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Climate change has caused fundamental changes in the pattern of rainfall worldwide. Climate change can alter precipitation patterns, consequently intensifying the frequency and severity of flash floods in specific regions, including Iran. It is important for communities to be prepared for these events and to take steps to mitigate their impact. Full control or damage management of the resulted floods through structural measures is not always feasible due to economic, technological, environmental and social limitations. Therefore, often non-structural measures play an important role in reducing probable damages and casualties. The significance of advanced systems for both short- and long-term flood forecasting cannot be overstated. In this article, short-term flood prediction model is discussed using Ensemble Prediction Systems (EPSs) Machine Learning algorithms (ML) and HEC-HMS hydrological model. Also, in order to achieve high accuracy in the assessment of flood-damaged areas, remote sensing techniques have been used. The results show that the use of EPS improves the speed and accuracy of the daily prediction model (R2 = 0.8). Also, with the use of Sentinel-1 radar satellite images and the simultaneous use of supervised learning algorithms, a suitable estimate of the evaded area has been made for seven selected floods in the Kan basin, which is a mountainous region in the north of Tehran, in 2015–2022 period. [ABSTRACT FROM AUTHOR]

Measurements with a 763 nm channel, located within the oxygen A-band and equipped on the Second-generation Global Imager (SGLI) onboard the JAXA's Global Change Observation Mission – Climate (GCOM-C) satellite, have the potential to retrieve cloud base height (CBH) and cloud geometric thickness (CGT) through passive remote sensing. This study implemented an algorithm to retrieve the CBH using the SGLI 763 nm channel in combination with several other SGLI channels in the visible, shortwave infrared, and thermal infrared regions. In addition to CBH, the algorithm can simultaneously retrieve other key cloud properties, including cloud optical thickness (COT), cloud effective radius, ice COT fraction as the cloud thermodynamic phase, cloud top height (CTH), and CGT. Moreover, the algorithm can be seamlessly applied to global clouds comprised of liquid, ice, and mixed phases. The SGLI-retrieved CBH exhibited quantitative consistency with CBH data obtained from the ground-based ceilometer network, ship-borne ceilometer, satellite-borne radar and lidar observations, as evidenced by sufficiently high correlations and small biases. These results provide practical evidence that the retrieval of CBH is indeed possible using the SGLI 763 nm channel. Moreover, the results lend credence to the future use of SGLI CBH data, including the estimation of the surface downward longwave radiative flux from clouds. Nevertheless, issues remain that must be addressed to enhance the value of SGLI-derived cloud retrieval products. These include the systematic bias of SGLI CTH related to cirrus clouds and the bias of SGLI CBH caused by multi-layer clouds. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and radar technology have benefitted from breakthroughs in recent decades. Both technologies have found applications independently of each other, but together, they also unlock new possibilities, especially for remote sensing applications. One of the key factors for a remote sensing system is the estimation of the flight attitude. Despite the advancements, accurate attitude estimation remains a significant challenge, particularly due to the limitations of a conventional Inertial Measurement Unit (IMU). Because these sensors may suffer from issues such as drifting, additional effort is required to obtain a stable attitude. Against that background, this study introduces a novel methodology for making an attitude estimation using radar data. Herein, we present a drone measurement system and detail its calculation process. We also demonstrate our results using three flight scenarios and outline the limitations of the approach. The results show that the roll and pitch angles can be calculated using the radar data, and we conclude that the findings of this research will help to improve the flight attitude estimation of remote sensing flights with a radar sensor. [ABSTRACT FROM AUTHOR]

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications. [ABSTRACT FROM AUTHOR]

High-frequency radars (HFRs) provide remote information on ocean surface velocity in extended coastal areas at high resolutions in space (O (km)) and time (O (h)). They directly produce radial velocities (in the radar antenna's direction) combined to provide total vector velocities in areas covered by at least two radars. HFRs are a key element in ocean observing systems, with several important environmental applications. Here, we provide an assessment of the HFR-TirLig network in the NW Mediterranean Sea, including results from the gap-filling open-boundary modal analysis (OMA) using in situ velocity data from drifters. While the network consists of three radars, only two were active during the assessment experiment, so the test also includes an area where the radial velocities from only one radar system were available. The results, including several metrics, both Eulerian and Lagrangian, and configurations, show that the network performance is very satisfactory and compares well with the previous results in the literature in terms of both the radial and total combined vector velocities where the coverage is adequate, i.e., in the area sampled by two radars. Regarding the OMA results, not only do they perform equally well in the area sampled by the two radars but they also provide results in the area covered by one radar only. Even though obviously deteriorated with respect to the case of adequate coverage, the OMA results can still provide information regarding the velocity structure and speed as well as virtual trajectories, which can be of some use in practical applications. A general discussion on the implications of the results for the potential of remote sensing velocity estimation in terms of HFR network configurations and complementing gap-filling analysis is provided. [ABSTRACT FROM AUTHOR]

