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Snow water equivalent (SWE) can be measured using low-cost Global Navigation Satellite System (GNSS) sensors with one antenna placed below the snowpack and another one serving as a reference above the snow. The underlying GNSS signal-based algorithm for SWE determination for dry- and wet-snow conditions processes the carrier phases and signal strengths and additionally derives liquid water content (LWC) and snow depth (HS). So far, the algorithm was tested intensively for high-alpine conditions with distinct seasonal accumulation and ablation phases. In general, snow occurrence, snow amount, snow density and LWC can vary considerably with climatic conditions and elevation. Regarding alpine regions, lower elevations mean generally earlier and faster melting, more rain-on-snow events, and shallower snowpack. Therefore, we assessed the applicability of the GNSS-based SWE measurement at four stations along a steep elevation gradient (820, 1185, 1510 and 2540 m a.s.l.) in the eastern Swiss Alps during two winter seasons (2018–2020). Reference data of SWE, LWC and HS were collected manually and with additional automated sensors at all locations. The GNSS-derived SWE estimates agreed very well with manual reference measurements along the elevation gradient, and the accuracy (RMSE = 34 mm, RMSRE = 11 %) was similar under wet- and dry-snow conditions, although significant differences in snow density and meteorological conditions existed between the locations. The GNSS-derived SWE was more accurate than measured with other automated SWE sensors. However, with the current version of the GNSS algorithm, the determination of daily changes of SWE was found to be less suitable compared to manual measurements or pluviometer recordings and needs further refinement. The values of the GNSS-derived LWC were robust and within the precision of the manual and radar measurements. The additionally derived HS correlated well with the validation data. We conclude that SWE can reliably be determined using low-cost GNSS sensors under a broad range of climatic conditions, and LWC and HS are valuable add-ons.

Accurate measurements of ice hydrometeors are required to improve the representation of clouds and precipitation in weather and climate models. In this study, a newly developed, synergistic retrieval algorithm that combines radar with passive millimeter and sub-millimeter observations is applied to observations of three frontally generated, mid-latitude cloud systems in order to validate the retrieval and assess its capabilities to constrain the properties of ice hydrometeors. To account for uncertainty in the assumed shapes of ice particles, the retrieval is run multiple times while the shape is varied. Good agreement with in situ measurements of ice water content and particle concentrations for particle maximum diameters larger than 200 µm is found for one of the flights for the large plate aggregate and the six-bullet rosette shapes. The variational retrieval fits the observations well, although small systematic deviations are observed for some of the sub-millimeter channels pointing towards issues with the sensor calibration or the modeling of gas absorption. For one of the flights the quality of the fit to the observations exhibits a weak dependency on the assumed ice particle shape, indicating that the employed combination of observations may provide limited information on the shape of ice particles in the observed clouds. Compared to a radar-only retrieval, the results show an improved sensitivity of the synergistic retrieval to the microphysical properties of ice hydrometeors at the base of the cloud. Our findings indicate that the synergy between active and passive microwave observations may improve remote-sensing measurements of ice hydrometeors and thus help to reduce uncertainties that affect currently available data products. Due to the increased sensitivity to their microphysical properties, the retrieval may also be a valuable tool to study ice hydrometeors in field campaigns. The good fits obtained to the observations increase confidence in the modeling of clouds in the Atmospheric Radiative Transfer Simulator and the corresponding single scattering database, which were used to implement the retrieval forward model. Our results demonstrate the suitability of these tools to produce realistic simulations for upcoming sub-millimeter sensors such as the Ice Cloud Image or the Arctic Weather Satellite.

In recent years, machine learning theory has set off a wave of research in the field of radar signal processing. In this study, a novel algorithm for sea‐surface target detection based on a stacked autoencoder (SAE) is proposed, which has already been applied in the authors’ coastal defense radar system. In the proposed algorithm, the sea surface echo data are cut into a large number of two‐dimensional (2‐D) images through a sliding window, mapping the cell under test (CUT) and the 2‐D images one by one and performing classification or detection from the perspective of 2‐D signal processing. Experimental results of simulated data and real radar data show that the proposed algorithm based on the SAE has better target detection performance compared with the traditional cell averaging constant false alarm rate (CA‐CFAR) algorithm. Besides, the proposed algorithm shows certain interference suppression ability in real data processing. As far as it is known, there is no public report on the detection of 2‐D sea surface targets based on the SAE algorithm in coastal defense radar.

