serves as a powerful SAR imaging tool due to its high efficiency and flexibility. The cosine similarity between the RaySAR-simulated SAR image and Google Maps achieves 0.93, while cosine similarity reaches 0.85 between Sentinel-1 SAR-focused images with our parallel RD algorithm and multi-look SAR image processed using SNAP software. This article can provide valuable assistance for SAR system performance evaluation, SAR imaging algorithm improvement, and remote sensing applications. [ABSTRACT FROM AUTHOR]

Ultra-wideband (UWB) antennas have recently gained prominence in communication, radar technology, and electronic warfare domains. The quick development of these antennas is due to the wide bandwidth requirements of pulse radar, ground penetrating radar, electromagnetic compatibility, spaceborne communication systems, stealth target detection, and more. Aiming to address the defects of existing UWB antennas, which often have narrow bandwidth and low gain, a planar ultra-wideband microstrip array antenna was designed to achieve good ultra-wideband characteristics and effectively improve the gain of the antenna. The initial bandwidth of the rectangular monopole antenna was 10 GHz–20 GHz. After loading multiple steps on the monopole patch, the bandwidth was increased to between 10 and 38 GHz. Using the new ultra-wideband array method that combines series feed and angle feed and the defective ground structure (DGS), the array maintains the ultrawide bandwidth span of 10–38 GHz of the array element, and the maximum gain of the antenna in the bandwidth was increased from 5.18 dBi to 9.55 dBi. The challenge of impedance matching of antenna units in ultra-wideband is resolved by the novel array technique, which also increases the antenna's gain within the bandwidth. The antenna simulation is consistent with the measurement results. With its extensive operating frequency band, high gain, compactness, and favorable radiation attributes, this newly designed antenna holds significant promise for application in UWB radar systems. [ABSTRACT FROM AUTHOR]

The Doppler characteristics of sea surface echoes reflect the time-varying characteristics of the sea surface and can be used to retrieve ocean dynamic parameters and detect targets. On airborne, spaceborne and shipborne radar platforms, radar moves along with the platforms while illuminating the sea surface. In this case, the area of the sea surface illuminated by radar beam changes rapidly with the motion, and the coherence of the backscattered echoes at different times decreases significantly. Therefore, the Doppler characteristics of the echoes would also be affected by the radar motion. At present, the computational requirements needed to simulate the Doppler spectrum of the microwave scattering field from the sea surface based on numerical methods are huge. To overcome this problem, a new method based on the sub-scattering surface elements has been proposed to simulate the Doppler spectrum of sea echoes acquired by a moving microwave radar. A comparison with the results evaluated by the SSA demonstrate the availability and superiority of the new method proposed by us. The influences induced by radar motion, radar beamwidth, incident angle, and thermal noise on the Doppler characteristics are all considered in this new method. The simulated results demonstrate that the spectrum bandwidth of sea surface echoes acquired by radar on the dive staring motion platform becomes somewhat narrower. [ABSTRACT FROM AUTHOR]

Approaching space target tracking is a typical and challenging mission in the space situational awareness (SSA) field. As the space-based radar is able to monitor the space targets of interest full-weather all-time, the space-based radar system is utilized in this paper. However, most multi-target tracking (MTT) filters in target tracking studies merely utilize the location or narrow measurements, and many potentially valuable electromagnetic scattering characteristics are missed, which leads to space target false tracking problems. The space-based radar transmits a wide-band signal, and the measured high-resolution range profile (HRRP) information is an effective characteristic for different target discrimination. Therefore, the HRRP characteristics of space targets are implemented into the update recursion of the MTT filter, which can be utilized to improve the tracking performance. Then, to predict the target HRRP sequence, the geometrical theory of diffraction (GTD) model is utilized. Additionally, a modified spatial spectrum method with a novel covariance matrix is designed to improve the scattering parameter estimation accuracy. Finally, an adapting threshold is devised for merging the Gaussian mixture (GM) components weights. The proposed threshold is on the basis of the proposed HRRP characteristic-aided probability hypothesis density (PHD) filter, and it can tackle the problem of space target discrimination. Simulation results validate the effectiveness and robustness of the proposed probability hypothesis density (HGI-PHD) filter aided by HRRP information and improved with GM weights. [ABSTRACT FROM AUTHOR]