T he interferometric Shuttle Radar Topography Mission (SRTM) satellite's digital elevation model (DEM) is an important tool for studying topographic features on a medium-spacing scale. Data were collected and processed using the satellite's orbital and navigation parameters with selected global GPS stations for verification. Distortion may be expressed by surveying measurements, such as position, distance, area, and shape. This study focuses on this distortion and proposes a new registration method to reduce its effect. Because of generality, the purpose shapes were excluded from this study. The proposed registration method depends on precise GPS control points that act as the ground truth for describing the considered surveying measurements. The processing was carried out using deep artificial neural networks (DANN) to produce a new registered DEM. A comparison was made between the original DEM and the new one, focusing on the selected surveying measurements. Another comparison was made between the GPS coordinates and SRTM polynomials to determine the potential of the proposed system. Some statistical investigations were applied to determine the level of significance of the distortion in each surveying measurement. The study shows that the distortion is highly significant; therefore, the proposed registration method is recommended to fix the distortion. An important finding is the enhancement in local coordinates scope. [ABSTRACT FROM AUTHOR]

A THz radar, with its wide bandwidth, is capable of high‐resolution imaging down to the centimeter scale. In this study, a THz radar is applied to detect hydrometeors generated in a spray chamber. The observed backscattering signals show fluctuations at centimeter scales, indicating various hydrometeor distribution patterns along the radar beam. A co‐located High‐Speed Imaging (HSI) sensor is used to measure the Drop Size Distributions (DSD) in the spray chamber. The radar sampling beam is well aligned with the HSI probes, allowing an objective comparison between the remote sensing and in situ observations. In this study, the observed radar power is compared with the power estimated from the HSI measurements. Results show great consistency, with power difference smaller than 0.5 dB. This study demonstrates the feasibility and great potential of using a THz radar for ultra‐high‐resolution observations of clouds in a laboratory facility, and in the real atmosphere. Plain Language Summary: An improved understanding of how precipitation forms in clouds calls for new observational instruments and novel measurement strategies. Radars operating at ultra‐high frequency (a.k.a. THz radars) are known for their unprecedented resolution down to centimeter scale. Here, for the first time, we applied a THz radar to detect hydrometeors generated in a spray chamber. An additional in situ probe is utilized within the chamber to validate the THz radar observations. The radar beam is directed through the same volume that is sampled by the in situ probe, thus enabling an objective comparison between the in situ and remote sensing measurements. Different hydrometeor distributions are generated in the chamber, each being jointly observed by the radar and the in situ probes. Results show that the THz radar measurements agree well with the radar power calculated from the in situ observations. This unique experiment indicates promising scenarios of applying THz radar to cloud and precipitation studies in a laboratory facility and in the real atmosphere. Key Points: A novel experiment is conducted by applying a THz radar to detect hydrometeors generated in a spray chamberGood agreement between in situ and radar observations demonstrates the unique capability of THz radar for cloud and precipitation studiesImplications of applying THz radar to design novel observational strategies for solving fundamental gaps in cloud physics are demonstrated [ABSTRACT FROM AUTHOR]

This paper recommends end to end quality assurance methods and quality control tests for High Frequency Radar Networks. We focus on the network that is operated by the Mid Atlantic Regional Association Coastal Ocean Observing System (MARACOOS). The network currently consists of 38 radars making real-time measurements of the surface currents over the continental shelf for a variety of applications including search and rescue planning, oil spill trajectory modelling and providing a transport context for marine biodiversity observing networks. MARACOOS has been delivering surface current measurements to the United States Coast Guard (USCG) since May 2009. Data quality is important for all applications; however, since the USCG uses this surface current information to plan life-saving missions, delivery of the best quality data is crucial. We have mapped the components of the HF radar data processing chain onto the data levels presented in the NASA Earth Science Reference Handbook and have applied quality assurance and quality control techniques at each data level to achieve the highest quality data. There are approximately 400 High Frequency radars (HFRs) deployed globally and the presented techniques can provide a foundation for data quality checks and standardization of the data collected by the large number of systems operating today. [ABSTRACT FROM AUTHOR]

