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This review article provides an overview of various aspects of lunar exploration, including missions to the Moon, collection and analysis of lunar sample data in laboratories, and the processing and analysis of remote sensing data, particularly using radar techniques. Both remote sensing and in-situ methods are critical for advancing our understanding of the lunar surface and its properties. This review article focuses on the identification and quantification of water–ice deposits located in areas such as Permanently shadowed areas (PSRs) and the lunar poles (section " Lunar Poles and PSRs: A Special Environment "). These volatile resources have the potential to serve as valuable sources of fuel for future missions, making it crucial for the lunar community to determine their abundance and distribution. After thoroughly examining lunar samples using high-precision laboratory techniques, many preconceptions were dispelled which is highlighted in the section " Laboratory Investigation of Lunar samples ". But as in-situ observations are difficult to acquire, especially terrestrial bodies samples, remote sensing techniques allow the global understanding of the surface. The article specifically highlights the importance of understanding the electrical characteristics of the lunar surface and how radar inversion can provide valuable information in this regard. The " Conclusion " section of this review article serves as a key takeaway for readers, underscoring the critical role that both in-situ and remote sensing techniques play in advancing our understanding of the Moon. [ABSTRACT FROM AUTHOR]

• A review of track detection algorithms across different research fields. • Technical description of the initiation, maintenance and termination of a radar track in the BIRALES radar. • Presentation of a new track detection strategy that is used for the detection of orbital debris using a bi-static continuous-wave radar. • Performance evaluation of this new track detection method on synthetic data. The world's economy has become heavily dependent on the services provided by satellites. With the exponential increase in satellite launches, the population of defunct or inactive hardware in space has grown substantially. This is especially true in sensitive orbits such as the Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. These objects, collectively known as orbital debris, can reach speeds of up to 28 000km h−1 in LEO. At these orbital speeds, even the smallest of objects can pose a considerable threat to operational satellites or astronauts. This makes the monitoring, and detection, of these objects of the utmost importance. This work describes the latest detection strategy used in one of Europe's largest Space Situational Awareness (SSA) installation; the BIstatic RAdar for LEo Survey (BIRALES) space debris radar. We present a novel bottom-up approach that makes use of single-linkage clustering to identify faint radar streaks in spectrogram data. Tests on synthetic data have shown that the detection strategy presented in this study obtains a higher detection rate when it is compared against existing methods. Unlike other approaches, this detection strategy, using the Multi-beam streak detection strategy (MSDS) algorithm, was still able to recall 90% of the track information at an Signal-to-Noise Ratio (SNR) of 2dB. [ABSTRACT FROM AUTHOR]

The assessment of morphological mapping capacity of X-band radars is performed by comparing radar-derived shoreline estimates with in-situ data measured from DGPS-RTK surveys in Cala Millor beach (October 22–24, 2018). Radar estimates are obtained by applying an automatic method based on digital image processing, threshold segmentation and first-order derivative filters on X-Band radar variance images. The study site is covered by the endemic Posidonia oceanica seagrass meadow at the seabed. It represents a challenge on the estimation of local shoreline. Results reveal that X-band radar estimate the shoreline location with a mean bias of 4.66 m, a relative error of 2.95% and a RMSE of 14.07 m. Also, the beach morphology is assessed by the identification of surrounding rocky outcrops and a submerged sandbar. Since the obtained radar estimation errors are comparable with the reported by previous studies but using video-cameras, results reveal a potential complementary use of both marine radars and optical cameras to provide higher-resolution and more accurate shoreline measurements in a broader area. • An automated shoreline detection method for nearshore areas using X-Band marine radar is proposed. • We combine threshold segmentation, edge detection, digital processing and statistical properties of the surrounding. • Shoreline location is detected with a mean bias of 4.66 m, a relative error of 2.95% and a RMSE of 14.07 m. • We suggest the complementary use of marine radars and optical camera for shoreline measurements. [ABSTRACT FROM AUTHOR]

The Chinese Gaofen-3 satellite was launched into space in 2016. Based on the corner reflector (CR) evaluation, it was found that the co-pol channel imbalance phase may exceed the maximum tolerance in the distributed standard product, i.e., ± 10°. The ground-deployed CRs are the most precise way to calibrate the phase error. However, the Mongolia calibration site of Gaofen-3 is a temporal calibration field, and the calibration time is limited for the periodic evaluation over the 8-year satellite life. In this paper, to accelerate the Gaofen-3 calibration mission, we propose an improved unitary zero helix (IUZH) framework to fully replace the use of CRs when estimating the co-pol channel imbalance phase by the use of natural media. Compared with our previous works (Shi et al., 2014; Shi et al., 2020), this novel algorithm presents three main contributions to remote sensing society. Firstly, we believe that this natural media calibration technique could replace the use of CRs, to satisfy the radar design specification, and that this natural media calibration technique could be more than just a substitute in an emergency event when there are no CRs deployed in-scene. Secondly, the IUZH framework is less dependent on choosing the particular reference targets, such as isolated point-like objects or Amazon forest. As a result, it will help radar researchers to relax the constraint of selecting references, and will make the non-CR calibration task easier in satellite or airborne cases. Last but not least, the proposed technique simultaneously derives the calibration phase after the sensor has completed a long-strip observation, which will significantly accelerate the Gaofen-3 external calibration mission, at little cost and time-consuming. The experiments with more than 500 images demonstrate that the phase bias of the Gaofen-3 standard product can be robustly calibrated, even when CRs are unavailable in the scene. The HH and VV imbalance phase with the IUZH method is generally found to be less than 4°. The potential phase error of the Gaofen-3 product is thus significantly suppressed, and the calibration accuracy satisfies the 10° design specification. [ABSTRACT FROM AUTHOR]

Abstract In this paired study, we apply a recently developed high-resolution bathymetry estimation algorithm ("cBathy") to X-band marine radar observations at two nearshore field sites. The algorithm exploits observations of the spatial structure of wave phase to attain wavenumber estimates, inverts the linear water wave dispersion relation for depth, and then applies a Kalman filter to objectively update the bathymetry estimates. Previously, performance has only been tested using optical video observations. In this first of two papers, performance of the algorithm using X-band radar image time series is tested at two disparate barred beach environments: Duck, NC, USA, and Benson Beach, WA, USA. Each of the test beaches is either co-located with (Duck, NC) or geographically close to (Benson Beach, WA) those utilized in the original algorithm verification. Concurrent echosounder surveys are used as ground truth. The bulk performance of the radar-derived bathymetry estimate at Duck, NC, achieves 0.49 m root-mean-square error (RMSE) with 0.26 m bias deep. This compares well with the bulk performance of the concurrent estimate derived using optical video (0.44 m RMSE and 0.23 m bias deep). The radar-derived bathymetry estimate performance at Benson Beach, is similar (0.35 m RMSE and 0.11 m bias shallow), and is comparable to that of an optical video derived estimate at a similar Pacific Northwest beach (0.56 m RMSE and 0.41 m bias shallow). At both beaches, significantly higher performance is achieved at locations deeper than 2 m (offshore of the surfzone) than at locations shallower than 2 m (surfzone), where errors are often a large fraction of the total depth. Lastly, several weeks of observations are utilized to assess the sensitivity of algorithm quality control to environmental conditions. Thresholds based on the shoreward component of wind stress and offshore wave steepness are identified and shown to impact the areal coverage of radar-derived bathymetric estimates. Overall, these results demonstrate the viability of marine radar observations as input to the cBathy algorithm and delineate some environmental constraints on algorithm performance. In the companion paper, the algorithm is extended to areas where tidal currents are important, including an ebb tidal shoal and an estuary mouth. Highlights • Depth estimation algorithm, cBathy, is applied to X-band radar image time series. • Performance is assessed at two beaches (North Carolina and Washington, USA). • Accuracy via radar is comparable to video-based performance (reported previously). • Frequency of updates increases with wave height and when wind blows toward shore. [ABSTRACT FROM AUTHOR]

Abstract As one of the most important ecosystems, wetlands are threatened from both natural and anthropogenic activities. Mapping wetland is one of the curtail needs in order to prevent further loss. Since the beginning of the Remote Sensing revolution, different approaches using satellite images have been used for mapping and monitoring wetlands. In this paper we investigate the potential of the recently launched Sentinel satellites, both separate and in combination, for accurately mapping of different wetland classes using Support Vector Machines (SVMs) learning classifier. For investigating the influence of the Sentinel-2 red-edge bands, and the radar bands from Sentinel-1, three different datasets have been analyzed. The results showed that for more accurate mapping of different wetland classes, different datasets should be used. Thus, the red-edge bands have significant influence over the intensive vegetated wetland classes such as swamps, and the radar bands have significant influence over partially decayed vegetated wetland areas such as bogs. For future studies, in addition to the analyzed datasets, we recommend adding and investigating several vegetation indices for mapping and monitoring wetland areas. Highlights • Combination of optical and radar data for wetland monitoring • Supported Vector Machine algorithm for wetlands classification • Significant influence of the Red-edge bands over intense vegetated wetlands • Significant influence of the Radar data over decayed vegetated wetlands [ABSTRACT FROM AUTHOR]

