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In this paper, a 3D conformal meta-lens designed for manipulating electromagnetic beams via height-to-phase control is proposed. The structure consists of a 40 × 20 array of tunable unit cells fabricated using 3D printing, enabling full 360° phase compensation. A novel automatic synthesizing method (ASM) with an integrated optimization process based on genetic algorithm (GA) is adopted here to create the meta-lens. Simulation using CST Microwave Studio and MATLAB reveals the antenna's beam deflection capability by adjusting phase compensations for each unit cell. Various beam scanning techniques are demonstrated, including single-beam, dual-beam generation, and orbital angular momentum (OAM) beam deflection at different angles of 0°, 10°, 15°, 25°, 30°, and 45°. A 3D-printed prototype of the dual-beam feature has been fabricated and measured for validation purposes, with good agreement between both simulation and measurement results, with small discrepancies due to 3D printing's low resolution and fabrication errors. This meta-lens shows promise for low-cost, high-gain beam deflection in mm-wave wireless communication systems, especially for sensing applications, with potential for wider 2D beam scanning and independent beam deflection enhancements. [ABSTRACT FROM AUTHOR]

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection. [ABSTRACT FROM AUTHOR]

The tropical forests in the Amazon store large amounts of carbon and are still considered a carbon sink. There is evidence that deforestation can turn a forest landscape into a carbon source due to land use and forest degradation. Deforestation causes fragmented forest landscapes. It is known from field experiments that forest dynamics at the edge of forest fragments are altered by changes in the microclimate and increased tree mortality ("edge effects"). However, it is unclear how this will affect large fragmented forest landscapes, and thus the entire Amazon region. The aim of this study is to investigate different forest attributes in edge and core forest areas at high resolution, and thus to identify the large-scale impacts of small-scale edge effects. Therefore, a well-established framework combining forest modelling and lidar-generated forest structure information was combined with radar-based forest cover data. Furthermore, forests were also analyzed at the landscape level to investigate changes between highly fragmented and less-fragmented landscapes. This study found that the aboveground biomass in forest edge areas is 27% lower than in forest core areas. In contrast, the net primary productivity is 13% higher in forest edge areas than in forest core areas. In the second step, whole fragmented landscapes were analyzed. Nearly 30% of all forest landscapes are highly fragmented, particularly in the regions of the Arc of Deforestation, on the edge of the Andes and on the Amazon river banks. Less-fragmented landscapes are mainly located in the central Amazon rainforest. The aboveground biomass is 28% lower in highly fragmented forest landscapes than in less-fragmented landscapes. The net primary productivity is 13% higher in highly fragmented forest landscapes than in less-fragmented forest landscapes. In summary, fragmentation of the Amazon rainforest has an impact on forest attributes such as biomass and productivity, with mostly negative effects on forest dynamics. If deforestation continues and the proportion of highly fragmented forest landscapes increase, the effect may be even more intense. By combining lidar, radar and forest modelling, this study shows that it is possible to map forest structure, and thus the degree of forest degradation, over a large area and derive more detailed information about the carbon dynamics of the Amazon region. [ABSTRACT FROM AUTHOR]

The surface currents in coastal areas are closely related to the ecological environment and human activities, and are influenced by both local and remote factors of different timescales, resulting in complex genesis and multi-timescale characteristics. In this research, 9-year-long, hourly high-frequency radar (HFR) surface current observations are utilized together with satellite remote sensing reanalysis products and mooring data, and based on the Empirical Orthogonal Function (EOF) and correlation analysis, we revealed the multi-timescale characteristics of the surface currents in Fremantle Sea (32°S), Southwestern Australia, and explored the corresponding driving factors as well as the impact of El Niño-Southern Oscillation (ENSO) on the nearshore currents. Results show that the currents on the slope are dominated by the southward Leeuwin Current (LC), and the currents within the shelf are dominated by winds, which are subject to obvious diurnal and seasonal variations. The strong bathymetry variation there, from a wide shelf in the north to a narrow shelf in this study region, also plays an important role, resulting in the frequent occurrence of nearshore eddies. In addition, the near-zonal winds south of 30°S in winter contribute to the interannual variability of the Leeuwin Current at Fremantle, especially in 2011, when the onshore shelf circulation is particularly strong because of the climatic factors, together with the wind-driven offshore circulation, which results in significant and long-lasting eddies. The southward Leeuwin Current along Southwestern Australia shows a strong response to interannual climatic variability. During La Niña years, the equatorial thermal anomalies generate the westward anomalies in winds and equatorial currents, which in turn strengthen the Leeuwin Current and trigger the cross-shelf current as well as downwelling within the shelf at Fremantle, whereas during El Niño years, the climate anomalies and the response of coastal currents are opposite. This paper provides insights into the multi-timescale nature of coastal surface currents and the relative importance of different driving mechanisms. It also demonstrates the potential of HFR to reveal the response of nearshore currents to climate anomalies when combined with other multivariate data. Meanwhile, the methodology adopted in this research is applicable to other coastal regions with long-term available HFR observations. [ABSTRACT FROM AUTHOR]

The proliferation of radar technology has given rise to a growing demand for advanced, high-performance transmitter front-ends operating in the 24 GHz frequency band. This paper presents a design analysis of a radio frequency (RF) transmitter (TX) front-end operated at a 24 GHz frequency and designed using 65 nm complementary metal-oxide-semiconductor (CMOS) technology for radar applications. The proposed TX front-end design includes the integration of an up-conversion mixer and power amplifier (PA). The up-conversion mixer is a Gilbert cell-based design that translates the 2.4 GHz intermediate frequency (IF) signal and 21.6 GHz local oscillator (LO) signal to the 24 GHz RF output signal. The mixer is designed with a novel technique that includes a duplex transconductance path (DTP) for enhancing the mixer's linearity. The DTP of the mixer includes a primary transconductance path (PTP) and a secondary transconductance path (STP). The PTP incorporates a common source (CS) amplifier, while the STP incorporates an improved cross-quad transconductor (ICQT). The integrated PA in the TX front-end is a class AB tunable two-stage PA that can be tuned with the help of varactors as a synchronous mode to increase the PA bandwidth or stagger mode to obtain a high gain. The PA is tuned to 24 GHz as a synchronous mode PA for the TX front-end operation. The proposed TX front-end showed an excellent output power of 11.7 dBm and dissipated 7.5 mW from a 1.2 V supply. In addition, the TX front-end achieved a power-added efficiency (PAE) of 47% and 1 dB compression point (OP1dB) of 10.5 dBm. In this case, the output power is 10.5 dBm higher than the linear portion of the response. The methodologies presented herein have the potential to advance the state of the art in 24 GHz radar technology, fostering innovations in fields such as autonomous vehicles, industrial automation, and remote sensing. [ABSTRACT FROM AUTHOR]