Along the western coastal plain of the Red Sea, the discovery of lithic tools proved that the occurrence of archaeological remains belonged to the Stone Age era. Unfortunately, until recently, most of the Stone Age site plans in the western Red Sea periphery have been unknown because of change in climate in addition to covering these sites with a large layer of sand. Recently, remote sensing (RS) data have proven to be effective at identifying buried archaeological sites using radar satellite imagery due to their ability to penetrate the land surface. This paper aims to identify and reconstruct several potential Stone Age sites using radar satellite images in the Tokar region, North-Eastern Sudan, on the western plain of the Red Sea. Radar (Sentinel-1 SAR GRD: C-band) data were processed utilizing the advantages of the Google Earth Engine platform and Snap software. The results of this research showed that some potential settlements belonging to the Stone Age era (expectedly dating back to the end of the Pleistocene and/or the initial phase of the Holocene) have subcircular shapes with some interior subcircles. These results shed new light on the potential construction technique, size, and function of the settlement in the Stone Age era and can provide some initial insights into the ancient lifestyle in this region. Additionally, this study can aid in future archaeological surveys and excavation missions in this region. [ABSTRACT FROM AUTHOR]

The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross‐regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U‐shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach. [ABSTRACT FROM AUTHOR]

Instruments such as Doppler lidars, radar wind profilers, and uncrewed aircraft systems could be used in observation networks to fill in the temporal and spatial gap that exists for low-level wind observations. These instruments, however, do not directly observe the wind and require a retrieval to be used to obtain wind estimates from their observations. Also, the depth and uncertainty of observations collected by these instruments vary depending on the environment that they are sampling. Optimal estimation is a variational retrieval method that combines information from a prior dataset and observations to retrieve an atmospheric state. This technique can be beneficial to use when observations have large uncertainties or provide insufficient information to obtain the atmospheric state by themselves. A new optimal estimation retrieval for obtaining wind profiles from typical lower atmospheric wind profiling instrumentation has been developed. This retrieval allows for more observations from wind profiling instrumentation to be used when retrieving wind profiles, increases the depth of retrieved profiles, and eliminates vertical data gaps. This retrieval can also be used to easily combine observations from different instruments or even with model data to create combined data wind retrievals that leverage the strengths of the different data sources to retrieve a wind profile that is superior to those obtained by the individual observations or data sources. It is envisioned that this retrieval will be continued to be developed and maintained as community software as lower atmospheric wind observing capabilities further develop and expand. [ABSTRACT FROM AUTHOR]

This paper considers theoretical and observed uncertainties in the estimates of ZDR and ρHV(0) using data from an operational S-band radar and a mobile X-band radar. Cases of widespread uniform precipitation including bright-band, clear air, and ash echoes from forest fires are all considered in order to obtain a wide range of ρHV(0) values as this along with the radar frequency and spectrum width determines the uncertainties. The theoretical uncertainties in these parameters provide a good estimate of the lower bound of the standard deviations of the observed values where these have been estimated using the adjacent data to the target pixel. The implications for the accuracy of precipitation estimation, particle identification, and estimates of drop-size distributions are discussed. Significance Statement: High-quality quantitative precipitation and particle size/classification retrievals using weather radar are strongly dependent on the accuracy of ZDR and ρHV(0). This paper examines the theoretical limits to the measurement accuracy and verifies these limits with radar data at 10- and 3-cm wavelengths. [ABSTRACT FROM AUTHOR]