Fire has a diverse range of impacts on Earth's physical and social systems. Accurate and up to date information on areas affected by fire is critical to better understand drivers of fire activity, as well as its relevance for biogeochemical cycles, climate, air quality, and to aid fire management. Mapping burned areas was traditionally done from field sketches. With the launch of the first Earth observation satellites, remote sensing quickly became a more practical alternative to detect burned areas, as they provide timely regional and global coverage of fire occurrence. This review paper explores the physical basis to detect burned area from satellite observations, describes the historical trends of using satellite sensors to monitor burned areas, summarizes the most recent approaches to map burned areas and evaluates the existing burned area products (both at global and regional scales). Finally, it identifies potential future opportunities to further improve burned area detection from Earth observation satellites. • A review of burned area trends in past 40 years of RS is performed. • Different sensors used for BA mapping presented, including Radar and Lidar. • Main burned area products are commented [ABSTRACT FROM AUTHOR]

Abstract LabOSat (acronym for "Laboratory On a Satellite") is a type of electronic platform designed to perform experiments in harsh, remote environments. Up to now, LabOSat platforms have been used mainly for characterizing and validating custom and commercial electronic devices at Low Earth Orbits (LEO). Many of these platforms are running experiments aboard ÑuSat satellites, which are built and operated by Argentine company Satellogic. A brief description of LabOSat platforms is presented here, along with details of their validation both at LEO and at a nuclear reactor. The electrical characterization of custom non-volatile memory cells — as performed by a LabOSat platform at LEO — is also described, and the results of such experiment are compared with results obtained on Earth. The memory cells studied were TiO 2 -based and La 1/3 Ca 2/3 MnO 3 -based ReRAM devices. Highlights • LabOSat, a characterization platform, was tested under radiation environments. • Satellite payload: excellent performance at Low Earth Orbits (LEO) in a 3-year mission. • LabOSat platform was used to study ReRAM cells on board microsatellites. • Measurements on ReRAM cells at LEO are in agreement with on-Earth measurements. [ABSTRACT FROM AUTHOR]

Abstract Permanently shadowed regions (PSRs) at the poles of the Moon are potential reservoirs of trapped volatile species, including water ice. Knowledge of the distribution and abundance of water ice at the poles provides key scientific background for understanding the evolution of volatiles in the Earth-Moon system and for human exploration efforts. The Lunar Reconnaissance Orbiter Camera (LROC) acquired images of the terrain within PSRs to search for indications of water ice. In addition, the LRO Miniature Radio-Frequency (Mini-RF) instrument acquired S-band radar observations to further characterize these regions. Specifically, the m-chi decomposition was used to assess the distribution of materials within and around PSRs based on the type of backscatter. Double-bounce backscatter is indicative of water ice, but could also be produced by randomly distributed blocks at the wavelength scale. To ascertain whether these signatures are due to water ice or blocks, we quantified the abundance of detectable blocks in areas with double-bounce backscatter using the LROC Narrow Angle Camera (NAC). Block populations were measured for a suite of craters with different ages, sizes, and radar characteristics. For fresh craters, a correlation between block size, block density and double-bounce backscatter was found. Within PSRs exhibiting double-bounce backscatter, no blocks were found. Additionally, no albedo variations were observed at PSRs, in contrast to observations of PSRs on Mercury. While the possibility of water ice in some lunar craters still exists, these results indicate that they are likely small-scale, and that the observed radar anomalies at PSR-bearing craters are most likely due to the presence of wavelength-scale blocks. Highlights • Radar signatures at the lunar poles interpreted as water ice could be due to blocks. • This study assessed block abundance in permanently shadowed regions (PSRs) using radar and visible images. • Observations of PSRs are consistent with block populations rather than large ice deposits. [ABSTRACT FROM AUTHOR]

Ground-based snowfall observations over Antarctica are rare due to the harsh environment and high logistical, equipment maintenance, and operational costs. Satellite measurements are crucial to provide continent-wide precipitation estimates, and this highlights the importance of validating the satellite estimates with measurements collected by ground-based Antarctic stations. The NASA CloudSat satellite, launched in 2006, is equipped with a 94 GHz (W-band) Cloud Profiling Radar (CPR) that provides measurements of reflectivity profiles of clouds and precipitation, whereas the incoming ESA/JAXA EarthCARE mission will add Doppler capability to a 94 GHz radar. This study explores how the synergy between two instruments available at most Antarctic stations, i.e., Micro Rain Radar (24 GHz, K-band) and laser disdrometer, can be used to validate satellite-borne W-band radar measurements, including Doppler estimates. A new validation methodology (K2W) was proposed to combine these instruments for simulating the 94 GHz reflectivity and Doppler measurements from Micro Rain Radar spectra. Assessment of the proposed K2W conversion methodology showed that the CloudSat Z e profiles can be simulated by the method with 0.2 dB mean difference at the lowest satellite radar range bin when time lag within ±12.5 min and the distance within 25 km around the CloudSat overpass were considered. With the K2W method, the 94 GHz Doppler velocity below 1 km altitude that would be observed by EarthCARE was obtained, and the standard deviation of the simulated Doppler velocity was found to be smaller than about 0.2 m s−1. The simulated 94 GHz Doppler radar profile information, which is less affected by attenuation compared to ground-based 94 GHz radar, will significantly improve the quantification of precipitation over Antarctica. This methodology will be applied to further assess the EarthCARE CPR Doppler velocity measurement accuracy as well as the Level 2 standard products for precipitation in Antarctica and at many other ground observation sites. • Synergy between 24 GHz Micro Rain Radar and disdrometer is used by K2W methodology • 94 GHz Doppler Spectra below 1 km were obtained in Antarctica • CloudSat radar reflectivity was evaluated within 0.2 dB by K2W • EarthCARE radar Doppler velocities are expected to be evaluated within 0.2 m s−1 • The wide applicability of K2W hugely raises opportunities for EarthCARE validation [ABSTRACT FROM AUTHOR]

Despite the capability of Ocean Color Monitor aboard Oceansat-2 satellite to provide frequent, high-spatial resolution, visible and near-infrared images for scientific research on coastal zones and climate data records over the global ocean, the generation of science quality ocean color products from OCM-2 data has been hampered by serious vertical striping artifacts and poor calibration of detectors. These along-track stripes are the results of variations in the relative response of the individual detectors of the OCM-2 CCD array. The random unsystematic stripes and bandings on the scene edges affect both visual interpretation and radiometric integrity of remotely sensed data, contribute to confusion in the aerosol correction process, and multiply and propagate into higher level ocean color products generated by atmospheric correction and bio-optical algorithms. Despite a number of destriping algorithms reported in the literature, complete removal of stripes without residual effects and signal distortion in both low- and high-level products is still challenging. Here, a new operational algorithm has been developed that employs an inverted gaussian function to estimate error fraction parameters, which are uncorrelated and vary in spatial, spectral and temporal domains. The algorithm is tested on a large number of OCM-2 scenes from Arabian Sea and Bay of Bengal waters contaminated with severe stripes. The destriping effectiveness of this approach is then evaluated by means of various qualitative and quantitative analyses, and by comparison with the results of the previously reported method. Clearly, the present method is more effective in terms of removing the stripe noise while preserving the radiometric integrity of the destriped OCM-2 data. Furthermore, a preliminary time-dependent calibration of the OCM-2 sensor is performed with several match-up in-situ data to evaluate its radiometric performance for ocean color applications. OCM-2 derived water-leaving radiance products obtained after calibration show a good consistency with in-situ and MODIS-Aqua observations, with errors less than the validated uncertainties of ±5% and ±35% endorsed for the remote-sensing measurements of water-leaving radiance and retrieval of chlorophyll concentrations respectively. The calibration results show a declining trend in detector sensitivity of the OCM-2 sensor, with a maximum effect in the shortwave spectrum, which provides evidence of sensor degradation and its profound effect on the striping artifacts in the OCM-2 data products. [ABSTRACT FROM AUTHOR]