The recent development of satellite products for observing precipitation based on different technologies (microwaves, infrared, etc.) allows for near-real-time meteorological studies. The purpose of this article is to evaluate 11 satellite products (GHE, PDIR, IMERG-Early v6, IMERG-Late v6, CMORPH v0.x, CMORPH-RT v0.x, GSMaP-NRT v7, GSMaP-NRT-GC v7, GSMaP-NOW v7, GSMaP-NOW-GC v7, and DATABOURG) currently available and compare them to 2 ground-based radar networks (PANTHERE and OPERA) and the French rain-gauge network RADOME. Two case studies of intense precipitation over France (22 to 25 April 2022 and 24 to 29 June 2022) were selected. The radar estimations are closer to the RADOME observations than the satellite-based estimations, which tend to globally underestimate the precipitation amounts over the areas of interest while OPERA tends to strongly overestimate precipitation amounts during the June case study. The PANTHERE radar product and the carrier-to-noise product DATABOURG shows promising results. Near-real-time satellite products tend to have closer precipitation amounts to the reference dataset than satellite products with a shorter latency. The use of these datasets for nowcasting developments is plausible but further analyses must be conducted beforehand. [ABSTRACT FROM AUTHOR]

In classical radar imaging, such as in Earth remote sensing, electromagnetic waves are usually assumed to propagate in free space. However, in numerous applications, such as ground penetrating radar or non-destructive testing, this assumption no longer holds. When there is a multi-material background, the subsurface image reconstruction becomes considerably more complex. Imaging can be performed in the spatial domain or, equivalently, in the wavenumber domain (k-space). In subsurface imaging, to date, objects with a non-planar surface are commonly reconstructed in the spatial domain, by the Backprojection algorithm combined with ray tracing, which is computationally demanding. On the other hand, objects with a planar surface can be reconstructed more efficiently in k-space. However, many non-planar surfaces are partly planar. Therefore, in this paper, a novel concept is introduced that makes use of the efficient k-space-based reconstruction algorithms for partly planar scenarios, too. The proposed algorithm forms an image from superposing sub-images where as many image parts as possible are reconstructed in the wavenumber domain, and only as many as necessary are reconstructed in the spatial domain. For this, a segmentation scheme is developed to determine which parts of the image volume can be reconstructed in the wavenumber domain. The novel concept is verified by measurements, both from monostatic synthetic aperture radar data and multiple-input–multiple-output radar data. It is shown that the computational efficiency for imaging irregularly shaped geometries can be significantly augmented when applying the proposed concept. [ABSTRACT FROM AUTHOR]

As an important part of remote sensing data, weather radar plays an important role in convective weather forecasts to reduce extreme precipitation disasters. The existing radar echo extrapolation methods do not utilize the local natural characteristics of the radar echo effectively but only roughly extract the whole characteristics of the radar echo. To address these challenges, we design a spatiotemporal difference and generative adversarial fusion model (STDGAN). Specifically, a spatiotemporal difference module (STD) is designed to extract local weather patterns and model them in detail. In our model, spatiotemporal difference information and spatiotemporal features captured by the model itself are fused together. In addition, our model is trained in a generative adversarial network (GAN) framework; it helps to generate a clearer map of future radar echoes at the image level. The discriminator consists of multi-scale feature extractors, which can simulate weather models of various scales more completely. Finally, extrapolation experiments were conducted using actual radar echo data from Shijiazhuang and Nanjing. The experiments have shown that our model has a more accurate prediction performance for predicting local weather patterns and overall echo change trajectories compared with previous research models. Our model achieved MSE, PSNE, and SSIM values of 132.22, 37.87, and 0.796, respectively, on the Shijiazhuang radar echo dataset. In addition, our model also showed better performance results on the Nanjing radar echo dataset. The results show that the MSE was 49.570, the PSNR was 0.714, and the SSIM was 30.633. The CC value was 0.855. [ABSTRACT FROM AUTHOR]

The measurement of global precipitation is important for quantifying and understanding the Earth's systems. While gauges form the basis of conventional measurements, global measurements are only truly possible using satellite observations. Over the last 50–60 years, satellite systems have evolved to provide a comprehensive suite of observing systems, including many sensors that are capable of precipitation retrievals. While much progress has been made in developing and implementing precipitation retrieval schemes, many techniques have concentrated upon retrievals over regions with well-defined precipitation systems, such as the tropics. At higher latitudes, such retrieval schemes are less successful in providing accurate and consistent precipitation estimates, especially due to the large diversity of precipitation regimes. Furthermore, the increasing dominance of snowfall at higher latitudes imposes a number of challenges that require further, urgent work. This paper reviews the state of the current observations and retrieval schemes, highlighting the key factors that need to be addressed to improve the estimation and measurement of precipitation at mid-to-high latitudes. [ABSTRACT FROM AUTHOR]

The monitoring of Natura 2000 habitats (Habitat Directive 92/43/EEC) is a key activity ensuring the sufficient protection of European biodiversity. Reporting on the status of Natura 2000 habitats is required every 6 years. Although field mapping is still an indispensable source of data on the status of Natura 2000 habitats, and very good field-based data exist in some countries, keeping the field-based habitat maps up to date can be an issue. Remote sensing techniques represent an excellent alternative. Here, we present a new method for detecting habitats that were likely misclassified during the field mapping or that have changed since then. The method identifies the possible habitat mapping errors as the so-called "attribute outliers", i.e., outlying observations in the feature space of all relevant (spectral and other) characteristics of an individual habitat patch. We used the Czech Natura 2000 Habitat Layer as field-based habitat data. To prepare the feature space of habitat characteristics, we used a fusion of Sentinel-1 and Sentinel-2 satellite data along with a Digital Elevation Model. We compared outlier ratings using the robust Mahalanobis distance and Local Outlier Factor using three different thresholds (Tukey rule, histogram-based Scott's rule, and 95% quantiles in χ2 distribution). The Mahalanobis distance thresholded by the 95% χ2 quantile achieved the best results, and, because of its high specificity, appeared as a promising tool for identifying erroneously mapped or changed habitats. The presented method can, therefore, be used as a guide to target field updates of Natura 2000 habitat maps or for other habitat/land cover mapping activities where the detection of misclassifications or changes is needed. [ABSTRACT FROM AUTHOR]

We present a machine-learning approach to detect and analyze meteor echoes (MADAME), which is a radar data processing workflow featuring advanced machine-learning techniques using both supervised and unsupervised learning. Our results demonstrate that YOLOv4, a convolutional neural network (CNN)-based one-stage object detection model, performs remarkably well in detecting and identifying meteor head and trail echoes within processed radar signals. The detector can identify more than 80 echoes per minute in the testing data obtained from the Jicamarca high power large aperture (HPLA) radar. MADAME is also capable of autonomously processing data in an interferometer mode, as well as determining the target's radiant source and vector velocity. In the testing data, the Eta Aquarids meteor shower could be clearly identified from the meteor radiant source distribution analyzed automatically by MADAME, thereby demonstrating the proposed algorithm's functionality. In addition, MADAME found that about 50 percent of the meteors were traveling in inclined and near-inclined circular orbits. Furthermore, meteor head echoes with a trail are more likely to originate from shower meteor sources. Our results highlight the capability of advanced machine-learning techniques in radar signal processing, providing an efficient and powerful tool to facilitate future and new meteor research. [ABSTRACT FROM AUTHOR]

