Search

Extensive efforts have been made to observe the accumulation and melting of seasonal snow. However, making accurate observations of snow water equivalent (SWE) at global scales is challenging. Active radar systems show promise, provided the dielectric properties of the snowpack are accurately constrained. The dielectric constant (k) determines the velocity of a radar wave through snow, which is a critical component of time-of-flight radar techniques such as ground penetrating radar and interferometric synthetic aperture radar (InSAR). However, equations used to estimate k have been validated only for specific conditions with limited in situ validation for seasonal snow applications. The goal of this work was to further understand the dielectric permittivity of seasonal snow under both dry and wet conditions. We utilized extensive direct field observations of k, along with corresponding snow density and liquid water content (LWC) measurements. Data were collected in the Jemez Mountains, NM; Sandia Mountains, NM; Grand Mesa, CO; and Cameron Pass, CO from February 2020 to May 2021. We present empirical relationships based on 146 snow pits for dry snow conditions and 92 independent LWC observations in naturally melting snowpacks. Regression results had r2 values of 0.57 and 0.37 for dry and wet snow conditions, respectively. Our results in dry snow showed large differences between our in situ observations and commonly applied equations. We attribute these differences to assumptions in the shape of the snow grains that may not hold true for seasonal snow applications. Different assumptions, and thus different equations, may be necessary for varying snowpack conditions in different climates, suggesting that further testing is necessary. When considering wet snow, large differences were found between commonly applied equations and our in situ measurements. Many previous equations assume a background (dry snow) k that we found to be inaccurate, as previously stated, and is the primary driver of resulting uncertainty. Our results suggest large errors in SWE (10–15%) or LWC (0.05–0.07 volumetric LWC) estimates based on current equations. The work presented here could prove useful for making accurate observations of changes in SWE using future InSAR opportunities such as NISAR and ROSE-L.

Most previous studies tend to simplify the lunar regolith as a homogeneous medium. However, the lunar regolith is not completely homogeneous, because there are weak reflections from the lunar regolith layer. In this study, we examined the weak heterogeneity of the lunar regolith layer using a self-organization model by matching the reflection pattern of both the lunar regolith layer and the top of the ejecta layer. After a series of numerical experiments, synthetic results show great consistency with the observed Chang’E-4 lunar penetrating radar data and provide some constraints on the range of controlling parameters of the exponential self-organization model. The root mean square permittivity perturbation is estimated to be about 3% and the correlation distance is about 5–10 cm. Additionally, the upper layer of ejecta has about 1–2 rocks per square meter, and the rock diameter is about 20–30 cm. These parameters are helpful for further study of structural characteristics and the evolution process of the lunar regolith. The relatively small correlation distance and root mean square perturbation in the regolith indicate that the regolith is mature. The weak reflections within the regolith are more likely to be due to structural changes rather than material composition changes.

Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.

Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

The navigation information about a vessel’s position in the waters covered by the vessel traffic system operation is obtained through supervisory services, mainly from coastal navigation radars. Although today it is possible to simultaneously acquire data from many independent radars, the obtained radar image is inconsistent and consists of several echoes next to each other. This makes it difficult to establish which echo represents the monitored unit. Another problem is the method of determining radar distances, which significantly affect the quality of determining the observation position. Errors in radar distance may occur when determining the radar echoes from large vessels, where the position of the unit is not the same as the edge of the radar echo to which the observation is made. In this article, the authors present a method of improving the measured radar distance. The presented proposal was verified in navigation and maneuvering simulation conditions. It could support the process of determining the ship position in vessel traffic service (VTS) systems with increased accuracy.

Evaluating satellite ability in capturing sudden natural disasters such as heavy snowstorms is a topic of societal interest. This paper presents a rapid qualitative analysis of an intense snowfall in Madrid using data from the Global Precipitation Measurement (GPM) mission, specifically the GPM IMERG (Integrated Multi-satellitE Retrievals for GPM) Late Precipitation L3 Half Hourly 0.1° × 0.1° V06 estimates of precipitation (IMERG-Late), and Sentinel-2 imagery. The main research question addressed is the consistency of ground observations, model outputs and satellite data, a topic of major interest for an appropriate and timely societal response to severe weather episodes. Indeed, the choice of the ‘Late’ product over the IMERG ‘Final’ or other GPM datasets was motivated by the availability of data for near real-time response to the storm. Additionally, the 30-min temporal resolution of the product would in principle allow for a detailed analysis of the dynamic processes involved in the snowstorm. Using several complementary data sources, it is shown that optical remote sensing sensors (Sentinel) add value to existing ground data and that is invaluable for rapid response to severe meteorological events such as Filomena. Regarding the GPM precipitation radar, the sampling of the GPM-core satellite was insufficient to provide the IMERG algorithm with enough quality data to correctly represent the actual sequence of precipitation. Without corrections, the total precipitation differs from observations by a factor of two. The difficulties of retrieving precipitation with radiometers over snow-covered surfaces was a major factor for the mismatch. Thus, the calibrated precipitation product did not fully capture the historic storm, and neither did the IR-based element of the IMERG-Late product, which is a neural network merging of microwave and infrared data. It follows that increased temporal resolution of spaceborne microwave sensors and improved retrieval of precipitation from radiometers are critical in order to provide a complete account of these sorts of extreme, significant, short-duration cases. Otherwise, the high-quality, radar and radiometer data feeding the high temporal resolution algorithms simply slip through the grasp of the ascending and descending orbits, leaving little quality data to be interpolated into successive overpasses.

The motivation for improving gridded precipitation data lies in weather now-casting and flood forecasting. Therefore, over the past decade, Commercial Microwave Link (CML) attenuation data have been used to determine rain rates between microwave antennas, and to produce more accurate countrywide precipitation grids. CML networks offer a unique advantage for precipitation measurements due to their high density. However, these data experience uncertainty from several sources as reported in earlier research. This current work determines the reliability of rainfall measurements for each link by comparing CML-derived rain rates to adjusted weather radar rainfall at the link location, over three months. Dynamic Time Warping (DTW) is applied to the pair of CML/radar time-series data in two study areas, Israel and Netherlands. Based on the DTW amplitude and temporal distance, unreliable links are identified and flagged, and interpolated gridded precipitation data are derived in each country after filtering out those unreliable links. Correlations between CML-derived grids and rain observations from an independent set of gauges, tested over several rain events in both study areas, are higher for the reliable subset of CML than the full set. For certain storm events, the Kendall rank correlation for the set of reliable CML is almost double that of the complete set, demonstrating that improved gridded precipitation data can be obtained by removing unreliable links.

This research focused on the quantitative assessment of the surface deformation velocities and rates and their natural and man-made controlling factors at Tengiz Oilfield in Kazakhstan using the Small Baseline Subset remote sensing technique followed by 3D and 2D decompositions and cosine corrections to derive vertical and horizontal movements from line-of-sight (LOS) measurements. In the present research we applied time-series of Sentinel-1 satellite images acquired during 2018–2020. All ground deformation derivatives showed the continuous subsidence at the Tengiz oilfield with increasing velocity. 3D and 2D decompositions of LOS measurements to vertical movement showed that the Tengiz Oil Field 2018–2020 continuously subsided with the maximum annual vertical deformation velocity around 70 mm. Based on the LOS measurements, the maximum annual subsiding velocity was observed to be 60 mm. Cosine corrections of LOS measurements to vertical movement, however, revealed a maximum annual vertical deformation velocity of 77 mm. The vertical deformation confirmed typical patterns of subsidence caused by oil extraction. Detected east-west and north-south horizontal movements at the Tengiz field clearly indicated that the study area crossed by seismic faults is affected by natural tectonic processes. The overall RMSE of 3D decomposed vertical deformation in relationship to LOS measurements and cosine corrections were in the range of 10–13 mm and 6–8 mm, correspondingly. The results of the present research will support operators of oil and gas fields and also other types of infrastructure to evaluate the actual differences of InSAR ground deformation measurements against the required standards and the precision of measurements depending on the operational needs, timeframes and availability of radar imagery.