We have studied the regolith landform distribution in the area of Gaoua, western Burkina Faso, using an integration of geophysical and remote sensing data. Concentration maps of K, Th, U, as well as their ratios, were computed from airborne gamma-ray spectrometry data to assess the geochemical composition of the regolith. The mineralogy of the surfaces was mapped via the analysis of multispectral ASTER and Landsat scenes. Pauli-decomposition data retrieved from polarimetric ALOS PALSAR and Radarsat-2 images were included to characterize the surface properties of the regolith material. Morphometric variables such as slope, curvature, and relative relief were derived from the SRTM digital elevation model to quantify the topographic parameters of the different regolith landforms. An artificial neural network implementation, ADVANGEO, was then employed to extract four basic regolith landform units from the satellite and airborne data. Relic ferruginous duricrusts rich in hematite and goethite belonging to the High glacis, erosional surfaces represented by rock outcrops and suboutcrops, alluvial sediments, and soft pediment materials of the Middle and Low glacis were mapped successfully in the region. The results were compared with the existing geomorphological maps, an independent visual classification, and field observations. We found that the distribution and shape of the iron-rich duricrusts are more accurate than portrayed in the current maps. The best results, with an overall accuracy of 94.21% and a kappa value of 0.92, were obtained for a dataset consisting of gamma-ray spectrometry data combined with derivatives of the SRTM digital elevation model augmented by Landsat, and polarimetric radar data. The approach demonstrates for the first time the potential of machine learning in regolith landform mapping. The proposed combined analysis of airborne geophysics and remote sensing data can be adopted easily in other regions with similar long-term lateritic weathering histories worldwide. [ABSTRACT FROM AUTHOR]

There is a growing need for lightweight airborne platforms that could provide precise information about the environment (topography, presence of obstacles, etc.) filling the data gap between aerial/satellite remote sensing and terrestrial systems. A major limitation of classical sensors such as vision or laser is that they are ineffective in degraded visual conditions. Millimeter-wave radar provides an alternative solution to overcome the shortcomings of optical solutions, because in the microwave range, data can be acquired independently of atmospheric conditions and time of the day. The intended application of a new radar sensor is the construction of digital elevation models of the overflown environments. As the design of new radar sensors for light airborne platforms is subject to specific technological constraints, a simulator of airborne radar surveys is developed. The objective of the simulator is to help the designer in defining the main parameters of the future airborne radar, and in developing radar signal processing algorithms. [ABSTRACT FROM AUTHOR]

In this work, we evaluate landfast sea ice dynamics using ground-based radar interferometry. During two field campaigns in Utqiaġvik, Alaska, we collected three ∼24-h series of measurements on 16 May 2012 and 21–23 April 2021 using the Gamma portable radar interferometer (GPRI). These data enable examination of progressive strain of landfast ice on the sub-cm scale. The results indicate near spatially uniform divergence strains exceeding 10−6 in response to an increase in offshore winds from 0 to 7 m s−1. Exceptions to uniform divergence are grounded ridges with either negligible or reduced motion. We also track uniform vertical shifts in response to cm-scale sea level change correlated with NOAA model tidal predictions. We evaluate and take steps to remove contributions from moisture variability, thereby reducing potential errors from atmospheric effects to less than 5 mm. The results suggest that the GPRI can be a valuable tool for monitoring ice-covered coastal zones. The system has possible applications for monitoring tides and ocean surges and tracking small-scale deformation of otherwise stationary ice that can lead to fracture and destabilization. • We resolve wind-induced ice strain over kilometers with cm-scale accuracy. • We track vertical displacement of sea ice in microtidal environment. • We obtain minute-scale temporal resolution and sub-meter range resolution. • Allows accurate location and timing of episodic events e.g., cracks and detachments. • Atmospheric effects can be mitigated by removing phase change from static targets. [ABSTRACT FROM AUTHOR]

Aiming at the task scheduling problem in the DAR (digital array radar), an online task interleaving scheduling algorithm is proposed. The full structure of the DAR task is explicitly considered in a way that the waiting duration can be utilized to transmit or receive subtasks, which is called the task interleaving, as well as the receiving durations of different tasks can be overlapped. The algorithm decomposes the task interleaving analysis into the time resource constraint analysis and the energy resource constraint analysis, and online schedules all kinds of tasks that can be interleaved. Thereby the waiting durations and receiving durations can be fully utilized. The simulation results demonstrate that the proposed algorithm improves the successfully scheduling ratio by 73%, the high value ratio by 86% and the time utilization ratio by 55% compared with the HPEDF (highest priority and earliest deadline first) algorithm. [ABSTRACT FROM AUTHOR]

The accuracy of spatial predictions of rainfall by merging rain-gauge and radar data is partly determined by the sampling design of the rain-gauge network. Optimising the locations of the rain-gauges may increase the accuracy of the predictions. Existing spatial sampling design optimisation methods are based on minimisation of the spatially averaged prediction error variance under the assumption of intrinsic stationarity. Over the past years, substantial progress has been made to deal with non-stationary spatial processes in kriging. Various well-documented geostatistical models relax the assumption of stationarity in the mean, while recent studies show the importance of considering non-stationarity in the variance for environmental processes occurring in complex landscapes. We optimised the sampling locations of rain-gauges using an extension of the Kriging with External Drift (KED) model for prediction of rainfall fields. The model incorporates both non-stationarity in the mean and in the variance, which are modelled as functions of external covariates such as radar imagery, distance to radar station and radar beam blockage. Spatial predictions are made repeatedly over time, each time recalibrating the model. The space-time averaged KED variance was minimised by Spatial Simulated Annealing (SSA). The methodology was tested using a case study predicting daily rainfall in the north of England for a one-year period. Results show that (i) the proposed non-stationary variance model outperforms the stationary variance model, and (ii) a small but significant decrease of the rainfall prediction error variance is obtained with the optimised rain-gauge network. In particular, it pays off to place rain-gauges at locations where the radar imagery is inaccurate, while keeping the distribution over the study area sufficiently uniform. [ABSTRACT FROM AUTHOR]

Utilizing remote sensing techniques to extract soil properties can facilitate several engineering applications for large-scale monitoring and modeling purposes such as earthen levees monitoring, landslide mapping, and off-road mobility modeling. This study presents results of statistical analyses to investigate potential correlations between multiple polarization radar backscatter and various physical soil properties. The study was conducted on an approximately 3 km long section of earthen levees along the lower Mississippi river as part of the development of remote levee monitoring methods. Polarimetric synthetic aperture radar imagery from UAVSAR was used along with an extensive set of in situ soil properties. The following properties were analyzed from the top 30–50 cm of soil: texture (sand and clay fraction), penetration resistance (sleeve friction and cone tip resistance), saturated hydraulic conductivity, field capacity, permanent wilting point, and porosity. The results showed some correlation between the cross-polarized (HV) radar backscatter coefficients and most of these properties. A few soil properties, like clay fraction, showed similar but weaker correlations with the co-polarized channels (HH and VV). The correlations between the soil properties and radar backscatter were analyzed separately for the river side and land side of the levee. It was found that the magnitude and direction of the correlation for most of the soil properties noticeably differed between the river and the land sides. The findings of this study can be a good starting point for scattering modelers in a pursuit of better models for radar scattering at cross polarizations which would include more diverse set of soil parameters. [ABSTRACT FROM AUTHOR]

Civil marine radar, which operates at a low grazing-incidence angle and with horizontal polarization in transmitting and receiving, can be modified and used as a primary tool for surface wave monitoring. The high spatial resolutions of sea-clutter image sequences from X-band radar offer a means of deriving individual waves and wave field at low-cost. However, the performance of X-band radar is impaired under rainy conditions, which are usually accompanied by the severe weather at sea. In the present study, we examine the effectiveness of S-band radar for wave measurements under precipitation. The results of comprehensive comparative studies with sea-truth data show that S-band radar is capable of carrying out wave measurements in rainy conditions. Although the longer wavelength of the S-band leads to a coarser resolution of radar imagery, the S-band radar features at least the equivalent performance of the X-band system in non-rainy conditions, in terms of wave height measurement. The results suggest that the S-band and X-band could be complementary systems. In rainy conditions the S-band is more efficient but in the non-rainy periods the X-band gives more confident results. The relationship of significant wave height with radar signal-to-noise ratio (SNR), and the modulation transfer function (MTF) between radar spectrum and wave spectrum for the used X-band and S-band radars are established and discussed in this paper. [ABSTRACT FROM AUTHOR]

Summary Hydrological forecasting systems require accurate initial conditions, particularly for real time precipitation data, which are problematic to retrieve. This is especially difficult for high temporal and spatial resolutions, e.g. sub-daily and less than 10–20 km. Forecasting fast processes such as flash flood are, however, dependent on such high resolution data. Gridded gauge data produces too smooth fields and underestimates small scale phenomena, such as convection, whereas radar composites contain the small scale information, but suffer from inconsistencies between individual radars and have poor long term statistics. Here, we present a method to merge a radar composite with daily resolution gridded gauge data for Sweden for the time period 2009–2014 to produce a one hourly 4 × 4 km 2 data set. The method consists of a main step where monthly accumulations of the radar data are scaled by those retrieved from the gridded data for each month. An optional quantile mapping based bias correction step makes sure that the daily intensity distribution agrees with the gridded observations. Finally, the data are dis-aggregated to an hourly time resolution. This results in a data set which has the same long-term spatial properties as the gridded observations, but with the spatial and temporal details of the radar data. Validation of the method is performed with high resolution gauge data, and shows a high quality of the derived product. [ABSTRACT FROM AUTHOR]