Extensive studies have been carried out on the characteristics of quantum radar cross section (QRCS) of targets. However, one crucial question related to multi-photon quantum radar cross section (M-QRCS) for targets in the atmospheric medium has not been explored yet. Understanding this question is vital for target detection and identification of quantum radar. This paper presents a universal method to solve M-QRCS in a homogeneous atmospheric medium (HAM-QRCS). The process is based on the photon wave function in a homogeneous atmospheric medium and the interaction mechanism of multi-photon and multiple atoms. It is suitable for analyzing the HAM-QRCS characteristics of targets of arbitrary shapes. The simulation results show that the molecules, particles, and other factors in the atmospheric medium cause the signal photons' energy to decrease and the propagation direction to change, leading to a decrease in the target return responses. However, in a specific angle range, as the photon number increases, the main lobe and first side lobe structures of the bistatic HAM-QRCS response are enhanced. These findings can be utilized to design target detection strategies and optimize stealth target structures of the quantum radar in the atmospheric medium. [ABSTRACT FROM AUTHOR]

X‐band marine radar observations and a hindcast simulation from a 3D hydrostatic model are used to provide an overview of Connecticut River (USA) ebb plume front expansion into the strong tidal crossflow of eastern Long Island Sound. The model performance is evaluated against in situ and remote sensing observations and demonstrates dominant control of the front by semidiurnal tides. The recurring frontal evolution is classified into three dynamical stages of arrest, propagation, and advection. A conceptual model that follows this progressing balance between outflow buoyancy and crossflow momentum qualitatively reproduces frontal evolution in both the radar observations and the hindcast. The majority of the residual, intertidal variability of front timing and geometry is explained by co‐varying tidal amplitude, freshwater discharge, and wind stress using a multi‐linear regression analysis of the radar observation record. Intrinsic front speeds in the modeled frontal propagation are compared with the analytical model of Benjamin (1968, https://doi.org/10.1017/s0022112068000133), with better agreement achieved after accounting for ambient near‐surface shear associated with wind forcing. Plain Language Summary: The fresh buoyant water that exits the Connecticut River mouth (USA) during each ebb tide expands as a plume, and floats above the denser waters of Long Island Sound. Currents in the Sound flow back and forth along the coast each tide, pushing the plume along the shore first to the east and then to the west. The location of the plume boundary (front) goes through a similar cycle almost every tide, initially being held stationary by eastward tidal currents, then expanding freely around slack tide, and subsequently being aided as it moves to the west by the ambient flow. Small changes to the arrival time and shape of the front are linked to changes in river discharge, tidal strength, and winds. Realistic simulations using a type of numerical model that aids coastal decision making are able to reproduce the tidal progression of front movement that was observed in 6 weeks of marine radar images of the water surface. Front movement in the numerical model also fits a simple analytical theory to a degree not previously seen on such a large scale, despite the complicating effects of wind. Key Points: Buoyant ebb plume front kinematics in a tidal cross‐flow are characterized by stages of arrest, propagation, and advectionWind, tide, and discharge variations explain modifications to the dominant semidiurnal control of the Connecticut River plume frontFront propagation in a hydrostatic numerical model reproduces both radar remote sensing observations and gravity current theory [ABSTRACT FROM AUTHOR]

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]

Observations of clouds and precipitation in the microwave domain from the active dual-frequency precipitation radar (DPR) and the passive Global Precipitation Measurement (GPM) Microwave Imager (GMI) onboard the GPM Core Observatory satellite are used in synergy with cloud tracking information derived from infrared imagery from the GOES-13 and Meteosat-7 geostationary satellites for analysis of the life cycle of precipitating cloud systems, in terms of temporal evolution of their macrophysical characteristics, in several oceanic and continental regions of the tropics. The life cycle of each one of the several hundred thousand cloud systems tracked during the 2-yr (2015–16) analysis period is divided into five equal-duration stages between initiation and dissipation. The average cloud size, precipitation intensity, precipitation top height, and convective and stratiform precipitating fractions are documented at each stage of the life cycle for different cloud categories (based upon lifetime duration). The average life cycle dynamics is found remarkably homogeneous across the different regions and is consistent with previous studies: systems peak in size around midlife; precipitation intensity and convective fraction tend to decrease continuously from the initiation stage to the dissipation. Over the three continental regions, Amazonia (AMZ), central Africa (CAF), and Sahel (SAH), at the early stages of clouds' life cycle, precipitation estimates from the passive GMI instrument are systematically found to be 15%–40% lower than active radar estimates. By highlighting stage-dependent biases in state-of-the-art passive microwave precipitation estimates over land, we demonstrate the potential usefulness of cloud tracking information for improving retrievals and suggest new directions for the synergistic use of geostationary and low-Earth-orbiting satellite observations. [ABSTRACT FROM AUTHOR]

