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Radar technology is critical for subsurface sensing applications since it enables the extraction of surface information remotely without being affected by environmental factors such as illumination and weather conditions. Hence, their usage on drones has become extremely essential as these vehicles need to accumulate data while flying over different and possibly difficult-to-access terrains regardless of visibility conditions. A frequency-modulated continuous-wave (FMCW) single input multiple output (SIMO) radar system is developed to provide near real-time radar images onboard. The system is comprised of a transceiver module, specifically designed for synthetic aperture radar (SAR) applications, to produce images in real-time. GPU-based computation is utilized to optimize processing power, while enabling range profiles to be generated and SIMO SAR images to be created during flight. The system can be easily mounted on UAV or multirotor systems. The detection and ranging performance of the system are examined by performing several experiments at the campus. Objects up to 100 metres are successfully detected by the radar system. The system demonstrates excellent ability to generate 2D SIMO SAR images, effectively identifying building and guard-box exteriors that are consistent with images from Google Earth. [ABSTRACT FROM AUTHOR]

Radar signatures of rain over the ocean have a complex structure since they receive contributions from surface scattering and volume scattering and attenuation by hydrometeors in the atmosphere. These contributions overlap and are often difficult to detangle. While most of the mechanisms contributing to radar signatures of rain over the ocean are well understood, there is one remaining issue that has been discussed controversially in the literature for a long time. It is the question what scattering mechanism causes the areas of strongly enhanced radar backscatter, also called 'bright blobs' or 'bright patches', which are frequently observed on spaceborne C-band SAR images acquired over tropical and subtropical oceans in the presence of convective rain. Recently, papers have been published in which it is hypothesized that they are caused by radar backscattering at hydrometeors in the melting layer (ML). Although many observational facts seem to support this hypothesis, there exists one strong argument against this hypothesis: It is the observation that the position of the ML radar signatures (bright blob) in the SAR image is not shifted in anti-range from the position, where the rain column hits the sea surface. This absence of a shift is observed when 1) comparing Sentinel-1 SAR images on which rain cells are visible with quasi-concurrently acquired weather radar images and 2) when inter-comparing of SAR images of rain cells acquired concurrently at different frequencies and polarizations. Based on these observations, we discard the hypothesis that the bright blobs are due to volume scattering at hydrometeors in the ML and hypothesize instead that they are due to scattering at splash products at the sea surface. This hypothesis is supported by radar backscattering measurements carried out in the laboratory and from a shore-based platform, which show that, at C-and X-band, strong rain can give rise to strong radar returns also at cross-polarization. [ABSTRACT FROM AUTHOR]

The Radar Cross Section (RCS) plays a crucial role in radar systems, serving to detect and characterize radar targets. Moreover, the need to penetrate foliage and capture images of man-made structures like roads and buildings has driven the development of very high frequency/ultra high frequency (VHF/UHF)-based radar systems. In this context, we propose a novel polyphase-coded spread spectrum (PCSS) radar system designed for RCS measurements and ISAR imaging over VHF/UHF radio channels in open environments. To provide a comprehensive understanding, we examined the open range, considering factors such as amplitude distribution function, root mean square (RMS) delay, and coherence bandwidth, followed by a link budget analysis. We also conducted a comparative study of RCS measurements to evaluate the performance of the proposed radar system, demonstrating its effectiveness through theoretical calculations. Furthermore, we assessed the radar imaging capabilities by applying the CLEAN algorithm to improve the quality of the generated images. [ABSTRACT FROM AUTHOR]

The Sissingué Gold Project is in Northern Côte d'Ivoire and part of the southern extension, a large peneplain of the landscape in South Mali. The area has experienced extensive weathering and erosion over a long time, resulting in a widespread and complex regolith cover. This diverse and vast regolith cover poses considerable difficulties for exploration. To overcome this problem, we adopted mapping of regolith units using machine learning (ML) with support vector machine (SVM) and artificial neural network (ANN) algorithms. The main objectives were to (1) make the predictive regolith map of the study region using these algorithms with data obtained from Landsat-8 operational land imager, Advanced Land Observing Satellite Phased Array Type L-Band Synthetic Aperture Radar digital elevation model and Sentinel-1A; (2) show the method of pre-processing and processing the required data and (3) develop the regolith landform unit (RLU) map from the predictive regolith map and collected data based on the Relict – Erosional – Depositional model. Tests using the SVM and ANN algorithms showed that both ML tools could accurately map regolith landforms. An innovative method of optimizing data using parameters is presented. The results showed that ANN outperformed SVM with an overall accuracy of 87.01% and a kappa coefficient of 0.84, whereas the corresponding values for SVM were 86.69% and 0.84, respectively. However, the validation data obtained from SVM-based prediction exhibited a better score than that of validation data obtained from ANN-based prediction. The Sentinel-1A radar band combined with Landsat-8 data reduced the vegetation-masking effect and improved the classification results. The RLUs of this area are composed primarily of relicts at 24.91%, including lateritic residuum and soil, depositional material at 36.39% with exotic sediments and ferrierite and the remaining 38.7% of erosional material made of saprolite, colluvial fragments, and mottled zones on flanks. [ABSTRACT FROM AUTHOR]