The dataset collected during the Radar Snow Experiment (RadSnowExp) presents the first-ever airborne triple-frequency radar observations combined with almost perfectly co-located and coincident airborne microphysical measurements from a single platform, the National Research Council Canada (NRC) Convair-580 aircraft. The potential of this dataset is illustrated using data collected from one flight during an Arctic storm, which covers a wide range of snow habits from pristine ice crystals and low-density aggregates to heavily rimed particles with maximum size exceeding 10 mm. Three different flight segments with well-matched in situ and radar measurements were analyzed, giving a total of 49 min of triple-frequency observations. The in situ particle imagery data for this study include high-resolution imagery from the Cloud Particle Imager (CPI) probe, which allows accurate identification of particle types, including rimed crystals and large aggregates, within the dual-frequency ratio (DFR) plane. The airborne triple-frequency radar data are grouped based on the dominant particle compositions and microphysical processes (level of aggregation and riming). The results from this study are consistent with the main findings of previous modeling studies, with specific regions of the DFR plane associated with unique scattering properties of different ice habits, especially in clouds where the radar signal is dominated by large aggregates. Moreover, the analysis shows close relationships between the triple-frequency signatures and cloud microphysical properties (particle characteristic size, bulk density, and level of riming).

This paper presents a radar cross-section (RCS)-based statistical recognition system for identifying/ classifying unmanned aerial vehicles (UAVs) at microwave frequencies. First, the paper presents the results of the vertical (VV) and horizontal (HH) polarization RCS measurement of six commercial UAVs at 15 GHz and 25 GHz in a compact range anechoic chamber. The measurement results show that the average RCS of the UAVs depends on shape, size, material composition of the target UAV as well as the azimuth angle, frequency, and polarization of the illuminating radar. Afterward, radar characterization of the target UAVs is achieved by fitting the RCS measurement data to 11 different statistical models. From the model selection analysis, we observe that the lognormal, generalized extreme value, and gamma distributions are most suitable for modeling the RCS of the commercial UAVs while the Gaussian distribution performed relatively poorly. The best UAV radar statistics forms the class conditional probability densities for the proposed UAV statistical recognition system. The performance of the UAV statistical recognition system is evaluated at different signal noise ratio (SNR) with the aid of Monte Carlo analysis. At an SNR of 10 dB, the average classification accuracy of 97.43% or better is achievable., Comment: one column, 39 pages

Upcoming Synthetic Aperture Radar (SAR) missions with larger swath like NASA-ISRO Synthetic Aperture Radar (NISAR) (~250 km) requires a large, flat and homogeneous low-background site, free from human-made structures for point targets deployment for calibration. Finding out such a large, flat, homogeneous area devoid of perceived sources of radar clutter is a challenging task. In this regard, Antarctica is a potential site for calibration as it fulfills many of the criteria required for the ideal calibration site and suitable for setting up a corner reflector (CR) network for SAR calibration. This network will help in the calibration study and aid the studies related to Interferometric SAR (InSAR) viz. ice velocity estimation. As a part of this activity, in-house designed and developed CRs were installed near Indian research base stations Maitri and Bharati at Antarctica during the 38th Indian Scientific Expedition to Antarctica (ISEA) during 2018–2019. Corner reflectors were designed keeping in mind the harsh environment of Antarctica, such as high katabatic winds, blizzards, snowfall and low temperature. Temporal and seasonal analysis of radar backscatter data using available Indian Radar Imaging Satellite (RISAT-1) and European SENTINEL-1 SAR satellite over Maitri and Bharati was carried out to find out the suitable CR deployment site. Super-hydrophobic microwave transparent cover was designed and installed over CRs to protect snow accumulation. Hydrophobic radar absorbing materials were installed over central-mount to decrease the background noise due to it. Various tests such as structural analysis of total CR system, thermal analysis of materials and Radar Cross Section (RCS) characterization of CR were done before sending it to Antarctica. Integrated Wide Swath (IW) mode data of Sentinel-1 satellite was used for analyzing time-series response of CR in SAR image.

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

Multiple-elevation-beam synthetic aperture radar (SAR) is a concept based on digital beamforming (DBF) in elevation and simultaneous recording of the echoes of multiple transmitted pulses. It enables high-resolution imaging of wide areas and is therefore ideal for the systematic observation of dynamic processes on the Earth's surface. Furthermore, if the pulse repetition interval (PRI) is continuously varied (staggered SAR), it is possible to map a wide continuous swath rather than multiple subswaths separated by ``blind'' ranges. Within the design of multiple-elevation-beam SAR, however, it is fundamental to consider how nadir echoes affect the mapping capabilities of systems with constant PRI and the image quality of staggered SAR systems, where nadir echoes are intrinsically smeared due to the PRI variation. This article addresses the characterization of nadir echoes in multiple-elevation-beam SAR with constant and variable PRI by presenting a parametric model for the nadir echo profile based on real radar measurements, a formulation of the nadir echo location and smearing in staggered SAR, and realistic simulations based on TerraSAR-X data, which show that nadir echo are likely to be barely visible in staggered SAR images. The results of this work are relevant to both the design of future SAR systems and the interpretation of the acquired data.