When forming the radar situation of a terrain, in order to increase its information content and to extract useful information, multi-position spatially distributed systems for integrating multi-angle radar data established by small-sized UAV-based airborne radars are used. In this case, each radar station belonging to a multi-position system as a probing signal must have its own unique marked signal. Such a setup will allow the signals reflected from ground objects and zones to be "attached" to specific receiving-transmitting positions of the multi-position system. This requirement results from the fact that each transceiver position emits one probing signal, and then receives all the echo signals reflected from the underlying surface and previously emitted by other radar devices of the multi-position system. Such a setup of multi-position systems requires the researcher to look for and investigate specialized systems of marked code structures used to modulate the probing signals in order to identify them in a joint radar channel. Thus, the problem at hand is how to look for and investigate novel marked code structures used to generate a system of probing signals, the use of which will allow it to be "attached" to a specific receiving-transmitting position of a multi-position onboard system and to identify them in a joint radar channel in the course of the remote sensing of the underlying surface. The purpose of this work is to conduct a study on the subject of the squeak and choice of a system of code structures that have a low level of side lobes of autocorrelation functions and uniformly distributed values of the levels of the cross-correlation function. To achieve this goal, the relevance of the study is substantiated in the introduction. The second section analyzes the level of side lobes for classical and modified Barker codes with an asymmetric alphabet. On the basis of this analysis, it was concluded that it is expedient to apply this approach for codes longer than Barker codes. Therefore, in the third section, the features of the generation of M-sequences are considered. The fourth section presents a technique for searching for new marked code structures, taking into account their mutual correlation properties for modifying M-sequences in order to implement multi-positional systems. The fifth section presents computer experiments on the search for marked code structures based on the modifications of M-sequences and presents the numerical characteristics of the correlation properties of the considered marked codes. And finally, in the sixth section, the final conclusions of the study are presented and recommendations are given for their further practical application. The practical significance of this study lies in the fact that the proposed new marked code structures are necessary for the synthesis of probing signals in the implementation of spatially distributed systems that function for the high-probability detection and high-precision determination of the coordinates of physical objects and are also necessary for the formation of radar images in a multi-position mode. [ABSTRACT FROM AUTHOR]

Clouds cover substantial parts of the Earth's surface and they are one of the most essential components of the global climate system impacting the Earth's radiation balance as well as the water cycle redistributing water around the globe as precipitation. Therefore, continuous observation of clouds is of primary interest in climate and hydrological studies. This work documents the first efforts in Italy in remote sensing clouds and precipitation using a combination of K- and W-band (24 and 94 GHz, respectively) radar profilers. Such a dual-frequency radar configuration has not been widely used yet, but it could catch on in the near future given its lower initial cost and ease of deployment for commercially available systems at 24 GHz, with respect to more established configurations. A field campaign running at the Casale Calore observatory at the University of L'Aquila, Italy, nestled in the Apennine mountain range is described. The campaign features are preceded by a review of the literature and the underpinning theoretical background that might help newcomers, especially in the Italian community, to approach cloud and precipitation remote sensing. This activity takes place in interesting time for radar sensing clouds and precipitation, stimulated both by the launch of the ESA/JAXA EarthCARE satellite missions scheduled in 2024, which will have on-board, among other instruments, a W-band Doppler cloud radar and the proposal of new missions using cloud radars currently undergoing their feasibility studies (e.g., WIVERN and AOS in Europe and Canada, and U.S., respectively). [ABSTRACT FROM AUTHOR]

Inundation dynamics are the primary control on greenhouse gas emissions from peatlands. Situated in the central Congo Basin, the Cuvette Centrale is the largest tropical peatland complex. However, our knowledge of the spatial and temporal variations in its water levels is limited. By addressing this gap, we can quantify the relationship between the Cuvette Centrale's water levels and greenhouse gas emissions, and further provide a baseline from which deviations caused by climate or land-use change can be observed, and their impacts understood. We present here a novel approach that combines satellite-derived rainfall, evapotranspiration and L-band Synthetic Aperture Radar (SAR) data to estimate spatial and temporal changes in water level across a sub-region of the Cuvette Centrale. Our key outputs are a map showing the spatial distribution of rainfed and flood-prone locations and a daily, 100 m resolution map of peatland water levels. This map is validated using satellite altimetry data and in situ water table data from water loggers. We determine that 50% of peatlands within our study area are largely rainfed, and a further 22.5% are somewhat rainfed, receiving hydrological input mostly from rainfall (directly and via surface/sub-surface inputs in sloped areas). The remaining 27.5% of peatlands are mainly situated in riverine floodplain areas to the east of the Congo River and between the Ubangui and Congo rivers. The mean amplitude of the water level across our study area and over a 20-month period is 22.8 ± 10.1 cm to 1 standard deviation. Maximum temporal variations in water levels occur in the riverine floodplain areas and in the inter-fluvial region between the Ubangui and Congo rivers. Our results show that spatial and temporal changes in water levels can be successfully mapped over tropical peatlands using the pattern of net water input (rainfall minus evapotranspiration, not accounting for run-off) and L-band SAR data. [ABSTRACT FROM AUTHOR]

Due to its maneuverability and agility, the shipborne high-frequency surface wave radar (HFSWR) provides a new way of monitoring large-area marine dynamics and environment information. However, wind direction ambiguity is problematic when using monostatic shipborne HFSWR for wind direction inversion. In this article, an unambiguous wind direction measurement method based on wind direction interval limitation is proposed. The two first-order spectral wind direction estimation methods are first presented using the relationship between the normalized amplitude differences or ratios of the broadened Doppler spectrum and the wind direction. Moreover, based on the characteristic of a small wind direction estimation error in a large included angle between the spectral wind direction and the radar beam, the wind direction interval is obtained by counting the distribution of radar-measured wind direction within this included angle. Furthermore, the eliminated ambiguity of wind direction is transformed to judge the relationship between the wind direction interval and the two curves, which represent the relationship between the spreading parameter and the wind direction. Therefore, the remote sensing monitoring of ocean surface wind direction fields can be realized by shipborne HFSWR. The simulation results are used to evaluate the performance of the proposed method and the multi-beam sampling method for wind direction inversion. The experimental results show that the errors of wind direction estimated by the multi-beam sampling method and the equivalent dual-station model are large, and the proposed method can improve the accuracy of wind direction measurement. Three widely used wave directional spreading models have been applied for performance comparison. The wind direction field measured by the proposed method under a modified cosine model agrees well with that observed by the China-France Oceanography Satellite (CFOSAT). [ABSTRACT FROM AUTHOR]

Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced. [ABSTRACT FROM AUTHOR]