In the Ferret Valley (NW Italy), anthropic activities coexist close to the Grandes Jorasses massif’s glaciological complex. In the past, break-off events have caused damage to people and infrastructure. These events concerned two specific sectors: the Montitaz Lobe (Planpincieux Glacier) and the Whymper Serac (Grandes Jorasses Glacier). Since 2010, permanent and discontinuous survey campaigns have been conducted to identify potential failure precursors, investigate the glacier instability processes, and explore different monitoring approaches. Most of the existing terrestrial apparatuses that measure the surface kinematics have been adopted in the Grandes Jorasses area. The monitoring sites in this specific area are characterized by severe weather, complex geometry, logistic difficulties, and rapid processes dynamics. Such exceptional conditions highlighted the limitations and potentialities of the adopted monitoring approaches, including robotic total station (RTS), GNSS receivers, digital image correlation applied to time-lapse imagery, and terrestrial radar interferometry (TRI). We examined the measurement uncertainty of each system and their monitoring performances. We discussed their principal limitations and possible use for warning purposes. In the Grandes Jorasses area, the use of a time-lapse camera appeared to be a versatile and cost-effective solution, which, however is not suitable for warning applications, as it does not guarantee data continuity. RTS and GNSS have warning potentialities, but the target installation and maintenance in remote environments remain challenging. TRI is the most effective monitoring system for early warning purposes in such harsh conditions, as it provides near-real-time measurements. However, radar equipment is very costly and requires extreme logistic effort. In this framework, we present data integration strategies to overcome the abovementioned limits and we demonstrate that these strategies are optimal solutions to obtain data continuity and robustness.

Conventionally, in situ rainfall data are used to develop Intensity Duration Frequency (IDF) curves, which are one of the most effective tools for modeling the probability of the occurrence of extreme storm events at different timescales. The rapid recent technological advancements in precipitation sensing, and the finer spatio-temporal resolution of data have made the application of remotely sensed precipitation products more dominant in the field of hydrology. Some recent studies have discussed the potential of remote sensing products for developing IDF curves. This study employs a 19-year NEXRAD Stage-IV high-resolution radar data (2002–2020) to develop IDF curves over the entire state of Texas at a fine spatial resolution. The Annual Maximum Series (AMS) were fitted to four widely used theoretical Extreme Value statistical distributions. Gumble distribution, a unique scenario of the Generalized Extreme Values (GEV) family, was found to be the best model for more than 70% of the state’s area for all storm durations. Validation of the developed IDFs against the operational Atlas 14 IDF values shows a ±27% difference in over 95% of the state for all storm durations. The median of the difference stays between −10% and +10% for all storm durations and for all return periods in the range of (2–100) years. The mean difference ranges from −5% for the 100-year return period to 8% for the 10-year return period for the 24-h storm. Generally, the western and northern regions of the state show an overestimation, while the southern and southcentral regions show an underestimation of the published values.

Cloud-top heights (CTH), as one of the representative variables reflecting cloud macro-physical properties, affect the Earth–atmosphere system through radiation budget, water cycle, and atmospheric circulation. This study compares the CTH from passive- and active-spaceborne sensors with ground-based Ka-band zenith radar (KAZR) observations at the Semi-Arid Climate and Environment Observatory of Lanzhou University (SACOL) site for the period 2013–2019. A series of fundamental statistics on cloud probability in different limited time and areas at the SACOL site reveals that there is an optimal agreement for both cloud frequency and fraction derived from space and surface observations in a 0.5° × 0.5° box area and a 40-min time window. Based on the result, several facets of cloud fraction (CF), cloud overlapping, seasonal variation, and cloud geometrical depth (CGD) are investigated to evaluate the CTH retrieval accuracy of different observing sensors. Analysis shows that the CTH differences between multi-satellite sensors and KAZR decrease with increasing CF and CGD, significantly for passive satellite sensors in non-overlapping clouds. Regarding passive satellite sensors, e.g., Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua, the Multi-angle Imaging SpectroRadiometer (MISR) on Terra, and the Advanced Himawari Imager on Himawari-8 (HW8), a greater CTH frequency difference exists between the upper and lower altitude range, and they retrieve lower CTH than KAZR on average. The CTH accuracy of HW8 and MISR are susceptible to inhomogeneous clouds, which can be reduced by controlling the increase of CF. Besides, the CTH from active satellite sensors, e.g., Cloud Profiling Radar (CPR) on CloudSat, and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), agree well with KAZR and are less affected by seasonal variation and inhomogeneous clouds. Only CALIPSO CTH is higher than KAZR CTH, mainly caused by the low-thin clouds, typically in overlapping clouds.

International audience; In the past decades, Bragg scattering has been considered to be an important scattering mechanism of microwave backscattering from sea surfaces. However, as reported in many recent literatures, non-Bragg scattering (which is often attributed to wave breaking) also makes a significant impact on radar scattering, especially for Horizontal–Horizontal (HH) polarized radar signals. To date, we know far less about non-Bragg scattering than Bragg scattering. Herein, this paper carries out an investigation on non-Bragg scattering and its effect on radar echoes at moderate incidence angles, both for oil-free and oil-covered sea surfaces. This paper firstly presents a systematic comparison of several sea spectra commonly used for the simulation of microwave scattering from sea surfaces. It is found that none of them perform well for the description of Bragg waves. Then, the “pure” Bragg wave spectra are inverted in the framework of the two-scale model (TSM) and geophysical model functions (GMFs). The normalized radar cross sections (NRCS) related to total scattering, non-Bragg scattering, and “pure” Bragg scattering in C, X, and Ku-bands are simulated under various conditions (i.e., incidence angles, wind speeds, and wind directions). Quantitative assessments of the relative contributions of non-Bragg scattering to total scattering are conducted. We also perform a survey on the non-Bragg scattering from the oil-covered sea surface. This article provides some new insights for a better understanding of the non-Bragg microwave scattering from rough sea surfaces at moderate incidence angles.

Strong variability of Ka-band radar backscattering from short wind waves on the surface of water covered with surfactant films in the presence of internal waves (IW) was studied in wave tank experiments. It has been demonstrated that modulation of Ka-band radar return due to IW strongly depends on the relationship between the phase velocity of IW and the velocity of drifting surfactant films. An effect of the strong increase in surfactant concentration was revealed in convergent zones, associated with IW orbital velocities in the presence of a “resonance” surface steady current, the velocity of which was close to the IW phase velocity. A phenomenological model of suppression and modulations in the spectrum of small-scale wind waves due to films and IW was elaborated. It has been shown that backscatter modulation could not be explained by the modulation of free (linear) millimeter-scale Bragg waves, but was associated with the modulation of bound (parasitic) capillary ripples generated by longer, cm–dm-scale waves—a “cascade” modulation mechanism. Theoretical analysis based on the developed model was found to be consistent with experiments. Field observations which qualitatively illustrated the effect of strong modulation of Ka-band radar backscatter due to IW in the presence of resonance drift of surfactant films are presented.

Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people’s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly limits their effective application. In this study, we propose a novel framework of a deep learning-based model to retrieve the radar composite reflectivity factor (RCRF) maps from the Fengyun-4A new-generation geostationary satellite data. The suggested framework consists of three main processes, i.e., satellite and radar data preprocessing, the deep learning-based regression model for retrieving the RCRF maps, as well as the testing and validation of the model. In addition, three typical cases are also analyzed and studied, including a cluster of rapidly developing convective cells, a Northeast China cold vortex, and the Super Typhoon Haishen. Compared with the high-quality precipitation rate products from the integrated Multi-satellite Retrievals for Global Precipitation Measurement, it is found that the retrieved RCRF maps are in good agreement with the precipitation pattern. The statistical results show that retrieved RCRF maps have an R-square of 0.88-0.96, a mean absolute error of 0.3-0.6 dBZ, and a root-mean-square error of 1.2-2.4 dBZ.

The Global Precipitation Measurement mission is a major U.S.–Japan joint mission to understand the physics of the Earth’s global precipitation as a key component of its weather, climate, and hydrological systems. The core satellite carries a dual-precipitation radar and an advanced microwave imager which provide measurements to retrieve the drop size distribution (DSD) and rain rates using a Combined Radar-Radiometer Algorithm (CORRA). Our objective is to validate key assumptions and parameterizations in CORRA and enable improved estimation of precipitation products, especially in the middle-to-higher latitudes in both hemispheres. The DSD parameters and statistical relationships between DSD parameters and radar measurements are a central part of the rainfall retrieval algorithm, which is complicated by regimes where DSD measurements are abysmally sparse (over the open ocean). In view of this, we have assembled optical disdrometer datasets gathered by research vessels, ground stations, and aircrafts to simulate radar observables and validate the scattering lookup tables used in CORRA. The joint use of all DSD datasets spans a large range of drop concentrations and characteristic drop diameters. The scaling normalization of DSDs defines an intercept parameter NW, which normalizes the concentrations, and a scaling diameter Dm, which compresses or stretches the diameter coordinate axis. A major finding of this study is that a single relationship between NW and Dm, on average, unifies all datasets included, from stratocumulus to heavier rainfall regimes. A comparison with the NW–Dm relation used as a constraint in versions 6 and 7 of CORRA highlights the scope for improvement of rainfall retrievals for small drops (Dm < 1 mm) and large drops (Dm > 2 mm). The normalized specific attenuation–reflectivity relationships used in the combined algorithm are also found to match well the equivalent relationships derived using DSDs from the three datasets, suggesting that the currently assumed lookup tables are not a major source of uncertainty in the combined algorithm rainfall estimates.

Abandoned agricultural land (AAL) is a European problem and phenomenon when agricultural land is gradually overgrown with shrubs and forest. This wood biomass has not yet been systematically inventoried. The aim of this study was to experimentally prove and validate the concept of the satellite-based estimation of woody above-ground biomass (AGB) on AAL in the Western Carpathian region. The analysis is based on Sentinel-1 and -2 satellite data, supported by field research and airborne laser scanning. An improved AGB estimate was achieved using radar and optical multi-temporal data and polarimetric coherence by creating integrated predictive models by multiple regression. Abandonment is represented by two basic AAL classes identified according to overgrowth by shrub formations (AAL1) and tree formations (AAL2). First, an allometric model for AAL1 estimation was derived based on empirical material obtained from blackthorn stands. AAL2 biomass was quantified by different procedures related to (1) mature trees, (2) stumps and (3) young trees. Then, three satellite-based predictive mathematical models for AGB were developed. The best model reached R2 = 0.84 and RMSE = 41.2 t·ha−1 (35.1%), parametrized for an AGB range of 4 to 350 t·ha−1. In addition to 3214 hectares of forest land, we identified 992 hectares of shrub–tree formations on AAL with significantly lower wood AGB than on forest land and with simple shrub composition.

The Remote sensing of Electrification, Lightning, And Meso-scale/micro-scale Processes with Adaptive Ground Observations (RELAMPAGO) and the Cloud, Aerosol, and Complex Terrain Interactions Experiment Proposal (CACTI) field campaigns provided an unprecedented thirteen-disdrometer dataset in Central Argentina during the Intensive (IOP, 15 November to 15 December 2018) and Extended (EOP, 15 October 2018 to 30 April 2019) Observational Periods. The drop size distribution (DSD) parameters and their variability were analyzed across the region of interest, which was divided into three subregions characterized by the differing proximity to the Sierras de Córdoba (SDC), in order to assess the impact of complex terrain on the DSD parameters. A rigorous quality control of the data was first performed. The frequency distributions of DSD-derived parameters were analyzed, including the normalized intercept parameter (logNw), the mean volume diameter (D0), the mean mass diameter (Dm), the shape parameter (μ), the liquid water content (LWC), and the rain rate (R). The region closest to the SDC presented higher values of logNw, lower D0, and higher μ, while the opposite occurred in the farthest region, i.e., the concentration of small drops decreased while the concentration of bigger drops increased with the distance to the east of the SDC. Furthermore, the region closest to the SDC showed a bimodal distribution of D0: the lower values of D0 were associated with higher values of logNw and were found more frequently during the afternoon, while the higher D0 were associated with lower logNw and occurred more frequently during the night. The data were analyzed in comparison to the statistical analysis of Dolan et al. 2018 and sorted according to the classification proposed in the cited study. The logNw-D0 and LWC-D0 two-dimensional distributions allowed further discussion around the applicability of other mid-latitude and global precipitation classification schemes (startiform/convection) in the region of interest. Finally, three precipitation case studies were analyzed with supporting polarimetric radar data in order to relate the DSD characteristics to the precipitation type and the microphysical processes involved in each case.

Wet snow may cause significant damage to humans and property, and thus, it is necessary to estimate the corresponding liquid fraction (FL). Consequently, the FL of wet snow was estimated using a novel technique; specifically, the particle shape irregularity (Ir) was estimated through the particle coordinate information obtained using 2-D video disdrometer (2DVD) measurements. Moreover, the possibility of quantitively estimating FL via Ir, based on the temperature (T), was examined. Eight snowfall cases from 2014 to 2016 were observed through a 2DVD installed in Jincheon, South Korea, to analyze the dominant properties of physical variables of snowflakes (i.e., the terminal velocity (VT), particle density (ρs), Ir, and FL) and the corresponding relationships according to the T ranges (−4.5 < T (°C) < 2.5) in which wet snow can occur. It was clarified that the volume-equivalent particle diameter (D)–FL and D–Ir relationships depended on T, and a relationship existed between Ir and FL. The analysis results were verified using the Yong-In Testbed (YIT) S-band weather radar and T-matrix scattering simulation. The D–FL relationship was implemented in the scattering simulation, and the results indicated that the simulated reflectivity (ZS) was highly correlated with the observed reflectivity (ZO) under all T classes. These features can provide a basis for radar analysis and quantitative snowfall estimation for wet snow with various FL values.