With the continuous development of satellite payload and system-on-chip (SoC) technology, spaceborne real-time synthetic aperture radar (SAR) imaging systems play a crucial role in various defense and civilian domains, including Earth remote sensing, military reconnaissance, disaster mitigation, and resource exploration. However, designing high-performance and high-reliability SAR imaging systems that operate in harsh environmental conditions while adhering to strict size, weight, and power consumption constraints remains a significant challenge. In this paper, we introduce a spaceborne SAR imaging chip based on a SoC architecture with system fault-tolerant technology. The fault-tolerant SAR SoC architecture has a CPU, interface subsystem, memory subsystem, data transit subsystem, and data processing subsystem. The data processing subsystem, which includes fast Fourier transform (FFT) modules, coordinated rotation digital computer (CORDIC) modules (for phase factor calculation), and complex multiplication modules, is the most critical component and can achieve various modes of SAR imaging. Through analyzing the computational requirements of various modes of SAR, we found that FFT accounted for over 50% of the total computational workload in SAR imaging processing, while the CORDIC modules for phase factor generation accounted for around 30%. Therefore, ensuring the fault tolerance of these two modules is crucial. To address this issue, we propose a word-length optimization redundancy (WLOR) method to make the fixed-point pipelined FFT processors in FFT modules fault tolerant. Additionally, we propose a fault-tolerant pipeline CORDIC architecture utilizing error correction code (ECC) and sum of squares (SOS) check. For other parts of the SoC architecture, we propose a generic partial triple modular redundancy (TMR) hardening method based on the HITS algorithm to improve fault tolerance. Finally, we developed a fully automated FPGA-based fault injection platform to test the design's effectiveness by injecting errors at arbitrary locations. The simulation results demonstrate that the proposed methods significantly improved the chip's fault tolerance, making the SAR imaging chip safer and more reliable. We also implemented a prototype measurement system with a chip-included board and demonstrated the proposed design's performance on the Chinese Gaofen-3 strip-map continuous imaging system. The chip requires 9.2 s, 50.6 s, and 7.4 s for a strip-map with 16,384 × 16,384 granularity, multi-channel strip-map with 65,536 × 8192 granularity, and multi-channel scan mode with 32,768 × 4096 granularity, respectively, and the system hardware consumes 6.9 W of power to process the SAR raw data. [ABSTRACT FROM AUTHOR]

The NewSpace approach of utilizing CubeSats and smaller satellites for complex LEO missions that once were reserved only for bigger platform, paired with the requirement of enduring reliability and quality assurance on batches of tens of satellites, is paving the way for the use of commercial-off-the-shelf (COTS) components in micro-satellite missions. The recent global supply chain issues and extended lead times in the procurement of micro-controllers, memories and passive electronic components, caused by the pandemic and factories shutdowns, have resulted in a forced procurement diversification of several key elements, even in partially assembled products. GAUSS has been designing, manufacturing, and launching Small Satellites since early 2000. Its tenth satellite, UNISAT-7, was launched by a Soyuz-Fregat LV on March 22nd, 2021. Thanks to the vast experience gained on the application of commercial components in all the subsystems developed by the company, the typical overall costs for a complete platform are substantially lower compared to competitors, while retaining similar reliability. UNISAT-7, developed using lean-satellite methodologies, included more than 28 independent subsystems with their own telemetry, and most of them were developed in-house using ground COTS components; no space-grade products were employed, to save on overall mission costs. Some systems were devised from the start using open-source hardware, to expedite the prototyping process, while having the benefit of using readily available software, generally included with these platforms. Raspberry embedded devices were employed on the flight model for two remote sensing systems, to test how such systems, duly adapted to sustain space conditions, may perform and show reliability similar to commercial space systems worth thousand times the value. All systems have been activated and successfully verified for an entire year in LEO. They were hardened for vacuum, extreme temperatures, radiation, and launch stresses using common items found in general hardware stores. This work will delve into the open-source/COTS components used, the process employed to strengthen them, and the programmed flight firmware routines for maximizing embedded devices' reliability in space. • Use of commercial-off-the-shelf (COTS) components in micro-satellite missions • UNISAT-7 as a space-tug, to deploy CubeSats and PocketQubes in LEO • GAUSS experience on COTS usage for micro- and nano-satellite missions • Use of open-source embedded systems in micro and nano-satellite missions • Best practices to enhance low-cost COTS reliability [ABSTRACT FROM AUTHOR]

The Joint Center for Satellite Data Assimilation (JCSDA) Community Radiative Transfer Model (CRTM) is a fast, 1D radiative transfer model used in numerical weather prediction, calibration/validation, etc., across multiple federal agencies and universities. The key benefit of the CRTM is that it is a satellite simulator. It provides a highly accurate representation of satellite radiances by using the specific sensor response functions convolved with a Line-by-Line Radiative Transfer Model (LBLRTM). CRTM covers the spectral ranges consistent with all present operational and most research satellites, from visible to microwave. The capability to simulate ultraviolet radiances and support space-based radar sensors is being added over the next 2 years in CRTM version 3.0. In addition to simulated radiances, the CRTM also provides Jacobian outputs needed to interpret satellite observations for numerical weather prediction. The Jacobian estimates how changes in geophysical parameters affect simulated measurements from satellite sensors. Using the Jacobian in modeling and weather prediction improves the accuracy and efficiency of data analysis, leading to better weather predictions. The CRTM model's success and growth depend on community contributions and evaluation. To facilitate this, we have made the CRTM highly accessible through modular programming, clear documentation and tutorials, public domain licensing, unfettered public access via GitHub, and a clear path to operational implementation for innovative research. We encourage and welcome contributions from the community to help us continue to improve the CRTM. [ABSTRACT FROM AUTHOR]