• A fully non-contact medical radar-based vital sign monitoring system was developed for neonatal monitoring at an NICU. • An advanced signal processing algorithm to estimate respiration and cardiac peaks in time-series were proposed and implemented in the system. • The proposed system introduces a novel approach for NICU monitoring. Continuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU. A non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates. The results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland–Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms. The proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings. [ABSTRACT FROM AUTHOR]

[Display omitted] • UAV data accurately predicted SOC concentrations (RMSE < 2.44 %) • Radar data characterized spatial patterns of flood frequency in coastal areas. • The effect of flood frequency on SOC was highest at pioneer and seaward communities. • AGB showed a larger explanatory power than flood frequency in SOC predictions. • UAV-SAR fusion unveils site-specific fine-scale patterns of SOC in coastal meadows. Coastal wetlands are considered important stores of blue carbon, containing some of the largest stores of pedologic and biotic carbon per unit area on the planet. These ecosystems are however highly sensitive to Climate Change and changes in the management practices. It is of utmost importance to address relevant ecosystem scales in order to fully understand carbon dynamics in coastal wetlands. In this regard, Unoccupied Aerial Vehicles (UAVs) can provide spatial scales detailed enough to address the fine scale patters of topsoil organic carbon accumulation in coastal wetlands. This study demonstrates the use of multispectral and photogrammetric data derived from UAVs to accurately map plant communities and topsoil organic carbon concentration in coastal wetlands. The overall accuracies from the classification of plant communities ranged from 88% to 97% whereas RMSE for topsoil organic carbon concentration ranged from 2.44% to 0.74%. By combining both models, site-specific variations in topsoil carbon concentrations among plant communities were unveiled. Open pioneer communities consistently showed the lowest topsoil organic carbon concentration, while the concentrations vary considerably across plant communities characterized by denser vegetation coverage. Furthermore, Sentinel-1 radar data was used to assess the spatial patterns of flood frequency. GAMs were used to combine flood frequency with the plant communities and topsoil organic carbon models, as well as an aboveground biomass (AGB) model from a previous study. GAMs revealed a stronger effect than flood frequency on topsoil organic carbon. Regarding flooding, increased flood frequency generally led decreased topsoil organic concentrations across communities and sites. However, the relative contribution of flood frequency to topsoil organic carbon concentration in Baltic coastal wetlands depends strongly on the location of the wetland and the nature of the floods. Higher flood frequencies could lead to increased topsoil organic carbon in wetlands subject to the input of estuarine sediments. Lastly, the integration of remote sensing platforms constitutes an effective tool for revealing spatial heterogeneity of carbon storage in coastal wetlands. [ABSTRACT FROM AUTHOR]

Evaluation of the stress/strain state in alloys is usually performed by direct mechanical measurements, contact ultrasound or ionizing-radiation nondestructive testing (NDT). Safe and totally non-contact methods have considerable advantages and are worthy of further development. In this work, a novel active infrared NDT framework with a linear-frequency-modulated laser-induced thermal-wave radar (LFM-LTR) imaging modality was proposed to quantitatively analyze thermo-mechanical properties as a function of the changing external loading. Chirp-modulated laser emission generates a depth-resolved diffusion-wave field which is analyzed with a modified anisotropic LTR theory. Features of stress-induced thermal anisotropy within uniaxial loaded materials were parametrized and extracted from simulated LTR signals which form the basis for solving the inverse problem of determining tensorized thermo-mechanical properties. An LTR experimental setup was built and used to validate the foregoing theoretical model and numerical analysis. Experimental thermo-mechanical property variation throughout the tensile test was eventually determined in terms of the nominal conductivity tensor without using any contacting sensors. The proposed active infrared testing scheme is promising to facilitate remote and indirect sensing of residual stresses in materials. • Mechanical property of alloy was tested with laser infrared thermography. • Tensorized conductivity determined by thermo-infrared image parameterization. • Depth-sensitive and adjustable thermal wave radar enables high SNR. • Stress tensor was characterized quantitatively with noncontact measurement. [ABSTRACT FROM AUTHOR]

There is ongoing interest to develop remote sensing methods for mapping and monitoring the spatial distribution and biomass of mangroves. In this study, we develop a suite of methods to evaluate the combination of Landsat-8, ALOS PALSAR, and SRTM data for mapping spatial distribution of mangrove composition, canopy height, and aboveground biomass in the wide intertidal zones and coastal plains of Mimika district, Papua, Indonesia. Image segmentation followed by visual interpretation of composite PALSAR images was used to delineate mangrove areas, whereas a flexible statistical rule based classification of spectral signatures from Landsat-8 images was used to classify mangrove associations. The overall accuracy of land cover classification was 94.38% with a kappa coefficient of 0.94 when validated with field inventory data and Google Earth images. Mangrove height and aboveground biomass were mapped using the SRTM DEM, which were calibrated with field-measured data via quantile regression models. There was a strong correlation between the SRTM DEM and the 0.98 quantile of field canopy heights (H .98 ), which was used to represent the tallest trees in each of 196 10 m radius subplots (r = 0.84 and R 2 = 0.804). Model performance was evaluated through 10,000 bootstrapped simulations, producing a mean absolute error (MAE) of 3.0 m for canopy height estimation over 30 m pixels of SRTM data. Quantile regression revealed a relatively strong non-linear relationship between the SRTM derived canopy height model and aboveground biomass measured in 0.5 ha mangrove inventory plots (n = 33, R 2 = 0.46). The model results produced estimates of mean standing biomass of 237.52 ± 98.2 Mg/ha in short canopy ( Avicennia / Sonneratia ) stands to 353.52 ± 98.43 Mg/ha in mature tall canopy ( Rhizophora ) dominated forest. The model estimates of mangrove biomass were within 90% confidence intervals of area-weighted biomass derived from field measurements. When validated at the landscape scale, the difference between modeled and measured aboveground mangrove biomass was 3.48% with MAE of 105.75 Mg/ha. These results indicate that the approaches developed here are reliable for mapping and monitoring mangrove composition, height, and biomass over large areas of Indonesia. [ABSTRACT FROM AUTHOR]

Mapping of settlement areas from space is entering a new era. With the recently developed Global Urban Footprint (based on radar data from TanDEM-X) and the Global Human Settlement Layer (based on optical data), two new initiatives that promise to map complex settlement patterns at global scales and unprecedented spatial resolutions are about to enter the scientific and map user community. However, comparative studies on these layers' strengths and weaknesses, especially in terms of their potential added value with regard to existing lower resolution maps, as well as their assessed accuracy are still absent. In this regard, we introduce a multi-scale cross-comparison framework that uses the best existing urban maps as a benchmark. To paint a complete picture, we simultaneously address several components of map accuracy including relative inter-map agreement, absolute accuracies and pattern-based classification differences. This framework is applied to present regionally representative results from two Central European test sites. In this, we find that the new base maps bring decisive advancements in preserving the small-scale complexity of global human settlement patterns beyond urban core areas. Relative inter-map comparison exposes low density settlement regions traditionally under-represented by lower resolution maps that are now recognized. Absolute metrics such as the Kappa coefficient of agreement ( K ) show that accuracies of the new high resolution layers ( K ¯ = 0.56–0.58) nearly double those of existing products. Beyond, they feature substantial consistency between urban ( K ¯ = 0.46–0.50) and rural landscapes ( K ¯ = 0.41–0.45). Results from pattern-based exploration further reveal significant correlation of accuracies with physical pattern variations such as settlement density and mark a clear shift of accuracies from large to medium and small patch sizes. This differentiated view on classification accuracies shows that the new generation of urban maps constitutes a significantly enhanced spatial representation of large-scale settlement patterns. [ABSTRACT FROM AUTHOR]

Persistent Scatterer Interferometry (PSI) is a powerful remote sensing technique able to measure and monitor displacements of the Earth’s surface over time. Specifically, PSI is a radar-based technique that belongs to the group of differential interferometric Synthetic Aperture Radar (SAR). This paper provides a review of such PSI technique. It firstly recalls the basic principles of SAR interferometry, differential SAR interferometry and PSI. Then, a review of the main PSI algorithms proposed in the literature is provided, describing the main approaches and the most important works devoted to single aspects of PSI. A central part of this paper is devoted to the discussion of different characteristics and technical aspects of PSI, e.g. SAR data availability, maximum deformation rates, deformation time series, thermal expansion component of PSI observations, etc. The paper then goes through the most important PSI validation activities, which have provided valuable inputs for the PSI development and its acceptability at scientific, technical and commercial level. This is followed by a description of the main PSI applications developed in the last fifteen years. The paper concludes with a discussion of the main open PSI problems and the associated future research lines. [ABSTRACT FROM AUTHOR]