Snow is an important environmental variable and a primary water resource in many areas of the world. Monitoring seasonal snowpack properties is also crucial for properly managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, an innovative radar architecture, based on the use of two receivers, has been proposed for snowpack monitoring for the case of dry snow, where the snowpack depth and bulk density can be calculated with one single radar measurement, without any kind of external aid. This article presents the extension of this innovative radar architecture for the case of wet snow. The approach to determine, not only the snowpack depth and bulk density but also the liquid water content, is outlined and discussed in detail, along with the experimental validation of the operating principle for two cases., This work was supported in part by the Italian Ministry of Education, University and Scientific Research (MIUR) under Project SIR2014 "SNOWAVE" RBSI148WE5. © 2022, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.

One of the scientific payloads of Chang'E-5 (CE-5), i.e., the lunar penetrating array radar (LRPR), will carry out the in situ exploration of the regolith structure and guide the drilling sampling ...

The dual-frequency precipitation radar (DPR) onboard the global precipitation measurement (GPM) satellite provides valuable measurements of precipitation. In this study, the GPM DPR products (version 6) are validated against a ground-based S-band polarimetric radar in South China based on a volume-matching method. Good consistency is found for the reflectivity factor (Z) calibration of the two instruments. From the perspective of microphysics, the mass-weighted mean diameter ( $D_{m})$ estimates correspond well with those of the ground-based radar in the inner swath of the normal scan (NS); however, underestimation is found for the raindrop number concentration, indicated by the generalized intercept parameter ( $N_{w})$ , especially for the intense echoes. Thus, the GPM DPR product may fail to depict the microphysical characteristics of small-to-medium raindrops in high concentration for heavy rainfall in South China. This is attributed to the negative Z bias of the DPR caused probably by insufficient correction of attenuation, which also leads to clear underestimation in the liquid water content (W) and the rainfall rate (R) products for intense echoes. In the outer swath where only single-frequency retrieval is available, overestimation in $D_{m}$ exists regardless of echo intensity level, and more underestimation can be found in $N_{w}$ , W, and R especially for intense echoes. In the selected typhoon and squall line cases, better capability in revealing microphysical properties is also found for the inner swath of the NS. After adjusting the scan mode, the performance of the precipitation products in the outer swath can be improved by dual-frequency retrievals in the future.

In this article, an efficient radar fusion-before-imaging (RFBI) method for heterogeneous borehole radar systems is proposed. Different from conventional fusion-after-imaging processes, the sample sets collected from heterogeneous borehole radar systems (monostatic, bistatic, or multiple-input multiple-output) are first merged into one data set before the imaging process in RFBI, and a single imaging operation is demanded to obtain the target space image with high precision. Specifically, the diversity in heterogeneous borehole radar sample sets is taken into consideration, and the radar sample sets are inserted and fused into a high-dimensional sample set before imaging. The target space spectrum is generated according to the echo space-frequency constraint relationship, and the target space is extracted from one imaging process. The influence of clutters in RFBI results is reduced, and the imaging accuracy is satisfactory. Meanwhile, due to the fusion process ahead, the computational time of RFBI hardly increases with the number of radar sample sets. The synthetic and field experiment results show that RFBI demonstrates comparable accuracy as Kirchhoff migration and higher efficiency in processing large amounts of data sets at the cost of large memory requirement, which is suitable for the joint imaging of heterogeneous radar systems.

Undesired echo from a flying object (aerial clutter) significantly contaminates the received signal of a wind profiler radar (WPR) because it has high intensity and spreads over a wide Doppler velocity range. In this study, results of aerial clutter mitigation obtained by applying adaptive clutter suppression (ACS) to a 1.3-GHz WPR are shown. The 1.3-GHz WPR used in this study has a main antenna comprising 13 antenna subarrays (MSAs). five-element Yagi-Uda antennas were also used as antenna subarrays for detecting clutters from low elevation angles (CSAs). The CSAs were used only in reception and installed so that they covered most of the horizontal directions and the horizontal and vertical polarizations. The directionally constrained minimization of power (DCMP) method was used as the adaptive signal processing to mitigate clutter. By the DCMP method, the weighted sum of the signals collected by 13 MSAs and 11 CSAs was computed so that the power of output signals was minimized under the constraint of constant gain in the antenna beam direction. Results of a case study for an aerial clutter from a low elevation angle at 17:04:37 on October 1 2020 showed that an overlap of the aerial clutter over a desired echo (i.e., clear-air echo) was solved by decreasing the aerial clutter whose peak intensity was ~24 dB greater than that of the clear-air echo. In a case study at 09:30:27 on September 18 2020, effects of the DCMP method on the processed results were discussed.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2020-11-06, 資料番号: PA2110067000