We describe results from coastal radar systems that observed anomalous current flows generated by the volcanic eruption in the Tongan archipelago on 15 January 2022 UTC, reporting the first radar detection of a volcanic tsunami. The eruption caused small tsunamis along the western U.S. Coast, generating some damage in a few harbors. The highest tsunami signal in U.S. tide gauge data from the California coast occurred at Arena Cove, with significant heights detected at Port San Luis and Crescent City. We analyze correlated wave orbital velocity detections by High Frequency (HF) radars along the coast between Gerstle Cove and Santa Barbara. Signals observed by the radars indicate that the event had two phases, each with its own distinct genesis: an initial weak surface disturbance, most likely generated by the wave of atmospheric pressure that moved outward from the blast source at just below the speed of sound, followed by a stronger disturbance that arrived approximately 3.5 h later, matching the arrival time for a wave moving entirely through the water from the volcano to the U.S. West Coast. We conclude that this phase consists of a conventional water wave tsunami and weaker waves generated by the pulse. We also report the detection of a small pulse-generated event off the west coast of Florida. Radar observations are compared with water level measurements at nearby tide gauges and a DART buoy, and with observations of barometric pressure. We point out that a Proudman near-resonance at the Tonga Trench is unlikely to explain the second phase observations. Comparison with tide gauge signals at San Francisco, generated by the Krakatoa eruption in 1883, support our conclusions. [ABSTRACT FROM AUTHOR]

Background: One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers. Methods: This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances. Results: The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97–99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%. Conclusions: The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently. [ABSTRACT FROM AUTHOR]

Airborne scatterometer capability depends on not only the device's technical characteristics but also the scheme used for surface observations. Typically, a rotating-beam scatterometer uses a circular scheme for sampling normalized radar cross-sections (NRCS) at wind measurements over the sea. Here, we investigate wind retrieval using an updated semicircular scheme, providing the NRCS sampling at various combinations of incidence angles within the range 30° to 60°. The effectiveness of the wind retrieval using our semicircular sampling scheme was evaluated using Monte Carlo simulations, and we then developed corresponding wind algorithms that used a geophysical model function (GMF). As a result of the study, we found that a semicircular sampling scheme is well suited for wind retrieval over the sea using a rotating-beam scatterometer. We showed that a semicircular scheme can provide wind retrieval accuracies similar to those achievable with a conventional circular scheme, although the semicircular scheme requires approximately three times the number of NRCS samples integrated in each azimuth sector. Most importantly, however, the semicircular scheme enabled a maximum altitude for wind retrieval of twice the height possible with a circular scheme. In this study, we also demonstrate that the wind speed accuracy tends to increase with an increase in the incidence angle and similarly for the wind direction accuracy. Nonetheless, we then show that the simultaneous use of the NRCS sampling scheme at several incidence angles can increase the wind retrieval accuracy, especially when three or four incidence angles are used. The obtained results can be used to enhance airborne scatterometers and multimode radars operated in a scatterometer mode, including airborne high-altitude conical scanning radars, and can be applied to new remote sensing systems' development. [ABSTRACT FROM AUTHOR]

In this study, the spherical particle model and ten nonspherical particle models describing the scattering properties of snow are evaluated for potential use in precipitation estimation from spaceborne dual-frequency precipitation radar. The single scattering properties of nonspherical snow particles are computed using discrete dipole approximation (DDA), while those of spherical particles are determined using Mie theory. The precipitation profiles from WRF output are then input to a forward radiative transfer model to simulate the radar reflectivity at Ka-band and Ku-band. The results are validated with Global Precipitation Mission Dual-Frequency Precipitation Radar measurements. Greater consistency between the simulated and observed reflectivity is obtained when using the sector- and dendrite-shape assumptions. For the case in this study, when using the spherical-shape assumption, radar underestimates the error of the cloud's top by about 300 m and underestimates the error of the cloud's area by about 15%. As snowflake shapes change with temperature, we use the range between −40 °C and −5 °C to define three temperature layers. The relationships between reflectivity (Z) and precipitation rate (R) are fitted separately for the three layers, resulting in  Z = 134.59 · R 1.184  (sector) and  Z = 127.35 · R 1.221  (dendrite) below −40 °C. [ABSTRACT FROM AUTHOR]

The ash dieback pandemic, caused by the invasive fungus Hymenoscyphus fraxineus, represents one of Europe's biggest threats to preserving natural biodiversity. To ensure the suppression of forest damage caused by fungi, timely recognition of the symptoms of ash dieback and further continuous monitoring on an adequate spatial scale are essential. Visual crown damage assessment is currently the most common method used for identifying ash dieback, but it lacks the spatial and temporal coverage required for effective disease suppression. Remote sensing technologies, with the capabilities of fast and repetitive retrieval of information over a large spatial scale, could present efficient supplementary methods for ash damage detection and disease monitoring. In this study, we provided a synthesis of the existing remote sensing methods and applications that considers ash dieback disease, and we described the lifecycle of the disease using the major symptoms that remote sensing technologies can identify. Unfortunately, although effective methods of monitoring biotic damage through remote sensing have been developed, ash dieback has only been addressed in two research studies in the United Kingdom and Germany. These studies were based on single-date hyperspectral and very-high-resolution imagery in combination with machine learning, using previously specified ground-truth information regarding crown damage status. However, no study exists using high-resolution imagery such as Sentinel-2 or radar Sentinel-1, although some preliminary project results show that these coarser sources of information could be applicable for ash dieback detection and monitoring in cases of Fraxinus angustifolia, which forms pure, more homogenous stands in Southern Europe. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar with polarimetric diversity is a powerful tool in remote sensing. Each pixel is described by the scattering matrix corresponding to the emission/reception polarization states (usually horizontal and vertical). The algebraic real representation, a block symmetric matrix form, is introduced to adopt a more comprehensive framework (non-restricted by reciprocity assumptions) in mapping the scattering matrix by the consimilarity equivalence relation. The proposed representation can reveal potentially new information. For example, its eigenvalue decomposition, which is itself a necessary step in obtaining the consimilarity transformation products, may be useful in characterizing the degree of reciprocity/nonreciprocity. As a consequence, it can be employed in testing the reciprocity compliance assumed with monostatic PolSAR data. Full-wave simulated polarimetric data confirm that oriented scatterers can present complex eigenvalues, even with the monostatic geometry. [ABSTRACT FROM AUTHOR]

Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status. [ABSTRACT FROM AUTHOR]

As an important remote sensing technology, millimeter-wave radar is used for environmental sensing in many fields due to its advantages of all-day, all-weather operation. With the increasing use of radars, inter-radar interference becomes increasingly critical. Severe mutual interference degrades radar signal quality and affects the performance of post-processing, e.g., synthetic aperture radar (SAR) imaging and target tracking. Aiming to deal with mutual interference, we propose an interference mitigation method based on variational mode decomposition (VMD). With the characteristics that the target is a single-frequency sine wave and the interference is a broadband signal, VMD is used for decomposing the radar received signal and separating the target from the interference. Interference mitigation is then implemented in each decomposed mode, and an interference-free signal is obtained through the reconstruction process. Simulation results of multi-target scenarios demonstrate that the proposed method outperforms existing decomposition-based methods. This conclusion is also confirmed by the experimental results on real data. [ABSTRACT FROM AUTHOR]