Terrestrial radar interferometry (TRI) provides accurate observations of displacements in the line-of-sight (LOS) direction and is therefore used in various monitoring applications. However, relating these displacements directly to the 3d world is challenging due to the particular imaging process. To address this, the radar results are projected onto a 3d model of the monitored area, requiring georeferencing of the 3d model and radar observation. However, georeferencing relies on manual alignment and resource-intensive on-site measurements. Challenges arise from the significant disparity in spatial resolution between radar images and 3d models, the absence of identifiable common natural features and the relationship between image and spatial coordinates depending on the topography and instrument pose. Herein, we propose a method for data-driven, automatic and precise georeferencing of TRI images without the need for manual interaction or in situ installations. Our approach (i) uses the radar amplitudes from the TRI images and the angle of incidence based on the 3d point cloud to identify matching features in the datasets, (ii) estimates the best-fitting transformation parameters using Kernel Density Correlation (KDC) and (iii) requires only rough initial approximations of the radar instrument's pose. Additionally, we present the correct relation between cross-range and azimuth for ground-based radar instruments. We demonstrate the application on a geomonitoring case using TRI data and a point cloud of a large rock cliff. The results show that the positions of the radar image can be localized in the monitored 3d space with a precision of a few metres at distances of over 1 k m. This is an improvement of almost one order of magnitude compared to what had been achieved using standard approaches and direct observation of the radar instrument's pose. The proposed method thus contributes to the automation of TRI data processing and improved localization of small-scale deformation areas detected in radar images. [ABSTRACT FROM AUTHOR]

In this paper, the shadow statistical method, which has the merit of without calibration, is further investigated for retrieving significant wave height (SWH) based on shadowing from X-band marine radar images. For the shadow statistical method, the Smith fitting function (SFF) is utilized to fit the illumination probability obtained from the radar image and calculate the SWH. By deeply investigating the estimation of wave steepness from the shadow image, it is found that the analytic solution of wave steepness is not obtained, due to the complementary error function and exponential term in the SFF. Meanwhile, several screening processes of gradually discarding illumination probability are required to obtain accurate root mean square (RMS) wave steepness, which makes the calculation time-consuming. To solve these problems, both the complementary error function and exponential term in the SFF are approximated under the working conditions of marine radar in practice, and the analytic solution of the wave steepness is described with the illumination probability. The shore-based X-band radar images at Pingtan station from November 9 to 17, 2014 are collected to validate the validity of the proposed method. By using 254 sets of radar data with the SWH range of 0.5 ~ 3.5 m, the experimental results demonstrate that the correlation coefficient (CC) of the estimated SWH between the proposed method and the traditional method is 0.996, and the root mean square error (RMSE) is 0.034. The average running time is significantly reduced from 2.76 s to 0.44 s. [ABSTRACT FROM AUTHOR]

Crop type mapping visualizes the spatial distribution pattern and proportion of planting areas of different crop types, which is the basis for subsequent agricultural applications. Although optical remote sensing has been widely used to monitor crop dynamics, data are not always available due to cloud and other atmospheric effects on optical sensors. Satellite microwave systems such as Synthetic Aperture Radar (SAR) have all-time and all-weather advantages in monitoring ground and crop conditions, combining optical imagery and SAR imagery for crop type classification is of great significance. Our study mainly proposes seven feature combination schemes based on the combination of multi-temporal spectral features and texture features of Sentinel-2 (S2), and radar backscattering features of Sentinel-1 (S1) evaluate the influence of different data sources and different features on classification accuracy, obtains the optimal classification strategy and analyses the contribution of different features to classification result, in the aim of providing a new technical approach for the fine identification of crops from multi-source remote-sensing data. Results show that the crop classification accuracy of combined multi-time series spectral, texture, and radar features is higher than that of combining two types of features. The features subset selected by multi-period spectral, texture, and radar features have the best classification result, the overall accuracy (OA) and kappa coefficients reach 96.40% and 0.93, respectively. The study provides a method reference for future research on larger-scale remote-sensing crop precise extraction. [ABSTRACT FROM AUTHOR]

In this study, a new method combining wave-number energy spectrum (WES) and Genetic algorithm-back propagation neural network (GA-BPNN) is proposed to retrieve the rainfall intensity level from rain-contaminated X-band marine radar image. Since the intensity of spatial rainfall can be reflected by the distribution of energy in the wavenumber frequency domain, the obtained WES is divided into three wavenumber segments (low, medium and high wavenumber segments), and the ratio of the wavenumber in each wavenumber segment to the total wavenumber is calculated separately as the characteristic parameters. Based on the excellent network convergence speed and data prediction accuracy of the GA-BPNN, these calculated parameters are input into the constructed GA-BPNN for training to complete the task of rainfall intensity level retrieval. The proposed method is tested using data collected at the ocean observation station of Haitan Island in Pingtan County. Referring to the actual rainfall intensity synchronously recorded by the rain gauge, the retrieval accuracy of the proposed method is 97.4%, which is 4.3% higher than that of back propagation neural network (BPNN) not optimized by Genetic algorithm (GA). In addition, compared with the retrieval performance of the ratio of zero intensity to echo (RZE) method based on the occlusion area of radar image, the retrieval accuracy of the proposed method is improved by about 12.9%. [ABSTRACT FROM AUTHOR]

Gaussian Process Regression (GPR) emerged as a powerful algorithm since last decade in many applications. However, it is not fully explored in remote sensing applications, especially for predicting plant biophysical variables in the heterogeneous environment of India. This kernel-based machine learning technique has effectively replaced conventional approaches for estimating vegetation characteristics from remotely sensed data. In this work, an attempt has been made to test the ability of GPR to estimate the leaf area index (LAI) of wheat crops using the Sentinel-1 (S1) derived Dual-Polarized Radar Vegetation Index (DpRVI) and Sentinel-2 (S2) Top of Atmosphere (TOA) products. Further, the ability of the atmospheric correction procedure was tested by S2 Bottom of Atmosphere (BOA) images. To accomplish this, the field measurements of LAI were carried out from January to March 2020. Further comparisons of the GPR's performance were made with the Artificial Neural Network (ANN) coupled with the PROSAIL (PROSPECT+SAIL) radiative transfer model, available through the Sentinel Application Platform (SNAP) Biophysical processor. The accuracy of the estimated LAI was evaluated using the statistical indicators, for example, coefficient of determination (R2), Root Mean Square Error (RMSE), and Nash Sutcliffe Efficiency (NSE). The results showed that the synergy of the GPR and DpRVI provided the most accurate result (R2 = 0.822, RMSE = 0.503 m2 m−2, NSE = 0.831) as compared to the GPR and TOA (R2 = 0.816, RMSE = 0.596 m2 m−2, NSE = 0.803) and SNAP biophysical processor (based on ANN) (R2 = 0.696, RMSE = 0.760 m2 m−2, NSE = 0.578). Therefore, the study demonstrated the importance of S1 SAR images and GPR as an alternative tool among the other well-established machine learning algorithms to estimate the crop biophysical parameters. [ABSTRACT FROM AUTHOR]

