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With the advent of phased array radar (PAR) technology, it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than traditional Doppler radar. Research on the assimilation of PAR observations in numerical weather prediction model is still very few in China. In this study, the impact of assimilating PAR data on the model forecasts is investigated with a case study of a local heavy rainfall system that occurred in Foshan city, Guangdong Province on 26 August 2020. A series of sensitive experiments for this study is conducted. Both of the retrieved three-dimensional wind and hydrometeor fields are assimilated through nudging method for the TRAMS_RUC_1km (Tropical Regional Assimilation Model for South China sea_The Rapid Update Cycle_1km). The temperature and moist fields are also adjusted accordingly. The results show that significant improvements are made by the experiments with latent heat nudging and adjustment of moisture vapor field, which implies the importance of thermodynamic balance in the initialization of convective system and highlights the need to assimilate PAR radar observation in a continuous manner to maximize the impact of the data. Sensitivity tests also indicate that the relaxation time should be less than 5 minutes. In general, the assimilation of PAR data can significantly improve the nowcasting of regional heavy precipitation in this case. This paper is the first step toward operational PAR data assimilation in the numerical weather prediction model of southern China.

In this paper, a scheme of dual-Doppler radar wind analysis based on a three-dimensional variational method is proposed and performed in two steps. First, the horizontal wind field is simultaneously recovered through minimizing a cost function defined as a radial observation term with the standard conjugate gradient method, avoiding a weighting parameter specification step. Compared with conventional dual-Doppler wind synthesis approaches, this variational method minimizes errors caused by interpolation from radar observation to analysis grid in the iterative solution process, which is one of the main sources of errors. Then, through the accelerated Liebmann method, the vertical velocity is further re-estimated as an extra step by solving the Poisson equation with impermeable conditions imposed at the ground and near the tropopause. The Poisson equation defined by the second derivative of the vertical velocity is derived from the mass continuity equation. Compared with the method proposed by O'Brien, this method is less sensitive to the uncertainty of the boundary conditions and has better stability and reliability. Furthermore, the method proposed in this paper is applied to Doppler radar observation of a squall line process. It is shown that the retrieved vertical wind profile agrees well with the vertical profile obtained with the velocity–azimuth display (VAD) method, and the retrieved radial velocity as well as the analyzed positive and negative velocity centers and horizontal wind shear of the squall line are in accord with radar observations. There is a good correspondence between the divergence field of the derived wind field and the vertical velocity. And, the horizontal and vertical circulations within and around the squall line, as well as strong updrafts, the associated downdrafts, and associated rear inflow of the bow echo, are analyzed well. It is worth mentioning that the variational method in this paper can be applied to simultaneously synthesize the three-dimensional wind field from multiple-Doppler radar observations.

This study mainly focused on the structure and evolution of desert clouds, and was the first to conduct the high-resolution consecutive detection of clouds over the hinterland of the Taklimakan Desert (TD) from April to June 2018 by using the ground-based Ka-band Millimeter Wave Cloud Radar (MMCR). The reflectivity factor (Z), cloud boundary, and liquid water content (LWC) were calculated based on the power spectrum data observed by the MMCR, which were verified by comparing the detection data of the cloud profile radar (CPR) on the CloudSat board. The results showed that clouds over the TD were dominated by medium and high clouds, with their thicknesses generally being less than 2 km; moreover, the mean LWCs of the medium and high clouds were less than 0.01 g m−3, which implied that cirrus and stratiform clouds were predominant. However, for the low clouds, the average thickness was 3166 m and the drizzles were concentrated within 2.5–4.5 km, which indicated that precipitation would be more likely to occur in the low clouds. The mean LWC in the clouds over the TD was 0.0196 g m−3, which was less than that of clean clouds. Compared to the other periods, the average durations and LWCs in clouds increased significantly around noon owing to the obvious surface heating by the sensible heat flux. The average duration for the evolution of high to low clouds was approximately 2 h, and the average maximum LWC increased from 0.008 to 0.139 g m−3. These results provide a key database for further studies on the desert cloud structure and evolution characteristics of clouds over TD.

In this paper, we devise a unitary noise subspace fitting method for monostatic multiple-input multiple-output radar, which can obtain high-resolution direction-of-arrival (DOA) estimation with non-orthogonal waveforms. Firstly, the cross-covariance matrix without spatially colored noise can be obtained by the spatial cross-correlation technique of two transmit subarrays. Secondly, unitary transformation is introduced to convert the singular value decomposition from the complex-valued matrix to the real-valued matrix. Finally, DOAs are achieved by solving the optimization problem. Some numerical simulation results confirm the effectiveness and superiority of the proposed approach, which can provide higher angular resolution, more accurate estimation performance and low computational cost.

The sparse Multiple-Input–Multiple-Output radar has large array element spacing, which has a great significance for reducing the mutual coupling effects. In this paper, the authors propose a compensation sparse arrays with flexible inter-element spacing (CSA-FIS). Compared with the sparse arrays with flexible inter-element spacing (SA-FIS), the CSA-FIS improves the situation in that the number of virtual sensors significantly decreases when the inter-element spacing of two sub-array is not coprime. Under the guarantee of lower mutual coupling effects, the system degrees of freedom (DOFs) and the number of consecutive virtual sensors are also improved. Meanwhile, a pre-processing DFT-compressed sensing (D-CS) algorithm is proposed, with the purpose to solve the problem that the traditional CS algorithm has a high calculation cost when all non-zero parameters must be estimated. Numerical simulations demonstrate the superiority of the CSA-FIS in terms of DOFs and estimation accuracy when compared with SA-FIS. Moreover, compared with the traditional CS algorithm, the D-CS has lower computational complexity.

Carefully engineered radiofrequency (RF) pulses play a key role in a number of systems such as mobile phone, radar, and magnetic resonance imaging. The design of an RF waveform, however, is often posed as an inverse problem with no general solution. As a result, various design methods each with a specific purpose have been developed based on the intuition of human experts. In this work, we propose an artificial intelligence (AI)-powered RF pulse design framework, DeepRF, which utilizes the self-learning characteristics of deep reinforcement learning to generate a novel RF pulse. The effectiveness of DeepRF is demonstrated using four types of RF pulses that are commonly used. The DeepRF-designed pulses successfully satisfy the design criteria while reporting reduced energy. Analyses demonstrate the pulses utilize new mechanisms of magnetization manipulation, suggesting the potentials of DeepRF in discovering unseen design dimensions beyond human intuition. This work may lay the foundation for an emerging field of AI-driven RF waveform design., Published at Nature Machine Intelligence

The Advanced Driving Assistance System has been widely applied on various kinds of vehicles, since it can provide timely protection and burden relief to the passengers and drivers. Multiple different sensors are adopted to realize specific functions, while millimeter-wave radar and vision sensor are one of the most common sensor combinations. The scheme of sensor data fusion for this combination becomes necessary to contribute with more accurate and complementary detection results. A data fusion model designed for the pair of radar and vision is proposed with a three-level hierarchical structure, which includes screening of original sensor data, sensor data correlation, and target track forecast. The concrete fusion scheme applied on each layer is presented in details. The results of simulation and real circumstances test validate and prove the presented sensor data fusion model is efficient.

