Search

Spatiotemporal distribution of soil moisture is important for hydrometeorological and agricultural applications. There is growing interest in monitoring soil moisture in relation to soil- and land-based natural flood management (NFM), to understand the soil’s ability, via land-use and management changes, and to delay the arrival of flood peaks in nearby watercourses. This article monitors relative surface soil moisture (rSSM) across the Thames Valley, U.K., using Sentinel-1 data, and the Vienna University of Technology (TU-Wien) Change Detection Algorithm, with a novel exploration of monthly and annual normalization factors and spatial averaging. Two pairs of normalization factors are introduced to remove impacts from varying local incidence angles through direct and multiple regression slopes. The spatiotemporal distribution of rSSM values at various spatial resolutions (1000, 500, 250, and 100 m) is assessed. Comparisons with in situ soil moisture data from the COSMOS-UK network show that, while general temporal trends agree, the difference in effective depth of measurements, coupled with vegetation impacts during the growing season, makes comparison with soil moisture observations difficult. Temporal rSSM trends can be retrieved at spatial resolutions down to 100 m, and the rSSM RMSE was found to decrease as the spatial resolution increases. The vegetation effects upon the rSSM are further explored by comparing the two dominant land cover types: Arable and Horticulture, and Improved Grassland. It was found that, while the rSSM retrieval for these land covers was possible, and the general soil moisture trend is clear, overlying vegetation during the summer artificially increased the rSSM values. [ABSTRACT FROM AUTHOR]

Registration for multisensor or multimodal image pairs with a large degree of distortions is a fundamental task for many remote sensing applications. To achieve accurate and low-cost remote sensing image registration, we propose a multiscale framework with unsupervised learning, named MU-Net. Without costly ground truth labels, MU-Net directly learns the end-to-end mapping from the image pairs to their transformation parameters. MU-Net stacks several deep neural network (DNN) models on multiple scales to generate a coarse-to-fine registration pipeline, which prevents the backpropagation from falling into a local extremum and resists significant image distortions. We design a novel loss function paradigm based on structural similarity, which makes MU-Net suitable for various types of multimodal images. MU-Net is compared with traditional feature-based and area-based methods, as well as supervised and other unsupervised learning methods on the optical-optical, optical-infrared, optical-synthetic aperture radar (SAR), and optical-map datasets. Experimental results show that MU-Net achieves more comprehensive and accurate registration performance between these image pairs with geometric and radiometric distortions. We share the code implemented by Pytorch at https://github.com/yeyuanxin110/MU-Net. [ABSTRACT FROM AUTHOR]

This paper describes a technique for identifying hydrometeor particle types using airborne HIAPER Cloud Radar (HCR) and High Spectral Resolution Lidar (HSRL) observations. HCR operates at a frequency of 94 GHz (3 mm wavelength), while HSRL is an eye‐safe lidar system operating at a wavelength of 532 nm. Both instruments are deployed on the NSF‐NCAR HIAPER aircraft. HCR is designed to fly in an underwing pod and HSRL is situated in the cabin. The HCR and HSRL data used in this study were collected during the Southern Ocean Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES). Comprehensive observations of the vertical distributions of liquid and mixed‐phase clouds were obtained using in situ probes on the aircraft and remote sensing instruments. Hydrometeor particle types were retrieved from HCR, HSRL, and temperature fields with a newly developed fuzzy logic particle identification (PID) algorithm. The PID results were validated with in situ measurements collected onboard the HIAPER aircraft by a 2D‐Stereo (2D‐S) cloud probe. Particle phases derived from the PID results compare well with those obtained from the 2D‐S observations and agree in over 70% of cases. Size distributions are also consistent between the two methods of observation. Knowledge of the particle type distribution gained from the PID results can be used to constrain microphysical parameterizations and improve the representation of cloud radiative effects in weather and climate models. Key Points: A fuzzy logic hydrometeor particle identification method is presented that uses airborne cloud radar and lidar observationsThe method classifies cloud and precipitation particles into eleven types of different phases and sizesA comparison of hydrometeor classifications with in situ measurements of Southern Ocean clouds show agreement in over 70% of cases [ABSTRACT FROM AUTHOR]