In previous research, the identification of the Bragg region, which is often achieved using image recognition, was easily affected by ionospheric interference and different manual parameter settings. Strong disturbances from the ionosphere and other environmental noise may interfere with high-frequency radar (HFR) systems when determining first-order Bragg regions and thus may directly influence the surface current mapping performance. To avoid human intervention in first-order Bragg-region recognition, deep learning methods were used to extract multiple levels of feature abstractions of the first-order Bragg region. In this study, to assist in developing sufficient training data, a procedure integrating a morphological approach and Otsu's method was proposed to provide manual labelled data for a U-Net deep learning model. Additionally, several sets of activation functions and optimizers were used to achieve optimal deep learning model performance. The best combination of model parameters results in an accuracy of over 90%, an F1 score of over 80%, and an intersection over union (IoU) reaching 60%. The identification time of one image is approximately 70 ms. These results demonstrate that this deep learning model can predict the position of first-order Bragg regions under ionospheric interference and avoid being affected by strong noise that could cause prediction errors. Hence, deep learning and image fusion processing can effectively recognize the first-order Bragg regions under strong interference and noise and can thereby improve the surface current mapping accuracy. [ABSTRACT FROM AUTHOR]

Very-high frequency (VHF) radar echoes are known to be aspect sensitive in general, which could be either the manifestation of a thin stable layer providing sharp refractive index gradient or shear-driven steep layer structures. The present study for the first time shows the volume imaging of aspect sensitivity in the recently upgraded Advanced Indian Mesosphere Stratosphere Troposphere (MST) radar (AIR). The present configuration of radar can provide a better understanding of three-dimensional structures of turbulence and instabilities, which was not possible when the radar was operated in two orthogonal planes. Both isotropic and anisotropic layers/turbulence are observed at various height levels over the radar volume. Enhanced aspect sensitivity is observed between 6 and 9 km, which is attributed to the presence of dynamic instability arising due to strong wind shear and atmospheric stability. All the scatterers tend to become isotropic when both the square of wind shear and stability parameters are above 0.25 × 10−3 s−2. Due to the high aspect sensitivity, there may be an underestimate of 5–10% in wind velocity measurements, and it is higher for the low off-zenith beam angle. [ABSTRACT FROM AUTHOR]

Interferometric Synthetic Aperture Radar (InSAR) came into practical use as a geohazard monitoring tool in the 1990s. The uptake of this remote sensing technique however is geographically varying, and Australia has been one of many continents not fully utilizing the available InSAR data. As a result, limited knowledge exists on the application of InSAR data to obtain insights and benefits in geohazard risk monitoring and management. Due to the said limitation, this research carried out a concise technical background study and investigated a suite of case studies to demonstrate the potential for geohazard monitoring and assessment in Australia using InSAR. The differential InSAR workflow was applied to sites that experienced heaving and subsiding of the ground surface, mining, earthquake, landslides, and tunnelling in West Metro Melbourne (VIC), Musgrave (SA), Crinum mine (QLD), Pelham (TAS), Lake Muir (WA), and Sydney (NSW). The results of the Differential InSAR analysis resulted in acceptable ground movement and deformation estimates when compared with measured field data. Future opportunities related to new applications of the InSAR technique on geohazard reduction considering climate change contributions and probable coupling with artificial intelligence techniques were also discussed. This research has formed a conducive and scientific learning medium, which may encourage the growth of multidisciplinary research combining the fields of remote sensing and geotechnical engineering to investigate the impact of geohazards. [ABSTRACT FROM AUTHOR]

Aeolus is European Space Agency's unique and novel wind measuring satellite mission providing near real-time wind profiles from near surface to an altitude of 30 km. This paper presents the validation of Aeolus wind profiles over Cochin (10.04° N, 76.9°E), India using the 205 MHz wind profile radar. The Aeolus wind profiles (baselines 10 and 11) have been validated for an altitude range of 1 to 18 km during June, 2019 to September, 2021. Aeolus Rayleigh wind for clear ( R a y l e i g h c l e a r ) and Mie wind for cloudy ( M i e c l o u d y ) show very good agreement with the radar wind profiles. The Pearson correlation coefficient between radar and Aeolus wind for R a y l e i g h c l e a r and M i e c l o u d y are 0.93 and 0.94, respectively. The systematic and random errors in R a y l e i g h c l e a r are found to be −0.15 m s − 1 and 4.87 m s − 1 , respectively, while these values are −0.06 m s − 1 and 3.68 m s − 1 for M i e c l o u d y . A detailed error characterization of Aeolus wind profiles with respect to radar is presented in this study. The bias in Aeolus wind is provided in terms of observation altitude, seasons, different windy conditions and ascending/descending orbits. [ABSTRACT FROM AUTHOR]

Space-time adaptive processing (STAP) is an important clutter suppression technique in airborne multichannel radar. It can be a challenging problem to apply STAP to the maritime airborne radar without knowledge of sea clutter space-time coupling characteristics. The radar sea clutter data with space-time coupling characteristics can be obtained by airborne multichannel radar, but the flight testing is very expensive. A cost-effective technique is presented for emulating airborne motion based on shore-based multichannel radar. The technique is implemented by the same array shared with transmit and receive antenna, where the transmit array uses an inverse displaced phase centre antenna to emulate platform motion and the receive array is used for receiving data. Based on the above technique, a sea clutter model of aircraft motion emulation is proposed. Then, we verify the model by quantitatively comparing the number of eigenvalue and the spread width of the space-time power spectrum between the simulated and measured data. The result shows that the eigenvalue number and the spread width of 3 dB clutter ridge of measured data are more than 79.5% of that belonging to simulated data. It means that the simulated radar sea clutter data generated by this model can better fit the measured data under sea states classes 1–4, which can provide support for the analysis of sea clutter space-time characteristics. [ABSTRACT FROM AUTHOR]