Recent research revealed that western Greece and NW Peloponnese are regions that favor prefrontal tornadic incidence. On March 25, 2009 a tornado developed approximately at 10:30 UTC near Varda village (NW Peloponnese). Tornado intensity was T4–T5 (TORRO scale) and consequently caused an economic impact of 350,000 € over the local society. The goals of this study are: (i) to analyze synoptic and remote sensing features regarding the tornado event over NW Peloponnese and (ii) to investigate the role of topography in tornadogenesis triggered under strong synoptic scale forcing over that area. Synoptic analysis was based on the European Centre for Medium-Range Weather Forecasts (ECMWF) data sets. The analysis of daily anomaly of synoptic conditions with respect to 30 years' climatology (1981–2010), was based on the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data sets. In addition, numerous remote sensing data sets were derived by the Hellenic National Meteorological Service (HNMS) weather station network in order to better interpret the examined tornado event. Finally, numerical modeling was performed using the non-hydrostatic Weather Research and Forecasting model (WRF), initialized by ECMWF gridded analyses, with telescoping nested grids that allow the representation of atmospheric circulations ranging from the synoptic scale down to the meso-scale. The two numerical experiments were performed on the basis of: (a) the presence and (b) the absence of topography (landscape), so as to determine whether the occurrence of a tornado – identified by diagnostic instability indices – could be indicated by modifying topography. The energy helicity index (EHI), the bulk Richardson number (BRN) shear, the storm-relative environmental helicity (SRH), and the maximum convective available potential energy (MCAPE, for parcels with maximum θ e ) were considered as principal diagnostic instability variables and employed in both numerical experiments. Furthermore, model verification was conducted, accompanied by analysis of the absolute vorticity budget. Synoptic analysis revealed that the synoptic weather conditions on March 25, 2009 are in agreement with the composite synoptic climatology for tornado days over western Greece. In addition, maximum daily anomalies at the barometric levels of 500, 700, 850 and 925 hPa were found, compared to the climatology of composite mean anomalies for tornado days over western Greece. Numerical simulations revealed that the topography of NW Peloponnese did not constitute an important factor during the tornado event on March 25, 2009, based on EHI, SRH, BRN, and MCAPE analyses. [ABSTRACT FROM AUTHOR]

Two years of sea surface current measurements acquired since August 2012 by High-Frequency SeaSonde radars over the relatively shallow shelf area dividing the Maltese Islands from Sicily (the Malta - Sicily Channel), are used to characterize the surface tidal currents in the region. Tidal currents are generally weak and concentrated in the semidiurnal and diurnal bands, barely exceeding 3 cm s −1 in the semidiurnal band (M 2 , S 2 ), and below 6 cm s −1 in the diurnal band (K 1 , O 1 ). In the middle part of the basin, the M 2 currents oscillate along the main Channel axis; on the contrary the S 2 oscillations are oriented along the energetic Atlantic Ionian Stream (AIS) flow. Diurnal tides have a more circular pattern, also following the AIS path and with a significant intensification in proximity of the shelf break, thus suggesting a substantial contribution from internal tides in the region. Phase contours suggest the presence of amphidromic points for both the semidiurnal and diurnal constituents, located in the central area of the Channel for S 2 , O 1 and K 1 , but slightly shifted towards north-west for the M 2 component. [ABSTRACT FROM AUTHOR]

In this paper, a cancelling system for linear and three nonlinear frequency modulated signals is proposed. According to the RCS (Radar Cross Section) characteristic of target, amplitude and phase modulation are made to acquire the signal having the same frequency as the echo pulse but with the opposite phase. This signal cancels the radar echo signal. The basic theory of active cancellation stealth is introduced. Based on two kinds of radar target fluctuation models (Swerling I and Swerling III), the formulas of radar detection probability are given. The influence of amplitude, phase and frequency error on the radar detection probability have been discussed. Simulation results show that the technology of active cancellation stealth is feasible in theory, it could reduce the radar detection probability. [ABSTRACT FROM AUTHOR]

For advanced offshore engineering applications the prediction with available nautical X-band radars of phase-resolved incoming waves is very much desired. At present, such radars are already used to detect averaged characteristics of waves, such as the peak period, significant wave height, wave directions and currents. A deterministic prediction of individual waves in an area near the radar from remotely sensed spatial sea states needs a complete simulation scenario such as will be proposed here and illustrated for synthetic sea states and geometrically shadowed images as synthetic radar images. The slightly adjusted shadowed images are used in a dynamic averaging scenario as assimilation data for the ongoing dynamic simulation that evolves the waves towards the near-radar area where no information from the radar is available. The dynamic averaging and evolution scenario is rather robust, very efficient and produces qualitatively and quantitatively good results. For study cases of wind waves and multi-modal wind-swell seas, with a radar height of 5 times the significant wave height, the correlation between the simulated and the actual sea is found to be at least 90%; future waves can be predicted up to the physically maximal time horizon with an averaged correlation of more than 80%. [ABSTRACT FROM AUTHOR]

This research investigates anomalous nighttime ion density behaviour over the Kharkiv, Ukraine incoherent scatter radar (49.6° N, 36.3° E, 45.3° inv) during the equinoxes of 2006–2010. The observations show that the altitude of the transition from O + to lighter ions was much lower than empirical and physical models predict. The standard physical model produces very good agreement for the O + densities but underestimates the H + densities by a factor of 2 in March 2006 and a factor of 3 in March 2009. The anomalously low transition height is a result of similar lowering of the ionospheric peak height and also of significantly increased H + density. The lower ionospheric peak height may be caused by weaker nighttime neutral winds. The calculations indicate that the higher measured topside ionosphere H + densities are most likely due to higher neutral hydrogen densities. Both factors could be the result of weaker than usual magnetic activity, which would reduce the energy input to high latitudes. Prolonged low activity periods could cause a global redistribution of hydrogen and also allow more neutral hydrogen to settle down from the exosphere into the mid-latitude ionosphere. The finding of the need for higher H densities agrees well with recent H-alpha airglow measurements and it is important for accurate modelling of plasmasphere refilling rates and night-time N m F 2 values. [ABSTRACT FROM AUTHOR]

As the number of space-borne SAR sensors increases, a rising number of different SAR acquisition modes is in use, resulting in a higher variation within the image products. This variability in acquisition geometry, radiometry, and last but not least polarimetry raises the need for a consistent SAR image description incorporating all available sensors and acquisition modes. This paper therefore introduces the framework of the Kennaugh elements to comparably represent all kinds of multi-scale, multi-temporal, multi-polarized, multi-frequency, and hence, multi-sensor data in a consistent mathematical framework. Furthermore, a novel noise model is introduced that estimates the significance and thus the (polarimetric) information content of the Kennaugh elements. This facilitates an advanced filtering approach, called multi-scale multi-looking, which is shown to improve the radiometric accuracy while preserving the geometric resolution of SAR images. The proposed methodology is finally demonstrated using sample applications that include TerraSAR-X (X-band), Envisat-ASAR, RADARSAT-2 (C-band) and ALOS-PALSAR (L-band) data as well as the combination of all three frequencies. Thus the suitability of the Kennaugh element framework for practical use in proved for advanced SAR remote sensing. [ABSTRACT FROM AUTHOR]

Construction was completed during summer 2013 on the Telescope Array RAdar (TARA) bi-static radar observatory for Ultra-High Energy Cosmic Rays (UHECR). TARA is co-located with the Telescope Array, the largest “conventional” cosmic ray detector in the Northern Hemisphere, in radio-quiet Western Utah. TARA employs an 8 MW Effective Radiated Power (ERP) VHF transmitter and smart receiver system based on a 250 MS/s data acquisition system in an effort to detect the scatter of sounding radiation by UHECR-induced atmospheric ionization. TARA seeks to demonstrate bi-static radar as a useful new remote sensing technique for UHECRs. In this report, we describe the design and performance of the TARA transmitter and receiver systems. [ABSTRACT FROM AUTHOR]

Abstract: The ambiguity function is derived analytically for waveforms from a chaotic oscillator that has an analytic solution. The chaotic solutions of this oscillator can be expressed as a superposition of basis functions, similar to conventional communication or phase coded radar waveforms. Example waveforms are considered to illustrate the variety of ambiguity functions obtainable from a free running oscillator. The mean and the variance of the ambiguity function for waveforms generated by a free running oscillator are derived to determine typical performance. The mean ambiguity function is shown to have a single, localized peak with low variance indicating that solvable chaos has significant potential as the basis of novel remote sensing technologies. [Copyright &y& Elsevier]