For radar modeling and simulation of sea clutter, there are a number of key characteristics used to describe the observed features. These include the mean backscatter power, amplitude statistics, spatial and long-time temporal correlation, and the Doppler spectrum. Regarding the latter, a common modeling approach is to use the mean Doppler characteristics (width and center point) to relate to the sea conditions. However, this does not capture the time- and range-varying fluctuations of the Doppler spectra, which are important for assessing the behavior of coherent detection schemes. The evolving Doppler spectrum model offers a way to model these variations and can be described with a moderate number of parameters. Relating these to the observed geometry, environment and radar characteristics are important to make the models accessible. Previous work has characterized X-band radar sea clutter over a range of sea conditions and azimuth and grazing angles. In this article, new models are presented that capture variations with both the radar resolved area and integration time. This enables the evolved Doppler spectra model to be used over a much wider range of applications.

Convective precipitation nowcasting remains challenging due to the fast change in convective weather. Radar images are the most important data source in nowcasting research area. This study proposes a radar data-based U-Net model for precipitation nowcasting. The nowcasting problem is first transformed into an image-to-image translation problem in deep learning under the U-Net architecture, which is based on convolutional neural networks (CNNs). The input of the model is five consecutive radar images; the output is the predicted radar reflectivity image. The model consists of three operations: upsampling, downsampling, and skip connection. Three methods, U-Net, TREC, and TrajGRU, are used for comparison in the experiments. The experimental results show that both deep learning methods outperform the TREC method, and the CNN-based U-Net can achieve almost the same performance as TrajGRU which is a recurrent neural network (RNN)-based model. With the advantages that U-Net is simple, efficient, easy to understand, and customize, this result shows the great potential of CNN-based models in addressing time-series applications.

The accumulation rate is a key parameter in computing the mass balance of glaciers and ice sheets to estimate sea level rise. A shallow-layers-detection ice sounding radar (SLDISR) is developed to measure the accumulation rate and shape of near-surface internal layers with high resolution. With a transmitting frequency from 500 to 2000 MHz, this frequency-modulated continuous wave (FMCW) radar provides a range resolution of about 16 cm in free space by using a Hanning window and a penetrating depth about 150 m under polar ice. The spectral analysis and coherent integration techniques are used to obtain a high processing gain and to improve the signal-to-noise ratio of the system. A phase-locked loop with wideband yttrium iron garnet (YIG) oscillator is applied to generate a sweeping chirp signal as an input source for the transmitter. A stable, low-frequency reference chirp signal is generated with a direct digital synthesizer (DDS) integrated in field-programmable gate array (FPGA). To reduce the high-speed requirement to the analog-to-digital converter (ADC), dechirp technology is adopted at the RF section of the receiver. The implementation of the digital unit is based on an FPGA chip. The designed radar has been successfully deployed in Antarctica during the 31st Chinese Antarctic Research Expedition (CHINARE 31) and CHINARE 33, mainly over the East Antarctic Ice Sheet (EAIS). The echograms indicate the effectiveness of the radar system on detecting clear internal reflecting horizons (IRHs) over ice sheets.

The sea clutter, referred to as the time-varying radar backscatter from the ocean surface, plays a significant role in marine radar development. The ocean's complex hydrodynamics cause it to exhibit non-Gaussian and nonstationary characteristics, which brings challenges in the sea clutter modeling, especially for establishing its spatial-temporal correlated and coherent model. In this article, a data-driven method based on the Koopman mode decomposition (KMD) is proposed for modeling spatial-temporal correlated complex sea clutter. The method decomposes the coherent sea clutter dynamic behavior in terms of Koopman modes and corresponding temporal patterns. Then, these spatiotemporal patterns are used to construct the sea clutter state over time according to the approximate solution. Furthermore, this proposed state-of-the-art data-driven approach is benchmarked by the measured sea clutter data from intelligent PIXel processing radar (IPIX). It is demonstrated that the proposed approach accurately models the complex sea clutter with actual statistic characteristics, phase information, and spatial-temporal correlations. The mean absolute error (MAE) and root mean square error (RMSE) between the obtained and actual sea clutter are only 0.1817 and 0.2349, respectively. This work offers a practical approach for modeling sea clutter, especially when the spatial-temporal correlation and coherence information is needed.