The effective normalized radar cross section (NRCS) of breaking waves, σwb, is empirically derived based on joint synchronized Ka-band radar and video records of the sea surface from a research tower. The σwb is a key parameter that, along with the breaker footprint fraction, Q, defines the contribution of non-polarized backscattering, NP =σwbQ, to the total sea surface NRCS. Combined with the right representation of the regular Bragg and specular backscattering components, the NP component is fundamental to model and interpret sea surface radar measurements. As the first step, the difference between NRCS values for breaking and non-breaking conditions is scaled with the optically-observed Q and compared with the geometric optics model of breaker backscattering. Optically-derived Q might not be optimal to represent the effect of breaking waves on the radar measurements. Alternatively, we rely on the breaking crest length that is firmly detected by the video technique and the empirically estimated breaker decay (inverse wavelength) scale in the direction of breaking wave propagation. A simplified model of breaker NRCS is then proposed using the geometric optics approach. This semi-analytical model parameterizes the along-wave breaker decay from reported breaker roughness spectra, obtained in laboratory experiments with mechanically-generated breakers. These proposed empirical breaker NRCS estimates agree satisfactorily with observations.

A melting layer detection algorithm is developed for the NCAR 94 GHz airborne cloud radar (HIAPER CloudRadar, HCR). The detection method is based on maxima in the linear depolarization ratio and a discontinuity in the radial velocity field. A melting layer field is added to the radar data, which provides detected, interpolated, and estimated altitudes of the melting layer and the altitude of the 0 °C isotherm detected in model temperature data. The icing level is defined as the lowest melting layer, and the cloud data are flagged as either above (cold) or below (warm) the icing level. Analysis of the detected melting layer shows that the offset between the 0 °C isotherm and the actual melting layer varies with cloud type: in heavy convection sampled in the tropics, the melting layer is found up to 500 m below the 0 °C isotherm, while in shallow clouds, the offset is much smaller or sometimes vanishes completely. A relationship between the offset and the particle fall speed both above and below the melting layer is established. Special phenomena, such as a lowering of the melting layer towards the center of storms or split melting layers, were observed.

The accurate representation of precipitation across the Earth’s surface is crucial to furthering our knowledge and understanding of the Earth System and its component processes. Precipitation poses a number of challenges, particularly due to the variability of precipitation over time and space and whether it falls as snow or rain. While conventional measures of precipitation are reasonably good at the location of their measurement, their distribution across the Earth’s surface is uneven with some regions having no surface measurements. Spaceborne sensors have the capability of providing regular observations across the Earth’s surface that can provide estimates of precipitation. However, the estimation of precipitation from satellite observations is not necessarily straightforward. Visible and/or infrared techniques rely upon imprecise cloud-top to surface precipitation relationships, while the sensitivity of passive microwave techniques to different precipitation types is not consistent. Active microwave (radar) observations provide the most direct satellite measurements of precipitation but cannot provide estimates close to the surface and are generally not sufficiently sensitive to resolve light precipitation. This is particularly problematic at mid to high latitudes, where light and/or shallow precipitation dominates. This paper compares measurements made by ground-based weather radars, Micro Rain Radars and the spaceborne Dual-frequency Precipitation Radar to study both light precipitation intensity and shallow precipitation occurrence and to assess their impact on satellites retrievals of precipitation at the mid to high latitudes.

Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.

The paper investigates the penetration properties of an airborne Ku-band frequency modulated continuous waveform (FMCW) profiling radar named Tomoradar and a satellite near-infrared lidar into the boreal forest of Finland. We achieve the accumulative energy distributions based on the Tomoradar waveforms and the satellite lidar waveforms generated from the high-density airborne lidar data within Tomoradar footprints. By comparing two groups of the height percentiles and energy percentiles derived from the accumulative energy distributions, we evaluate the relationship of penetrations between the Ku-band microwave and near-infrared laser according to the coefficients of the determination (COD), and the root mean square errors (RMSE) of linear regression analyses. The quantitative analysis results demonstrate that the height and energy percentiles derived from Tomoradar waveforms correlate well with those from satellite lidar waveforms with the mean correlation coefficients of more than 0.78 and 0.85. The linear regression models for the height and energy percentile produce excellent fits with the mean CODs of 0.95 and 0.90 and the mean RMSEs of 1.25 m and 0.03, respectively. Less than 15% of height percentiles and 87.54% of the energy percentiles in the sixth stratum near the ground derived from Tomoradar waveforms surpass those from satellite lidar waveforms. Hence, the Ku-band microwave can penetrate deeper into the forest than the near-infrared laser at the same spatial scale. In addition, quadratic fitting models are established to describe the differences of the height percentile (DHP) and the energy percentile (DEP) to expound the canopy height and closure contributions numerically. The facts that the CODs of the DHP and DEP individually are more than 0.96 and 0.89 and the fitting residual histograms approximate to normal distributions reveal the reliabilities of the proposed fitting models. Thus, the penetration analyses are valid for the explorations on the FMCW radar applications and the data fusion of the Ku-band radar and near-infrared lidar in the forest investigations.

Ground Penetrating Radar (GPR) has proved to be a successful technique for the detection of landmines and Improvised Explosive Devices (IEDs) buried in the ground. In the last years, novel architectures for safe and fast detection, such as those based on GPR systems onboard Unmanned Aerial Vehicles (UAVs), have been proposed. Furthermore, improvements in GPR hardware and signal processing techniques have resulted in a more efficient detection. This contribution presents an experimental validation of a hybrid Forward-Looking–Down-Looking GPR architecture. The main goal of this architecture is to combine advantages of both GPR architectures: reduction of clutter coming from the ground surface in the case of Forward-Looking GPR (FLGPR), and greater dynamic range in the case of Down-Looking GPR (DLGPR). Compact radar modules working in the lower SHF frequency band have been used for the validation of the hybrid architecture, which involved realistic targets.

The retrieval of precipitation (snowfall and rainfall) from satellite sensors on a global basis is essential in aiding our knowledge and understanding of the Earth System and for many societal applications. Measurements from surface-based instruments are essentially limited to populated regions, necessitating the use of satellite-based observations to provide estimates of precipitation across the whole of the Earth’s surface. The temporal and spatial variability of precipitation requires adequate sampling, especially at finer resolutions. It is, therefore, necessary to exploit all available data from precipitation-capable satellites to ensure the proper representation of precipitation. To date, the estimation of precipitation using passive microwave observations has been largely concentrated upon the conically scanning imaging instruments, with relatively few techniques exploiting the observations made from the cross-track sounders. This paper describes the development of the Precipitation Retrieval and Profiling Scheme (PRPS) to retrieve precipitation from cross-track sensors, together with its performance against surface radar data and other satellite precipitation retrievals.

A previous study has used the stable and peculiar echoes backscattered by a single “bright scatterer” (BS) during five winter days to characterize the hardware of C-band, the dual-polarization radar located at Monte Lema (1625 m altitude) in Southern Switzerland. The BS is the 90 m tall metallic tower on Cimetta (1633 m altitude, 18 km range). In this note, the statistics of the echoes from the BS were derived from other ten dry days with normal propagation conditions in winter 2015 and January 2019. The study confirms that spectral signatures, such as spectrum width, wideband noise and Doppler velocity, were persistently stable. Regarding the polarimetric signatures, the large values (with small dispersion) of the copolar correlation coefficient between horizontal and vertical polarization were also confirmed: the average value was 0.9961 (0.9982) in winter 2015 (January 2019); the daily standard deviations were very small, ranging from 0.0007 to 0.0030. The dispersion of the differential phase shift was also confirmed to be quite small: the daily standard deviation ranged from a minimum of 2.5° to a maximum of 5.3°. Radar reflectivities in both polarizations were typically around 80 dBz and were confirmed to be among the largest values observed in the surveillance volume of the Monte Lema radar. Finally, another recent 5-day data set from January 2020 was analyzed after the replacement of the radar calibration unit that includes low noise amplifiers: these five days show poorer characteristics of the polarimetric signatures and a few outliers affecting the spectral signatures. It was shown that the “historical” polarimetric and spectral signatures of a bright scatterer could represent a benchmark for an in-depth comparison after hardware replacements.