The aim of this study was to evaluate the impact of radar-detected radial velocity and reflectivity data assimilation (DA) on precipitation forecasts using the Weather Research and Forecasting (WRF) numerical model and its three-dimensional variational DA system (WRFDA-3DVAR). For this purpose, one heavy rain event in the city of Manaus in the Central Amazon on 25 December 2017 was analyzed. Numerical experiments were conducted using different initial conditions and both cyclical and non-cyclical assimilation processes. The evaluation of these experiments, conducted with and without radar DA, was based on comparisons between estimated radar precipitation and simulated precipitation. The results show that radial velocity plays a fundamental role in improving the representation of the convective system. Additionally, the best results were obtained when reflectivity was included alongside radial velocity in non-cyclical assimilation processes. Therefore, it can be concluded that the assimilation of radar data positively impacted the short-term forecast of precipitation associated with the studied convective system.

Roughness-induced emission from ocean surfaces is one of the main issues that affects the retrieval accuracy of sea surface salinity remote sensing. In previous studies, the correction of roughness effect mainly depended on wind speeds retrieved from scatterometers or those provided by other means, which necessitates a high requirement for accuracy and synchronicity of wind-speed measurements. The aim of this study is to develop a novel roughness correction model of ocean emissivity for the salinity retrieval application. The combined active/passive observations of normalized radar cross-sections (NRCSs) and emissivities from ocean surfaces given by the L-band Aquarius/SAC-D mission, and the auxiliary wind directions collocated from the National Centers for Environmental Prediction (NCEP) dataset are used for model development. The model is validated against the observations and the Aquarius standard algorithms of roughness-induced emissivity correction. Comparisons between model computations and measurements indicate that the model has better accuracy in computing wind-induced brightness temperature in the upwind/downwind directions or for the surfaces with smaller NRCSs, which can be better than 0.3 K. However, for crosswind directions and larger NRCSs, the model accuracy is relatively low. A model using HH-polarized NRCSs yields better accuracy than that using VV-polarized ones. For a fair comparison to the Aquarius standard algorithms using wind speeds retrieved from multi-source data, the maximum likelihood estimation is employed to produce results combining our model calculations and those using other sources. Numerical simulations show that combined results basically have higher accuracy than the standard algorithms.

This paper is devoted to solving the inverse problem of restoring the hydrometeor distribution function from radar measurements. We use the Tikhonov regularization algorithm based on the minimization of the residual functional by the conjugate gradient method.

The NASA Orbital Debris Program Office (ODPO) develops, maintains, and updates orbital debris environmental models, such as the NASA Orbital Debris Engineering Model (ORDEM), to support satellite designers and operators by estimating the risk from orbital debris impacts on their vehicles in orbit. Updates to ORDEM utilize the most recent validated datasets from radar, optical, and in situ sources to provide estimates of the debris flux as a function of size, material density, impact speed, and direction along a mission orbit. On-going efforts within the NASA ODPO to update the next version of ORDEM include a new parameter that highly affects the damage risk – shape. Shape can be binned by material density and size to better understand the damage assessments on spacecraft. The in situ and laboratory research activities at the NASA ODPO are focused on cataloging and characterizing fragments from a laboratory hypervelocity-impact test using a high-fidelity, mock-up satellite, DebriSat, in controlled and instrumented laboratory conditions. DebriSat is representative of present-day, low Earth orbit satellites, having been constructed with modern spacecraft materials and techniques. The DebriSat fragment ensemble provides a variety of shapes, bulk densities, and dimensions. Fragments down to 2 mm in size are being characterized by their physical and derived properties. A subset of fragments is being analyzed further in NASA’s Optical Measurement Center (OMC) using broadband, bidirectional reflectance measurements to provide insight into the optical-based NASA Size Estimation Model. Additionally, pre-impact spectral measurements on a subset of DebriSat materials were acquired for baseline material characterization. This paper provides an overview of DebriSat, the status of the project, and ongoing fragment characterization efforts within the OMC.

To make a better discrimination between target and false alarm, amplitude information (AI) of radar signal has been incorporated into many target tracking algorithms, such as probabilistic data association, multiple hypothesis tracking and probability density hypothesis (PHD). In this paper, we present the cubature integration based Gaussian mixture implementation of the PHD filter with AI, namely the GMPHD-AI filter. Since the utilization of multiple sensors is more effective than using only one sensor, we also extend the GMPHD-AI filter to the multi-sensor application where the iterated-corrector scheme is exploited for multi-sensor information fusion. The effectiveness of the presented method is demonstrated by a multiple targets tracking scenario.

Range side-lobes suppression which is responsible for detection ambiguity is a challenging task in pulse compression (PC) radar. Targets generating weak signals tend to hide in undesired side-lobes creating ambiguity. In general, the side-lobes behaviour performance of received signals is observed by their correlation with other contemporary signals. In this paper, a new polyphase code is proposed that exhibits good auto-correlation and cross-correlation properties to reduce peak side-lobe level (PSL) and integrated side-lobe level (ISL). The proposed polyphase code named P43code is produced by combining left and right shifted versions of $$P4$$ code. The amount of rotation depends upon the overall code length. The optimum rotation combination is the one that produces maximum side-lobes reduction. The proposed design is also applied to multiple-input multiple-output (MIMO) radar, where for the proposed $$P4_{3}$$ code, orthogonal waveforms are used to minimize cross-interference. Using ambiguity function (AF), the impact of proposed code on the delay-Doppler plane is investigated. Simulation results demonstrate the superiority of the proposed $$P4_{3}$$ code compared to existing alternatives.