Cross-polarized temporal coherence observations of a boreal forest, acquired using a tower-based radar, are presented in this article. Temporal coherence is analyzed with respect to frequency, temporal baseline, time of day of observation, season, meteorological variables, and biophysical variables. During the summer, P- and L-band temporal coherence exhibited diurnal cycles, which appeared to be due to high rates of transpiration and convective winds during the day. During the winter, freeze-thaw cycles and precipitation resulted in decorrelation. At temporal baselines of seconds to hours, a high temporal coherence was observed even at C-band. The best observation times of the day were midnight and dawn. Temporal coherence is the main limitation of accuracy in interferometric and tomographic forest applications. The observations from this experiment will allow for better spaceborne SAR mission designs for forest applications, better temporal decorrelation modeling, and more accurate forest parameter estimation algorithms using interferometric and tomographic SAR data.

Research based on Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data detected unusual radar bright basal reflections located at about 1.5 km depth in a Mars region denoted as Ultimi Scopuli. These reflections were interpreted as a signature of subglacial liquid water even though this interpretation is still being debated in the literature. In this article, we propose a novel approach to the detection and imaging of candidate subglacial liquid water from radar sounding data. The approach combines the radar echo power traces with a suitable digital elevation model to provide a bidimensional representation of the surface. Even if the imaging method reconstructs a representation of the surface, we prove that it can be used to identify subsurface bright reflections in icy regions. Imaging is feasible even if the basal interface is not directly included in the processed data for image generation. To support this experimental evidence, we show that a relationship exists between the value of the reflected echo power originating from the englacial layers and the basal-to-surface-echo-power ratio. The observed relationship holds on both Ultimi Scopuli radar sounding data acquired on Mars and Lake Vostok data acquired on Earth. Our results show that the 2-D imaging provides an alternative way for locating candidate subglacial liquid water bodies on Mars over large areas also where the basal interface is not directly measured. The proposed approach complements previous research for further evaluation of the actual presence of liquid water on Mars.

This article studies changes in microwave signals of oceanic snowfall in response to the formation of snow-covered sea ice using active and passive coincident data from the radar and radiometer onboard the CloudSat and the global precipitation measurement satellites. Using reanalysis data of liquid and ice water path as well as satellite retrievals of sea ice snow-cover depth, spectral regions are determined over which the snowfall signatures are likely to be obscured or falsely detected. Relying on an a priori database populated with the active-passive coincidences, a Bayesian snowfall retrieval algorithm is presented that links a k-nearest neighbor matching with the inverse Gaussian estimator used in the Goddard profiling algorithm. Without relying on any ancillary data of air temperature, the results demonstrate that over open oceans (sea ice), we can passively retrieve the CloudSat active snowfalls with a true positive rate of 92 (85%) and the root mean squared error of 0.24 (0.15) mm h⁻¹.

Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

A satellite mission onboard a radar sounder for the observation of the earth's polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.

The optical and radar satellite data based Global Digital Elevation Models (DEMs) serve as a main data source in hydrological studies today. Open access Remote Sensing (RS) products have increasing accuracy and improved spatial resolution. The main research purposes are to study the use of AW3D30, ASTER GDEM V003 and SRTMGL1 DEMs in modeling mountainous lake basin and comparative analysis of their spatial accuracy. Topographic maps and very high resolution satellite imagery of the study area have been applied for the spatial accuracy verification and surface referencing. The relative accuracies of three different DEMs were analyzed using 1284 elevation points on the terrain surfaces. Comparison of these DEMs with 30 m spatial resolution confirms that an AW3D30 DEM has a higher vertical accuracy compared to other models and can be used for modeling river and lake basins over high mountainous terrain. The detected unreliable sections of the modeled lake basin boundary were analyzed and corrected based on the basin delineation by different DEMs. Analysis of spatial accuracy of the delineated Issyk-Kul Lake basin provide further incentive for DEM modification based on higher resolution remote sensing data, considering the applied geoid height differences as well by using satellite positioning data.