In this study, the scattering mechanisms associated to internal waves (IWs) are investigated at L-band. IWs represent key geophysical factors for sea-air heat exchange and play a paramount role in the biological primary production and in the understanding of the evolution of climate ecosystem. In addition, a better understanding of IWs microwave scattering mechanisms can improve the modeling capability and, therefore, can boost the development on advanced synthetic aperture radar (SAR)-based added-value products to mitigate the risk for offshore drilling operations and aquaculture activities associated to IWs. The analysis of L-band multi-polarization SAR scattering of IWs under the influence of surface current straining is performed using a meaningful full-polarimetric Advanced Land Observing Satellite Phased Array type L-band 1 SAR data set collected over IWs observed under different imaging and wind conditions. Time and space co-located ancillary information is also available. Experimental results demonstrate that the non-polarized scattering mechanisms constitute a significant contribution to the total IW backscattering, especially in the case of surface current gradients owing to IWs (about 48–57%). It is also found that the non-polarized scattering contribution associated to IW concentrates along the wave crests, i.e. it is at least 60% larger than the one observed along the wave troughs. In addition, considering the IW traveling directions relative to that of the wind, the non-polarized scattering contribution associated to IWs is more remarkable at upwind direction while it is less significant at down/crosswind directions. The non-polarized scattering mechanisms also calls for a modulation induced by IWs which is much more significant,i.e. at least three times, that the one that characterizes the polarized scattering mechanism. [ABSTRACT FROM AUTHOR]

Remote sensing based retrieved of soil moisture from low frequency passive microwave observations is preferred in different aspects such as better spatial coverage and more measurement compared to traditional ground-based measurements. However, due to coarse spatial resolution of the observations, their applications are limited in local to regional studies. This paper provides a framework using random forest regression to disaggregate the daily SMAP enhanced soil moisture (SPL3SMP_E) utilizing several ancillary data to overcome the spatial resolution limits and cloudiness effects. Ancillaries were acquired from sentinel-1 radar, MODIS monthly NDVI, land cover, topography, and surface soil properties. To validate the downscaled results with 1-km spatial resolution, the OZNET soil moisture measurements and sparse TDR ground soil moisture measurements were collected from Murrumbidgee catchment (Australia) and Firozabad catchment (Iran), respectively. Downscaled soil moisture product unbiased root-mean-square error (UnbRMSE) of ensemble learning demonstrated a range of 0.023 and –0.07 cm3/cm3. The produced downscaled soil moisture exhibited better local heterogeneity when compared to the coarse data and tracked the dynamics of temporal changes in soil moisture. Furthermore, cumulative distribution function (CDF) analysis showed good accuracy of downscaled soil moisture in grassland and cropland. Taken together, the findings supported usefulness of the suggested methodology in downscaling the medium- resolution SMAP soil moisture product. [ABSTRACT FROM AUTHOR]

A measurement setup based on a high-precision, low-cost FMCW radar sensor has been developed for radar-based 1D deformation monitoring of engineering structures. The method enables contactless and time-continuous scans, but has lower acquisition costs compared to commercially available terrestrial laser and radar technologies. The article contains the instrument-related description of the sensor with accuracy tests for 1D distance measurements on a laboratory scale as well as a case study for monitoring the movements of a headframe over the course of the day. The detectability of deformations was statistically verified by means of a hypothesis test and applied to the case study under real conditions. With a defined target object (corner reflector), measuring accuracies of up to 10 μm with a 99,7% confidence level could be achieved. The case study shows absolute movements of the headframe during the day of 1 mm. Changes of 0,2 mm can be detected in time intervals of 9 min with a statistical significance level of 95%. Abbreviations : CA-CFAR: Cell-averaging constant false alarm rate; CR-I, CR-II: Corner reflectors; CUT: Cell Under Test; DFT: Discrete Fourier transformation; EXP: Experiment; FMCW: Frequency-modulated continuous-wave; iSDR-C:indurad ScanningDynamicRadar-Compact; LOD: Limit of detection; PI: Peak interpolation [ABSTRACT FROM AUTHOR]

Vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM), whose unique wavefront distribution exhibits angular diversity characteristics, which provides a richer degree of freedom for information modulation. Although several works have reported that the EM vortex imaging has superior performance in target detection and imaging with azimuthal super-resolution, the underlying physical mechanism needs to be further developed. This article offers a solution to significantly refine the imaging resolution for conventional real-aperture imaging radar (CRAIR) using multiple OAM-carrying beams. Firstly, it is deduced that the various spatial frequency distributions and time-varying phase wavefront distributions of OAM beams promote the realization of OAM-based imaging radar (OAMIR) super-resolution imaging. Secondly, a scheme for adjusting the directivity of EM vortex beam is proposed, which can effectively acquire most spatial frequency components of the probed target, resulting in the target can be illuminated simultaneously by the main lobes of various beams carrying different topological charges. Thirdly, the point-spread function (PSF) comparison for CRAIR and OAMIR is carried out by theoretical analysis and numerical simulation, and the spatial azimuth resolution for OAMIR is derived. Finally, the signal mathematical model for OAMIR is established and analysed. Simulation results validate that the proposed OAM-based imaging paradigm breaks through the Rayleigh limit associated with conventional real-aperture imaging technology and achieves imaging performance superior to OAMIR in previous works. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the most recent passive radars based on long-term evolution (LTE). To begin, this paper investigates the various characteristics and requirements of 4 G LTE signals for radar, taking performance aspects such as range, velocity, range resolution, and velocity resolution into account. An ambiguity function analysis is performed on a measured LTE signal using the synchronization and reference signal components to evaluate key performance parameters such as Doppler and range characteristics. We also discuss how LTE passive radar can be used in a variety of applications. The detailed analysis of the LTE downlink signal, its structural overview, and the effect on cross- and self-ambiguity functions are all discussed. The paper investigates related standard development proposals, with a focus on performance evaluation criteria for existing passive radar technologies. As a result, this survey paper serves as a starting point for evaluating the performance of current and future passive radar innovations, including an emerging 5 G radar. [ABSTRACT FROM AUTHOR]