In this paper, we build on past efforts with regard to the implementation of an efficient feature tracking algorithm for the mass processing of satellite images. This generic open-source feature tracking routine can be applied to any type of imagery to measure sub-pixel displacements between images. The routine consists of a feature tracking module (autoRIFT) that enhances computational efficiency and a geocoding module (Geogrid) that mitigates problems found in existing geocoding algorithms. When applied to satellite imagery, autoRIFT can run on a grid in the native image coordinates (such as radar or map) and, when used in conjunction with the Geogrid module, on a user-defined grid in geographic Cartesian coordinates such as Universal Transverse Mercator or Polar Stereographic. To validate the efficiency and accuracy of this approach, we demonstrate its use for tracking ice motion by using ESA’s Sentinel-1A/B radar data (seven pairs) and NASA’s Landsat-8 optical data (seven pairs) collected over Greenland’s Jakobshavn Isbræ glacier in 2017. Feature-tracked velocity errors are characterized over stable surfaces, where the best Sentinel-1A/B pair with a 6 day separation has errors in X/Y of 12 m/year or 39 m/year, compared to 22 m/year or 31 m/year for Landsat-8 with a 16-day separation. Different error sources for radar and optical image pairs are investigated, where the seasonal variation and the error dependence on the temporal baseline are analyzed. Estimated velocities were compared with reference velocities derived from DLR’s TanDEM-X SAR/InSAR data over the fast-moving glacier outlet, where Sentinel-1 results agree within 4% compared to 3–7% for Landsat-8. A comprehensive apples-to-apples comparison is made with regard to runtime and accuracy between multiple implementations of the proposed routine and the widely-used “dense ampcor" program from NASA/JPL’s ISCE software. autoRIFT is shown to provide two orders of magnitude of runtime improvement with a 20% improvement in accuracy.

Snow makes a great contribution to the hydrological cycle in cold regions. The parameter to characterize available the water from the snow cover is the well-known snow water equivalent (SWE). This paper presents a near-surface-based radar for determining the SWE from the measured complex spectral reflectance of the snowpack. The method is based in a stepped-frequency continuous wave radar (SFCW), implemented in a coherent software defined radio (SDR), in the range from 150 MHz to 6 GHz. An electromagnetic model to solve the electromagnetic reflectance of a snowpack, including the frequency and wetness dependence of the complex relative dielectric permittivity of snow layers, is shown. Using the previous model, an approximated method to calculate the SWE is proposed. The results are presented and compared with those provided by a cosmic-ray neutron SWE gauge over the 2019–2020 winter in the experimental AEMet Formigal-Sarrios test site. This experimental field is located in the Spanish Pyrenees at an elevation of 1800 m a.s.l. The results suggest the viability of the approximate method. Finally, the feasibility of an auxiliary snow height measurement sensor based on a 120 GHz frequency modulated continuous wave (FMCW) radar sensor, is shown.

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Zh, R–(Zh, ZDR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates.

Profile radar allows direct characterization of the vertical forest structure. Short-wavelength, such as Ku or X band, microwave data provide opportunities to detect the foliage. In order to exploit the potential of radar technology in forestry applications, a helicopter-borne Ku-band profile radar system, named Tomoradar, has been developed by the Finnish Geospatial Research Institute. However, how to use the profile radar waveforms to assess forest canopy parameters remains a challenge. In this study, we proposed a method by matching Tomoradar waveforms with simulated ones to estimate forest canopy leaf area index (LAI). Simulations were conducted by linking an individual tree-based forest gap model ZELIG and a three-dimension (3D) profile radar simulation model RAPID2. The ZELIG model simulated the parameters of potential local forest succession scene, and the RAPID2 model utilized the parameters to generate 3D virtual scenes and simulate waveforms based on Tomoradar configuration. The direct comparison of simulated and collected waveforms from Tomoradar could be carried out, which enabled the derivation of possible canopy LAI distribution corresponding to the Tomoradar waveform. A 600-m stripe of Tomoradar data (HH polarization) collected in the boreal forest at Evo in Finland was used as a test, which was divided into 60 plots with an interval of 10 m along the trajectory. The average waveform of each plot was employed to estimate the canopy LAI. Good results have been found in the waveform matching and the uncertainty of canopy LAI estimation. There were 95% of the plots with the mean relative overlapping rate (RO) above 0.7. The coefficients of variation of canopy LAI estimates were less than 0.20 in 80% of the plots. Compared to lidar-derived canopy effective LAI estimation, the coefficient of determination was 0.46, and the root mean square error (RMSE) was 1.81. This study established a bridge between the Ku band profile radar waveform and the forest canopy LAI by linking the RAPID2 and ZELIG model, presenting the uncertainty of forest canopy LAI estimation using Tomoradar. It is worth noting that since the difference of backscattering contribution is caused by both canopy structure and tree species, similar waveforms may correspond to different canopy LAI, inducing the uncertainty of canopy LAI estimation, which should be noticed in forest parameters estimation with empirical methods.

The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band) and NCU C-POL (C-band) dual-polarization radars in northern Taiwan. The radar data quality control (QC) procedures, including the corrections of attenuation, the systematic bias, and the wet-radome effect, have large impact on the QPE accuracy. With the proper QC procedures, the values of normalized root mean square error (NRMSE) decrease about 10~40% for R(ZHH) and about 5~15% for R(KDP). The QPE error from the RSD variability is mitigated by applying seasonal coefficients derived from eight-year disdrometer data. Instead of using discrete QPEs (D-QPE) from one radar, the synthetic QPEs are derived via discretely combined QPEs (DC-QPE) from S- and C-band radars. The improvements in DC-QPE compared to D-QPE are about 1.5&ndash, 7.0% and 3.5&ndash, 8.5% in R(KDP) and R(KDP, ZDR), respectively. A novel algorithm, Lagrangian-evolution adjustment (LEA), is proposed to compensate D-QPE from a single radar. The LEA-QPE shows 1&ndash, 4% improvements in R(KDP, ZDR) at the C-band radar, which has a larger scanning temporal gap (up to 10 minutes). The synthetic LEA-QPEs by combining two radars have outperformed both D-QPEs and DC-QPEs.

Over the past several decades, as sensor technology has improved, the spatial resolution of satellite images has been steadily improving [...]