A bistatic high-frequency surface wave radar (HFSWR) with both receiving and transmitting stations placed on different ships (platforms) is a new radar system and referred to as shipborne bistatic HFSWR. In this paper, a first-order ocean surface cross section of shipborne bistatic HFSWR was derived. The first-order cross-section models for three different cases, i.e., ships moving with uniform, periodic, and hybrid motion states, respectively, are presented. The corresponding first-order Doppler spectra were simulated, and the spread width of the first-order spectrum was investigated. The simulation results show that the characteristics of the first-order spectrum are similar to those of a shore-based bistatic HFSWR when the transmitting and receiving platforms move in opposite directions. The first-order spectral spread width in the case of platforms with opposite directions is much smaller than that in the case of platforms with the same direction. This finding is useful for reducing HFSWR first-order spectrum spread due to platform motion, thus improving the target detection performance of the shipborne bistatic HFSWR. In addition, periodic oscillation motion of both platforms will cause complex motion-induced peaks in the first-order spectrum, which may be detrimental to target detection and ocean remote sensing. These results have important implications for the application of shipborne bistatic HFSWR. [ABSTRACT FROM AUTHOR]

Due to the semi-enclosed nature of the Mediterranean Sea, natural disasters and anthropogenic activities impose stronger pressures on its coastal ecosystems than in any other sea of the world. With the aim of responding adequately to science priorities and societal challenges, littoral waters must be effectively monitored with high-frequency radar (HFR) systems. This land-based remote sensing technology can provide, in near-real time, fine-resolution maps of the surface circulation over broad coastal areas, along with reliable directional wave and wind information. The main goal of this work is to showcase the current status of the Mediterranean HFR network and the future roadmap for orchestrated actions. Ongoing collaborative efforts and recent progress of this regional alliance are not only described but also connected with other European initiatives and global frameworks, highlighting the advantages of this cost-effective instrument for the multi-parameter monitoring of the sea state. Coordinated endeavors between HFR operators from different multi-disciplinary institutions are mandatory to reach a mature stage at both national and regional levels, striving to do the following: (i) harmonize deployment and maintenance practices; (ii) standardize data, metadata, and quality control procedures; (iii) centralize data management, visualization, and access platforms; and (iv) develop practical applications of societal benefit that can be used for strategic planning and informed decision-making in the Mediterranean marine environment. Such fit-for-purpose applications can serve for search and rescue operations, safe vessel navigation, tracking of marine pollutants, the monitoring of extreme events, the investigation of transport processes, and the connectivity between offshore waters and coastal ecosystems. Finally, future prospects within the Mediterranean framework are discussed along with a wealth of socioeconomic, technical, and scientific challenges to be faced during the implementation of this integrated HFR regional network. [ABSTRACT FROM AUTHOR]

Carrier modulation, a variant of intermodulation distortion, is described and exploited for the remote detection of radio-frequency (RF) electronics. To generate carrier modulation, a single frequency illuminates an electronic device, which is actively transmitting, and mixing of the applied probe tone with the device's emission produces intermodulation distortion. Theory predicts that carrier modulation is detectable from an emitter at a greater distance away from a radar than the traditional intermodulation distortion. Benchtop measurements taken using commercial RF electronic devices confirm this theory. The experiments demonstrate that carrier modulation may be stimulated remotely from a variety of RF electronics. The spectrum of the distortion may be used to pinpoint an emitter's frequency-of-operation. [ABSTRACT FROM AUTHOR]