In this letter, a multibeam folded transmitarray antenna based on an improved phase matching method is presented. A Ku‐band magneto‐electric dipole antenna is designed as the primary source for the proposed antenna. Besides, a Fabry–Perot metasurface with high transmission efficiency is proposed. Finally, a multibeam folded transmitarray antenna is designed, simulated, fabricated, and measured. By exciting different ports, the proposed antenna achieves a ±25° scanning range. The impedance bandwidth is 14%, and the measured peak gain is 23.5 dBi, which means the aperture efficiency is about 17.8%. Measured results show that the proposed antenna achieves a good multibeam performance while maintaining a low profile. [ABSTRACT FROM AUTHOR]

The fracturing of glaciers and ice shelves in Antarctica influences their dynamics and stability. Hence, data on the evolving distribution of crevasses are required to better understand the evolution of the ice sheet, though such data have traditionally been difficult and time-consuming to generate. Here, we present an automated method of mapping crevasses on grounded and floating ice with the application of convolutional neural networks to Sentinel-1 synthetic aperture radar backscatter data. We apply this method across Antarctica to images acquired between 2015 and 2022, producing a 7.5-year record of composite fracture maps at monthly intervals and 50 m spatial resolution and showing the distribution of crevasses around the majority of the ice sheet margin. We develop a method of quantifying changes to the density of ice shelf fractures using a time series of crevasse maps and show increases in crevassing on Thwaites and Pine Island ice shelves over the observational period, with observed changes elsewhere in the Amundsen Sea dominated by the advection of existing crevasses. Using stress fields computed using the BISICLES ice sheet model, we show that much of this structural change has occurred in buttressing regions of these ice shelves, indicating a recent and ongoing link between fracturing and the developing dynamics of the Amundsen Sea sector. [ABSTRACT FROM AUTHOR]

The technical and technological progress achieved in the field of photogrammetry and teledection has resulted in remarkable advancements in various areas of activity, including topographic measurements. In what concerns detailed topographic measurements, typically performed with GNSS equipment and electronic total stations, the use of Mobile Mapping technology - LiDAR proves to be a significant advantage due to the reduction of working hours, the surveillance area coverage, and the complexity of acquired data. These measurements are performed with similar accuracy as any conventional equipment. To provide proof and test the proposed model, a section of the town of Lipova in Arad County was selected for investigation using mobile scanning equipment, specifically the Leica Pegasus: Backpack, in conjunction with a Leica Viva GS14 GNSS device. The Leica Pegasus: Backpack, a Mobile Mapping system, is a sensor platform designed for extreme ergonomic convenience. It combines five cameras providing a fully calibrated 360-degree view and two LiDAR sensors on an ultra-lightweight carbon fiber chassis. This system allows for extensive and efficient data collection both indoors and outdoors with a high level of accuracy and professionalism. Today, mobile mapping technology has become an indispensable part of our digital society. Using mobile devices and specialized applications, mobile scanning provides a fast, precise, and accessible method of recording and analyzing geographical information in real-time. [ABSTRACT FROM AUTHOR]

This paper presents a new model of seismic rupture surface for the Chignik Mw 8.2 earthquake which occurred off the Alaska Peninsula on July 29, 2021. The model is based on data on surface displacements obtained by satellite radar interferometry from Sentinel-1A and 1B images and on horizontal GPS displacements in the area of this earthquake. We used satellite radar images for the period between July 17 and August 10, 2021, as well as GPS data for the period between July 18 and August 8, 2021. All of these displacements include both coseismic movements and some postseismic displacements. The rupture surface model was developed using F. Pollitz' solution to the problem concerning the displacement field at the surface of a spherical radially stratified planet due to a rectangular rupture inside the planet. The inversion was regularized by requiring that the direction of displacement at each element of the rupture plane be close to a specified direction as given by seismological data. The seismic rupture surface in this model is fitted by a plane extending for 225 km along strike and 126 km along dip, and which is subdivided into 48 identical elements. According to the model derived here, the type of displacement is a nearly pure thrust, with the displacement in general involving all of the rupture zone. The maximum slip was 5.7 m, with the mean slip as averaged over the entire rupture plane being 2.2 m. Assuming the shear modulus to be 32 GPa, we found the seismic moment based on the parameters of the plane we have derived and on the displacements on it as 1.95 × 1021 N m (Мw = 8.13), which is near the USGS and GCMT estimates based on seismological data. [ABSTRACT FROM AUTHOR]