Satellite radar altimetry has immense potential for monitoring fresh surface water resources and predicting the intra-seasonal, seasonal, and inter-annual variability of inundated surface water over large river basins. As part of the Preparation for the Surface Water and Ocean Topography mission scheduled for launch in mid-2022, the present study aimed to evaluate the performance of radar altimetry over the inland water bodies of India. The Joint Altimetry Satellite Oceanography Network (Jason) and Satellite with ARgos and ALtiKa (SARAL/AltiKa) data were used to derive the water levels of 18 major reservoirs in India by incorporating the geophysical and propagation corrections into the radar range. In situ gauge data were used to evaluate the performance of the altimetry-derived water level time series from 2008 to 2019. The results showed a strong correlation between Jason-2 and in situ data with the determination coefficient (R2) and root mean squared error (RMSE) ranging from 0.96 to 0.99 and from 0.28 m to 1.62 m, respectively. The Jason-3 data had the highest correlation with the in situ observation (R2 = 0.99) and the lowest correlation (R2 = 0.82), with RMSE values ranging from 0.11 m to 1.18 m. With an R2 range of 0.93–0.99 and an RMSE range of 0.20–1.05 m, the SARAL/AltiKa mission presented greater accuracy than the Jason altimetry mission. The estimated water levels can be utilized in remote, inaccessible, or ungauged areas and in international transboundary rivers for water storage and river discharge estimations. However, the accuracy of remotely sensed data depends on such factors as along-track distance, water body area, and geographical and terrain conditions near water bodies. publishedVersion

Synthetic aperture radar (SAR) is a valuable tool for lake ice monitoring. The recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric (FP) SAR for operational ice applications, including lake ice. Given its enhanced radar target information compared with conventional SAR systems over wider swath coverage compared with FP SAR, CP systems could play important role in the new generation of Earth observation systems. Herein, we study the evolution of CP SAR parameters from simulated CP SAR data in relation to early ice growth. Focus of the study is on four lakes located in Cornwallis Island, Canadian Central Arctic. We adopt parameters extracted from dual circular polarimetric and right circular transmit, linear (horizontal and vertical) receive configurations. In this study, we consider the ice thickness calculated from an established empirical model. Meteorological and ice climatological data were used to support the analysis. Results demonstrated a potential connection between a number of CP parameters and lake ice growth. Furthermore, we were able to highlight the relationship between the density of air bubbles in ice layer and the intensity of volume scattering mechanism, leading to the identification of lakes with increased gas production activities. Thus, differences between lakes in terms of density of air bubbles were detected and statistically evaluated.

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 ...

Continuous and spatially distributed data of snow mass (water equivalent of snow cover, SWE) from automatic ground-based measurements are increasingly required for climate change studies and for hydrological applications (snow hydrological-model improvement and data assimilation). We present and compare four new-generation sensors, now commercialized, that are non-invasive and based on different radiations that interact with snow for SWE monitoring: cosmic-ray neutron probe (CRNP), gamma ray monitoring (GMON) scintillator, frequency-modulated continuous-wave radar (FMCW radar) at 24 GHz and global navigation satellite system (GNSS) receivers (GNSSr). All four techniques have relatively low power requirements, provide continuous and autonomous SWE measurements, and can be easily installed in remote areas. A performance assessment of their advantages, drawbacks and uncertainties is discussed from experimental comparisons and a literature review. Relative uncertainties are estimated to range between 9 % and 15 % when compared to manual in situ snow surveys that are also discussed. Results show the following. (1) CRNP can be operated in two modes of functioning: beneath the snow, it is the only system able to measure very deep snowpacks (> 2000 mm w.e.) with reasonable uncertainty across a wide range of measurements; CRNP placed above the snow allows for SWE measurements over a large footprint (∼ 20 ha) above a shallow snowpack. In both cases, CRNP needs ancillary atmospheric measurements for SWE retrieval. (2) GMON is the most mature instrument for snowpacks that are typically up to 800 mm w.e. Both CRNP (above snow) and GMON are sensitive to surface soil moisture. (3) FMCW radar needs auxiliary snow-depth measurements for SWE retrieval and is not recommended for automatic SWE monitoring (limited to dry snow). FMCW radar is very sensitive to wet snow, making it a very useful sensor for melt detection (e.g., wet avalanche forecasts). (4) GNSSr allows three key snowpack parameters to be estimated simultaneously: SWE (range: 0–1000 mm w.e.), snow depth and liquid water content, according to the retrieval algorithm that is used. Its low cost, compactness and low mass suggest a strong potential for GNSSr application in remote areas.

The NOAA Wide Swath Radar Altimeter (WSRA) and its processing are described. The WSRA provides real-time measurements of sea surface significant wave height and directional wave spectra during flights in hurricanes and other environments. The characteristics of near nadir scattering from the sea surface and the resulting distortion of the wave topography measured by the WSRA are discussed, as well as the simulation which generated a matrix to correct the directional wave spectra produced from the WSRA wave topography.