In order to improve the accuracy of hail forecasting for mountainous and plateau areas in China, this study presents a novel fusion forecast model based on machine learning techniques. Specifically, known mechanisms of hail formation and two newly proposed elevation features calculated from radar data, sounding data, automatic station data, and terrain data, are firstly combined, from which a hail/short-duration heavy rainfall (SDHR) classification model based on the random forest (RF) algorithm is built up. Then, we construct a hail/SDHR probability identification (PI) model based on the Bayesian minimum error decision and principal component analysis methods. Finally, an “and” fusion strategy for coupling the RF and PI models is proposed. In addition to the mechanism features, the new elevation features improve the models’ performance significantly. Experimental results show that the fusion strategy is particularly notable for reducing the number of false alarms on the premise of ensuring the hit rate. A comparison with two classical hail indexes shows that our proposed algorithm has a higher forecasting accuracy for hail in mountainous and plateau areas. All 19 hail cases used for testing could be identified, and our algorithm is able to provide an early warning for 89.5% (17 cases) of hail cases, among which 52.6% (10 cases) receive an early warning of more than 42 minutes in advance. The PI model sheds new light on using Bayesian classification approaches for high-dimensional solutions.

The significant wave height (SWH) is one of the main parameters that describe wave characteristics and is widely used in wave research fields. Wave parameters measured by radar are influenced by the offshore distance and sea state. Validation and calibration are of great significance for radar data applications. The nadir beam of surface wave investigation and monitoring (SWIM) detects the global-ocean-surface SWH. To determine the product quality of SWIM SWH, this paper carried out time-space matching between SWIM and buoy data. The data qualities were evaluated under different offshore distances and sea states. An improved calibration method was proposed based on sea state segmentation, which considered the distribution of the point collocation numbers in various sea states. The results indicate that (1) the SWIM SWH accuracy at offshore distances greater than 50 km is higher than that at distances less than 50 km, with an RMSE of 0.244 4 m, SI of 0.115 6 and RE of 9.97% at distances greater than 50 km and those of 0.446 0 m, 0.223 0 and 18.66% at distances less than 50 km. (2) SWIM SWH qualities are better in moderate and rough sea states with RMSEs of 0.284 8 m and 0.316 9 m but are worse in slight and very rough sea states. (3) The effect of the improved calibration method is superior to the traditional method in each sea state and overall data, and the RMSE of SWIM SWH is reduced from the raw 0.313 5 m to 0.285 9 m by the traditional method and 0.198 2 m by the improved method. The influence of spatiotemporal window selection on data quality evaluation was analyzed in this paper. This paper provides references for SWIM SWH product applications.

Environmental perception is a key technology for autonomous driving. Owing to the limitations of a single sensor, multiple sensors are often used in practical applications. However, multi-sensor fusion faces some problems, such as the choice of sensors and fusion methods. To solve these issues, we proposed a machine learning-based fusion sensing system that uses a camera and radar, and that can be used in intelligent vehicles. First, the object detection algorithm is used to detect the image obtained by the camera; in sequence, the radar data is preprocessed, coordinate transformation is performed, and a multi-layer perceptron model for correlating the camera detection results with the radar data is proposed. The proposed fusion sensing system was verified by comparative experiments in a real-world environment. The experimental results show that the system can effectively integrate camera and radar data results, and obtain accurate and comprehensive object information in front of intelligent vehicles.

Because many vessels use the Automatic Identification System (AIS) to broadcast GPS positions, recent advances in satellite technology have enabled us to map global fishing activity. Understanding of human activity at sea, however, is limited because an unknown number of vessels do not broadcast AIS. Those vessels can be detected by satellite-based Synthetic Aperture Radar (SAR) imagery, but this technology has not yet been deployed at scale to estimate the size of fleets in the open ocean. Here we combine SAR and AIS for large-scale open ocean monitoring, developing methods to match vessels with AIS to vessels detected with SAR and estimate the number of non-broadcasting vessels. We reveal that, between September 2019 and January 2020, non-broadcasting vessels accounted for about 35% of the longline activity north of Madagascar and 10% of activity near French Polynesia and Kiribati’s Line Islands. We further demonstrate that this method could monitor half of the global longline activity with about 70 SAR images per week, allowing us to track human activity across the oceans.

Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socioeconomic needs of many sectors reliant on weather-dependent decision-making1,2. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations3,4. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints5,6. While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor performance on rarer medium-to-heavy rain events. Here we present a deep generative model for the probabilistic nowcasting of precipitation from radar that addresses these challenges. Using statistical, economic and cognitive measures, we show that our method provides improved forecast quality, forecast consistency and forecast value. Our model produces realistic and spatiotemporally consistent predictions over regions up to 1,536 km × 1,280 km and with lead times from 5–90 min ahead. Using a systematic evaluation by more than 50 expert meteorologists, we show that our generative model ranked first for its accuracy and usefulness in 89% of cases against two competitive methods. When verified quantitatively, these nowcasts are skillful without resorting to blurring. We show that generative nowcasting can provide probabilistic predictions that improve forecast value and support operational utility, and at resolutions and lead times where alternative methods struggle., A deep generative model using radar observations is used to create skilful precipitation predictions that are accurate and support real-world utility.

When an active landslide is first identified in an artificial reservoir, a comprehensive study has to be quickly conducted to analyse the possible hazard that it may represent to such a critical infrastructure. This paper presents the case of the El Arrecife Landslide, located in a slope of the Rules Reservoir (Southern Spain), as an example of geological and motion data integration for elaborating a preliminary hazard assessment. For this purpose, a field survey was carried out to define the kinematics of the landslide: translational in favour of a specific foliation set, and rotational at the foot of the landslide. A possible failure surface has been proposed, as well as an estimation of the volume of the landslide: 14.7 million m3. At the same time, remote sensing and geophysical techniques were applied to obtain historical displacement rates. A mean subsidence rate of the landslide around 2 cm/year was obtained by means of synthetic aperture radar interferometry (InSAR) and ground-penetrating radar (GPR) data, during the last 5 and 22 years, respectively. The structure-from-motion (SfM) technique provided a rate up to 26 cm/year during the last 14 years of a slag heap located within the foot of the landslide, due to compaction of the anthropical deposits. All of this collected information will be valuable to optimise the planning of future monitoring surveys (i.e. differential global positioning systems, inclinometers, ground drilling, and InSAR) that should be applied in order to prevent further damage on the reservoir and related infrastructures., This work was mainly supported by the European Regional Devel-opment Fund (ERDF) through the project "RISKCOAST" (SOE3/P4/ E0868) of the Interreg SUDOE Programme. The work of J.P.G., M.M-S., P.R. and J.M.A. was also supported by the "Ramón y Cajal" Programme (RYC-2017–23335) of the Spanish Ministry of Science, the project "MOR-PHOMED"—PID2019-107138RB-I00 / SRA (State Research Agency / https://doi.org/10.13039/501100011033) and the project "RADANDA -LUS" (P18-RT-3632) and B-RNM-305-UGR1818 of the FEDER / Junta de Andalucía-Consejería de Transformación Económica, Industria, Cono-cimiento y Universidades.