For distributed satellite constellations, detection performance can be equivalently regarded as a single large satellite by the cooperative operation of multiple small satellites, which is a promising research topic of the Next-Generation Radar (NGR) system. However, dense grating lobes inevitably occur in the synthetic transmit pattern due to its distributed configuration, as a result of which the detection performance of dynamic coherent radar is seriously weakened. In this paper, a novel transmit beampattern optimization method for dynamic coherent radar based on a distributed satellite constellation is presented. Firstly, the effective coherent detection range interval is determined by several influence factors, i.e., coherent detection, far-field, and system link constraints. Then, we discuss the quantitative evaluation method for coherent integration in terms of synchronization error, beam pointing error, and high-speed motion characteristics and we allocate the corresponding terms in a reasonable way from the perspective of engineering. Finally, the space–time–frequency degrees of freedom (DOFs), which can be collected from satellite spacing, carrier frequencies, and platform motion characteristics, are utilized to realize a robust transmit beampattern with low sidelobe by invoking a genetic algorithm (GA). Simulation results validate the effectiveness of our theoretic analysis, and unambiguous coherent transmit beamforming with a satellite constellation of limited scale is accomplished. [ABSTRACT FROM AUTHOR]

Magma-filled dikes may feed erupting fissures that lead to alignments of craters developing at the surface, yet the details of activity and migrating eruptions at the crater row are difficult to monitor and are hardly understood. The 2021 Tajogaite eruption at the Cumbre Vieja, La Palma (Spain), lasted 85 days and developed a pronounced alignment of craters that may be related to changes within the volcano edifice. Here, we use COSMO-SkyMed satellite radar data and ground-based time-lapse photographs, offering a high-resolution dataset to explore the locations and characteristics of evolving craters. Our results show that the craters evolve both gradually and suddenly and can be divided into three main phases. Phase 1, lasting the first 6 weeks of the eruption, was characterized by a NW–SE linear evolution of up to seven craters emerging on the growing cone. Following two partial collapses of the cone to the northwest and a seismicity increase at depth, Phase 2 started and caused a propagation of the main activity toward the southeastern side, together with the presence of up to 11 craters along this main NW–SE trend. Associated with strong deep and shallow earthquakes, Phase 3 was initiated and continued for the final 2 weeks of the eruption, expressed by the development of up to 18 craters, which became dominant and clustered in the southeastern sector in early December 2021. In Phase 3, a second and oblique alignment and surface fracture was identified. Our findings that crater and eruption changes coincide together with an increase in seismic activity at depth point to a deep driver leading to crater and morphology changes at the surface. These also suggest that crater distributions might allow for improved monitoring of changes occurring at depth, and vice versa, such that strong seismicity changes at depth may herald the migration and new formation of craters, which have major implications for the assessment of tephra and lava flow hazards on volcanoes. [ABSTRACT FROM AUTHOR]

Rain type classification into convective and stratiform is an essential step required to improve quantitative precipitation estimations by remote sensing instruments. Previous studies with Micro Rain Radar (MRR) measurements and subjective rules have been performed to classify rain events. However, automating this process by using machine learning (ML) models provides the advantages of fast and reliable classification with the possibility to classify rain minute by minute. A total of 20,979 min of rain data measured by an MRR at Das in northeast Spain were used to build seven types of ML models for stratiform and convective rain type classification. The proposed classification models use a set of 22 parameters that summarize the reflectivity, the Doppler velocity, and the spectral width (SW) above and below the so-called separation level (SL). This level is defined as the level with the highest increase in Doppler velocity and corresponds with the bright band in stratiform rain. A pre-classification of the rain type for each minute based on the rain microstructure provided by the collocated disdrometer was performed. Our results indicate that complex ML models, particularly tree-based ensembles such as xgboost and random forest which capture the interactions of different features, perform better than simpler models. Applying methods from the field of interpretable ML, we identified reflectivity at the lowest layer and the average spectral width in the layers below SL as the most important features. High reflectivity and low SW values indicate a higher probability of convective rain. [ABSTRACT FROM AUTHOR]

Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship ( r 2 = 0.74 ) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years' worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon's forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the possibility of generating high-resolution mapping of urban (or extra-urban) environments by the application of synthetic aperture radar (SAR) processing concepts to the data collected by mm-wave automotive radars installed on-board commercial vehicles. The study is motivated by the fact that radar sensors are becoming an indispensable component of the equipment of modern vehicles, being characterized by low cost, good performance, and affordable processing; therefore, in the future, nearly every single vehicle could be potentially equipped with radar devices capable of high-resolution imaging, enabled by application of SAR processing methodologies. Throughout this paper, we aim to discuss the role of SAR imaging in the automotive context under a theoretical and experimental perspective. First, we present the resulting benefits in terms of angular resolution and signal-to-noise ratio. Then, we discuss relevant technological aspects, such as suppression of angular ambiguities, fine estimation of platform motion, and SAR processing architectures, and we present a preliminary evaluation of the required computational costs. Finally, we will present a number of experimental results based on open road campaign data acquired using an 8-channel MIMO radar at 77 GHz, considering the cases of side-looking SAR, forward SAR, and SAR imaging of moving targets. [ABSTRACT FROM AUTHOR]

The accuracy of weather radar-measured products, such as reflectivity, plays a crucial factor in obtaining good quality, derived remote sensing products, such as hydrometeor classification. A slight mismatch of a few decibels in reflectivity may cause the hydrometeor classification to deviate from the actual truth. In order to obtain accurate remote-sensing measurements, calibration of weather radars should be carried out at regular intervals of time. The dual-frequency, dual-polarization, Doppler radar (D3R) is a well-established tool for measuring light rain and snow. There are various methods which are used for the calibration of weather radars, such as suspending a metallic sphere from a weather balloon or using corner reflectors on top of a tower or structure. In this work, we have shown the potential of using a UAV to suspend a calibration sphere, which is then used for calibrating the D3R radar. In this work, the advantages along with the practical aspects to be considered for calibration using UAV are discussed in detail. From the calibration results, it was observed that an offset of 2.2 dB was present in the Ku H-Pol reflectivity, Ku V-Pol was well calibrated and offsets within 2 dB were observed in the Ka H-Pol and V-Pol reflectivities. [ABSTRACT FROM AUTHOR]