Ensuring the safety and operational efficiency of hydraulic infrastructures is paramount, considering the widespread consequences that damages can inflict on people, communities, and the environment. To mitigate risks and prevent significant losses, continuous surveillance is vital. While some damages might appear minor, they can jeopardize the complete operational reliability of dams, leading to substantial economic losses, especially in energy production and related activities. The rapid growth in 20th-century infrastructure development globally has made security monitoring a necessity for numerous civil structures. Rigorous inspection programs, particularly for reservoir dams, are essential for safeguarding citizens and their properties. However, individually monitoring each dam is often impractical due to the associated costs and time constraints, potentially posing safety risks. Fortunately, satellite-based differential radar interferometry (DInSAR) offers an effective and cost-efficient remote sensing solution. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques, particularly utilizing Persistent Scatterers, have proven successful in monitoring various infrastructures, natural phenomena, and geological activities. MT-InSAR provides precise measurements without the need for fieldwork, utilizing historical SAR image archives dating back to the 1990s. Technological advancements, such as the Sentinel-1 C-band with a six-day revisit time until the end of 2021, have enhanced monitoring capabilities. Additionally, commercial radar images in the X-band and the development of multi-interferometric InSAR techniques have opened new avenues for monitoring. This study showcases the adaptation and application of MT-InSAR for monitoring dams and large ponds constructed with loose materials. By assessing vertical displacements and consolidation rates, the technique identifies potential issues, aiding in further field investigations. Case studies involving dams and large reservoirs in Andalusia illustrate the effectiveness of satellite radar interferometry in monitoring their structural stability from space as a routine practice. [ABSTRACT FROM AUTHOR]

Surface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach. Plain Language Summary: Pressure is an important atmospheric variable. A radar‐based remote sensing techinque to measure surface pressure is proposed. The methodology is based on the darkening of the surface return when moving closer to the center of the 60 GHz oxygen absorption line. Results demonstrate that measurements of surface pressure with errors of few hPa are feasible. Key Points: Differential absorption radars with multiple tones within the oxygen band to enable surface pressure measurements with few hPa accuracyCloud, rain and water vapor produce biases in the surface pressure that must be correctedErrors can be minimized by adopting tunable frequencies and frequency diversity [ABSTRACT FROM AUTHOR]

Global concern on extreme events is increasing the need for real timemonitoring of the wave fields in coastal areas. High Frequency (HF) radars, a remote sensing technology widely applied to measure near real time surface coastal currents with demonstrated accuracy, can also play a major role in the operational monitoring of waves height, period and direction. However, the ability of HF radar to measure waves can be jeopardized by specific ocean-meteorological and environmental conditions. Thus, a case-to-case analysis and parameterization is necessary to ensure the best data in each study area. In the southeastern (SE) Bay of Biscay, the EuskOOS HF radar network, composed by two compact HF radar stations provides hourly surface waves data in near real time. In this work, we analyze the effects of wind and noise levels on the radar skills for wave measurement, compared with existing in-situ data obtained by an offshore buoy. Then, the HF radar wave measurements for 2022 are analyzed with special focus on the most energetic observed wave events. The analysis performed versus in-situ data shows that both stations present reliable and accurate data for waves over 1.5 m, in agreement to what can be expected for a 4.46 MHz radar. The highest correlations are observed for waves > 4 m significant wave height, which demonstrates the capabilities for monitoring highly energetic events. Interference and noise detected on very precise time slots significantly reduced the availability and reliability of the measurements. Also, local winds blowing from land direction were found to affect the agreement between radar and in-situ measurements. Recommendations extracted from the analysis are provided, with the aim that they can be extended to other HF networks for more accurate wave monitoring. [ABSTRACT FROM AUTHOR]