Calibration error is one of the primary sources of bias in echo intensity measurements by ground-based radar systems. Calibration errors cause data discontinuity between adjacent radars and reduce the effectiveness of the radar system. The Global Precipitation Measurement Kuband Precipitation Radar (GPM KuPR) has been shown to provide stable long-term observations. In this study, GPM KuPR observations were converted to S-band approximations, which were then matched spatially and temporally with ground-based radar observations. The measurements of stratiform precipitation below the melting layer collected by the KuPR during Typhoon Ampil were compared with those of multiple radar systems in the Yangtze River Delta to determine the deviations in the echo intensity between the KuPR and the ground-based radar systems. The echo intensity data collected by the ground-based radar systems was corrected using the KuPR observations as reference, and the correction results were verified by comparing them with rain gauge observations. It was found that after the correction, the consistency of the echo intensity measurements of the multiple radar systems improved significantly, and the precipitation estimates based on the revised ground-based radar observations were closer to the rain gauge measurements.

Since the advent of assisted reproductive technology (ART), the utilization of ART procedures has become increasingly popular among women seeking to establish pregnancy. Radiologists are therefore likely to encounter the various complications of ART therapy. The most common is ovarian hyperstimulation syndrome; others are multiple, ectopic, and heterotopic pregnancies. Ultrasonography is considered the initial modality to investigate ART complications, However, nonspecific symptoms might need the use of an additional imaging modality, such as computed tomography or magnetic resonance imaging, as a problem-solving tool. This article briefly discusses the steps involved in assisted reproduction. Its aim is to help radiologists become familiarized with the multimodality imaging features of the spectrum of ART-related complications. Their key imaging features and differential considerations are emphasized. This will facilitate the provision of precise and timely diagnoses, and aid the avoidance of fatal consequences.

To ensure the proper adoption of new technologies in identifying the potential geologic hazard on tourist routes, convolutional neural network (CNN) is applied in the radar image geologic hazard information extraction. A scientific and practical geologic hazard radar identification model is built based on identification of CNN image and calculation of big data algorithm, which can effectively improve the geologic hazard identification accuracy. Through experiments, the geologic hazard radar image data are verified, and the practicality of radar image intelligent identification under CNN and big data is also verified. The results show that the images of different resolution sizes all play a significant role in identification of geologic hazard performed by CNN. However, there are differences in the performance of different CNN models. With the continuous increase in training samples, the identification accuracy of various network models is also improved. Through radar image test, the identification capability of CNN model is the best, the highest precision is 93.61%, and the geologic hazard recall rate is 98.27%. Apriori algorithm is proposed for data processing, and the running speed and efficiency of identification models are improved, with favorable identification effect in variable data sets. To sum up, this research can provide theoretical ideas and practical value for the development of potential geologic hazard identification on tourist routes.

This paper deals with the joint design of transmit and receive beamforming for airborne transmit subaperturing (TS) multiple-input multiple-output (MIMO) radar to improve the moving target detection performance in the presence of signal-dependent clutter background. The considered system is referred to as an airborne TS–MIMO radar, whose transmit array is divided into several subarrays to form the steerable waveforms with directional gain. The joint design of transmit and receive beamforming problem is formulated by maximizing the signal-to-interference-plus-noise ratio under the energy constraint. To resolve the resulting non-convex joint design problem, we devise an efficient iterative optimization algorithm within the majorization-minimization framework. To obtain the desired transmit beampattern property, we extend the iterative optimization algorithm to resolve the energy and similarity constrained transmit beamforming design. Simulation results demonstrate that the proposed two methods are monotonically increasing and convergent, and exhibit superior property to the compared methods.

Wheat and rapeseed are significant crops in Czech agriculture and remote sensing has huge potential for their management, given Sentinel-1 can overcome issues of cloudiness and monitor vegetation development via radar backscatter. This study compares radar and optical data characterizing the development of wheat and rapeseed in an agricultural cooperative in the Czech Republic. Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) time-series of the main vegetation seasons between 2015 and 2018 are processed, analysed, and compared with each other. In 2018, the comparison of data with ground measurement by camera was also used. The temporal development of RVI is affected by noise, which is caused by the composition of imagery from different Relative orbits. The separation of imagery according to the Relative orbit used seemed to provide results more comparable to the phenological curve. Simple linear regression between NDVI and RVI illustrated that considering Relative orbit can slightly increase the Coefficient of determination. By selecting a suitable Relative orbit, the coefficient of determination between NDVI and RVI increased from 0.281 to 0.387 in the case of wheat and from 0.233 to 0.316 in the case of rape monitoring. The RVI for rapeseed and the height of canopy correlation was 0.392. The results for RVI presented in this article demonstrated that monitoring wheat and rapeseed development by Sentinel-1 has potential, however more research needs to be conducted in the areas of spatial and temporal noise removal.

A head-mounted display cannot cover an angle of visual field as wide as that of natural view (out-of-view problem). To enhance the visual cognition of an immersive environment, previous studies have developed various guidance designs that visualize the location or direction of items presented in the users’ surroundings. However, two issues regarding the guidance effects remain unresolved: How are the guidance effects different with each guided direction? How much is the cognitive load required by the guidance? To investigate the two issues, we performed a visual search task in an immersive environment and measured the search time of a target and time spent to recognize a guidance design. In this task, participants searched for a target presented on a head-mounted display and reported the target color while using a guidance design. The guidance designs (a moving window, 3D arrow, radiation, spherical gradation, and 3D radar) and target directions were manipulated. The search times showed an interaction effect between guidance designs and guided directions, e.g., the 3D arrow and radar shorten the search time for targets presented at the back of users. The recognition times showed that the participants required short times to recognize the details of the moving window and radiation but long times for the 3D arrow, spherical gradation, and 3D radar. These results suggest that the moving window and radiation are effective with respect to cognitive load, but the 3D arrow and radar are effective for guiding users’ attention to necessary items presented at the out-of-view.