The availability of free and high temporal resolution satellite data and advanced SAR techniques allows us to analyze ground displacement cost-effectively. Our aim was to properly define subsidence and uplift areas to delineate a geothermal field and perform time-series analysis to identify temporal trends. A Persistent Scatterer Interferometry (PSI) algorithm was used to estimate vertical displacement in the Brady geothermal field located in Nevada by analyzing 70 Sentinel-1A Synthetic-Aperture Radar (SAR) images, between January 2017 and December 2019. To classify zones affected by displacement, an unsupervised Self-Organizing Map (SOM) algorithm was applied to classify points based on their behavior in time, and those clusters were used to determine subsidence, uplift, and stable regions automatically. Finally, time-series analysis was applied to the clustered data to understand the inflection dates. The maximum subsidence is –19 mm/yr with an average value of –6 mm/yr within the geothermal field. The maximum uplift is 14 mm/yr with an average value of 4 mm/yr within the geothermal field. The uplift occurred on the NE of the field, where the injection wells are located. On the other hand, subsidence is concentrated on the SW of the field where the production wells are located. The coupling of the PSInSAR and the SOM algorithms was shown to be effective in analyzing the direction and pattern of the displacements observed in the field.

Soil salinization is a global problem, which seriously damages the ecological environment and considerably reduces agricultural productivity, especially in arid regions. Synthetic aperture radar (SAR) has been widely used in remote sensing due to its weather and sunlight independence. Polarimetric SAR has great potential for large-scale mapping and monitoring salt-affected soils. In this study, we investigate the characteristics of saline soil in extremely arid regions using dual-band quadrature-polarimetric (quad-pol) SAR images acquired by GF-3 (C-band) and ALOS-2 (L-band). Firstly, the effectiveness of the modified dielectric mixing model and integral equation model (IEM) in describing saline soil is evaluated. Secondly, the potential relationships between polarimetric parameters and salinity are discussed in both the C- and L-band, respectively, such as co-polarization ratio, scattering entropy H, and scattering angle α. Finally, a linear regression model for monitoring salt content is established. The main contributions of this article are as follows: (1) Simulation results suggest that the radar backscattering coefficient is a weak function of salinity at low water content, but our experimental data show that soil salinity significantly contributes to the radar backscattering coefficient, which indicates the modified dielectric mixing model and IEM model is not applicable in extremely arid areas. (2) A negative correlation between the co-polarization ratio and salinity is observed, and the correlation coefficients are 0.64 (C-band) and 0.71 (L-band). Besides, scattering entropy and scattering angle exhibit a positive correlation with salinity in the C-band with correlation coefficients 0.686 and 0.669, respectively, whereas a negative correlation is found in the L-band with correlation coefficients 0.682 and 0.680, respectively. This can be attributed to the different penetration depths and sensitivity to the surface roughness of the electromagnetic waves at two frequencies. (3) A regression model for salinity estimating based on radar backscattering coefficient, co-polarization ratio, and scattering entropy is established, with a determination coefficient (R2) of 0.79 and a root mean square error (RMSE) of 6.56%, allowing us to determine soil salinity from quad-pol SAR images without using backscattering models. Therefore, our results can be a reference for future soil salinity monitoring and inversion.

In light of recent Arctic change, there is a need to better understand sea ice dynamic processes at the floe scale to evaluate sea ice stability, deformation, and fracturing. This work investigates the use of the Gamma portable radar interferometer (GPRI) to characterize sea ice displacement and surface topography. We find that the GPRI is best suited to derive lateral surface deformation due to mm-scale horizontal accuracy. We model interferometric phase signatures from sea ice displacement and evaluate possible errors related to noise and antenna motion. We compare the analysis with observations acquired during a drifting ice camp in the Beaufort Sea. We used repeat-scan and stare-mode interferometry to identify two-dimensional shear and to track continuous uni-directional convergence. This paper demonstrates the capacity of the GPRI to derive surface strain on the order of 10−7 and identify different dynamic regions based on sub-mm changes in displacement. The GPRI is thus a promising tool for sea ice applications due to its high accuracy that can potentially resolve pre- and post-fracture deformation relevant to sea ice stability and modeling.

Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval. The PolInSAR models have proven their potential in the accurate measurement of forest vegetation height. Spaceborne monostatic multifrequency data of different SAR missions and the Global Ecosystem Dynamics Investigation (GEDI)-derived forest canopy height map were used in this study for vegetation height retrieval. This study tested the performance of PolInSAR complex coherence-based inversion models for estimating the vegetation height of the forest ranges of Doon Valley, Uttarakhand, India. The inversion-based forest height obtained from the three-stage inversion (TSI) model had higher accuracy than the coherence amplitude inversion (CAI) model-based estimates. The vegetation height values of GEDI-derived canopy height map did not show good relation with field-measured forest height values. It was found that, at several locations, GEDI-derived forest height values underestimated the vegetation height. The statistical analysis of the GEDI-derived estimates with field-measured height showed a high root mean square error (RMSE; 5.82 m) and standard error (SE; 5.33 m) with a very low coefficient of determination (R2; 0.0022). An analysis of the spaceborne-mission-based forest height values suggested that the L-band SAR has great potential in forest height retrieval. TSI-model-based forest height values showed lower p-values, which indicates the significant relation between modelled and field-measured forest height values. A comparison of the results obtained from different SAR systems is discussed, and it is observed that the L-band-based PolInSAR inversion gives the most reliable result with low RMSE (2.87 m) and relatively higher R2 (0.53) for the linear regression analysis between the modelled tree height and the field data. These results indicate that higher wavelength PolInSAR datasets are more suitable for tree canopy height estimation using the PolInSAR inversion technique.

Satellite sensor systems for soil moisture measurements have been continuously evolving. The Soil Moisture Active Passive (SMAP) mission represents one of the latest advances in this regard. Thus far, much of our knowledge of the accuracy of SMAP soil moisture over the Great Lakes region of North America has originated from evaluation studies using in situ data from the U.S. Department of Agriculture (USDA) Natural Resources Conservation Service Soil Climate Analysis Network and/or the U.S. Climate Reference Network, which provide only several in situ sensor stations for this region. As such, these results typically underrepresent the accuracy of SMAP soil moisture in this region, which is characterized by a relatively large soil moisture variability and is one of the least studied regions. In this work, SMAP Level 2‒4 soil moisture products: SMAP/Sentinel-1 L2 Radiometer/Radar Soil Moisture (SPL2SMAP_S), SMAP Enhanced L3 Radiometer Soil Moisture (SPL3SMP_E), and SMAP L4 Surface and Root-Zone Soil Moisture Analysis Update (SPL4SMAU) are evaluated over the southern portion of the Great Lakes region using in situ measurements from Michigan State University’s Enviro-weather Automated Weather Station Network. The unbiased root-mean-square error (ubRMSE) values for both SPL4SMAU surface and root zone soil moisture estimates are below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of 0.045 m3 m−3 (0.037 m3 m−3) for the surface (root-zone) soil moisture against the sparse network. The ubRMSE values for SPL3SMP_E a.m. (i.e., descending overpasses) soil moisture retrievals are close to or below 0.04 m3 m−3 at the 36-km scale, with an average ubRMSE of ~0.06 m3 m−3 against the sparse network. The average ubRMSE values are ~0.05‒0.06 m3 m−3 for high-resolution SPL2SMAP_S soil moisture retrievals against the sparse network, with the skill of the baseline algorithm-based soil moisture retrievals exceeding that of the optional algorithm-based counterparts. Clearly, the skill of SPL4SMAU surface soil moisture exceeds that of the SPL3SMP_E and SPL2SMAP_S soil moisture retrievals.