This paper focuses on the automated extraction of textural features from segmented Sentinel-1A Synthetic Aperture Radar (SAR) imagery, which was captured on 25th August 2017. Textural properties play a pivotal role in discerning regions of interest in satellite imagery, essential for precise classification in diverse applications, including remote sensing, medical image analysis, biometric analysis, and document image analysis. To achieve this, the Local Adaptive Threshold (LAT) technique was employed for segmenting the foreground and background areas of the pre-processed vertical transmit-vertical receive (VV) polarized Sentinel-1A SAR data. The approach relies on second-order statistics, specifically co-occurrence measures, which evaluate the relationships between pairs of pixels within their local neighbourhood's in the input image. Four fundamental second-order statistical measures, namely correlation, energy, homogeneity, and contrast, were computed using the Grey Level Co-occurrence Matrix (GLCM). The GLCM was generated by quantizing the foreground region of the segmented grey-scale image into different levels, including 8, 16, and 64 grey levels, considering two inter-pixel distances, d=1 and d=2, and adopting an omni-directional orientation (θ = 00, 450, 900, 1350).The results underscore the effectiveness of the GLCM-based approach in computing second-order statistical texture measures from the segmented SAR image. Notably, the findings highlight that quantizing the image to Ng=8 with d=1 and considering omni-directional statistical measures (θ = 00, 450, 900, 1350) significantly enhances performance. [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]

Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN's accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning. [ABSTRACT FROM AUTHOR]

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

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 use of WiFi signals for sensing purposes has attracted a lot of interest from both the radar and communications communities and several techniques have been explored. In the attempt of meeting the requirements for small sensor size, compactness, and easy deployment, the authors consider reference‐free approaches, namely approaches that do not require a good copy of the transmitted waveform to be available at the radar receiver. To this end, the authors first resort to a passive radar‐based processing scheme that only exploits the invariant a priori known initial portion of the physical layer protocol data unit, that is, the PHY Preamble, and its limitations in practical applications is investigated. Specifically, the authors show that, with this approach, an accurate time, phase, and frequency synchronization is essential and a possible strategy is investigated. As an alternative solution the authors consider a forward scatter radar‐based approach where only the amplitude modulation of the received signal is exploited to detect the presence of a moving target thus avoiding the need to know the transmitted signal. For the first time, the authors comparatively investigate advantages and drawbacks of the two reference‐free approaches and present practical strategies to handle the limitations observed. The results are reported for experimental tests with people and drones using WiFi transmissions in the 2.4 and 5 GHz band. [ABSTRACT FROM AUTHOR]

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services. [ABSTRACT FROM AUTHOR]

This paper presents studies on using Sentinel-1 imagery data to determine the attributes of mixed forest stands. The fieldwork is carried out in Kostroma, Vologda, and Arkhangelsk oblasts and the Udmurt Republic. The study reveals that quantitative and qualitative forest characteristics correlate with radar survey parameters; the value of this correlation is identified. The results make it possible to zone a study area according to the standing volume and forest density. [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]

We present a comprehensive quality-controlled 15-month dataset of remote sensing observations of low-level mixed-phase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region and extensively affect the energy budget. However, our understanding of the ice microphysical processes taking place in these clouds is incomplete. The dual-wavelength and polarimetric Doppler cloud radar observations, which are the cornerstones of the dataset, provide valuable fingerprints of ice microphysical processes, and the high number of cases included allows for the compiling of robust statistics for process studies. The radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer, and wind fields from large-eddy simulations. All data are quality controlled, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter, and range folding. We finally present an analysis of the temperature dependence of Doppler, dual-wavelength, and polarimetric radar variables, to illustrate how the dataset can be used for cloud microphysical studies. The dataset has been published in and is freely available at: 10.5281/zenodo.7803064. [ABSTRACT FROM AUTHOR]