This paper provides a review of using Interferometric Synthetic Aperture Radar (InSAR), a microwave remote sensing technique, for deformation monitoring of hydroelectric power projects, a critical infrastructure that requires consistent and reliable monitoring. Almost all major dams around the world were built for the generation of hydropower. InSAR can enhance dam safety by providing timely settlement measurements at high spatial-resolution. This paper provides a holistic view of different InSAR deformation monitoring techniques such as Differential Synthetic Aperture Radar Interferometry (DInSAR), Ground-Based Synthetic Aperture Radar (GBInSAR), Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), Multi-Temporal Interferometric Synthetic Aperture Radar (MTInSAR), Quasi-Persistent Scatterer Interferometric Synthetic Aperture Radar (QPSInSAR) and Small BAseline Subset (SBAS). PSInSAR, GBInSAR, MTInSAR, and DInSAR techniques were quite commonly used for deformation studies. These studies demonstrate the advantage of InSAR-based techniques over other conventional methods, which are laborious, costly, and sometimes unachievable. InSAR technology is also favoured for its capability to provide monitoring data at all times of day or night, in all-weather conditions, and particularly for wide areas with mm-scale precision. However, the method also has some disadvantages, such as the maximum deformation rate that can be monitored, and the location for monitoring cannot be dictated. Through this review, we aim to popularize InSAR technology to monitor the deformation of dams, which can also be used as an early warning method to prevent any unprecedented catastrophe. This study also discusses some case studies from southern India to demonstrate the capabilities of InSAR to indirectly monitor dam health. [ABSTRACT FROM AUTHOR]

Convective motions and hydrometeor microphysical properties are highly sought-after parameters for evaluating atmospheric numerical models. With most of Earth’s surface covered by water, space-borne Doppler radars are ideal for acquiring such measurements at a global scale. While these systems have proven to be useful tools for retrieving cloud microphysical and dynamical properties from the ground, their adequacy and specific requirements for spaceborne operation still need to be evaluated. Comprehensive forward simulations enable us to assess the advantages and drawbacks of six different Doppler radar architectures currently planned or under consideration by space agencies for the study of cloud dynamics. Radar performance is examined against the state-of-the-art numerical model simulations of well-characterized shallow and deep, continental, and oceanic convective cases. Mean Doppler velocity (MDV) measurements collected at multiple frequencies (13, 35, and 94 GHz) provide complementary information in deep convective cloud systems. The high penetration capability of the 13 GHz radar enables to obtain a complete, albeit horizontally under-sampled, view of deep convective storms. The smaller instantaneous field of view (IFOV) of the 35 GHz radar captures more precise information about the location and size of convective updrafts above 5–8 km height of most systems which were determined in the portion of storms where the mass flux peak is typically located. Finally, the lower mean Doppler velocity uncertainty of displaced phase center antenna (DPCA) radars makes them an ideal system for studying microphysics in shallow convection and frontal systems, as well as ice and mixed-phase clouds. It is demonstrated that a 94 GHz DCPA system can achieve retrieval errors as low as 0.05–0.15 mm for raindrop volume-weighted mean diameter and 25% for rime fraction (for a −10 dBZ echo).

In 2014 a catastrophic ice storm occurred in the forests of Börzsöny Mts., Hungary. In this study we analyzed the potential of Synthetic Aperture Radar (SAR) data, complemented, and compared with optical imagery, in mapping this event. Great emphasis was put on reference data: three types of field-based reference datasets were used and the damaged patches were delineated manually based on the visual interpretation of pre- and post-event orthophotos. Four classifications with different set-ups were carried out by applying the eXtreme Gradient Boosting method. Combinations of radar backscatter coefficients, polarimetric descriptors, interferometric coherence, and optical data variables were tested. All classifications were suitable for identifying uprooted trees properly (1–11% underestimation), but none of them could detect crown loss accurately (55–58% overestimation), based on the validation of the most damaged area. Proper differentiation of healthy forests with various levels of canopy closure in the reference data seems crucial for accurate canopy loss estimation. In the case of methods using only Sentinel-1 imagery, interferometric coherence together with polarimetric descriptors provided the best results (OA: 65.7%). This setup can be useful for immediate uproot damage detection for planning salvage logging if a natural disturbance happens outside the vegetation period.