Ground-penetrating radar (GPR) has been used for estimating root biomass, estimating pull-out resistance force of roots, and reconstructing root system architecture. Although GPR can estimate the diameter of a single root, it has not yet been verified whether it can successfully estimate the diameters of adjacent roots or whether rock fragments occurring in the soil matrix can be misidentified as roots. This study aimed to (1) evaluate the visual properties of GPR images of root bundles and rock fragments, and (2) clarify whether they have the potential to create non-negligible errors in GPR surveys. Root bundle samples buried in a sandy soil bed were scanned using 900 MHz and 1500 MHz antennas. Sole roots of Cryptomeria japonica and rock fragments in the bed were also scanned. A bundle of root formed one hyperbola with an apex in the radar profile, like a sole root. However, the contrast of the hyperbolas was weak and GPR indices relating to diameter were reduced compared to those of the comparably sized sole root. Stones did not form clear images, but boulders were faintly visible with non-negligible GPR indices. For evaluation of root biomass, adjacent roots had the potential to lead to significant errors. Similarly, in reconstructions of root system architecture, adjacent roots might lead to incorrect modelling of root point connections, because of incorrect diameters. For pull-out resistance force, adjacent roots could lead to only underestimation, i.e., safe-side error. Misidentification of rock fragments as roots depends on their sizes.

Sequences with low correlation have important applications in communications, radar, cryptography, and also in compressed sensing. The ultimate objective of this paper is to design a new family of polyphase sequences with low correlation. Our main contribution is the construction of such a family using additive and multiplicative characters over Galois rings. The proposed sequences have lengths N = pm − 1, family size M = pkm − 1, and a maximum magnitude $\theta _{\max \limits }=p^{k-1}\sqrt {p^{m}}$ , where k is an integer with 1 ≤ k < m.

Binary sequences with low aperiodic autocorrelation play an important role in communication and radar. The search of long low autocorrelation binary sequences (LABS) is a classical but challenging research problem. Exhaustive sequence search is infeasible for large lengths due to its prohibitively high complexity. Albeit many algorithms on LABS search in the literature, they are effective for lengths up to a few hundred only. In this paper, we propose an efficient hybrid algorithm which combines the strength of coordinate descent algorithm and the simulated annealing algorithm. Our proposed hybrid algorithm can efficiently generate long LABS of lengths up to several thousand and outperforms existing approaches in most cases.

Cross-hole radar is a method for geophysical exploration of geological conditions between two holes that can meet the need for refined detection of fractures. Full waveform inversion and reverse time migration are usually used in geophysical data processing and imaging. Full waveform inversion can invert the high-resolution relative permittivity image of the underground using the full-amplitude and phase information of the radar wave. Reverse time migration can migrate each reflection point in the radar record to its original position, thereby reflecting the real location of the underground interface. In this paper, we combined these two methods to achieve the purpose of refined detection of cross-hole radar for fractures in the tunnel side wall. Through several numerical simulation examples, we found that when there is a certain degree of electrical difference between the fractures and the surrounding rock, full waveform inversion can accurately reflect the distribution of the fractures and the relative permittivity value. Reverse time migration is more prominent in the description of the low-angle fractures interface, which facilitates the accurate division of the fracture. The combination approach of the two methods can accurately reflect the information of fractures in the wall, but the imaging effect for high-angle fractures is not ideal. The engineering data processing results of the cross-hole radar measured on the tunnel side wall of the Yinsong, Jilin, China prove the reliability of the method.

Concrete is one of the popular construction materials in the world. Performance and durability are the main factors that contribute to its demand. However, long-term issues of weakening and corrosion. In this study, three samples of rectangular beam size of 400 mm × 1200 mm × 6000 mm are checked in terms of wave velocity, elementary, chemical and radio spectrum. Apparently, one of the beams inspected is detected with poor quality from ultrasonic pulse velocity. In addition, the defects are supported by crack evaluation and abnormalities in the samples using radio spectrum. Further check on the concrete quality is done using X-ray fluorescent techniques and carbonation reading. As a conclusion, the outcome of this findings will help to determine any structure failure and factors contributing to the problem. Finally, the relation between wave and radar analysis can identified and solved the structure elements.

The application of frequency diverse array and multiple-input multiple-output (FDA-MIMO) radar to achieve the joint estimation of target's range and angle is receiving more and more attention. However, its performance is generally affected by the periodicity and time-varying of its beam pattern. Therefore, this paper implements a joint estimation algorithm of target's range and angle based on a new waveform synthesis model of time modulation and range compensation FDA-MIMO (TMRC-FDA-MIMO) radar. At the same time, the multiple signal classification (MUSIC) algorithm is mainly used in FDA-MIMO radar. The huge amount of calculation also brings difficulties to the implementation of the system. Therefore, this paper proposes an improved estimating signal parameters via rotation invariance technique algorithm to solve the problem of large amount of calculation on the basis of ensuring system performance. In addition, the closed form expressions of the Cramer–Rao lower bound and root-mean-squared error of range and angle estimation are derived. Theoretical analysis and simulation verify the effectiveness of the proposed method and demonstrate the excellent performance of the proposed radar system and algorithm.

There is a necessity for new methods and technology for several agricultural applications and decision support systems. Technological development at the global, national, and regional level has shown a path for the application of spaceborne remote sensing for agriculture purposes. Remote sensing technology eases to detect areas within the plots for a reliable data supply to analyze the data. Spaceborne synthetic aperture radar (SAR) data are capable to provide a reliable data supply throughout the year. The global repetition rate is up to 12 days. The datasets acquired from the Sentinel-1 platform facilitates a ground resolution of 20 × 20 m and these sufficient for the synchronization of spaceborne multispectral and radar datasets for enhanced agricultural cropland monitoring applications and decision support systems. Sentinel-1 data is a C-band radar data sets in dual-polarizations. The signal intensity changes over according to humidity in the soil or vegetation cover and the surface structure. The variations in signal information being recovered at the sensor end help in the informed decision making. Besides, SAR datasets are capable to provide information on the phenological stage of agricultural cropland along with crop-type differentiation for specific use cases. The synchronous utilization of spaceborne multispectral and radar datasets for enhanced agricultural cropland monitoring applications and decision support systems can be transformed into informative map products for several processes. The same technology can be beneficial for monitoring crop conditions during any disaster and early warning systems for proper management of available resources.