The radar on the Global Precipitation Measurement (GPM) mission observes precipitation at 13.6 GHz (Ku-band) and 35.6 GHz (Ka-band) and also receives echoes from the earth’s surface. Statistics of surface measurements for non-raining conditions are saved in a database for later use in estimating the precipitation path-integrated attenuation. Previous work by Meneghini and Jones (2011) showed that while averaging over larger latitude/longitude bins increase the number of samples, it can also increase sample variance due to spatial inhomogeneity in the data. As a result, Meneghini and Kim (2017) proposed a new, adaptive method of database construction, in which the number of measurements averaged depends on the spatial homogeneity. The purpose of this work is to re-visit previous, single-frequency results using dual-frequency data and optimal interpolation (kriging). Results include that (1) temporal inhomogeneity can create similar results as spatial, (2) Ka-band behavior is similar to Ku-band, (3) the Ku-/Ka-band difference has less spatial inhomogeneity than either band by itself, and (4) kriging and the adaptive method can reduce the sample variance. The author concludes that finer spatial and temporal resolution is necessary in constructing the database for single frequencies but less so for the Ku-/Ka-band difference. The adaptive approach reduces sample standard deviation with a relatively modest computational increase.

Recent work has demonstrated a passive radio sounding approach that uses the Sun as a source for echo detection and ranging. As the Sun is a moving source with a position that is known a priori , we evaluate this technique’s capabilities to measure the echo’s phase history, map topography, and perform synthetic aperture radar (SAR) focusing. Here, we present our approach to implementing passive SAR using a compact, temporally incoherent radio-astronomical source as a signal of opportunity. We first evaluate the passive system’s capabilities to obtain an echo from a rough surface by determining the critical signal-to-noise ratio (SNR) for reliably observing the Sun’s echo reflection with our passive instrument. We then demonstrate that our technique can detect the necessary changes in range, phase, and reflectivity of an echo from the Sun. We next present the experimental results of our passive radar testing using the Sun at Dante’s View, Death Valley, to highlight this technique’s ability to perform 2-D imaging. Finally, with synthetic data, we demonstrate that we can use time-domain backprojection to focus a planar white noise signal, perform passive SAR imaging, and improve the measurement’s SNR and azimuth resolution. The results of passive SAR focusing on white noise highlight the potential for the Sun and Jupiter’s radio emissions to perform surface and subsurface imaging for planetary and terrestrial observations.

In the complex electromagnetic environment of cities, detection and positioning of slow and small unmanned aerial vehicles (UAVs) have always been a very challenging research topic. Usually, multiple radar observation stations are required to detect and track targets. Moreover, each radar observation station requires strict synchronisation, and the system is complex and costly. In view of this, under the condition of known prior information of urban geography, this study proposes a method to locate UAVs in complex electromagnetic environments, which use multiple virtual mirror synchronous radars constructed by multipaths and created by the complex buildings in urban environments. First, the simulation model is constructed, and the corresponding simulation parameters are given. Then, the algorithm for solving the single‐station radar location is proposed, and the algorithm is verified by the simulation of a ray propagation model. Finally, the proposed algorithm is further verified based on the measured data captured by the radar system in Section 2. The results show that the proposed algorithm based on the single‐station radar is able to locate the UAV in both line‐of‐sight and non‐line‐of‐sight site, and its positioning accuracy is better than 1 m.

The multipath effect deteriorates the performance of low elevation target altitude estimation with a ubiquitous radar strongly, especially in complex terrain environments in which multiple reflection paths exist simultaneously. This work shows how the authors reconstructed a multipath model for ubiquitous radar with a known waveform and took advantage of it to improve the altitude measurement performance when locating low‐altitude small targets in complex terrain environments. The authors first present a multipath propagation model for target elevation angle estimation in which not only received multipaths but also transmitted multipaths are taken into consideration. Then, a reconstructed elevation angle estimation model in which the waveform is known and the target elevation information is as same as the original model is derived. Finally, an innovative low elevation angle estimation algorithm based on a maximum likelihood estimator and a sparse representation is proposed. Simulations and experiment results verify that the proposed algorithm has a much more robust low elevation angle estimation performance than existing methods in complex terrain environments. Index Terms—Target altitude measurement, complex multipath effect, known transmitted waveform, sparse representation, ubiquitous radar.