Deep learning-based methods have demonstrated exceptional performance in the field of synthetic aperture radar automatic target recognition (SAR ATR). However, obtaining a sufficient number of labeled SAR images remains a significant challenge that can negatively affect the performance of these methods. This is because most deep learning models use the entire target image as input. However, but research has shown that with limited training data, the model may not be able to capture discriminative regions of the image. Instead, they might focus on more useless even harmful image regions for recognition, leading to poor recognition results. In this study, we propose a novel SAR ATR framework that addresses the limitation of limited training data. The proposed framework primarily comprises a global assisted branch, locally enhanced branch, feature capture module, and feature discrimination module. The global-assisted branch conducts an initial recognition and provides a loss based on the entire SAR image during each training epoch and the feature capture module automatically segments and captures crucial image regions for current recognition, which we refer to as the "golden key" of the image. The local enhanced branch then performs another recognition and provides another loss function based on the image parts. Instead of updating the model with two basic recognition losses to roughly search for and capture crucial image parts, a feature discrimination module is proposed to combine the global and local branches in a subtle manner to improve local feature separability or compactness for similar inter-class or dissimilar inner-class sample pairs in global features. This adaptively forced the model to capture more crucial image parts and extract more effective features. Experimental results and comparisons of the MSTAR and OpenSARship datasets indicated that the proposed method achieves superior recognition performance compared to existing methods. The effectiveness of our method was further demonstrated through the visualization of the golden key of the testing images and the recognition performance in ablation experiments. We will release our codes and more experimental results at https://github.com/cwwangSARATR/SARATR_FeaCapture_Discrimination. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper explores advanced shape control techniques for ultra-lightweight electro-actuated polymers with composite ferroelectric thin films. It begins with an overview of PVDF-TrFE film actuators used in the development of thin-shell composites, emphasizing the need to overcome constraints related to the electrode size for successful scalability. Strain generation in thin-film actuators is investigated, including conventional electrode-based methods and non-contact electron flux excitation. Numerical studies incorporate experimentally calibrated ferroelectric parameters, modeling non-contact actuation with an equivalent circuit representation. The potential distribution generated by electron flux injection highlights its potential for reducing print-through actuation issues. Additionally, the paper outlines a vision for the future of large thin-shell reflectors by integrating the discussed methods for charging ferroelectric polymer films. A hierarchical control strategy is proposed, combining macro- and micro-scale techniques to rectify shape errors in lightweight reflectors. These strategies offer the potential to enhance precision and performance in future spaceborne observation systems, benefiting space exploration and communication technologies. [ABSTRACT FROM AUTHOR]

Turbulent fluctuations of the electron number density in the Earth's ionosphere may hamper the performance of spaceborne synthetic aperture radar (SAR). Previously, we have quantified the extent of the possible degradation of transionospheric SAR images as it depends on the state of the ionosphere and parameters of the SAR instrument. Yet no attempt has been made to mitigate the adverse effect of the ionospheric turbulence. In the current work, we propose a new optimizationbased autofocus algorithm that helps correct the turbulence-induced distortions of spaceborne SAR images. Unlike the traditional autofocus procedures available in the literature, the new algorithm allows for the dependence of the phase perturbations of SAR signals not only on slow time but also on the target coordinates. This dependence is central for the analysis of image distortions due to turbulence, but in the case of traditional autofocus where the distortions are due to uncertainties in the antenna position, it is not present. [ABSTRACT FROM AUTHOR]

This paper presents the application of a Structural Health Monitoring (SHM) strategy for bridges affected by slow deformation phenomena to a curved roadway bridge undergoing slow landslide‐induced movements. The chosen multidisciplinary approach is based on the combination of remote sensing data through satellite Synthetic Aperture Radar Interferometry (InSAR) and structural analysis up to collapse investigated through the Applied Element Method (AEM). The results of the application to the considered case study demonstrate that the integration of the displacement information obtained through InSAR technique with the numerical analysis allows for improving the comprehension of the health state of the bridge undergoing slow movements and identifying potentially critical conditions for the structure. [ABSTRACT FROM AUTHOR]

The inverse synthetic aperture radar (ISAR) image is a kind of target feature data acquired by radar for moving targets, which can reflect the shape, structure, and motion information of the target, and has attracted a great deal of attention from the radar automatic target recognition (RATR) community. The identification of ISAR image components in radar satellite identification missions has not been carried out in related research, and the relevant segmentation methods of optical images applied to the research of semantic segmentation of ISAR images do not achieve ideal segmentation results. To address this problem, this paper proposes an ISAR image part recognition method based on semantic segmentation and mask matching. Furthermore, a reliable automatic ISAR image component labeling method is designed, and the satellite target component labeling ISAR image samples are obtained accurately and efficiently, and the satellite target component labeling ISAR image data set is obtained. On this basis, an ISAR image component recognition method based on semantic segmentation and mask matching is proposed in this paper. U-Net and Siamese Network are designed to complete the ISAR image binary semantic segmentation and binary mask matching, respectively. The component label of the ISAR image is predicted by the mask matching results. Experiments based on satellite component labeling ISAR image datasets confirm that the proposed method is feasible and effective, and it has greater comparative advantages compared to other classical semantic segmentation networks. [ABSTRACT FROM AUTHOR]