Non-precipitation echoes due to ground and sea clutter, chaff, anomalous propagation, biological targets, and interference in weather radar observations are major issues causing a decline in the accuracy of meteorological and hydrological applications based on radar data. Statistically based quality control techniques using polarimetric variables have improved the accuracy of radar echo classification, however their performance is affected by attenuation, nonuniform beam filling, and hydrometeor diversity as well as terrain blockage, beam broadening, and noise correction issues due to the quality degradation of polarimetric measurements. To address this, a new quality control algorithm, named clutter elimination algorithm for non-precipitation echo of radar data (CLEANER), was designed by employing independent feature parameters and variable classification conditions with spatial and temporal observation environments to adapt to these meteorological artifacts and observational limitations. CLEANER was applied to several precipitation cases with various non-precipitation echoes, showing improved performance compared with results from the fuzzy logic-based quality control algorithm in terms of non-precipitation echo removal as well as in precipitation echo conservation. In addition, CLEANER shows better computational efficiency and robustness, as well as an excellent expandability for different radar networks.

Currently, shore-based HF radars are widely used for coastal observations, and airborne radars are utilized for monitoring the ocean with a relatively large coverage offshore. In order to take the advantage of airborne radars, the theoretical mechanism of airborne HF/VHF radar for ocean surface observation has been studied in this paper. First, we describe the ocean surface wave height with the linear and nonlinear parts in a reasonable mathematical form and adopt the small perturbation method (SPM) to compute the HF/VHF radio scattered field induced by the sea surface. Second, the normalized radar cross section (NRCS) of the ocean surface is derived by tackling the field scattered from the random sea as a stochastic process. Third, the NRCS is simulated using the SPM under different sea states, at various radar operating frequencies and incident angles, and then the influences of these factors on radar sea echoes are investigated. At last, a comparison of NRCS using the SPM and the generalized function method (GFM) is done and analyzed. The mathematical model links the sea echoes and the ocean wave height spectrum, and it also offers a theoretical basis for designing a potential airborne HF/VHF radar for ocean surface remote sensing.

The skills of CODAR SeaSonde coastal high-frequency radars (HFR) located in the West Iberian Peninsula on measuring wave parameters are compared to in situ (buoy) and satellite altimeters (SA) wave observations. Significant wave heights (SWH), wave periods, and wave directions are compared over a time window of 36-months, from January 2017 to December 2019. The ability of HFR systems to capture extreme wave events is also assessed by comparing SWH measurements during the Emma storm, which hit the Iberian Peninsula in March 2018. The analysis presented in this study shows a slight overestimation of the SWH by the HFR systems. Comparisons with in situ observations revealed correlation coefficients (R) of the order of 0.69–0.87, biases below 0.60 m, root-mean-squared errors (RMSE) between 0.89 m to 1.18 m, and a slope regression between 1.01 and 1.26. Using buoy observations as reference ground truth, the comparisons with SA revealed Rs higher than 0.94, biases under 0.19 m, and RMSEs between 0.17 m and 0.42 m. Since in situ observations do not overlap all the HFR range cells (RC), and its correlation coefficients with SA have shown good agreement (R > 0.94), Sentinel-3 SA (SRAL) SWH measurements are further used for the validation of the HFR systems SWH observations. The comparison between the HFR and the SA collocated SWH observations allowed the evaluation of the ability of the radars to retrieve wave data as a function of the distance to the coast, particularly during extreme wave events. The comparison of the lower frequency (4.86 MHz) HFR coastal radars with the SA measurements showed an R of 0.94–0.99, a negative but reduced bias (−0.37), and an RMSE of 0.53 m. The higher frequency HFR systems (12–13.5 MHz) showed R between 0.53 and 0.82, and a clear overestimation of the SWH by the HFR sites.

Radar-based Quantitative Precipitation Estimates (QPE) provide rainfall products with high temporal and spatial resolutions as opposed to sparse observations from rain gauges. Radar-based QPE’s have been widely used in many hydrological and meteorological applications; however, using these high-resolution products in the development of Precipitation Frequency Estimates (PFE) is impeded by their typically short-record availability. The current study evaluates the robustness of a spatial bootstrap regional approach, in comparison to a pixel-based (i.e., at site) approach, to derive PFEs using hourly radar-based multi-sensor precipitation estimation (MPE) product over the state of Louisiana in the US. The spatial bootstrap sampling technique augments the local pixel sample by incorporating rainfall data from surrounding pixels with decreasing importance when distance increases. We modeled extreme hourly rainfall data based on annual maximum series (AMS) using the generalized extreme value statistical distribution. The results showed a reduction in the uncertainty bounds of the PFEs when using the regional spatial bootstrap approach compared to the pixel-based estimation, with an average reduction of 10% and 2% in the 2- and 5-year return periods, respectively. Using gauge-based PFE’s as a reference, the spatial bootstrap regional approach outperforms the pixel-based approach in terms of robustness to outliers identified in the radar-based AMS of some pixels. However, the systematic bias inherent to radar-based QPE especially for extreme rainfall cases, appear to cause considerable underestimation in PFEs in both the pixel-based and the regional approaches.

Global navigation satellite systems reflectometry (GNSS-R) is a relatively novel remote sensing technique, but it can be understood as a multi-static radar using satellite navigation signals as signals of opportunity. The scattered signals over sea ice, flooded areas, and even under dense vegetation show a detectable coherent component that can be separated from the incoherent component and processed accordingly. This work derives an analytical formulation of the response of a GNSS-R instrument to a step function in the reflectivity using well-known principles of electromagnetic theory. The evaluation of the spatial resolution then requires a numerical evaluation of the proposed equations, as the width of the transition depends on the reflectivity values of two regions. However, it is found that results are fairly constant over a wide range of reflectivities, and they only vary faster for very high or very low reflectivity gradients. The predicted step response is then satisfactorily compared to airborne experimental results at L1 (1575.42 MHz) and L5 (1176.45 MHz) bands, acquired over a water reservoir south of Melbourne, in terms of width and ringing, and several examples are provided when the transition occurs from land to a rough ocean surface, where the coherent scattering component is no longer dominant. This work was funded by the Spanish MCIU and EU ERDF project (RTI2018-099008-B-C21/AEI/10.13039/501100011033) “Sensing with pioneering opportunistic techniques” and grant to ”CommSensLab-UPC” Excellence Research Unit Maria de Maeztu (MINECO grant MDM-2016-600).

The role of wave breaking in microwave backscattering from the sea surface is a problem of great importance for the development of theories and methods on ocean remote sensing, in particular for oil spill remote sensing. Recently it has been shown that microwave radar return is determined by both Bragg and non-Bragg (non-polarized) scattering mechanisms and some evidence has been given that the latter is associated with wave breaking, in particular, with strong breaking such as spilling or plunging. However, our understanding of mechanisms of the action of strong wave breaking on small-scale wind waves (ripples) and thus on the radar return is still insufficient. In this paper an effect of suppression of radar backscattering after strong wave breaking has been revealed experimentally and has been attributed to the wind ripple suppression due to turbulence generated by strong wave breaking. The experiments were carried out in a wind wave tank where a frequency modulated wave train of intense meter-decimeter-scale surface waves was generated by a mechanical wave maker. The wave train was compressed according to the gravity wave dispersion relation (“dispersive focusing”) into a short-wave packet at a given distance from the wave maker. Strong wave breaking with wave crest overturning (spilling) occurred for one or two highest waves in the packet. Short decimeter-centimeter-scale wind waves were generated at gentle winds, simultaneously with the long breaking waves. A Ka-band scatterometer was used to study microwave backscattering from the surface waves in the tank. The scatterometer looking at the area of wave breaking was mounted over the tank at a height of about 1 m above the mean water level, the incidence angle of the microwave radiation was about 50 degrees. It has been obtained that the radar return in the presence of short wind waves is characterized by the radar Doppler spectrum with a peak roughly centered in the vicinity of Bragg wave frequencies. The radar return was strongly enhanced in a wide frequency range of the radar Doppler spectrum when a packet of long breaking waves arrived at the area irradiated by the radar. After the passage of breaking waves, the radar return strongly dropped and then slowly recovered to the initial level. Measurements of velocities in the upper water layer have confirmed that the attenuation of radar backscattering after wave breaking is due to suppression of short wind waves by turbulence generated in the breaking zone. A physical analysis of the effect has been presented.