Estimates of forest biomass are needed for various technical and scientific applications, ranging from carbon and bioenergy policies to sustainable forest management. As local measurements are costly, there is a great interest in obtaining reliable estimates over large areas from remote sensing data. Currently, such estimates are obtained with a variety of data sources, statistical methods and prediction standards, and there is no agreement on what are best practices for this task. To improve our understanding of how these different methods affect prediction quality, we first conducted a systematic review of the available literature to identify the most common sensor types and prediction methods. Based on the review, we identified sample size of the reference points on the ground, prediction method (stepwise linear regression, support vector machines, random forest, Gaussian processes and k-nearest neighbor), and sensor type as the main differences that could potentially affect predictive quality. We then compared those factors in two case study areas in Germany and Chile, for which airborne discrete return Light Detection And Ranging (LiDAR) and airborne hyperspectral as well as airborne discrete return LiDAR and spaceborne hyperspectral data were available. For each factor combination, we calculated Pearson's coefficient of correlation between observations and predictions (r 2 ) and root mean squared error (RMSE) for bootstrapped estimates using k-fold cross-validation with a varying number of folds. Finally, Analysis of Variance (ANOVA) was used to quantify the influence of the factors on the predictive error of the biomass models. Our results confirm previous findings that predictor data (sensor) type is the most important factor for the accuracy of biomass estimates, with LiDAR being preferable to hyperspectral data. In contrast to some previous studies, complementing LiDAR with hyperspectral data did not improve predictive accuracy. Also the prediction method had a substantial effect on accuracy and was generally more important than the sample size. In most cases, random forest performed best and stepwise linear models worst, judging from r 2 and RMSE under cross-validation. Additional results suggested that r 2 may deliver unrealistically large values when the hold-out sample during the cross-validation is too small. In conclusion, our literature review revealed that different methods for biomass estimation are currently used, with no general agreement on best practices. In our case studies, we found substantial accuracy differences between those methods, with LiDAR data, in combination with a random forest algorithm and a large number of reference sample units on the ground yielding the lowest error for biomass predictions. The comparatively high importance of the statistical prediction method seems particularly relevant, as they suggest that choosing the appropriate statistical method may be more effective than obtaining additional field data for obtaining good biomass estimates. Considering the costs of improving accuracy of global and regional biomass estimates by ground measurements, it seems sensible to invest in further comparative studies, preferably with a wider range of sites and including also RADAR sensors, to establish robust best-practice recommendations for obtaining regional and global biomass estimates from remote-sensing data. [ABSTRACT FROM AUTHOR]

During the PHOCUS rocket campaign, on 20-22 July 2011, the observations of polar mesosphere summer echoes (PMSE) were made by three mesosphere-stratosphere-troposphere radars, operating at about 50MHz. One radar, ESRAD is located at Esrange in Sweden, where the rocket was launched, two other radars, MAARSY and MORRO, are located 250km north-west and 200km north of the ESRAD, respectively, on the other side of the Scandinavian mountain ridge. We compared PMSE as measured by these three radars in terms of their strength, spectral width and wave modulation. Time-altitude maps of PMSE strength look very similar for all three radars. Cross-correlations with maximum values 0.5-0.6 were found between the signal powers over the three days of observations for each pair of radars. By using cross-spectrum analysis of PMSE signals, we show that some waves with periods of a few hours were observed by all three radars. Unlike the strengths, simultaneous values of PMSE spectral width, which is related to turbulence, sometimes differ significantly between the radars. For interpretation of the results we suggested that large-scale fields of neutral temperature, ice particles and electron density, which are more or less uniform over 150-250km horizontal extent were 'modulated' by waves and smaller patches of turbulence. [ABSTRACT FROM AUTHOR]

Networks of Windprofiler Radars have the capability to make significant contributions to severe weather forecasting (both on the ground and in the air) through the determination of real-time turbulence strengths, but the potential has still not been fully realized. In order to better understand the accuracy of profilers in determination of turbulence strengths, we have compared radar measurements made at the Harrow radar in Canada (located in Southwestern Ontario as part of the O-QNet radar network) with in-situ measurements made by multiple aircraft. These included measurements made both by commercial aircraft and dedicated research aircraft. Research aircraft (instrumented with accelerometers and GPS tracking devices) and radar data were analysed using structure function, spectral and spectral-width methods. Data were also recorded on-board commercial aircraft using accelerometer-based studies, and results were recorded for subsequent analyses. Over 92,000 commercial aircraft measurements, 4000h of radar data, and 15 days of research-aircraft measurements were available for this study, although only a subset of the commercial aircraft data were useable. The radar-based spectral-width method occasionally produced anomalous negative values of the turbulence strength, usually associated with weak turbulence coupled with significant wind variability over scales of tens of kms, but the aircraft data also had limitations. For the commercial aircraft, frequent zeros were common, also associated with weak turbulence. With regard to the research aircraft measurements, it was found through both spectral and structure function analyses that spectral contaminants exist out to scales of many tens of metres (larger than often assumed), but proper allowance for these effects permitted good estimates of turbulence strength. Spatial and temporal variability was large, however, complicating comparisons with the radar. By comparing the in-situ data to the radar data, it has been possible to place stronger limits on the constants used to determine radar turbulence strengths. Subsequent comparisons with the commercial aircraft data over a six-month period have then been used to show that aircraft and radar probability distributions agree in form, but that the aircraft data are three to five times larger. Corrections for this bias lead to good agreement, both in form and absolute values, for all three data sets. [ABSTRACT FROM AUTHOR]

This paper presents an accessible and reliable method to measure surface roughness of agricultural soils with a setup designed to tackle some of the challenges posed by roughness to SAR remote sensing. The method relies on Structure from Motion (SfM). From a large collection of unconstrained images (∼700 images) acquired with a commercial-grade camera, digital elevation models (DEMs) are generated for a SAR-pixel-size plot ( 2 × 11 m ), with horizontal and vertical RMS errors of respectively 1.5 mm and 3.1 mm. Example results highlight the need for individually detrending all sampled sub-DEMs when studying the convergence of the roughness parameters for increasing DEM length. This point appears to be missing in previous publications. The efficiency of the Fourier-based method used to compute the roughness parameters allows investigating anisotropy at a 1° angular resolution. This could benefit investigations on the flashing fields phenomenon observed within narrow direction bands over tilled fields. The inclusion of permanent reference targets into the soil makes multitemporal measurements over the same plot straightforward. Ten acquisitions from April to July 2013 show noticeable natural changes in roughness with cracking during dry periods and smoothing during rainfalls. As expected, changes in RMS height and correlation length appear inversely correlated and can be related to in situ measurements of soil moisture, soil temperature, and rainfall. Analysis of changes in power spectral density indicates that the observed roughness changes only affect scales below 50 cm, i.e. scales relevant for microwave scattering. Even though it seems that millimetric changes for horizontal scales below 1 cm are not observable, measurement performance could be improved by adding more detailed pictures to the image set. This SfM-based method appears to be well-suited to study the dynamics and characterization of roughness for SAR and more generally for geosciences. [ABSTRACT FROM AUTHOR]

Radar satellite-borne snow avalanche detection has rapidly grown into an important method for monitoring of avalanche activity over large spatial and long temporal scales. With increased application of Sentinel-1 data for avalanche detection, the need for improved performance evaluation arises. In this study, we make use of a unique dataset of field-based avalanche observations from eight adjacent avalanche paths at lake Tyin in central Norway. The dataset is a complete record of 318 dry slab avalanches that released during the winters 2016–2020, with information on release timing and extent. The dataset thus allows for detailed evaluation of the performance of automatic and manual avalanche detection in Sentinel-1 images, were both techniques make use of relative temporal backscatter intensity increase in case of an avalanche. Both automatic and manual detection underperform compared to previous studies with a probability of detection (POD) of 5.9% (false alarm rate (FAR) of 5.9%) and 11.3% (FAR of 19.27%) respectively. From the low relative backscatter intensity contrast of field-observed dry slab avalanches, it becomes evident that a higher backscatter contrast between avalanches and surrounding is needed for detectability in Sentinel-1 images. Neither the lag time between avalanche release and detection in a Sentinel-1 image, nor local incidence angle can explain the low POD's. Moreover, an analysis of meteorological conditions prior a during avalanche release and/or detection can explain the low POD's, given that differing snow conditions influence radar backscatter intensity. Finally, we cannot rule out that the small dataset of field-observed avalanches and the one-directional slope aspect of the study area influence our results, however, we believe that a physical limit of detectability of dry snow avalanches in C-band radar satellite data is reached. • We compare a complete dataset of field-observed avalanches in central Norway to Sentinel-1 detections of these avalanches. • We show that automatic and manual detection of dry snow avalanches yields an unsatisfyingly low probability of detection. • Neither lag time between release and S1 acquisition, nor meteorological conditions prior to release explain the low PODs. [ABSTRACT FROM AUTHOR]

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF. [ABSTRACT FROM AUTHOR]