The interferometric radar altimeter (IRA) is an innovative remote sensing sensor that enables the observation of mesoscale and submesoscale (meso–submesoscale) ocean dynamic phenomena. The charge-neutral atmosphere introduces path delay and bending in signal propagation. In this study, three types of sea surface height (SSH) errors caused by charge-neutral atmosphere propagation path for IRA were identified: differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Among them, DDE exhibits a proportionality to the negative zenith neutral delay (ZND) and demonstrates a significant increase with the incident angle; PDE is solely reliant on the ZND; PBE is like DDE in trend and magnitude resembling a ramp. Intriguingly, PBE exhibits insensitivity to variations in the charge-neutral atmosphere, behaving more like a systematic error. Theoretically, PBE leads to an increase in the SSH error of about 1.2 cm at far range for SWOT. The ZND spectrum fitted from the Jason-3 zenith delay correction data is additionally utilized to simulate the spatial distribution of ZND anomaly within the SWOT observation swaths. Then, the impact of PDE anomaly (PDEA), PBE, and DDE anomaly (DDEA) on the observation performance of SWOT is also considered in conjunction with SSH data provided by HYCOM. The findings indicate that both PDEA and PBE significantly reduce IRA's performance in oceanic phenomena, while the impact of DDEA can be disregarded. The PBE can distort the sea surface trend and increases the mean sea level within the range, requiring further attention. Significance Statement: This paper focuses on how the signal path affects the accuracy of measuring sea surface height (SSH) using the interferometric radar altimeter (IRA) in the charge-neutral atmosphere. The present paper defines three types of SSH errors caused by propagation path to IRA, namely, differential delay error (DDE), path delay error (PDE), and path bending error (PBE). Three types of SSH errors will make a significant impact on the altimetry performance of IRA. Among them, PDE spatial anomaly and PBE will reduce the expected observation performance of the meso–submesoscale ocean phenomena in IRA's swath, while PBE has not received enough attention in previous studies. Therefore, the research will provide a theoretical basis for IRA to correct the above SSH errors. [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]

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods, these observations offer the opportunity to address the challenge of estimating precipitation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model's robustness for global applications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model's superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 min. [ABSTRACT FROM AUTHOR]

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

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]

This paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies. Key Points: Demonstration database of individual thunderstorm‐like features combines independent data from multiple moving and fixed observing platformsPreliminary features include precipitation and lightning data collected from Global Precipitation Measurement and Lightning Imaging Sensor on board the International Space Station satellite instruments and ground‐based radarsDatabase enables cross‐platform, broad statistical thunderstorm‐centric analyses as well as detailed single‐storm case study reconstruction [ABSTRACT FROM AUTHOR]

These studies focused on the quantitative assessment of the surface displacement velocities and rates and their natural and man-made controlling factors as the potential risks along the seismically active 70 km section of buried oil and gas pipeline in Azerbaijan using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) and Small Baseline Subset (SBAS-InSAR) remote sensing analysis. The diverse spatial distribution and variation of ground movement processes along pipelines demonstrated that general geological and geotechnical understanding of the study area is not sufficient to find and mitigate all the critical sites of subsidence and uplifts for the pipeline operators. This means that both techniques outlined in this paper provide a significant improvement for ground deformation monitoring or can significantly contribute to the assessment of geohazards and preventative countermeasures along petroleum and gas pipelines. [ABSTRACT FROM AUTHOR]

Abstract Surface air pressure is one of the most important parameters used in Numerical Weather Prediction (NWP) models. Although it has been measured using weather stations on the ground for many decades, the numbers of measurements are sparse and concentrated on land. Few measurements from buoys and ships are available over ocean. Global measurements can only be achieved by using remote sensing from Space, which is challenging; however, a novel design using Differential Absorption Radar (DAR) can provide a potential solution. The technique relies on two facts: first the electromagnetic fields are absorbed mainly by oxygen and water vapor, and second that oxygen is well mixed. In this work we discuss a space‐borne concept, which aims at providing, over the ocean, consistent, and regular observations for determining surface air pressure from space by a design of a multi‐tone radar operating on the upper wing of the O2 absorption band with tones from 64 to 70 GHz. Simulations of radar vertical profiles based on the output of a state of‐the‐art microphysical retrievals applied to the A‐Train suite of sensors are exploited to establish the performance of such a system for surface pressure determination. In particular the identification and quantification of errors introduced by the presence of water vapor, cloud liquid water and rain water and the potential of a correction via the three‐tone method is discussed. Errors introduced by surface measurement noise and temperature profile uncertainties are discussed as well. Results show that accuracy between 2 and 5 hPa is at reach.