Given that the recent rapid growth of offshore production, especially in the Arctic region of the Russian Federation, is causing increased concern about oil spills on the water surface, this issue is especially relevant and important today. These pollutants have a devastating impact on the world’s marine biosphere. Therefore, effective and reliable methods and instruments must be used for operational spill detection in order to detect a remote oil spill. Several methods for oil spill monitoring and Russian developments in this area were described, including their features, advantages, and drawbacks. In cases when use in difficult Arctic conditions was anticipated, due to the harsh climate and ice-covered water surface, it was not always possible for spill detection instruments to be utilized. Despite this, such methods as radar, infrared, and ultraviolet were proven to be effective during this research. Ultimately, the combination of these methods returned the greatest volume of information to offshore platform staff about a detected oil spill. The information provided includes the spread area of the spill, the thickness of the leak, and the chemical composition of the oil.

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

Respiration rate (RR) and respiration patterns (RP) are considered early indicators of physiological conditions and cardiorespiratory diseases. In this study, we addressed the problem of contactless estimation of RR and classification of RP of one person or two persons in a confined space under realistic conditions. We used three impulse radio ultrawideband (IR-UWB) radars and a 3D depth camera (Kinect) to avoid any blind spot in the room and to ensure that at least one of the radars covers the monitored subjects. This article proposes a subject localization and radar selection algorithm using a Kinect camera to allow the measurement of the respiration of multiple people placed at random locations. Several different experiments were conducted to verify the algorithms proposed in this work. The mean absolute error (MAE) between the estimated RR and reference RR of one-subject and two-subjects RR estimation are 0.61±0.53 breaths/min and 0.68±0.24 breaths/min, respectively. A respiratory pattern classification algorithm combining feature-based random forest classifier and pattern discrimination algorithm was developed to classify different respiration patterns including eupnea, Cheyne-Stokes respiration, Kussmaul respiration and apnea. The overall classification accuracy of 90% was achieved on a test dataset. Finally, a real-time system showing RR and RP classification on a graphical user interface (GUI) was implemented for monitoring two subjects. [ABSTRACT FROM AUTHOR]

A quantum-state-based technique for change detection and analysis is presented. The method establishes a framework for change detection based on quantum states’ entanglement concept. The approach is applicable to quantum sensing (QS) (i.e., low field intensity, photon pairs) but is contingent to feasibility of quantum implementations and measurements. The main objective in this work is to develop a new change detection technique for classical radar/synthetic aperture radar (SAR) remote sensing that is inspired by quantum illumination (QI) and exploits the underlying characteristics and mathematical analogy between quantum-states and radar scatter-field-states. The described application of QI concept to develop a quantum signal processing (QSP) algorithm for classical radar/SAR data exploitations introduces a new change detection approach for SAR multi-pol exploitations. [ABSTRACT FROM AUTHOR]

This article presents the pioneering results of successful target detection using a fully operational and cooperative 5G cellular network as a source of illumination in a passive radar system. The electromagnetic wave generated by the real-life base station allowed for remote sensing of a moving car in bistatic geometry, which shows the feasibility of passive radar in which new generation radio networks serve as an illuminator of opportunity. This article describes passive radar principles, the theoretical fundamentals of the 5G signal structure, and the proposed processing for 5G network-based passive radar. Also, the 5G passive radar demonstrator is described, and the trial results show the possibilities and limitations of using new radio standards in the context of passive radars. [ABSTRACT FROM AUTHOR]