Reductions in deforestation and forest degradation are advocated as a means to mitigate climate change. The formulation and implementation of policies to achieve such reductions requires an understanding of current and historic land-use change and associated greenhouse gas emissions. In addition, it is often proposed that any reduction in emissions be measured against a reference scenario that describes future land-use in the absence of intervention. However, the information needed to progress this agenda is rarely available, as robust data on the extent and causes of land-use change, and the associated changes in carbon stocks, are sparse, particularly in African woodlands. Here we present a novel method for obtaining such information by combining data from radar remote sensing and ground surveys with a simple aspatial model. Using this approach we quantify changes in woody biomass and investigate the land-use activities that caused these changes in a 7500 km 2 area of Manica province, Mozambique. We use the data to construct a model linking the activities causing biomass loss to hypothesised drivers, allowing the definition of future scenarios. Within the study area, biomass was lost at a rate of 2.8 ± 1.9% per year, with stocks changing from 19.4 ± 0.9 TgC in 2007 to 17.6 ± 0.9 TgC in 2010. Small-scale agriculture was the direct cause of 46 ± 17% of the total biomass loss, followed in magnitude by construction and miscellaneous activities (24 ± 11%), charcoal production (18 ± 9%), logging (9 ± 5%) and commercial agriculture (3 ± 2%). Uncertainties remain on the biomass accumulated by regrowing vegetation. Extrapolating into the future, a scenario that includes projected population growth shows 41% of biomass being lost from 2010 to 2020 (a loss of 7.2 TgC). A scenario of intensive policy interventions gives reduced losses of 3.8 TgC by factoring in improvements in crop yields, charcoal production efficiency, and sustainable timber harvesting. Our case study demonstrates the importance of low intensity losses of biomass in African woodlands, and highlights the broad range of activities that will need to be addressed to develop locally appropriate mitigation actions. The simple modelling framework allows for the transparent creation of scenarios in data sparse areas, which could be used as local or national reference emissions levels under REDD+. [ABSTRACT FROM AUTHOR]

Historic rates of habitat change and growing exploitation of natural resources threaten avian biodiversity in the Brazilian Atlantic Forest, a global biodiversity hotspot. We implemented a twostage framework for conservation planning in the Atlantic Forest. First, we used ecological niche modeling to predict the distributions of 23 endemic bird species using 19 climatic metrics and 12 spectral and radar remote sensing metrics. Second, we utilized the principle of complementarity to prioritize new sites to augment the Atlantic Forest's existing reserves. The best predictors of bird distributions were precipitation metrics (the seasonality of rainfall) and radar remote sensing metrics (QSCAT). The existing protected areas do not include 10% of the habitat of each of the 23 endemic species. We propose a more economical set of protected areas by reducing the extent to which new sites duplicate the biodiversity content of existing protected areas. There is a high concordance between the proposed conservation areas that we designed using computerized algorithms and Important Bird Areas prioritized by BirdLife International. Insofar as deforestation in the Atlantic Forest is similar to land conversion in other biodiversity hotspots, our methodology is applicable to conservation efforts elsewhere in the world. [ABSTRACT FROM AUTHOR]

Glaciers in polar regions are sensitive to climate and ocean changes and can thin rapidly as a consequence of global warming. Digital Elevation Models (DEMs) from remote sensing observations have been widely used to detect changes in polar glaciers. DEMs from Terrestrial Radar Interferometer (TRI) have recently been used for high frequency glacier change and glacier-ocean interaction studies. However, it is unclear whether TRI DEM over a large study area can be combined directly with remote sensing observations to investigate glacier changes as well as the accuracy of TRI DEM at far range. In this study, we deployed a TRI close to Helheim Glacier, East Greenland and generated DEMs using TRI and satellite laser altimetry. We analyzed the accuracy of the TRI DEM using theoretical calculations, comparisons based on repeat observations, and comparisons with a high accurate ArcticDEM. The validation results suggest that for stable ground surfaces, the uncertainty (standard deviation) is <5 m at range < 9.8 km. Averaging across time (e.g. one hour) decreases the uncertainty almost linearly with range, over 0.5 m to 1.2 m when the range increases from 7.0 km to 10.0 km. Increasing the correlation coefficient threshold for phase unwrapping does not significantly reduce uncertainty. TRI DEMs are influenced by systematic error at far range primarily due to coarse azimuth resolution and phase unwrapping difficulties in discontinuous interferograms. As the absolute accuracy of TRI DEMs is not uniformly distributed in the range direction (farther points have worse uncertainty), our findings indicate that TRI DEMs within range of 10 km can reach <5 m uncertainty, which can be compared with DEMs obtained from remote sensing satellites to detect glacier thinning. • A method to generate TRI DEM referring to WGS-84 ellipsoid is presented. • Averaging across time decreases the DEM uncertainty almost linearly with range. • Increasing correlation coefficient threshold does not significantly improve TRI DEM. • Large uncertainty at far range is caused by coarser azimuth resolution and discontinuous interferogram. • TRI DEMs with range < 10 km have <5 m uncertainty and can match satellite observations. [ABSTRACT FROM AUTHOR]

• Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. • Noncontact radar-based discharge estimates can be as accurate as in-situ, conventional streamgauges. • There was a notable phase sequencing between the surface velocity and stage, indicative of unsteady flows. • Real-time alerting capabilities of an impending flash flood are demonstrated in a vulnerable, ungauged basin. Accounting for freshwater resources and monitoring floods are vital functions for societies throughout the world. Remote-sensing methods offer great prospects to expand stream monitoring in developing countries and to smaller, headwater streams that are largely ungauged worldwide. This study evaluates the potential to estimate discharge using eight radar units that have been installed over streams in diverse hydrologic and hydraulic settings across the United States. The research highlights error characteristics associated with the measurements of stage using pulsed wave radars, mean channel velocity from continuous wave Doppler radars, and their combined use to estimate discharge at sites that were collocated with conventional streamgauges. Potential stage biases caused by the thermal expansion and contraction of supporting structures due to diurnal temperature changes were examined. A dry concrete, flume showed the temperature-dependent stage variations were no more than 2 cm. Surface velocity retrievals needed to be adjusted to represent the mean channel velocity when estimating discharge. Different approaches were evaluated and application of two different, depth-dependent adjustment factors was found to yield the most accurate estimates. This study found that it is possible to get accurate discharge estimates from noncontact radar measurements, providing cost-effective solutions for remote sensing of ungauged streams. Lastly, radar measurements of the raw variables (i.e., stage and surface velocity) can be used in an early alerting context to detect flash floods in ungauged streams. [ABSTRACT FROM AUTHOR]

Sensor technologies will play a key role in the success of Remote Maintenance (RM) systems for future fusion reactors. In this paper, three key types of sensor technologies of particular interest in the robotics field at the moment are evaluated, namely: Colour-Depth cameras, LIDAR (Light Detection And Ranging), and Millimetre-Wave (mmWave) RADAR. The evaluation of the sensors is performed based on the following criteria: the types of data they provide, the accuracy at different distances, and the potential environmental resistance of the sensor (namely gamma radiation). The authors review the progress in making these three types of sensor capable of operating in Fusion facilities and discuss possible mitigations. Experiments are performed to demonstrate the pros and cons of each type of sensor by collecting data from radar, colour-depth camera and LIDAR, simultaneously. The paper concludes with a performance comparison between sensors, as well as discussing the possibility of combining them, fostering redundancy in case of failure of any individual sensor device. [ABSTRACT FROM AUTHOR]

Radar altimeters are important tools to monitor the volume of the ice sheets. The penetration of radar waves in the snowpack is a major source of uncertainty to retrieve surface elevation. To correct this effect, a better understanding of the sensitivity of the radar waveforms to snow properties is needed. Here, we present an extension of the Snow Model Radiative Transfer (SMRT) to compute radar waveforms and conduct a series of simulations on the Antarctic ice sheet. SMRT is driven by snow and surface roughness properties measured over a large latitudinal range during two field campaigns on the Antarctic Plateau. These measurements show that the snowpack is rougher, denser, less stratified, warmer, and has smaller snow grains near the coast than on the central Plateau. These simulations are compared to satellite observations in the Ka, Ku, and S bands. SMRT reproduces the observed waveforms well. For all sites and all sensors, the main contribution comes from the surface echo. The echo from snow grains (volume scattering) represents up to 40% of the amplitude of the total waveform power in the Ka band, and less at the lower frequencies. The highest amplitude is observed on the central Plateau due to the combination of higher reflection from the surface, higher scattering by snow grains in the Ka and Ku bands, and higher inter-layer reflections in the S band. In the Ka band, the wave penetrates in the snowpack less deeply on the central Plateau than near the coast because of the strong scattering caused by the larger snow grains. The opposite is observed in the S band, the wave penetrates deeper on the central Plateau because of the lower absorption due to the lower snow temperatures. The elevation bias caused by wave penetration into the snowpack show a constant bias of 10 cm for all sites in the Ka band, and a bias of 11 cm, and 21 cm in the Ku band for sites close to the coast and the central Plateau, respectively. Now that SMRT is performing waveform simulations, further work will address how the snowpack properties affect the parameters retrieved by more advanced retracking algorithms such as ICE-2 for different snow cover surfaces. • The snow radiative transfer model (SMRT) is extended for altimetry applications. • Waveform simulations are in good agreement with satellite observations in Antarctica. • The surface echo is the most important contribution in the Ka, Ku and S bands. • Smoother surfaces at the southern sites lead to higher waveform amplitudes. • In the Ka (resp. S) band, grain size (temperature) regulates the wave penetration depth. [ABSTRACT FROM AUTHOR]