Differential absorption radar (DAR) near the 183 GHz water vapor absorption line is an emerging measurement technique for humidity profiling inside of clouds and precipitation with high vertical resolution, as well as for measuring integrated water vapor (IWV) in clear air regions. For radar transmit frequencies on the water line flank away from the highly attenuating line center, the DAR system becomes most sensitive to water vapor in the planetary boundary layer (PBL), which is a region of the atmosphere that is poorly resolved in the vertical by existing spaceborne humidity and temperature profiling instruments. In this work, we present a high-fidelity, end-to-end simulation framework for notional spaceborne DAR instruments that feature realistically achievable radar performance metrics, and apply this simulator to assess DAR's PBL humidity observation capabilities. Both the assumed instrument parameters and radar retrieval algorithm leverage recent technology and algorithm development for an existing airborne DAR instrument. To showcase the capabilities of DAR for humidity observations in a variety of relevant PBL settings, we implement the instrument simulator in the context of large eddy simulations (LES) of 5 different cloud regimes throughout the trade-wind subtropical-to-tropical cloud transition. Three distinct DAR humidity observations are investigated: IWV between the top of the atmosphere and the first detected cloud bin or Earth's surface; in-cloud water vapor profiles with 200 meter vertical resolution; and IWV between the last detected cloud bin and the Earth's surface, which can provide a precise measurement of the sub-cloud humidity. We provide a thorough assessment of the systematic and random errors for all 3 measurement products for each LES case, and analyze the humidity precision scaling with along-track measurement integration. While retrieval performance depends greatly on the specific cloud regime, we find generally that for a radar with cross-track scanning capability, in-cloud profiles with 200 m vertical resolution and 10–20 % uncertainty can be retrieved for horizontal integration distances of 100–200 km. Furthermore, column IWV can be retrieved with 10 % uncertainty for 10–20 km of horizontal integration. Finally, we provide some example science applications of the simulated DAR observations, including estimating near-surface relative humidity using the cloud-to-surface column IWV, and inferring in-cloud temperature profiles from the DAR water vapor profiles by assuming a fully saturated environment.

In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson–Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.

The soil roughness and two-way vegetation transmissivity parameters affect the radar backscattered signal; therefore, the results of the soil moisture retrieval from Synthetic Aperture Radar (SAR) ...

Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce three-dimensional distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude, and vary across different arrays, which makes direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or lognormal distributions. Based upon the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross-track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter-limited to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Finally, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development which needs to be robust across multiple different arrays., 12 pages, 11 figures

The dechirp‐receiving radar measures range by the frequency difference between the target echo and the reference signal. It can operate at a low sampling frequency while transmitting a wideband signal, which simplifies the radar hardware. However, when detecting high‐speed and highly maneuvering targets, residual video phase (RVP), frequency mismatch, frequency modulation (FM) rate mismatch, and across range‐Doppler unit (ARDU) problems occur after dechirp receiving, which cause difficulties in integrating the target echo and result in severe performance loss. To solve these problems, the dechirp generalized Radon‐Fourier transform (DGRFT) and an algorithm for its fast implementation are proposed. The DGRFT compensates for the frequency and FM rate mismatch in the fast‐time domain and the RVP in the fast‐time frequency domain, and then implements joint envelope and phase compensations to overcome the ARDU phenomenon. After these compensations, the target echo can be effectively integrated. Experiments with simulated and measured data show that the performance of the DGRFT is better than that of the conventional moving target detector.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-08-10, 資料番号: PA2220049000

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-08-07, 資料番号: PA2210062000

This work presents the design of a 24-GHz digitally controlled oscillator (DCO) in an advanced 28-nm bulk CMOS technology for short-range frequency-modulated continuous-wave radar system-on-chip for mobile and Internet-of-Things devices. The power minimization is therefore the primary focus. The oscillator consumes a record low power of 1.2 mW at a 0.65-V supply voltage. It achieves a very large frequency tuning range (TR) of 5.8 GHz (27%) and a 150 kHz resolution without significantly degrading the phase noise (PN). The proposed design methodology results in a state-of-the-art -193 dBc/Hz FoM(T).

With the enrichment of land subsidence monitoring means, data fusion of multisource land subsidence data has gradually become a research hotspot. The Interferometry Synthetic Aperture Radar (InSAR) is a potential Earth observation approach, and it has been verified to have a variety of applications in measuring ground movement, urban subsidence, and landslides but similar to Global Positioning System (GPS). The InSAR observation accuracy and measurements are affected by the tropospheric delay error as well as by the Earth’s ionospheric and tropospheric layers. In order to rectify the InSAR result, there is a need to interpolate the GPS-derived tropospheric delay. Keeping in view of the above, this research study has presented an improved Inverse Distance Weighting (IIDW) interpolation method based on Inverse Distance Weighting (IDW) interpolation by using Sentinel-1 radar satellite image provided by European Space Agency (ESA) and the measured data from the Continuously Operating Reference Stations (CORS) provided by the Survey and Mapping Office of the Lands Department of Hong Kong. Furthermore, the corrected differential tropospheric delay correction is used to correct the InSAR image. The experimental results show that the correction of tropospheric delay by IIDW interpolation not only improves the accuracy of Differential Interferometry Synthetic Aperture Radar (D-InSAR) but also provides a new idea for the solution of InSAR and GPS data fusion.

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.