Fire is one of the significant drivers of vegetation loss and threat to Amazonian landscapes. It is estimated that fires cause about 30% of deforested areas, so the severity level is an important factor in determining the rate of vegetation recovery. Therefore, the application of remote sensing to detect fires and their severity is fundamental. Radar imagery has an advantage over optical imagery because radar can penetrate clouds, smoke, and rain and can see at night. This research presents algorithms for mapping the severity level of burns based on change detection from Sentinel-1 backscatter data in the southeastern Peruvian Amazon. Absolute, relative, and Radar Forest Degradation Index (RDFI) predictors were used through singular polarization length (dB) patterns (Vertical, Vertical-VV and Horizontal, Horizontal-HH) of vegetation and burned areas. The Composite Burn Index (CBI) determined the algorithms' accuracy. The burn severity ratios used were estimated to be approximately 40% at the high level, 43% at the moderate level, and 17% at the low level. The validation dataset covers 384 locations representing the main areas affected by fires, showing the absolute and relative predictors of cross-polarization (k = 0.734) and RDFI (k = 0.799) as the most concordant in determining burn severity. Overall, the research determines that Sentinel-1 cross-polarized (VH) data has adequate accuracy for detecting and quantifying burns. [ABSTRACT FROM AUTHOR]

Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic–land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil–water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated ("true") values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions. [ABSTRACT FROM AUTHOR]

Migratory shorebirds are notable consumers of benthic invertebrates on intertidal sediments. The distribution and abundance of shorebirds will strongly depend on their prey and on landscape and sediment features such as mud and surface water content, topography, and the presence of ecosystem engineers. An understanding of shorebird distribution and ecology thus requires knowledge of the various habitat types which may be distinguished in intertidal areas. Here, we combine Sentinel-1 and Sentinel-2 imagery and a digital elevation model (DEM), using machine learning techniques to map intertidal habitat types of importance to migratory shorebirds and their benthic prey. We do this on the third most important non-breeding area for migratory shorebirds in the East Atlantic Flyway, in the Bijagós Archipelago in West Africa. Using pixel-level random forests, we successfully mapped rocks, shell beds, and macroalgae and distinguished between areas of bare sediment and areas occupied by fiddler crabs, an ecosystem engineer that promotes significant bioturbation on intertidal flats. We also classified two sediment types (sandy and mixed) within the bare sediment and fiddler crab areas, according to their mud content. The overall classification accuracy was 82%, and the Kappa Coefficient was 73%. The most important predictors were elevation, the Sentinel-2-derived water and moisture indexes, and Sentinel-1 VH band. The association of Sentinel-2 with Sentinel-1 and a DEM produced the best results compared to the models without these variables. This map provides an overall picture of the composition of the intertidal habitats in a site of international importance for migratory shorebirds. Most of the intertidal flats of the Bijagós Archipelago are covered by bare sandy sediments (59%), and ca. 22% is occupied by fiddler crabs. This likely has significant implications for the spatial arrangement of the shorebird and benthic invertebrate communities due to the ecosystem engineering by the fiddler crabs, which promotes two vastly different intertidal species assemblages. This large-scale mapping provides an important product for the future monitoring of this high biodiversity area, particularly for ecological research related to the distribution and feeding ecology of the shorebirds and their prey. Such information is key from a conservation and management perspective. By delivering a successful and comprehensive mapping workflow, we contribute to the filling of the current knowledge gap on the application of remote sensing and machine learning techniques within intertidal areas, which are among the most challenging environments to map using remote sensing techniques. [ABSTRACT FROM AUTHOR]

This paper shows the work carried out to obtain a methodology capable of monitoring the Common Agricultural Policy (CAP) aid line for the protection of steppe birds, which aims to improve the feeding and breeding conditions of these species and contribute to the improvement of their overall biodiversity population. Two methodologies were initially defined, one based on remote sensing (BirdsEO) and the other on Machine Learning (BirdsML). Both use Sentinel-1 and Sentinel-2 data as a basis. BirdsEO encountered certain impediments caused by the land's slope and the crop's height. Finally, the methodology based on Machine Learning offered the best results. It evaluated the performance of up to 7 different Machine Learning classifiers, the most optimal being RandomForest. Fourteen different datasets were generated, and the results they offered were evaluated, the most optimal being the one with more than 150 features, including a time series of 8 elements with Sentinel-1, Sentinel-2 data and derived products, among others. The generated model provided values higher than 97% in metrics such as accuracy, recall and Area under the ROC Curve, and 95% in precision and recall. The methodology is transformed into a tool that continuously monitors 100% of the area requesting aid, continuously over time, which contributes positively to optimizing the use of administrative resources and a fairer distribution of CAP funds. [ABSTRACT FROM AUTHOR]

We describe an extension of methods used for the derivation of wave information from compact high frequency radar systems and give examples of observations which are compared to measurements from wave buoys close to the radar coverage area. Previous methods were based on the application of the Pierson–Moskowitz wave energy model in the analysis of radar spectra. We describe the extension of the methods to apply a bimodal ocean wave spectral model which includes two different spectra covering different wave frequencies, e.g., from swell and wind-waves. Results are presented from data sets obtained from radar stations located in the USA and Portugal. [ABSTRACT FROM AUTHOR]

The optimization of normalized radar cross-section (NRCS) sampling by a scatterometer allows an increase in the accuracy of the wind retrieval over the water surface and a decrease in the time of the measurement. Here, we investigate the possibility of improving wind vector measurement with an airborne rotating-beam scatterometer mounted under the fuselage. For this purpose, we investigated NRCS sampling at various incidence angles, and the possibility of using NRCS samples obtained during simultaneous measurement at different incidence angles to perform wind retrieval. The proposed wind algorithms are based on a geophysical model function (GMF). Sea wind retrieval was carried out using Monte Carlo simulations with consideration of a single incidence angle or combinations of several incidence angles. The incidence angles of interest were 30°, 35°, 40°, 45°, 50°, 55°, and 60°. The simulation showed that the wind speed error decreased with an increase in the incidence angle, and the wind direction error tended to decrease with an increase in the incidence angle. The single incidence angle case is characterized by higher maximum wind retrieval errors but allows for a higher maximum altitude of the wind retrieval method's applicability to be achieved. The use of several neighboring incidence angles allows a better wind vector retrieval accuracy to be achieved. The combinations of three and four incidence angles provided the lowest maximum wind speed and direction errors in the range of the incidence angles from 45° to 60° but, unfortunately, provide the lowest maximum altitude of applicability of the wind retrieval method. At the same time, the combination of two incidence angles is characterized by slightly higher maximum wind retrieval errors than in the cases of three and four incidence angles, but they are lower than in the case of the single incidence angle. Moreover, the two incidence angles' combination is a simpler way to decrease the wind retrieval errors, especially for measurement near an incidence angle of 30°, providing nearly the highest maximum altitude of the wind retrieval method applicability. The results obtained can be used to enhance existing airborne radars and in the development of new remote sensing systems. [ABSTRACT FROM AUTHOR]