The understanding of yielding pillar behavior has been well documented in terms of pillar strength. The behavior has been documented using numerical simulations and empirical formulas. Nevertheless, the understanding of how fracture propagates in a matured yielding pillar is not well established. Indeed, very limited studies have striven to study this behavior using numerical simulation; however, the simulation appears to be a complex method and some models do not present the actual distribution. In this study, geophysical and optical instruments (ground-penetrating radar (GPR) and borescope respectively) were used to establish the distribution of extension fractures across the matured yielding pillar. The GPR survey has shown that the distribution of fractures across the length of the matured yielding pillar differs greatly, with extensive fracturing distributed along the middle section of the pillar; this distribution was observed to propagate to a maximum depth of about 1.7 m. However, the situation on the top and lower section of the pillar differs greatly from the middle section; in these sections, extensive fracturing was observed to propagate to the maximum depth of about 0.5 m, with moderate fracturing occurred from 0.6 to about 1.5 m toward the pillar core while the rest of the pillar is therefore dominated with less fracturing to solid rockmass. Borescope analysis was performed to validate the GPR survey; the results from the borescope analysis correlated with the GPR survey. Based on the GPR and borescope results obtained from several yielding pillars, a summation of the results is therefore presented in a form of an empirical chart categorizing the distribution of fractures along the yielding pillar. It is therefore argued that a developed summation chart of fracture distribution could be utilized to classify the maturity of yielding pillars based on fracturing. Indeed, the developed summation chart is simple to use and does not require complex analysis. It is therefore concluded that the developed summation chart could open more doors in exploring other simple alternative ways of classifying the matured yielding pillars.

Multi-fidelity approaches to orbit-state probability density prediction reduce computation time, but introduce a systematic error in the single-point prediction of a spacecraft state. An estimate of the systematic error may be quantified using cross-validation. Credibilistic filters based on Outer Probability Measures (OPMs) enable a principled and unified representation of random and systematic errors in object tracking. The quantified error of the multi-fidelity approach defines an OPM-based transition kernel, which is used in a credibilistic filter to account for the systematic error in the orbit determination process. An approach based on automatic domain splitting is proposed to reduce the error beyond what is normally achievable with multi-fidelity methods. A proof-of-concept for the approach is demonstrated for a simulated scenario tracking a newly-detected space object in low-Earth orbit via two ground stations generating radar measurements. An OPM-based definition of the admissible region combined with the multi-fidelity credibilistic filter establishes custody of the object.

Countries are increasingly interested in spacecraft surveillance and recognition which play an important role in on-orbit maintenance, space docking, and other applications. Traditional detection methods, including radar, have many restrictions, such as excessive costs and energy supply problems. For many on-orbit servicing spacecraft, image recognition is a simple but relatively accurate method for obtaining sufficient position and direction information to offer services. However, to the best of our knowledge, few practical machine-learning models focusing on the recognition of spacecraft feature components have been reported. In addition, it is difficult to find substantial on-orbit images with which to train or evaluate such a model. In this study, we first created a new dataset containing numerous artificial images of on-orbit spacecraft with labeled components. Our base images were derived from 3D Max and STK software. These images include many types of satellites and satellite postures. Considering real-world illumination conditions and imperfect camera observations, we developed a degradation algorithm that enabled us to produce thousands of artificial images of spacecraft. The feature components of the spacecraft in all images were labeled manually. We discovered that direct utilization of the DeepLab V3+ model leads to poor edge recognition. Poorly defined edges provide imprecise position or direction information and degrade the performance of on-orbit services. Thus, the edge information of the target was taken as a supervisory guide, and was used to develop the proposed Edge Auxiliary Supervision DeepLab Network (EASDN). The main idea of EASDN is to provide a new edge auxiliary loss by calculating the L2 loss between the predicted edge masks and ground-truth edge masks during training. Our extensive experiments demonstrate that our network can perform well both on our benchmark and on real on-orbit spacecraft images from the Internet. Furthermore, the device usage and processing time meet the demands of engineering applications.

The quality of wave measurements is of primary importance for the validation of wave forecasting models, satellite wave calibration and validation, wave physics, offshore operations and design and climate monitoring. Validation of global wave forecasts revealed significant regional differences, which were linked to the different wave buoy systems used by different countries. To fully understand the differences between the wave measurement systems, it is necessary to go beyond investigations of the integral wave parameters height, period and direction, into the frequency spectra and the four directional Fourier parameters that are used to estimate the directional distribution. We here analyse wave data measured from three different sensors (non-directional Datawell Waverider buoy, WaveRadar Rex, Optech laser) operating at the Ekofisk oil production platform located in the central North Sea over a period of several months, with significant wave height ranging from 1 to 10 m. In general, all three sensors provide similar measurements of the integral wave properties and frequency spectra, although there are some significant differences which could impact design and operations, forecast verification and climate monitoring. For example, the radar underestimates energy in frequency bands higher than 8 s by 3–5%, swell (12.5–16 s) by 5–13%, while the laser has 1–2% more energy than the Waverider in the most energetic bands. Lee effects of structures are also estimated. Lower energy at the frequency tail with the radar has an effect on wave periods (they are higher); wave steepness is seen to be reduced by 10% in the wind seas. Goda peakedness and the unidirectional Benjamin-Feir index are also examined for the three sensors.

The spatiotemporal study of the glaciers under the changing environment is critical from the freshwater availability, especially in the perennial rivers flowing out of them during the lean season. The Mrigthuni Glacier, one of the major glaciers in the Pindar River basin in Uttarakhand, has been selected as a study glacier for the present analysis. The long-term (1951–2018) meteorological data analysis indicated that the temperature increased in both the maximum ablation and accumulation period; however, the precipitation decreased during the primary accumulation period. The change detected in climate patterns provided an impetus to study its impact on the glacier using geospatial techniques. It was found that the glacier front retreated by 831.4 ± 42 m at the rate of 32.94 m yr−1 from 1990 to 2015. Further, the glacier accumulation area ratio has reduced from 55.63 to 53.22%, with a 66 m upward shift in equilibrium line altitude (ELA). The glacier's mass balance has reduced from 0.23 to 0.28 ± 0.035 m w.e. during the analysis period, based on a general equation developed for northwestern Himalaya using the optical remote sensing data. Due to the limitations of optical data, Synthetic Aperture Radar (SAR) data was used to map glacier radar zones. The distinct percolation/refreeze, transient firn, and ablation zones were identified on the SAR data composite of the different seasons. Considering SAR-based ELA as the most accurate, the analysis confirmed the negative mass balance with a value of 0.62 ± 0.036 m w.e. Therefore, it was concluded that SAR-based analysis might be vital for accurate glacier dynamics studies especially the mass balance.