Real-time detection of satellite remote sensing images is one of the key technologies in the field of remote sensing, which requires not only high-efficiency algorithms, but also low-power and high-performance hardware deployment platforms. At present, the image processing hardware acceleration platform mainly uses an image processing unit (GPU), but the GPU has the problem of large power consumption, and it is difficult to apply to micro-nano satellites and other devices with limited volume, weight, computing power, and power consumption. At the same time, the deep learning algorithm model has the problem of too many parameters, and it is difficult to directly deploy it on embedded devices. In order to solve the above problems, we propose a YOLOv4-MobileNetv3 field programmable gate array (FPGA) deployment scheme based on channel layer pruning. Experiments show that the acceleration strategy proposed by us can reduce the number of model parameters by 91.11%, and on the aerial remote sensing dataset DIOR, the average accuracy of the design scheme in this paper reaches 82.61%, the FPS reaches 48.14, and the average power consumption is 7.2 W, which is 317.88% FPS higher than the CPU and reduces the power consumption by 81.91%. Compared to the GPU, it reduces power consumption by 91.85% and improves FPS by 8.50%. Compared with CPUs and GPUs, our proposed lightweight algorithm model is more energy-efficient and more real-time, and is suitable for application in spaceborne remote sensing image processing systems. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative spaceborne isoflux scanning digital phased array (ISDPA) design with two-stage digital beamforming (DBF) for geostationary satellites. To achieve isoflux scanning, a novel technique is presented to obtain an isoflux beam for the ISDPA equivalent element using a DBF completely shared subarray architecture and the differential evolution (DE) algorithm. By reutilizing the radiating elements of adjacent subarrays, the radiation aperture and element number are augmented, enhancing the degrees of optimization freedom. To validate the proposed design, a linear ISDPA with 16 DBF completely shared subarrays is optimized and analyzed using two sets of excitation coefficients in different DBF stages. The numerical results demonstrate that the proposed ISDPA can adaptively compensate for space loss variations during beam scanning for geostationary communications with low sidelobes better than −20 dB. [ABSTRACT FROM AUTHOR]

Sparse-representation-based synthetic aperture radar (SAR) imaging technology has shown superior potential in the reconstruction of nonsparse scenes. However, many existing compressed sensing (CS) methods with sparse representation cannot obtain an optimal sparse basis and only apply to the sensing matrix obtained by exact observation, resulting in a low image quality occupying more storage space. To reduce the computational cost and improve the imaging performance of nonsparse scenes, we formulate a deep learning SAR imaging method based on sparse representation and approximated observation deduced from the chirp-scaling algorithm (CSA). First, we incorporate the CSA-derived approximated observation model and a nonlinear transform function within a sparse reconstruction framework. Second, an iterative shrinkage threshold algorithm is adopted to solve this framework, and the solving process is unfolded as a deep SAR imaging network. Third, a dual-path convolutional neural network (CNN) block is designed in the network to achieve the nonlinear transform, dramatically improving the sparse representation capability over conventional transform-domain-based CS methods. Last, we improve the CNN block to develop an enhanced version of the deep SAR imaging network, in which all the parameters are layer-varied and trained by supervised learning. The experiments demonstrate that our proposed two imaging networks outperform conventional CS-driven and deep-learning-based methods in terms of computing efficiency and reconstruction performance of nonsparse scenes. [ABSTRACT FROM AUTHOR]

Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R2) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies. [ABSTRACT FROM AUTHOR]

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Reflectivity bias of ground‐based weather radar (GR) is a common error source that can lead to biases in quantitative precipitation estimation (QPE). In this study, the Dual‐frequency Precipitation Radar onboard the Global Precipitation Measurement Mission Core Observatory (GPM‐DPR) is used to correct GR reflectivity bias. The reflectivity bias correction comprehensively considers variations in GR‐DPR reflectivity difference associated with radar frequency difference, data matching approach, precipitation type, GR orographic beam blockage, attenuation, and sample size. The criteria to perform a robust GR‐DPR comparison are presented. Disdrometer validation, based on eight typical rain events from April to August 2019, demonstrates that GPM‐DPR can effectively reduce GR reflectivity bias to ±1 dB. For the period from June to August 2021, the reflectivity correction method is applied to 17 operational GRs. The multiradar QPE shows a significant increase in correlation coefficient (from 0.62 to 0.71), and decreases in relative mean bias (from −0.28 to −0.19) and root mean square error (from 3.96 to 3.54 mm) compared to rain gauges. These results confirm that GPM‐DPR can effectively correct the reflectivity biases of a GR network and improve multiradar QPE accuracy and consistency. Key Points: Calibration (against GPM‐DPR) can reduce CINRAD radar reflectivity bias to ±1 dBProposed a robust algorithm to make GR‐DPR comparisonContinuity and accuracy of radar QPE can be significantly enhanced after the correction [ABSTRACT FROM AUTHOR]

Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a C-Band Frequency Generator developed for Space-borne Synthetic Aperture Radar. This Frequency Generator subsystem generates stable and coherent reference signals for all the sub-systems of C-Band Synthetic Aperture Radar payload. Frequency Generator based on frequency multiplication technique generates various coherent signals namely 500 MHz signal for digital clock, local oscillator (LO) signals of 900 MHz and 4500 MHz needed for receivers and chirp signal of 5400 ± 37.5 MHz. This chirp signal is generated by direct modulation of the full bandwidth baseband signal of DC-37.5 MHz at 4500 MHz and subsequently mixing with 900 MHz signal. Frequency generator unit is realized in a compact two-tier architecture, using a novel concept of full chirp modulation, resulting in 6◦ rms phase error in the transmit chirp signal along with in-band spurious rejection better than 20 dBc, whereas other coherent frequencies resulting in out of band spurious rejection better than 53 dBc against the specification of 40 dBc. [ABSTRACT FROM AUTHOR]

Two air‐sea interaction quantification methods are employed on synthetic aperture radar (SAR) scenes containing atmospheric‐turbulence signatures. Quantification performance is assessed on Obukhov length L, an atmospheric surface‐layer stability metric. The first method correlates spectral energy at specific turbulence‐spectrum wavelengths directly to L. Improved results are obtained from the second method, which relies on a machine‐learning algorithm trained on a wider array of SAR‐derived parameters. When applied on scenes containing convective signatures, the second method is able to predict approximately 80% of observed variance with respect to validation. Estimated wind speed provides the bulk of predictive power while parameters related to the kilometer‐scale distribution of spectral energy contribute to a significant reduction in prediction errors, enabling the methodology to be applied on a scene‐by‐scene basis. Differences between these physically based estimates and parameterized numerical models may guide the latter's improvement. Plain Language Summary: Global high‐resolution satellite observations from the radar‐carrying Sentinel‐1 constellation are used to estimate ocean‐atmosphere stability conditions over the oceans. These observations don't measure the atmospheric state directly. Rather, they capture the kilometer‐scale variability of the ocean‐surface roughness which is correlated to specific ocean‐atmosphere stability conditions. By combining parameters and statistics extracted from the radar measurements with machine learning, estimation performance largely exceeds that of a previously suggested approach. With these results, the disequilibrium between the air and sea surface temperature, central to determining sensible heat fluxes over the oceans, shall be more quantitatively and systematically assessed. These results have the potential of guiding improvements in climate models as differences between our physically based estimates and parameterized climate models may indicate and diagnose shortcomings in the latter. Key Points: Global‐scale estimation of atmospheric instability from synthetic aperture radar high‐resolution ocean‐roughness observationsFirst‐order roughness statistics enable estimation on a climatological scaleO(1 km) roughness characteristics enable estimation on a scene‐by‐scene basis [ABSTRACT FROM AUTHOR]

Ground-based radar has been used for Moon imaging for more than 60 years. Five-hundred-meter Aperture Spherical radio Telescope (FAST), as the largest radio telescope on Earth, holds significant potential for celestial imaging missions with its exceptional sensitivity. A bistatic Synthetic Aperture Radar (SAR) Moon imaging model that incorporates FAST and other transmitting radars is presented. The objective of this paper is to design the imaging parameters of this bistatic configuration based on the required resolution, and to estimate the resolution performance based on a given bistatic system capability. Considering the ultra-far range and the ultra-long observation time between the radars and the Moon, the geometric relationship involved in this bistatic configuration is significantly distinct from the bistatic configuration of airborne and spaceborne radars. Therefore, this paper accurately derives the two-dimensional resolution on the Moon's surface. First of all, the models of the Earth's surface and the Moon's surface, and the celestial motion of the Earth and Moon are established using WGS-84 and JPL-DE421, given by STK. Secondly, the bistatic range history within the observation time is calculated in terms of continuous celestial motion instead of the popular 'stop-and-go' assumption. Thirdly, no approximation is used in the resolution derivation process, and, in addition to the two-dimensional resolutions, the incident angle and the included angle are also given to describe the imaging performance. This method can also be extended to other bistatic-station and single-station celestial imaging, providing support for radar location and parameters design, for observation time span selection, for observation area selection, and for imaging performance estimation. The echo generation and imaging for point targets set on the Moon are shown. The simulation results prove the validity and accuracy of the proposed method in the paper. [ABSTRACT FROM AUTHOR]

Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff's approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future. [ABSTRACT FROM AUTHOR]

On 11 October and 3 November 2018, two large landslides occurred in Baige Village, Tibet, China, forcing the Jinsha River to be cut off and form a dammed lake, resulting in massive economic damages and deaths. This paper uses ground-based radar (GBR) and spaceborne interferometric synthetic aperture radar (InSAR) technologies to perform dynamic monitoring of the Baige landslide. Firstly, the GBR results suggest that the cumulative deformation from 4 to 10 December 2018 was 1.4 m, and the landslide still exhibits a risk of instability. Secondly, with the Sentinel-1A ascending and descending orbit images from December 2018 to February 2022, the InSAR-stacking technology assisted by the generic atmospheric correction online service (GACOS) and the multidimensional small baseline subset (MSBAS) method are utilized to obtain the annual deformation velocity and cumulative deformation in the satellite radar line of sight (LOS) direction of the landslide. Finally, according to the spatial–temporal deformation characteristics of feature points, combined with optical images, field investigation, and geological conditions, the development trend and inducing factors of the Baige landslide are comprehensively analyzed. It is shown that the Baige landslide is in constant motion at present, and the deformation is spreading from the slope to its right side. This research establishes a framework of combining emergency monitoring (i.e., GBR) with long-term monitoring (i.e., spaceborne InSAR). The framework is more conducive to obtaining the deformation and evolution of landslides, providing a greater possibility for studying the development trend and risk assessment of landslides, and assisting in reducing or even avoiding the losses caused by landslides. [ABSTRACT FROM AUTHOR]

The cloud amount, referred to as the frequency of cloud occurrences, is of great importance for the Earth–atmosphere system. It was conventionally quantified as the area fraction of clouds in a given region, discarding the three-dimensional nature of both cloud entities and their spatial distribution. Although the area fraction is explicit, it is the volume fraction that fully depicts cloud occurrences, and the area fraction is just related to a projection of the volume fraction. In this study, by using spaceborne radar measurements, the spatial distribution of cloud volume fraction throughout the troposphere was investigated, and the contributions of various cloud types at each location were clarified. Overall, the volume fraction of total clouds in the whole troposphere is 15.9%, while the corresponding area fraction relative to the global surface is 73.6%. The peak volume fraction occurs at 1 km altitude, mainly contributed by stratocumulus and cumulus. For a single cloud type, the maximum fraction is 48.8%, which is from stratocumulus and occurs at 1 km altitude above the Greenland Sea. Half of the eight cloud types, altostratus, cirrus, nimbostratus, and deep convective clouds, reach the nominal tropopause. In particular, the vertical distribution difference among multiple cloud types in each category (low-level, middle-level, and vertically extending) was clarified, and it was found that the dominant cloud type in a category varies notably with the location in the atmosphere. [ABSTRACT FROM AUTHOR]