X-band radars are in growing use for various oceanographic purposes, providing spatial real-time information about sea state parameters, surface elevations, currents, and bathymetry. Therefore, it is very appealing to use such systems as operational aids to harbour management. In an installation of such a remote sensing system in Haifa Port, consistent radially aligned spikes of brightness randomly distributed with respect to azimuth were identified. These streak noise patterns were found to be interfering with the common approach of oceanographic analysis. Harbour areas are regularly frequented with additional electromagnetic transmissions from other ship and land-based radars, which may serve as a source of such interference. A new approach is proposed for the filtering of such undesirable interference patterns from the X-band radar images. It was verified with comparison to in-situ measurements of a nearby wave buoy. Regardless of the actual source of the corresponding pseudo-wave energy, it was found to be crucial to apply such filtration in order to improve the performance of the standard oceanographic parameter retrieval algorithm. This results in better estimation of the mean sea state parameters towards lower values of the significant wave height. For the commercial WaMoSII system this enhancement was clearly apparent in the improvement of the built-in quality control criteria marks. The developed prepossessing procedure improves the robustness of the directional spectra estimation practically eliminating pseudo-wave energy components. It also extends the system's capability to measure storm events earlier on, a fact that is of high importance for harbour operational decision making. [ABSTRACT FROM AUTHOR]

In near-field remote sensing, noncontact methods (radars) that measure stage and surface water velocity have the potential to supplement traditional bridge scour monitoring tools because they are safer to access and are less likely to be damaged compared with in-stream sensors. The objective of this study was to evaluate the use of radars for monitoring the hydraulic conditions that contribute to bridge–pier scour in gravel-bed channels. Measurements collected with a radar were also leveraged along with minimal field measurements to evaluate whether time-integrated stream power per unit area (Ω) was correlated with observed scour depth at a scour-critical bridge in Colorado. The results of this study showed that (1) there was close agreement between radar-based and U.S. Geological Survey streamgage-based measurements of stage and discharge, indicating that radars may be viable tools for monitoring flow conditions that lead to bridge pier scour; (2) Ω and pier scour depth were correlated, indicating that radar-derived Ω measurements may be used to estimate scour depth in real time and predict scour depth based on the measured trajectory of Ω. The approach presented in this study is intended to supplement, rather than replace, existing high-fidelity scour monitoring techniques and provide data quickly in information-poor areas. [ABSTRACT FROM AUTHOR]

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number ( A C I N d ) and effective radius ( A C I r e f f ) of the droplets with altitude. Furthermore, A C I N d and A C I r e f f for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum A C I N d values are obtained when the activation of new droplets stops, while A C I r e f f reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of 30 % at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of 18 % cause an ACI variability of 30 % while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining A C I r e f f uncertainty below 16 % for the uncertainty of all LARSS input parameters of 10 % . Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index ( A C I R s ), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the A C I r e f f , which depends on the Angstrom exponent. The use of A C I R s to derive A C I r e f f presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources. [ABSTRACT FROM AUTHOR]

Subsurface imaging in arid regions is a well-known application of satellite Synthetic Aperture Radar (SAR). Archaeological prospection has often focused on L-band SAR sensors, given the ability of longer wavelengths to penetrate more deeply into sand. In contrast, this study demonstrates capabilities of shorter-wavelength, but higher spatial resolution, C-band and X-band SAR sensors in archaeological subsurface imaging at the site of 'Uqdat al-Bakrah (Safah), Oman. Despite having varying parameters and acquisitions, both the X-band and C-band images analyzed were able to identify a subsurface paleo-channel that is not visible on the ground surface. This feature was first identified through Ground Penetrating Radar (GPR) survey, then recognized in the SAR imagery and further verified by test excavations. Both the GPR and the excavations reveal the base of the paleo-channel at a depth of 0.6 m-0.7 m. Hence, both X-band and C-band wavelengths are appropriate for subsurface archaeological prospection in suitable (dry silt and sand) conditions with specific acquisition parameters. Moreover, these results offer important new insights into the paleo-environmental context of ancient metal-working at 'Uqdat al-Bakrah and demonstrate surface water flow roughly contemporary with the site's occupation. [ABSTRACT FROM AUTHOR]

Lake Chad, located in the middle of the African Sahel belt, underwent dramatic decreases in the 1970s and 1980s leaving less than ten percent of its 1960s surface water extent as open water. In this paper, we present an extended record (dry seasons 1988-2016) of the total surface water area of the lake (including both open water and flooded vegetation) derived using Land Surface Temperature (LST) data (dry seasons 2000-2016) from the NASA Terra MODIS sensor and EUMETSAT Meteosat-based LST measurements (dry seasons 1988-2001) from an earlier study. We also examine the total surface water area for Lake Chad using radar data (dry seasons 2015-2016) from the ESA Sentinel-1a mission. For the limited number of radar data sets available to us (18 data sets), we find on average a close match between the estimates from these data and the corresponding estimates from LST, though we find spatial differences in the estimates using the two types of data. We use these spatial differences to adjust the record (dry seasons 2000-2016) from MODIS LST. Then we use the adjusted record to remove the bias of the existing LST record (dry seasons 1988-2001) derived from Meteosat measurements and combine the two records. From this composite, extended record, we plot the total surface water area of the lake for the dry seasons of 1988-1989 through 2016-2017. We find for the dry seasons of 1988-1989 to 2016-2017 that the maximum total surface water area of the lake was approximately 16,800 sq. km (February and May, 2000), the minimum total surface water area of the lake was approximately 6400 sq. km (November, 1990), and the average was approximately 12,700 sq. km. Further, we find the total surface water area of the lake to be highly variable during this period, with an average rate of increase of approximately 143 km² per year. [ABSTRACT FROM AUTHOR]

High frequency surface wave radar (HFSWR) has been successfully developed for early warning and remote sensing. However, the ionospheric clutter is a difficult challenge that can make HFSWR system inefficient. The Generalized Side-lobe Canceller (GSC) has been proved to be an effective algorithm for clutter suppression in theory, but it suffers from the performance degradation for some non-ideal conditions in practice. The most intolerable shortcoming is the signal to noise ratio (SNR) loss caused by the residual signal in the secondary data. In this paper, a knowledge-based GSC (KB-GSC) method has been proposed via an adaptive single notch filter design to reject the residual signal for reducing the SNR loss. The feasibility and availability has been demonstrated by measured data. [ABSTRACT FROM AUTHOR]

The technical aspects of oil spill remote sensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day-night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable. [ABSTRACT FROM AUTHOR]