This study investigates the prediction of lightning flash rates using a ground-based, S-band polarimetric weather radar and storm cell-based lightning prediction. Both short-term cases during multiple years and long-term cases are used for training and testing by combining the radar and ground-based lightning detection network measurements. The identified lightning-generating storm cells are tracked during the data pre-processing to initiate our machine learning (ML) algorithm. The selection of features for ML algorithm implementation is discussed, and the performance of lightning rate prediction is evaluated using Pearson's correlation coefficient (r) and mean percentage error (MPE) metrics. The results show that using polarimetric measurement can improve prediction accuracy with short-term data sets, while for long-term datasets, the improvement is less significant. In general, reasonable prediction accuracy is achieved for up to 30 minutes of lead time. [ABSTRACT FROM AUTHOR]

Although the atmospheric compensation models developed to date have been generally robust and effective for InSAR, how to choose the right atmospheric correction method for ground-based InSAR is worth studying. This paper uses different methods for atmospheric phase screen (APS) compensation based on studies of ground-based radar (GBR) to zero-baseline acquisitions over the Woda landslide in Sichuan, China. This landslide has a steep topography, thick fog, turbulent rivers and strong rains. Data were acquired at the Ku-band using a GAMMA Portable Radar Interferometer II (GPRI-II) in multiple campaigns (4 July 2019–13 July 2019). In 2019, the Miyun open-pit mine was also studied; the maximum deformation reached 4.03 mm/hour. The collected data were processed within one unit based on the small baseline set (SBAS) approach. The experimental results show the following (1) For long-distance monitoring with complex atmospheric disturbances, range- and/or height-dependent models fail. (2) The iterative decomposition (ITD) method can effectively address complex atmospheric disturbances. (3) If the threshold of wavelength is set to be large, the ITD method becomes a stratified model. [ABSTRACT FROM AUTHOR]

Vertical-beam entomological radars provide precise measurements of the body alignment of individual overflying insects but are unable to distinguish which of the two axial directions the insect is heading towards. Insects migrating at altitude typically show common alignment, although with a broad spread. We show here that when observations from multiple individual insects are available, and the insects have airspeeds of about 2 ms–1 or greater, the spread in heading directions allows the heading ambiguity to be resolved (though at the sample rather than the individual level). A vector analysis of radar-measured track direction, track speed, and heading will provide consistent results for all the insects in a sample only when the heading direction is chosen correctly. With the heading then resolved, the analysis can continue to estimation of representative values for the airspeed of the insects in the sample and for the speed and direction of the wind they were flying in. This general approach can be implemented in two different ways, which we term the 'cluster' and 'projection' methods. When applied to an intense migration of large insects, probably moths, these methods produced highly consistent results from hour to hour and from one 150 m height interval to the next. Simulations show that the methods are not liable to directional bias and reveal when they are rendered ineffective by small sample sizes or low insect airspeeds; they also indicate that the cluster method handles small sample sizes better than the projection method, and its use is therefore recommended. A comparison with two previously proposed methods that use meteorological data to resolve the ambiguity shows that the new methods are more reliable. Use of this objective means of resolving the heading ambiguity will increase confidence in radar-based studies of the orientation behaviour of insect migrants and their responses to cues like sky illumination patterns, the Earth's magnetic field, and wind. [ABSTRACT FROM AUTHOR]

Snowpack in sloped mountain terrain may present stratification in which layers of snow accumulate over a certain period. The loosely adhering layers in snowpack may exhibit a severe avalanche risk, and detecting such stratification phenomena is key to avalanche detection. The danger of manual, in-situ sampling calls for investigation of alternative approaches, including the use of in-situ radars. Considering a buried radar sensor, this manuscript analyses the impact of multipath phenomena on the results of the sensing operation. Multiple bounces in snow layers may degrade the received signal and corrupt the estimation results. Multipath phenomena are well known and analysed in communication systems, but snow layering measurement is a different problem, with little previous research done on related multipath phenomena. This paper analyses the problem with a multi-receiver radar architecture, investigating the impact of multi-path effects from layered snow on measurements carried out by a multi-static radar of Frequency-Modulated Continuous Wave (FMCW) type. A few simplifying assumptions are formulated, in order to make the problem manageable, and a possible approach for solving the related issues is proposed, using a robust algorithm based on pattern matching to detect snow layering in such a challenging environment. Results obtained from various simulations carried out with realistic environmental and geometrical parameters appear to support the effectiveness of the proposed method. Results show that with 3 metres of snow thickness, the probability of successfully detecting the correct numbers of layers exceeds 80%. [ABSTRACT FROM AUTHOR]

In this paper, the possibilities of remote sensing of moisture profiles in the arable layer were theoretically and experimentally studied based on the nadir measurements of reflection coefficients at three frequencies of 1.26 GHz, 796 MHz and 641 MHz. The reflection coefficients were measured by the impulse method during natural cycles of evaporation and moistening of an arable layer at the agricultural field being under steam, located at 56°05ʹN, 92°40ʹ E in the area of the Minino village, Krasnoyarsk region, the Russian Federation. The soil moisture profiles were retrieved in the course of solving the inverse problem, in which the reflection coefficients at different frequencies acted as an informative sign. The root-mean-square error and the determination coefficient (R2) between retrieved and measured moisture values in the topsoil thickness of 0.15 m were 3.3% and 0.79, respectively. In the course of theoretical calculations, it was shown that in practice, it is impossible to predict the sensing depth of the arable layer without preliminary information on the form of moisture profile. Moreover, the sensing depth depends not only on the form of soil moisture profile but also on frequency. In this regard, it is impossible to correlate the effective soil moisture, retrieved from single-frequency measurements of the reflection coefficient in the approximation of homogeneous topsoil, with the specific thickness of topsoil. The study shows the promise of developing multi-frequency radar systems for remote sensing of soil moisture profiles in the arable layer, the potential of which can be realized on lightweight unmanned area vehicle (UAV) platforms. [ABSTRACT FROM AUTHOR]