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternative discharge algorithms with sensors capable of measuring surface velocity, streamgage networks can be established in regions where data collection was previously impractical or impossible. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from sUAS or fixed platforms. QCam is a Doppler (velocity) radar mounted and integrated on a 3DR© Solo sUAS. It measures the along-track surface velocity by spot dwelling in a river cross section at a vertical where the maximum surface velocity is recorded. The surface velocity is translated to a mean-channel (mean) velocity using the probability concept (PC), and discharge is computed using the PC-derived mean velocity and cross-sectional area. Factors including surface-scatterer quality, flight altitude, propwash, wind drift, and sample duration may affect the radar-returns and the subsequent computation of mean velocity and river discharge. To evaluate the extensibility of the method, five science flights were conducted on four rivers of varying size and dynamics and included the Arkansas River, Colorado (CO), USA (two events); Salcha River near Salchaket, Alaska (AK), USA; South Platte River, CO, USA; and the Tanana River, AK, USA. QCam surface velocities and river discharges were compared to conventional streamgaging methods, which represented truth. QCam surface velocities for the Arkansas River, Salcha River, South Platte River, and Tanana River were 1.02 meters per second (m/s) and 1.43 m/s; 1.58 m/s; 0.90 m/s; and 2.17 m/s, respectively. QCam discharges (and percent differences) were 9.48 (0.3%) and 20.3 cubic meters per second (m3/s) (2.5%); 62.1 m3/s (−10.4%); 3.42 m3/s (7.3%), and 1579 m3/s (−18.8%). QCam results compare favorably with conventional streamgaging and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and river discharge, if cross-sectional area is available.

It is essential to accurately estimate rainfall to predict and prevent hydrological disasters such as floods. In this paper, an electromagnetic wave rain gauge system and a method to estimate average rainfall using the system&rsquo, s multiple elevation observation data are presented. The compact electromagnetic wave rain gauge is a small-sized radar that performs very short-range observations using K-band dual-polarization technology. The method to estimate average rainfall is based on the concept of an average observation derived from multiple elevation scans with very short range and dual-polarization information. The proposed method was evaluated by comparing it with ground instruments, including a pit-gauge, tipping-bucket rain gauges, and a Parsivel disdrometer. The evaluation results demonstrated that the new methodology worked fairly well for various rainfall events.

Canada’s successful space-based earth-observation (EO) radar program has earned widespread and expanding user acceptance following the launch of RADARSAT-1 in 1995. RADARSAT-2, launched in 2007, while providing data continuity for its predecessor’s imaging capabilities, added new polarimetric modes. Canada’s follow-up program, the RADARSAT Constellation Mission (RCM), launched in 2019, while providing continuity for its two predecessors, includes an innovative suite of polarimetric modes. In an effort to make polarimetry accessible to a wide range of operational users, RCM uses a new method called hybrid compact polarization (HCP). There are two essential elements to this approach: (1) transmit only one polarization, circular; and (2) receive two orthogonal polarizations, for which RCM uses H and V. This configuration overcomes the conventional dual and full polarimetric system limitations, which are lacking enough polarimetric information and having a small swath width, respectively. Thus, HCP data can be considered as dual-pol data, while the resulting polarimetric classifications of features in an observed scene are of comparable accuracy as those derived from the traditional fully polarimetric (FP) approach. At the same time, RCM’s HCP methodology is applicable to all imaging modes, including wide swath and ScanSAR, thus overcoming critical limitations of traditional imaging radar polarimetry for operational use. The primary image data products from an HCP radar are different from those of a traditional polarimetric radar. Because the HCP modes transmit circularly polarized signals, the data processing to extract polarimetric information requires different approaches than those used for conventional linearly polarized polarimetric data. Operational users, as well as researchers and students, are most likely to achieve disappointing results if they work with traditional polarimetric processing tools. New tools are required. Existing tutorials, older seminar notes, and reference papers are not sufficient, and if left unrevised, could succeed in discouraging further use of RCM polarimetric data. This paper is designed to provide an initial response to that need. A systematic review of studies that used HCP SAR data for environmental monitoring is also provided. Based on this review, HCP SAR data have been employed in oil spill monitoring, target detection, sea ice monitoring, agriculture, wetland classification, and other land cover applications.

Following the mission science plan of EPS/Metop-SG C-band scatterometer for 2023&ndash, 2044, we consider the potential application of the sea ice/water discrimination method based on the minimum statistical distance of the measured normalized radar cross sections (NRCS) to the geophysical model functions (GMF) of the sea ice and water, respectively. The application of the method is considered for the classical spacecraft scatterometer geometry with three fixed fan-beam antennas and the hypothetical prospective scatterometer geometry with the five fixed fan-beam antennas. Joint vertical (VV) and horizontal (HH) transmit and receive polarization are considered for the spaceborne scatterometer geometries. We show explicitly that the hypothetical five fixed fan-beam antenna geometry combined with the dual VV and HH polarization for all antennas provides better estimates of the sea wind speed and direction as well as sea ice/water discrimination during single spacecraft pass. The sea ice/water discrimination algorithms developed for each scatterometer geometry and dual VV/HH polarization are presented. The obtained results can be used to optimize the design of new spaceborne scatterometers and will be beneficial to the forthcoming satellite missions.

Quantitative precipitation estimation (QPE) through remote sensing has to take rain microstructure into consideration, because it influences the relationship between radar reflectivity Z and rain intensity R. For this reason, separate equations are used to estimate rain intensity of convective and stratiform rain types. Here, we investigate whether incorporating synoptic scale meteorology could yield further QPE improvements. Depending on large-scale weather types, variability in cloud condensation nuclei and the humidity content may lead to variation in rain microstructure. In a case study for Bavaria, we measured rain microstructure at ten locations with laser-based disdrometers, covering a combined 18,600 h of rain in a period of 36 months. Rain was classified on a temporal scale of one minute into convective and stratiform based on a machine learning model. Large-scale wind direction classes were on a daily scale to represent the synoptic weather types. Significant variations in rain microstructure parameters were evident not only for rain types, but also for wind direction classes. The main contrast was observed between westerly and easterly circulations, with the latter characterized by smaller average size of drops and a higher average concentration. This led to substantial variation in the parameters of the radar rain intensity retrieval equation Z–R. The effect of wind direction on Z–R parameters was more pronounced for stratiform than convective rain types. We conclude that building separate Z–R retrieval equations for regional wind direction classes should improve radar-based QPE, especially for stratiform rain events.

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation.

Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.