A variety of data assimilation approaches have been applied to enhance modelling capability and accuracy using observations from different sources. The algorithms have varying degrees of complexity of implementation, and they improve model results with varying degrees of success. Very little work has been carried out on comparing the implementation of different data assimilation algorithms using High Frequency radar (HFR) data into models of complex inshore waters strongly influenced by both tides and wind dynamics, such as Galway Bay. This research entailed implementing four different data assimilation algorithms: Direct Insertion (DI), Optimal Interpolation (OI), Nudging and indirect data assimilation via correcting model forcing into a three-dimensional hydrodynamic model and carrying out detailed comparisons of model performances. This work will allow researchers to directly compare four of the most common data assimilation algorithms being used in operational coastal hydrodynamics. The suitability of practical data assimilation algorithms for hindcasting and forecasting in shallow coastal waters subjected to alternate wetting and drying using data collected from radars was assessed. Results indicated that a forecasting system of surface currents based on the three-dimensional model EFDC (Environmental Fluid Dynamics Code) and the HFR data using a Nudging or DI algorithm was considered the most appropriate for Galway Bay. The largest averaged Data Assimilation Skill Score (DASS) over the 6 h forecasting period from the best model NDA attained 26% and 31% for east-west and north-south surface velocity components respectively. Because of its ease of implementation and its accuracy, this data assimilation system can provide timely and useful information for various practical coastal hindcast and forecast operations. [ABSTRACT FROM AUTHOR]

High-frequency (HF) radars are routinely used for remotely sensing ocean surface currents. However, the performance of the most widely used direction-finding HF radar is degraded due to the effect of the inevitable deviations of actual antenna pattern on the direction of arrival (DOA) estimation. In this paper, we quantify the DOA estimation error resulting from the deviation of the actual antenna pattern from the ideal one. Theoretical analysis and field experiment results suggest that the ratio of the deviations for the two loops dominates the DOA estimation error. Thus, eliminating the effect of the antenna pattern deviations on DOA estimation error is transformed into eliminating the effect of this ratio. From this, a calibration method based on the time-averaged local spatial coverage rate (TLSCR) is proposed to reduce the effect of the antenna pattern deviations on current extraction, which uses the ideal antenna pattern to estimate the DOA of the echoes. To validate this proposed calibration method, we assess the radar-derived radial velocities by comparing with in situ observations. The comparison results indicate that the proposed TLSCR calibration method can effectively reduce the DOA estimation error and improve the performance of the direction-finding HF radar in current observation. [ABSTRACT FROM AUTHOR]

Multipath interference can occur in ground-based radar data acquired with systems with a large antenna beam width in elevation in an upward looking geometry, where the observation area and the radar are separated by a reflective surface. Radiation reflected at this surface forms a coherent overlay with the direct image of the observation area and appears as a fringe-like pattern in the data. This deteriorates the phase and intensity data and therefore can pose a considerable disadvantage to many ground-based radar measurement campaigns. This poses a problem for physical parameter retrieval from backscatter intensity and polarimetric data, absolute and relative calibration on corner reflectors, the generation of digital elevation models from interferograms and in the case of a variable reflective surface, differential interferometry. The main parameters controlling the interference pattern are the vertical distance between the radar antennas and the reflective surface, and the reflectivity of this surface. We used datasets acquired in two different locations under changing conditions as well as a model to constrain and fully understand the phenomenon. To avoid data deterioration in test sites prone to multipath interference, we tested a shielding of the antennas preventing the radar waves from illuminating the reflective surface. In our experiment, this strongly reduced but did not completely prevent the interference. We therefore recommend avoiding measurement geometries prone to multipath interferences. [ABSTRACT FROM AUTHOR]

Ocean wind and wave parameters can be measured by in-situ sensors such as anemometers and buoys. Since the 1980s, X-band marine radar has evolved as one of the remote sensing instruments for such purposes since its sea surface images contain considerable wind and wave information. The maturity and accuracy of X-band marine radar wind and wave measurements have already enabled relevant commercial products to be used in real-world applications. The goal of this paper is to provide a comprehensive review of the state of the art algorithms for ocean wind and wave information extraction from X-band marine radar data. Wind measurements are mainly based on the dependence of radar image intensities on wind direction and speed. Wave parameters can be obtained from radar-derived wave spectra or radar image textures for non-coherent radar and from surface radial velocity for coherent radar. In this review, the principles of the methodologies are described, the performances are compared, and the pros and cons are discussed. Specifically, recent developments for wind and wave measurements are highlighted. These include the mitigation of rain effects on wind measurements and wave height estimation without external calibrations. Finally, remaining challenges and future trends are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating component caused by cross-terms. This method can bring a better compromise in the auto-terms concentration and cross-terms suppressing, which contributes to the multi-component signal separation. Finally, the effective micro signal is extracted by threshold segmentation and envelope extraction. To verify the proposed method, six states of motion are separated by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 95.4% for two cases without interference. [ABSTRACT FROM AUTHOR]

Surface snowmelt causes changes in mass and energy balance, and endangers the stabilities of the ice shelves in the Antarctic Peninsula (AP). The dynamic changes of the snow and ice conditions in the AP were observed by Sentinel-1 images with a spatial resolution of 40min this study. Snowmelt detected by the special sensor microwave/imager (SSM/I) is used to study the relationship between summer snowmelt and winter synthetic aperture radar (SAR) backscatter. Radar glacier zones (RGZs) classifications were conducted based on their differences in liquid snow content, snow grain size, and the relative elevations. We developed a practical method based on the simulations of a microwave scattering model to classify RGZs by using Sentinel-1 images in the AP. The summer snowmelt detected by SSM/I and Sentinel-1 data are compared between 2014 and 2015. The SSM/I-derived melting days is used to validate the winter dry snow line (DSL). RGZs derived from Sentinel-1 images suggest that snowmelt expanded from inland of the Larsen C Ice Shelf to the coastal area, whereas an opposite direction was found in the George VI Ice Shelf. The long melting season in the grounding zone of the Larsen C Ice Shelf may result from the adiabatically-dried föhn winds on the east side of the AP. As the uppermost limit of summer snowmelt, DSL was mapped based on the winter Sentinel-1 mosaic of the AP. Compared with the SSM/I-derived melting days, the winter DSL mainly distributed in the areas melted for one to three days in summer. DSL elevations on the Palmer Land increased from south to north. [ABSTRACT FROM AUTHOR]

RapidScat is a National Aeronautics and Space Administration (NASA) Ku-Band scatterometer that was operated onboard the International Space Station between September 2014 and August 2016 when the mission effectively ended after an irrecoverable instrument failure. A unique non-Sun-synchronous orbit facilitated global contiguous geographical sampling between the ±56˚ latitude. For the first time, such an orbit enabled an overlap with other scatterometers flying in Sun-synchronous orbits. The double-difference technique was developed and successfully used for microwave radiometer calibration at the Remote Sensing Laboratory at the University of Central Florida, USA. This paper presents the extension of the double difference methodology to scatterometry. The methodology has been adopted for the cross-instrument calibration between RapidScat and QuikScat scatterometers simultaneously orbiting the Earth on-board two independent satellite platforms. The double-difference technique was deployed to compare measurements from these two scatterometers, as a more accurate alternative to the classic single difference approach. The work summarized in this paper addressed a cross-calibration algorithm developed and applied to RapidScat and QuikScat data in the period from January 2015 to March 2016. The initial results of the statistical analysis and biases between the two scatterometers are presented. Calculated biases may be used for measurement correction and reprocessing. [ABSTRACT FROM AUTHOR]