Recently, there are few reports on three-dimensional (3D) imaging method based on radar network. Aiming at the 3D imaging problem in radar network, a reconstruction method for 3D inverse synthetic aperture radar (ISAR) image based on radar network is proposed in this paper. The 2D ISAR image is considered as a geometric projection of a target on an imaging projection plane; thus, aerial points might be scatterers of targets if their geometric projection on an imaging projection plane is similar to the 2D ISAR image of the target. The proposed method makes use of the similarity between the ISAR Range-Doppler (RD) image and the target's geometric projection on the imaging projection plane to construct the 3D image reconstruction model. The reconstructed target 3D ISAR image is obtained by estimating the scattering centre position in 3D Cartesian space. In this paper, the projection formulas designed to calculate the geometric projection of the aerial points on the imaging projection plane are derived to first form the projection image. Depending on the similarity between the 2D ISAR image and the target's projection, the 3D ISAR image reconstruction model based on the radar network is constructed. Further, a corresponding algorithm for solving the 3D ISAR image reconstruction model is proposed. Finally, the radar distribution influence on reconstruction precision is analysed, and some experimental results are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Real-time water boundary extraction with high accuracy is still a challenging problem in remote-sensing field. This article presents a shipborne water surface boundary extraction technique by integrating civilian marine radar and GPS compass. Meanwhile, a framework is developed to automatically extract water boundary from radar image. In this framework, direct transformation is adopted to rectify the geometric distortion of the radar image, morphological operation is used to fill holes and filter speckles in image, an edge tracing algorithm is design to extract image boundaries, visibility analysis and optimizations are conducted to recognize the real water surface boundary and to produce a vector map. A case study in Nanjing reach of Yangtze River are intended to validate performances of the method, and the final output of this method is verified using synchronous and detailed GPS-RTK (real-time kinematics) survey points. The experimental results demonstrate that the proposed water boundary extraction method can reach a positioning accuracy within ±1 m, which is significantly better than that of satellite-based optical remote-sensing and synthetic aperture radar (SAR) techniques, and the algorithm is computationally efficient and has the capability of real-time, full-automatic water boundary generation for various applications. [ABSTRACT FROM AUTHOR]

High sidelobes exist in normal multiple-input multiple-output (MIMO) radar waveforms, which utilize linear frequency modulation (LFM) or non-linear frequency modulation (NLFM) waveforms with the same subcarrier durations. Since the waveforms have the same expression, and each segment of the subcarriers can be only controlled by a few parameters to make them distinctive. This will cause comparatively high auto-correlation sidelobes in each zone of subcarriers. In order to suppress the sidelobes, new piecewise NLFM waveforms have been proposed in this paper. These novel MIMO radar waveforms are composed of three different kinds of subcarriers with totally different mathematical expressions. This is verified by the fact that the cross-correlation sidelobes between different subcarriers are much lower than the auto-correlation of each subcarrier. In addition, the number of MIMO radar waveforms has been improved significantly. As different subcarriers have their own controllable parameters, a genetic algorithm will be applied to generate the novel MIMO radar waveforms. Numerical results demonstrate the effectiveness that the auto-correlation sidelobes and the cross-correlation sidelobes of our proposed waveforms are reduced. [ABSTRACT FROM AUTHOR]

By exploring the hyperbolic-logarithmic spiral (HLS) model of a streamline in a tropical cyclone (TC), maximum wind speeds in hurricane Irma (AL112017) were estimated using its rainband radar signatures acquired by the NOAA Hurricane Research Division during routine aircraft missions into the hurricane. The HLS-approximation of a signature consisted of determining the 'expected' (mean) spiral and most probable (modal) spiral of a set of probing HLSs 'fitted' into a signature pattern. The maximum wind speed was determined from coefficients of the modal and mean fitted ('signatural') HLSs. The HLS estimates were compared with the reference Best Track and aircraft in situ data. The comparison evaluated satisfactory correlation (0.87–0.95) of the outcome of the HLS approximation with the reference data. The results obtained suggest that the HLS-approach to determine the maximum wind speed in a TC, in addition to known empirical and instrumental methods, would be beneficial. [ABSTRACT FROM AUTHOR]

Global precipitation measurement (GPM) launched in February 2014 as a legacy of Tropical Rainfall Measuring Mission (TRMM). Both satellites carry precipitation radar (PR), which measures the three-dimension structure of precipitation from space. Compared to TRMM PR, GPM has dual-polarized radar (DPR) and provides the raindrop size distribution (DSD) including mass-weighted mean diameter (Dm, in mm) and normalized DSD scaling parameter for concentration (Nw, in mm–1 m–3) (DSD parameters), along with radar reflectivity factor (Ze) from the surface to 21.875 km. Here we investigated the regional differences in intense convective clouds over South East & South Asia (SESA) and explored the differences in the East and West coast of India, by selecting the various areas. We defined two types of clouds, namely Cumulonimbus towers (CbTs) and intense convective clouds (ICCs) based on the Ze and height thresholds. CbTs must consist of 20 dBZ at 12 km, with echo base height less than 3 km, where ICCs are classified based on Ze threshold at 8 km (ICC8) and 3 km (ICC3). The average vertical profiles of CbTs indicate a strong west to east gradient, as the west side/coast of India has intense CbTs, with higher hydrometeors size, and decreases at east side/coast of India. The results reveal that the western side of India (Western Himalaya Foothills and Western Ghats) consists of fewer CbTs, but they are more intense. ICC3s are distributed nearly uniformly over the SESA but show the regional differences in Ze and DSD parameters in the vertical profiles. Despite various height and Ze thresholds used in the present study, WHF has the strongest vertical profile for all type of cloud cells, and indicate the importance of specific features and orographic modulated flow. The hydrometeors size is highest over Western Himalaya Foothills and least over the Bay of Bengal. Hydrometeors concentration shows the north-south gradient and higher over oceanic areas. Two coastal areas, Western Ghats and Myanmar show the different characteristics. Western Ghats CbTs are more intense, with higher hydrometeors size, whereas Myanmar has weaker convection and consists of small-sized hydrometeors. Hydrometeors size and concentration show the opposite characteristics, as hydrometeors size is higher for intense average Ze profiles, whereas hydrometeors concentration is less for intense average Ze profiles. [ABSTRACT FROM AUTHOR]