In this study, an optically frequency-reconfigurable antenna with multiband characteristics is proposed utilizing photodiodes. It is developed on the basis of a Vivaldi antenna structure, while the composite radiation structure is realized by introducing three parallel branches in the antenna slot. Three photodiodes on the branches function as photoconductive switches to make the antenna reconfigurable at multiple low frequencies and stable at high frequencies. When the illumination irradiates different photodiodes, the proposed antenna is capable to switch between three narrowband modes, including 300 MHz, 677 MHz, and 1.02 GHz. The radiation gain is measured to reach 0.91 dB, 1.69 dB, 2.96 dB, respectively, while the variation in illumination states is 6.82 dB, 9.93 dB, 17.13 dB, respectively. Meanwhile, this antenna can continue to work stably at 3.2–3.8 GHz and 5.1–6.5 GHz regardless of illumination, with the maximum gain of 7.51 dB. Both simulation and experimental results substantiate the feasibility of the proposed design. This antenna design can transmit and shield the signal of specific frequency with optical control, and has good working characteristics at both high and low frequencies. In the future, it has promising application potential of communication and radar integration.

In this study, NixCo1−xFe2O4/Ni:ZnO (x:0.0, 0.5, and 1.0) epoxy-based composite structures were manufactured by sol–gel method. The structural, morphological and magnetic characteristics were investigated using X-ray diffraction (XRD), scanning electron microscopy (SEM) and vibrating sample magnetometer (VSM). Electromagnetic wave (EMW) absorption measurements were carried out using a vector network analyzer (VNA) between 8 and 12 GHz (X band). Ni substitution with Co decreases the crystallite size, average particle size, magnetic saturation (Ms) and the coercive field (Hc) values of cobalt-ferrite spinel structure. EMW characterization results showed that minimum reflection loss (RL) − 30.91 dB (99.9% absorption) at 10.76 GHz, and the maximum bandwidth of 1.81 GHz were observed for the RAM1 composite. In addition, the substitution of Ni2+ into the cobalt–ferrite structure resulted in the shifting absorption peaks to the higher frequency region (towards Ku band).

Land subsidence problems have become increasingly prominent. Traditional monitoring methods, such as level measurements, have high costs and low efficiency. Permanent scatterer interferometric synthetic aperture radar (PS-InSAR) has several advantages for land subsidence monitoring based on technological innovations. Aimed at resolving the key problems associated with PS-InSAR technology, the accuracy of three external digital elevations models (DEMs, namely, SRTM, ASTER GDEM, and PleiadesDEM) was analysed and compared. We found that the introduction of ground control points can significantly improve the elevation accuracy of DEMs. Herein, we introduce the specific processing steps and selection of the key parameters for IN-SAR data using the StaMPS software. We discuss the differences between IN-SAR technology and levelling measurements as well as the influence that the different external DEMs have on IN-SAR data. Based on 36 scenes of TerraSAR-X images, we obtained results for land deformation monitoring in Taiyuan City using PS-InSAR technology, which provided satisfactory monitoring results.

Identifying hitherto unknown palaeo-channels, especially in the arid regions of the Thar Desert, is crucial since these channels may form excellent aquifers, and are also associated with valuable ore deposits of many precious minerals. This study employed integrated C-band Synthetic Aperture Radar (SAR) of Sentinel-1A and high-resolution multispectral Sentinel-2A data of pre- and post-monsoon seasons (June and November) to delineate playas and palaeo-channels. This approach is the first of its kind for this area. The palaeo-channels were delineated through a detailed visual inspection of colour composite (CC) images of Sentinel-2A data, SAR backscatter (VH) images and fused SAR and optical images. Then, we studied the topographic profiles generated from the Shuttle Radar Topography Mission - Digital Elevation Model (SRTM-DEM) across the identified palaeo-channels, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) to further confirm the existence of a palaeo-channel's course and playas. As a result, several playas and palaeo-channels in the area were successfully identified, some of which were previously unmapped and undetected. The results indicate that the post-monsoon datasets are more useful for the precise delineation of palaeo-channels due to the presence of relatively higher moisture along the palaeo-channels' courses.

Difference far-field patterns represent a way for pin-pointing a target in both azimuth and elevation, extremely useful in radar applications. At the present work, an innovative method for synthesizing good compromise solutions among sum and difference patterns providing low complexity of the antenna feeding network for uniform thinned arrays is addressed. This procedure uses a hybrid version of the Simulated Annealing algorithm (hybrid SA) to optimize a cost function of radiation characteristics for both sum and difference patterns as peak directivity and side lobe level (SLL) while fixing deep nulls. In this framework, examples of half-wavelength spaced linear arrays from 40 to 120 elements were analyzed, as well as an extension to planar arrays by means of separable distributions was developed. The performance of the method is analyzed with different examples and its potential outlined, showing the ability of fixing deep nulls in both sum and difference patterns which share the same uniform excitation relative amplitudes.

The raindrop size distribution (DSD) parameters, which consists of the mass-weighted average diameter (Dm) and the scaling parameter for the concentration (Nw) are essential to estimate precipitation in numerical modelling and other research areas such as the Global Precipitation Measurement (GPM) core satellite. In the present work, we used the GPM Dual-Frequency Precipitation Radar algorithms (GPM-DPR), single (SF) and dual (DF) frequency, and in situ observations to derive the DSD parameters and evaluate the performance of algorithms under the complex orography and climate regime of the central Andes. We used data from optical disdrometer and Ka-band profiler radar over Huancayo Observatory during the austral summer monsoon. Our results indicate that the GPM-DPR algorithms have problems to correctly estimate the DSD parameters of convective rains due to the high variability in time and space of this type of rain and is the result of fixing the shape parameter (µ). The estimation of DSD parameters in stratiform rains, which are very common in the central Andes, is strongly affected by the limitation of the DF algorithm in light rain rates caused by its inability to estimate Dm

Detection of lane region under the road boundary is an imperative module for intelligent vehicle system. Lane markings provide separate regions on the road for the vehicles to avoid the possibility of accidents. Existing methods in lane detection have limited performance using various sensor-based approaches such as Radar and LiDAR and have high operational costs. To achieve a steady and optimal lane detection, the vision-based lane region detection scheme VLDNet is proposed which utilizes a encoder-decoder network using semantic segmentation architecture. In this direction, a hybrid model using UNet and ResNet has been adopted, where UNet is used as a segmentation model and ResNet-50 is used for down-sampling the image and identifying the required features. These identified features have been then applied into UNet for up-sampling and decoding the segments of the images. The publicly available KITTI dataset have been accessed for experiments and validation of the proposed network. The method outperforms the existing state-of-the-art methods in lane region detection. The network achieves better performance using standard evaluation measures such as accuracy of 98.87%, the precision of 98.24%, recall of 96.55%, frequency weighted IoU of 97.78%, and MaxF score of 97.77%.