Abstract This paper outlines the construction of a new Multiplatform Precipitation Feature (MPF) framework designed to facilitate storm‐based analyses of data sets from multiple independent observing platforms for convective studies. The MPF approach is applied to ground‐ and space‐based precipitation observations and retrievals from the Global Precipitation Measurement mission Validation Network with space‐based lightning measurements from the Lightning Imaging Sensor on board the International Space Station to create a prototype MPF database. The data are synthesized in a thunderstorm‐like, feature‐based framework that enables broad statistical analyses of the storm‐based relationships between the microphysics, kinematics, and electrical properties of convection that can contribute insight into their morphological, regional, and seasonal variations. This manuscript details how MPFs are defined from precipitation data and how observations from multiple additional platforms are combined within the context of an MPF thunderstorm boundary and used to characterize the feature. A demonstration of the prototype database as a proof‐of‐concept showcases its flexibility to conduct both large‐scale storm‐based statistical studies as well as MPF‐facilitated thunderstorm case studies.

Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.

Using global Sentinel-1 radar backscatter data, we systematically map the locations of glaciers with surge-type activity during 2017–22. Patterns of pronounced increases or decreases in the strongest backscatter between two winter seasons often indicate large changes in glacier crevassing, which we treat here as a sign of surge-type activity. Validations against velocity time series, terminus advances and crevassing found in optical satellite images confirm the robustness of this approach. We find 115 surge-type events globally between 2017 and 2022, around 100 of which on glaciers already know as surge-type. Our data reveal a pronounced spatial clustering in three regions, (i) Karakoram, Pamirs and Western Kunlun Shan (~50 surges), (ii) Svalbard (~25) and (iii) Yukon/Alaska (~9), with only a few other scattered surges elsewhere. This spatial clustering is significantly more pronounced than the overall global clustering of known surge-type glaciers. The 2017–22 clustering may point to climatic forcing of surge initiation. [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]

Wind retrievals from high‐frequency radars (HFRs) provide high‐density, hourly wind estimates over the ocean near the coast. These wind retrievals are a promising new source of near‐surface wind estimates in coastal regions where it is difficult to deploy large networks of buoys and where scatterometers cannot observe because of land contamination. In addition to improving ocean monitoring, these wind retrievals are also useful in numerical weather prediction. The wind estimates are retrieved from the HFR Doppler spectra in conjunction with the Simulating WAves Nearshore (SWAN) model, HFR forward model, and HFR adjoint model. In this work, wind retrievals were generated from three Coastal Ocean Dynamics Applications Radar (CODAR) SeaSonde HFR sites located near the mouth of the Chesapeake Bay in August 2017 and assimilated in the Coupled Ocean/Atmosphere Mesoscale Prediction System® (COAMPS) model. The impact of the assimilated HFR wind retrievals on near‐surface weather forecasts is measured with adjoint‐derived forecast sensitivity observation impact (FSOI) and a comparison to forecasts from a data‐denial observing system experiment (OSE). The FSOI measurement indicates the HFR winds had neutral impact on the 12‐h forecast while the OSE comparison suggests a small improvement in the 10‐m u and v winds at lead times up to 36 h. Compared to a similar study in the Southern California Bight, the small impact on the forecast seen in this Chesapeake Bay study could be related to there being a smaller number of wind retrievals in a smaller region during a period where the HFR wind retrievals were already similar to the background wind field. We suggest that HFR wind retrievals used for numerical weather prediction may be more beneficial when generated for long swaths of coastlines rather than small, isolated areas. [ABSTRACT FROM AUTHOR]

Close-range sensors are employed to observe glaciological processes that operate over short timescales (e.g. iceberg calving, glacial lake outburst floods, diurnal surface melting). However, under poor weather conditions optical instruments fail while the operation of radar systems below 17 GHz do not have sufficient angular resolution to map glacier surfaces in detail. This letter reviews the potential of millimetre-wave radar at 94 GHz to obtain high-resolution 3-D measurements of glaciers under most weather conditions. We discuss the theory of 94 GHz radar for glaciology studies, demonstrate its potential to map a glacier calving front and summarise future research priorities. [ABSTRACT FROM AUTHOR]

Ice-penetrating radar sounding is a powerful geophysical tool for studying terrestrial and planetary ice with a rich glaciological heritage reaching back over half a century. Recent years have also seen rapid growth in both the radioglaciological community itself and in the scope and sophistication of its analysis of ice-penetrating radar data. This has been spurred by a combination of growing datasets and improvements in computational resources as well as advances in radar sounding instrumentation and platforms. Together, these developments are transforming the field and highlight exciting paths forward for future innovation and investigation. [ABSTRACT FROM AUTHOR]