Incidence angle is one of the most important imaging parameters that affect polarimetric SAR (PolSAR) image classification. Several studies have examined the land cover classification capability of PolSAR images with different incidence angles. However, most of these studies provide limited physical insights into the mechanism how the variation of incidence angle affects PolSAR image classification. In the present study, land cover classification was conducted by using RADARSAT-2 Wide Fine Quad-Pol (FQ) images acquired at different incidence angles, namely, FQ8 (27.75°), FQ14 (34.20°), and FQ20 (39.95°). Land cover classification capability was examined for each single-incidence angle image and a multi-incidence angle image (i.e., the combination of single-incidence angle images). The multi-incidence angle image produced better classification results than any of the single-incidence angle images, and the different incidence angles exhibited different superiorities in land cover classification. The effect mechanisms of incidence angle variation on land cover classification were investigated by using the polarimetric decomposition theorem that decomposes radar backscatter into single-bounce scattering, double-bounce scattering and volume scattering. Impinging SAR easily penetrated crops to interact with the soil at a small incidence angle. Therefore, the difference in single-bounce scattering between trees and crops was evident in the FQ8 image, which was determined to be suitable for distinguishing between croplands and forests. The single-bounce scattering from bare lands increased with the decrease in incidence angles, whereas that from water changed slightly with the incidence angle variation. Consequently, the FQ8 image exhibited the largest difference in single-bounce scattering between bare lands and water and produced the fewest confusion between them among all the images. The single- and double-bounce scattering from urban areas and forests increased with the decrease in incidence angles. The increase in single- and double-bounce scattering from urban areas was more significant than that from forests because C-band SAR could not easily penetrate the crown layer of forests to interact with the trunks and ground. Therefore, the FQ8 image showed a slightly better performance than the other images in discriminating between urban areas and forests. Compared with other crops and trees, banana trees caused stronger single- and double-bounce scattering because of their large leaves. As a large incidence angle resulted in a long penetration path of radar waves in the crown layer of vegetation, the FQ20 image enhanced the single- and double-bounce scattering differences between banana trees and other vegetation. Thus, the FQ20 image outperformed the other images in identifying banana trees. [ABSTRACT FROM AUTHOR]

We analysed wind speed and direction off the coast of Japan using data from the satellite-borne Advanced Scatterometer (ASCAT) and the Weather Research and Forecasting model (WRF), validated these data using in situ wind measurements from 20 buoys, and evaluated the effect of the long time intervals from ASCAT observations on wind resource assessment. More than 25 km from the coast, and at heights of 10 m, the ASCAT wind speed has negative biases of up to 3.4% and root mean square errors of up to 18.5%; its wind direction has 11° to 27° of mean absolute error compared to buoy measurements at a height of 10 m. These accuracies are better than either the expected accuracies reported in the technical manual or those simulated with WRF with its spatial resolution of 10 km. We also evaluated long-term average ASCAT wind speeds in comparison to 4- and 5-year averages of in situ buoy wind speeds measured at three buoys, with resulting differences of -0.3%, -6.3%, and - 1.6%. Furthermore, wind roses show that appearance frequencies of the ASCAT wind direction for the long term are in a good agreement with those of the measurements at the three buoys. Our results show that the ASCAT-derived wind speed and direction are appropriate more than 25 km from the coast, and that the long time interval between ASCAT observations has an insignificant effect on wind resource assessment, if at least 4 or 5 years of averaged ASCAT data are used. [ABSTRACT FROM AUTHOR]

Ground-based interferometric radar is a popular technique for the deformation monitoring and analysis of civil engineering constructions. Many researchers have applied this technique to different operative scenarios, but only a few studies have analysed deformation time series derived through ground-based interferometric radar in consideration of the effect of coloured noise. In this study, deformation information was retrieved through the joint application of ground-based interferometric radar and maximum likelihood estimation (MLE) in consideration of the effects of white and coloured noises. A case study was conducted on the subsidence of East Lake High-tech Bridge during subway shield tunnel crossing underneath this bridge (from 16 to 18 November 2016). The subsidence time series was derived through ground-based interferometric radar. Afterward, the subsidence time series was verified by levelling at an accuracy better than 0.33 mm. Furthermore, white and coloured noises were detected in the denoized subsidence time series through a spectral analysis and MLE. For the subsidence time series of Nos. 7 and 8 piers, the coloured noise amplitudes were 0.3824 and 0.6261 mm, respectively, and the white noise values were 0.0414 and 0.0610 mm, respectively. Accurate subsidence rates and accumulative subsidence were derived through MLE by using the estimated noise characteristics in the subsidence time series. The subsidence rates of Nos. 7 and 8 piers were −0.0122 ± 0.0060 and −0.0065 ± 0.0058 mm hour−1, respectively, and the accumulative subsidence values were −0.6365 and −0.3370 mm, respectively. This finding suggests that the bridge is stable and safe. [ABSTRACT FROM PUBLISHER]