Generally, radar echo is analysed in the assumptions that echo comes from stationary processes. Signal processing algorithms of atmospheric radar echoes are based on stationarity assumptions. In this paper, we propose a technique for processing the radar echoes by still preserving their non-stationary nature. The proposed algorithm is based on the complex empirical mode decomposition (CEMD) of non-stationary complex atmospheric radar signals. The well-established technique Hilbert Huang transform has been applied for the practical radar echoes collected from the Mesosphere-Stratosphere-Troposphere radar located at Gadanki Andhra Pradesh. The data is subjected to proposed CEMD algorithm and results are obtained and Stationarity is tested using the wavelet transform. The inference from the obtained results is of two-fold: Hilbert spectrum is very well localized and can be used for stationarity test like Wavelet spectrum, the frequency components in Hilbert spectrum lies in the signal region of Fourier spectrum thereby resembling it. For better interpretation, the algorithm is also applied for simulated signals under both stationary and non-stationary environments. The results motivate us for better exploitation of HHT for the spectral analysis of atmospheric radar echoes.

Natural disasters like floods are happening worldwide. Due to their negative impact on different social, economic and environmental aspects need to monitor and map these phenomena have increased. In fact, to access the zones affected by the flood, we use open source remote sensing (RS) images acquired by optical and radar sensors. Furthermore, we present a method using Sentinel-1 images; we suggest applying Ground Range Detected (GRD) images. For this purpose, pre-processed built and provided by the European Space Agency (ESA), preserved by free software Sentinel Application Platform (SNAP) for data extraction around appropriate demand. Moreover, the principal objective of this article is to assess the capability of Sentinel-1 Synthetic Aperture Radar (SAR) images in order to visualize flood areas in the Inaouene watershed located in north-eastern of Morocco. The origin of this natural hazard is the combination of natural and anthropogenic factors that makes the watershed vulnerable with a sub-annual frequency. The results of this work help decision-makers and managers in the field of natural risk management and land-use planning to implement a strategy and action plan for the protection of the populations and the environment against the negative impact of floods.

Monitoring and estimating rainfall for river basins in developing countries, such as Thailand, during severe storms are important and challenging studies due to the need for high spatiotemporal information of rainfall estimates. There is no end-software to produce rainfall estimate products for the basin because sophisticated systems and algorithms are needed to manipulate spatiotemporal data. Radar-based quantitative precipitation estimation (RQPE) provided by ground-based weather radar is considered to be suitable data for monitoring rainfall over a basin because its system can observe a rainfall event with high resolution of spatiotemporal scanning. This study presents the implementation of mosaicked RQPE from two stations over the Yom River basin in the northern part of Thailand. The study was performed using free and open source for a geographic information system (GIS) software with developing Python programming language in QGIS (PyQGIS) during the severe storm of Sontihn that occurred in July 2018. Using four-times-per-hour radar scanning, the RQPE mosaicking products were created over the basin by developing an algorithm in PyQGIS. The efficiency of the Z-R relationship was evaluated over the basin with multitemporal aggregation in the validation process between the mosaicked RQPE and rainfall observed at gauges. The Rosenfeld Z-R relationship produced the best performance based on validation statistics of the Marshall-Palmer and Summer Deep Tropical Convection Z-R relationship. Spatial mean field bias using the Rosenfeld Z-R relationship was greatly reduced during the storm evolution of Sontihn compared with the other two Z-R relationships. However, the RQPE based on instantaneous observations using the Marshall-Palmer Z-R relationship performed the best for rainfall estimation compared with the others.

Forest ecosystems play a crucial role in mitigating global climate change by forming massive carbon sinks. Their carbon stocks and stock changes need to be quantified for carbon budget balancing and international reporting schemes. However, direct sampling and biomass weighing may not always be possible for quantification studies conducted in large forests. In these cases, indirect methods that use forest inventory information combined with remote sensing data can be beneficial. Synthetic aperture radar (SAR) images offer numerous opportunities to researchers as freely distributed remote sensing data. This study aims to estimate the amount of total carbon stock (TCS) in forested lands of the Kizildag Forest Enterprise. To this end, the actual storage capacities of five carbon pools, i.e. above- and below-ground, deadwood, litter, and soil, were calculated using the indirect method based on ground measurements of 264 forest inventory plots. They were then associated with the backscattered values from Sentinel-1 and ALOS-2 PALSAR-2 data in a Geographical Information System (GIS). Finally, TCS was separately modelled and mapped. The best regression model was developed using the HH polarization of ALOS-2 PALSAR-2 with an adjusted R2 of 0.78 (p −1. The map showed that the distribution of TCS was heterogenic across the study area. Carbon hotspots were mostly composed of pure stands of Anatolian black pine and mixed, over-mature stands of Lebanese cedar and Taurus fir. It was concluded that the total carbon stocks of forest ecosystems could be estimated using appropriate SAR images at acceptable accuracy levels for forestry purposes. The use of additional ancillary data may provide more delicate and reliable estimations in the future. Given the implications of this study, the spatiotemporal dynamics of carbon can be effectively controlled by forest management when coupled with easily accessible space-borne radar data.

The sparse recovery space–time adaptive processing (SR-STAP) can reduce the requirements of clutter samples and suppress clutter effectively using limited training samples for airborne radar. Commonly, the whole angle-Doppler plane is uniformly discretized into small grid points in SR-STAP methods. However, the clutter patches deviate from the pre-discretized grid points in a non-sidelooking SR-STAP radar. The off-grid effect degrades the SR-STAP performance significantly. In this paper, a gridless SR-STAP method based on reweighted atomic norm minimization is proposed, in which the clutter spectrum is precisely estimated in the continuous angle-Doppler domain without resolution limit. Numerical simulations are conducted and the results show that the proposed method can achieve better performance than the SR-STAP methods with discretized dictionaries and the SR-STAP methods utilizing atomic norm minimization.