Real-time monitoring of volcanic tephra fallout rate is an important factor to predict ash plume dispersion and to mitigate risk to air traffic. Ground-based weather radar has been one of the fundamental instruments to detect the plume and derive eruptive source parameters, such as the tephra fallout rate. The current work presents the use of two small and compact X-band Multi-Parameter (X-MP) radars for a new tephra fallout rate model development and the technical aspects of the system in Sinabung and Merapi Volcanoes. The new model estimates the tephra fallout rate using two radar parameters: the specific differential phase shift parameter and the reflectivity intensity factor. Total cumulated mass estimated from the radar-based tephra fallout rate model from the radar is compared with the plume height model and an empirical radar-based model. A volcanic eruptive index (VEI)-2 of Sinabung generated a plume exceeding 15 km, resulting in a maximum tephra fallout rate of 0.58 kg m−2 h−1 and a total tephra mass of 51 × 106 kg. The VEI 1 of Sinabung caused a plume height of 2.5 km, resulting in a maximum tephra fallout rate of 0.3 kg m−2 h−1 and a total cumulated tephra of 9 × 106 kg. In the last case, a VEI 1 eruption of Mt. Merapi produces a 6 km plume, resulting in a maximum tephra fallout rate of 0.28 kg m−2 h−1 and a total cumulated tephra of 35 × 106 kg. The sector range height indicator scan-mode strategy in the VEI 2 eruption of Mt. Sinabung ran at six degrees azimuth angles capturing only a partial volume of the plume. Thus, the total mass was only 22% of the result from the empirical plume height model, even though the plume height was assumed to be equally the same with the maximum height scanned of radar at 7 km. In contrast, the volumetric scan by a plan position indicator strategy gave a total cumulated tephra mass, that matches better to the result of the empirical plume height model at 65–92%. Based on these results and the ability of the X-MP radar to capture the volcanic plume at the same reported onset time, we can confirm the importance of an X-MP radar for real-time tephra fallout monitoring during an eruption. • Utilization of a small-compact dual polarimetric X-band weather radar for a real-time eruption monitoring • Deployment, data acquisition, and data processing of a small-compact dual polarimetric X-band weather radar • Real-time tephra fallout rate by multi parameter of radar • Multi-Parameter radar information of tephra • Two-dimensional (2D) and Three-dimensional (3D) visualization of a volcanic plume by a weather radar [ABSTRACT FROM AUTHOR]

The lack of reliable global E-region electron density or conductivity measurements has hampered a detailed and quantitative understanding of the E-region electrodynamic processes. The characterization of the global E-region ionospheric irregularities as a function of local time, longitude, and season remains a great challenge. In this study, we evaluate the electron densities retrieved by Wu (2018) in the D and E regions using ground-based radar data and empirical models. Unlike in the conventional top-down retrieval methodology, the new approach avoids the Abel weighting function that carries substantial sensitivity to the electron density residuals from the F-region even after they have been corrected. The new technique uses a bottom-up approach in which the F-region contributions in the excess phase are more effectively removed compared to the Abel inversion, and hence more accurate electron density retrievals in D and E regions are determined. The results are compared and evaluated with two low and mid-latitude incoherent scatter radar observations — the Arecibo Observatory's 430 MHz radar, located in Puerto Rico (18 ∘ N, 67 ∘ W) and the Millstone Hill Observatory's 440 MHz radar, located in Massachusetts (42 ∘ N, 72 ∘ W). In addition, the results are also compared with two models — the International Reference Ionosphere model (IRI-2016) and the Faraday International Reference Ionosphere model (FIRI-2018) at these two locations. The comparison of electron density height profiles from GPS-RO retrievals at the two locations show good agreement with radar measurements, as well as the FIRI-2018 model predictions, especially at ∼ 85–100 km. The seasonal and diurnal climatology comparisons of GPS-RO retrieved electron density show good agreement with radar observations at both locations, and also reflect the influence of the solar cycle on E-region electron density. It is noticed that the GPS-RO background electron density at Millstone Hill increases by ∼ 20% at around 105 km from solar minimum to the maximum during cycle 24 which is in agreement with radar observations and FIRI-2018 model predictions. • A novel GPS-RO technique estimates electron densities in D and E regions more accurately compared to conventional Abel inversion. • The technique is evaluated using radar observations and two empirical models — IRI-2016 and FIRI-2018. • Seasonal and diurnal climatology comparisons show good agreement with radar measurements and FIRI-2018 model predictions, especially at 85–100 km. • The influence of the solar cycle on the E-region can be clearly seen in the retrieved electron density. [ABSTRACT FROM AUTHOR]

Recently, the interest on snowfall remote sensing and quantitative precipitation estimation is becoming a popular topic by both the scientific and operational communities. As a matter of fact, snow plays a key role in the hydrological cycle and Earth energy budget and clearly represents a meteorological hazard that can seriously compromise human activities and properties. In this study, we used a dual-polarization X-band weather radar to quantify the near-surface liquid equivalent snowfall rate, proposing a new parameterization based on the use of radar reflectivity factor and specific differential phase shift. This effort adds to several recent works, mainly focused on S-band weather radar systems, demonstrating that the use of the radar specific differential phase shift (K dp) is able to enhance the estimation precision with respect to the more customary approaches making use of radar reflectivity factor alone. To demonstrate this concept also at X-band, some case studies were collected from December 2018 to May 2019 in the Southern Apennine Mountains in the area of Naples (Italy). They were used to compare the proposed radar based liquid equivalent snowfall rate estimations, based on Z and K dp , with reference laser-optical disdrometer time series collected in the close reference site of Montevergine observatory. Findings show that also at X band the use of K dp produces a better score between the radar-derived liquid equivalent snowfall rate and the reference one from the disdrometer. • The use of specific differential phase shift improves the radar-based estimation of snowfall rate. • Snow estimators based on equivalent radar reflectivity factor can be corrupted by melting layer effects. • In snow conditions, strong horizontal winds can corrupt laser-optical disdrometer measurements. [ABSTRACT FROM AUTHOR]

• Remote vital signs measurement using ultra wideband radar. • Efficient separation of heart beat signal and respiration signal by using Eigenvalues. • Peaks frequency of vital signal can be extracted based on Eigenvalues. • Vital signal can be reconstructed after selecting Eigenvalues. Ultra wideband impulse (UWB) radar for vital signs measurement applications is getting much attention because it is a contactless method. However, the heartbeat signal is usually be perturbed in strong noisy signals from stationary and non-stationary objects. In addition, the respiration signal, which has a much higher intensity than the heartbeat signal, makes it challenging to extract the heartbeat signal. Thus, this paper proposes an extraction method of heartbeat signal and frequency using eigenvalues. Eigenvalues of the vital signal were found to be strongly correlated with the fundamental and harmonic frequencies of respiration and heartbeat. Experimental results show that the proposed method could improve the signal-to-noise ratio of over 18 dB for different distances from 0.5 m to 3.0 m and different orientations of the person. Furthermore, the original signal of respiration and heartbeat can be reconstructed by the selection of the eigenvalues and the eigenvectors. [ABSTRACT FROM AUTHOR]

A Radar Inlet Observation System (RIOS) collected hourly X-band radar data at the wave dominated Oregon Inlet, NC, for 9 months from September 2016 to May 2017. The intensity of the radar backscatter, predominantly associated with surface wave shoaling and breaking, was time-averaged to provide an implied measure of bedforms superimposed on the ebb-tidal delta. Eulerian and Lagrangian motion tracking analyses determined the bedform alongshore migration rates over the study period; these were found to be significantly correlated to estimates of longshore sediment flux based on offshore wave parameters given by Ashton and Murray (2006). Although the exact magnitude of sediment transport cannot be directly estimated from bedform migration, the correlation does support an underlying assumption that sediment transport varies proportionally in both magnitude and direction to average bedform movements. The implications of these findings were further explored through application of the sediment transport model to a year long period of continuous offshore wave data (20 Oct 2016 to 20 Oct 2017). The predicted net transport of 90,000 m3 southward during this year is consistent with a previous estimate of inlet bypassing (Dolan and Glassen 1973). However, pronounced and opposing seasonal transport patterns were an order of magnitude higher than the yearly net, and could lead to significant inter-annual variability in alongshore sediment transport with similar dynamic response in ebb-delta morphodynamics at Oregon Inlet. • The morphology of a wave dominated ebb-tidal delta (Oregon Inlet, NC) was measured hourly for 9 months via remote sensing • A flow analysis of consecutive morphologic configurations provides a measure of superimposed bedform movements over time • The longshore bedform movements are significantly correlated to an estimate of wave-driven longshore sediment transport • A 90,000 m3/year net longshore transport results from significantly larger but opposing seasonal trends [ABSTRACT FROM AUTHOR]