Maps that accurately quantify aboveground vegetation biomass (AGB) are essential for ecosystem monitoring and conservation. Throughout Namibia, four vegetation change processes are widespread, namely, deforestation, woodland degradation, the encroachment of the herbaceous and grassy layers by woody strata (woody thickening), and woodland regrowth. All of these vegetation change processes affect a range of key ecosystem services, yet their spatial and temporal dynamics and contributions to AGB change remain poorly understood. This study quantifies AGB associated with the different vegetation change processes over an 8-year period, for a region of Kalahari woodland savannah in northern Namibia. Using data from 101 forest inventory plots collected during two field campaigns (2014-2015), we model AGB as a function of the Advanced Land Observing Satellite Phased Array L-band synthetic aperture radar (PALSAR and PALSAR-2) and dry season Landsat vegetation index composites, for two periods (2007 and 2015). Differences in AGB between 2007 and 2015 were assessed and validated using independent data, and changes in AGB for the main vegetation processes are quantified for the whole study area (75,501 km²). We find that woodland degradation and woody thickening contributed a change in AGB of -14.3 and 2.5 Tg over 14% and 3.5% of the study area, respectively. Deforestation and regrowth contributed a smaller portion of AGB change, i.e. -1.9 and 0.2 Tg over 1.3% and 0.2% of the study area, respectively. [ABSTRACT FROM AUTHOR]

Terrestrial biosphere carbon dynamics are the most uncertain elements of the global carbon budget. Carbon stocks estimated using spatially extensive remote sensing are crucial in reducing this uncertainty, and using these stocks as initial conditions to biosphere models can improve carbon flux predictions beyond the site level. Yet remote-sensing data are not always consistently available for large regions, so methods assessing carbon uncertainty using data sources in one location may not be transferable to another. This study assesses the use of multiple-source data from lidar, radar, imaging spectroscopy, and national forest inventories to derive forest structure and composition necessary to initialise the Ecosystem Demography model (ED2), and hence evaluate short-term carbon flux uncertainty over Harvard Forest, Massachusetts. ED2 was initialized using forest structure and composition derived from lidar and national forest inventories, radar and national forest inventories, lidar and imaging spectroscopy, and radar and imaging spectroscopy resulting in net ecosystem productivity uncertainty of 26.3%, 41.9%, 19.6%, and 20.2%, respectively, compared to ground-based forest inventory initializations. This study uniquely offers a multitude of methods to estimate forest ecosystem state, with resulting carbon uncertainties, transferable to regions with potentially different data availability. Furthermore, in preparation for satellite radar, lidar, and imaging spectrometer, this study highlights the importance of combining techniques deriving forest structure and composition at different scales, binding regional to potentially global carbon-fluxes with remote sensing, reducing this uncertainty source in global climate models. [ABSTRACT FROM PUBLISHER]

This article proposes a simple Ku-band wind and rain backscatter model at low-incidence angles for describing sea surfaces where rain is present. The data used consisted of the sea surface backscatter and averaged rain rates from Tropical Rainfall Mapping Mission precipitation radar (TRMM PR) measurements and the collocated 10 m height numerical prediction wind speeds from the European Centre for Medium-Range Weather Forecasts. From the rain effect analysis, it was found that the surface backscatter decreases with increases in the averaged rain rate. The rain-induced backscatter was clearly dependent of the wind speed and was slightly dependent of the incidence angle. On the basis of the analysis, the wind and rain backscatter model was simply defined as the sum of a wind backscatter model and a rain backscatter model. The wind backscatter model was provided with the given Ku-band low-incidence backscatter model. The rain-induced backscatter for specific incidence angles and wind speeds was modelled as a simple quadratic polynomial function of the averaged rain rate by using the collocated datasets. The wind and rain backscatter model was validated by other parts of the collocated datasets. Results showed that the new model-predicted total backscatter had a root mean square error of 1.02 dB and a correlation coefficient (r) of 0.73 with the collocated TRMM PR-measured rain-affected surface backscatter. Compared to the single wind backscatter model, the wind and rain backscatter model significantly improved the prediction accuracy for the surface backscatter in rain. [ABSTRACT FROM PUBLISHER]

High-frequency (HF) radars sense the surface of ocean using electromagnetic waves and provide data of current vectors over a large spatial domain in the form of current maps at short intervals of time. Due to reasons such as failure of hardware or software, vandalism, or environmental issues, small or large gaps can always be found in collected data. In-filling of such missing information calls for special procedures owing to the typical sensing and reporting style of these current maps. In this article HF radar observations made at five different locations in India are analysed with the aim of providing consistent and synthesized information after noticing that there were significant missing values. This task is accomplished by selecting the most appropriate and state of the art methods of spatial data interpolation. An exhaustive experimentation in this regard showed that the statistical method of ‘inverse distance weighting’ or the soft computing technique of artificial neural network can work satisfactorily for this purpose. [ABSTRACT FROM PUBLISHER]

Vertical-beam entomological radars incorporating rotating polarization and a very-narrow-angle conical scan generate information about both the trajectories of the insect targets and their identities. The identity information comprises four parameters, one a measure of target size, two of shape, and the last the wing-beat frequency. Transformations have been developed to convert these to a set of orthogonal variables that make the widths of peaks in the distributions similar. The existence of distinct target classes has been demonstrated from the radar observations themselves, using an 8-month data set. The analysis uses peak finding followed by association of the peaks found for different parameters, and is achieved through specially developed algorithms and procedures. Peaks have been identified in each parameter separately, in pairs of parameters, and in the four parameters together, the three approaches producing consistent results. Mainly single peaks were found in spring and the first month of summer, and also in late autumn, while multiple peaks occurred through most of summer and early autumn. All peaks are broad, so the number of target classes that can be resolved is not large. Classes were recognized by associating similar combinations of peak positions for the four parameters when these occurred on multiple nights. The most commonly occurring classes corresponded approximately to rules developed previously for identifying targets as locusts and moths. Two methods of using the identified classes to partition a target sample, with different purity-versus-yield trade-offs, are demonstrated. [ABSTRACT FROM PUBLISHER]