Reasonable allocation of space-based radar resources is a crucial aspect of improving the accuracy of space multi-target tracking and enhancing spatial awareness. The conventional resource allocation algorithm fails to exploit the high dynamic radar cross-section (RCS) characteristics, resulting in poor tracking robustness, tracking divergence, or even loss of tracking. However, the RCS of space targets fluctuates considerably in actual tracking scenarios, which cannot be disregarded for space target tracking tasks. To address this issue, we propose an adaptive allocation method that considers the dynamic RCS fluctuation characteristic for space-based radar tracking assignments. The proposed method exploits the predictable orbital information of space target to calculate the real-time observation angle of radar, and then obtains the multi-target dynamic RCS through the target RCS dataset. By combining the obtained RCS sequence, radar power, and bandwidth, an optimal model for radar tracking accuracy is established based on the multi-target posterior Cramér–Rao lower bound (PCRLB) to evaluate the tracking performance. By resolving the aforementioned multivariance optimization problem, we eventually acquire the results of power and bandwidth pre-allocation for tracking multiple space targets. Simulation results validate that, compared with the traditional methods, the proposed joint dynamic RCS power and bandwidth allocation (JRPBA) method can achieve superior tracking accuracy and minimize instances of missed tracking. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar (TomoSAR) obtains elevation resolution by adding multiple baselines successively in the direction perpendicular to the line of sight, thereby realizing three-dimensional (3D) reconstruction of complex scenes and significantly promoting the development of the 3D application field. However, a large data redundancy and long mapping time in traditional 3D imaging lead to a large data transmission burden, low efficiency, and high costs. To solve the above problems, this paper proposes a distributed SAR high-resolution and wide-swath (HRWS) 3D imaging technology scheme. The proposed scheme overcomes the size limitation of traditional single-satellite antennas through the multi-channel arrangement of multiple satellites in the elevation direction to achieve HRWS imaging; meanwhile, the distributed SAR system is integrated with tomographic processing technology to realize 3D imaging of difficult areas by using the elevation directional resolution of TomoSAR systems. HRWS 3D SAR increases the baseline length and channel number by transmission in turn, which leads to excessive pulse repetition frequency and causes echoes of different pulse signals to overlap in the same receiving cycle, resulting in range ambiguity and thus seriously affecting the quality of the 3D reconstruction. To solve this problem, this paper proposes a range ambiguity resolution algorithm based on multi-beam forming and verifies it on the measured data from airborne array SAR. Compared with the traditional TomoSAR, the distributed HRWS 3D SAR scheme proposed in this paper can obtain a greater mapping bandwidth with the same resolution in a single flight, thereby enhancing the time correlation, reducing data redundancy, and greatly improving mapping efficiency. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of flood simulations using rain gauge, ground- and space-borne radar precipitation products. The objectives are to assess the effectiveness of two radar-based data sources, namely the Radar-AWS Rainrates (RAR) and Integrated Multi-satellite Retrievals for GPM (IMERG), in a dam watershed with gauge observations, and explore the modeling feasibility of integrating the half-hourly IMERG satellite precipitation in regions with ungauged or limited observational area. Two types of HEC-HMS models were developed, considering areal-averaged and spatially distributed gridded data simulations utilizing eight selected storm events. The findings indicate that the RAR data, although slightly underestimate precipitation compared to the gauge measurements, accurately reproduce hydrographs without requiring parameter adjustments (Nash–Sutcliffe efficiency, ENS, 0.863; coefficient of determination, R2, 0.873; and percent bias, PBIAS, 7.49%). On the other hand, flood simulations using the IMERG data exhibit lower model efficiency and correlation, suggesting potential limitations in ungauged watersheds. Nevertheless, with available discharge data, the calibrated model using IMERG shows prospects for utilization (ENS 0.776, R2 0.787, and PBIAS 7.15%). Overall, this research offers insights into flood simulations using various precipitation products, emphasizing the significance of reliable discharge data for accurate hydrological modeling and the need for further evaluation of the IMERG product. [ABSTRACT FROM AUTHOR]

The article informs Radar remote sensing provides a new perspective on rainfall kinetic energy (RKE) and its role in soil erosion, challenging traditional empirical relations between RKE and rainfall intensity. The study presents global estimates of RKE based on raindrop microphysics, highlighting potential underestimations of RKE and rainfall erosivity with implications for soil erosion assessment and conservation practices, particularly in the context of climate change.

For a single-channel spaceborne synthetic aperture radar (SAR), the usage of a low pulse repetition frequency (PRF) is an effective technical way to extend the range swath. The sub-aperture imaging strategy is usually used to solve the problem of azimuth spectrum aliasing under the condition of a low PRF. However, the required up-sampling processing before the coherent synthesis of sub-images will lead to spectrum discontinuity between adjacent sub-images, which leads to an obvious grating lobe phenomenon after the process of sub-image synthesis, resulting in a significant decrease in image quality. For this issue, a high-resolution wide-swath (HRWS) imaging algorithm for a spaceborne SAR with a low PRF is proposed in this paper based on optimal spectrum shift processing. First, each sub-aperture is imaged using the typical range migration algorithm (RMA), and then all sub-images are up-sampled at the same time. Then, based on the criterion of the minimum grating lobe, the optimal spectrum shift is estimated. Finally, the spectrum of all sub-images is shifted and then all the shifted sub-images are synthesized coherently. The simulation data processing results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

General synthetic aperture radar (SAR) image automatic target recognition (ATR) methods perform well under standard operation conditions (SOCs). However, they are not effective in extended operation conditions (EOCs). To improve the robustness of the ATR system under various EOCs, an ATR method for SAR images based on the scattering parameter Gaussian mixture model (GMM) is proposed in this paper. First, an improved active contour model (ACM) is used for target–background segmentation, which is more robust against noise than the constant false alarm rate (CFAR) method. Then, as the extracted attributed scattering center (ASC) is sensitive to noise and resolution, the GMM is constructed using the extracted ASC set. Next, the weighted Gaussian quadratic form distance (WGQFD) is adopted to measure the similarity of GMMs for the recognition task, thereby avoiding false alarms and missed alarms caused by the varying number of scattering centers. Moreover, adaptive aspect–frame division is employed to reduce the number of templates and improve recognition efficiency. Finally, based on the public measured MSTAR dataset, different EOCs are constructed under noise, resolution change, model change, depression angle change, and occlusion of different proportions. The experimental results under different EOCs demonstrate that the proposed method exhibits excellent robustness while maintaining low computation time. [ABSTRACT FROM AUTHOR]