Rapid detection of landslides is critical for emergency response, disaster mitigation, and improving our understanding of landslide dynamics. Satellite-based synthetic aperture radar (SAR) can be used to detect landslides, often within days of a triggering event, because it penetrates clouds, operates day and night, and is regularly acquired worldwide. Here we present a SAR backscatter change approach in the cloud-based Google Earth Engine (GEE) that uses multi-temporal stacks of freely available data from the Copernicus Sentinel-1 satellites to generate landslide density heatmaps for rapid detection. We test our GEE-based approach on multiple recent rainfall- and earthquake-triggered landslide events. Our ability to detect surface change from landslides generally improves with the total number of SAR images acquired before and after a landslide event, by combining data from both ascending and descending satellite acquisition geometries and applying topographic masks to remove flat areas unlikely to experience landslides. Importantly, our GEE approach does not require downloading a large volume of data to a local system or specialized processing software, which allows the broader hazard and landslide community to utilize and advance these state-of-the-art remote sensing data for improved situational awareness of landslide hazards. [ABSTRACT FROM AUTHOR]

Radar imaging of typical planet landforms can be used for planetary exploration such as landform recognition and geometry measurement. This communication realizes high-resolution imaging simulations of typical planetary landforms, such as craters, hills, and volcanoes, by using parametric scattering center (SC) models. Based on the geometric models of these typical landforms, parametric SC models are constructed from scattered solutions of the physical optics (POs) method. Because the SC models take geometric parameters as model parameters, these models can simulate the scattered waves of landforms of different sizes. These SC models are validated by the PO results. In addition, the landform types can be recognized and their geometrical parameters are further extracted by SC model matching. The extracted geometric parameters show good agreement with real landforms, demonstrating that the SC models of typical planetary landforms have potential applications in planetary geomorphologic exploration. [ABSTRACT FROM AUTHOR]

This article reports the yield estimation of vineyards from using a mobile millimeter-wave radar system. The proximal radar sensor is a 122-GHz FM-CW radar mounted on a rover that moves along the furrows between vine plants. The proposed yield estimation is based on the classification of radar echoes measured for various grape varieties, for different meteorological conditions, and at different stages of grape development. As yield estimation errors are found to be lower than 10%, the proposed radar technique is suitable for precision viticulture applications. [ABSTRACT FROM AUTHOR]

Two years (2018–2019) of different precipitation products are assessed for their skill in capturing orographic precipitation over the western United States (30°–50°N & 105°–135°W) using two popular methods. The first method defines orographic indices using orographic enhancement and moisture content that represents the amount of moisture advected over sloping terrain. In contrast, the second method classifies precipitation events into orographic and non‐orographic events. NCEP Stage‐IV product is used as a reference. All of the evaluated products (radar, microwave imager/sounder, and infrared) show more significant errors for the orographic than the non‐orographic events. The Global Precipitation Mission (GPM) products; Dual‐frequency Precipitation Radar (DPR), combined radar and radiometer (COMBINE), and GPM Microwave Imager (GMI), and Microwave Humidity Sounder (MHS), severely underestimate the precipitation rates, especially for heavy precipitation (>4 mm/day), whereas infrared precipitation (used in the Integrated Multi‐satellite Retrievals for GPM; IMERG‐IR) and a reanalysis product (ERA5) show relatively better estimation. It was found that the amount of precipitation over the reference (rate BIAS) varies with seasons, so in cold seasons satellite precipitation products tend to underestimate while in warm‐season they (except DPR) tend to overestimate precipitation amount. Most of the satellite products severely underestimate precipitation volume at relatively colder surfaces (<10°C) and lower TPW (<15 mm), but ERA5 shows little rate BIAS in such cases. The underestimation tends to be larger for orographic than non‐orographic events. In contrast, ERA5 shows relatively large underestimation at warmer temperatures (>20°C), where satellite products tend to overestimate precipitation amounts. Key Points: Several satellite‐based precipitation products are compared in relation with orographic enhancementSatellite‐based precipitation BIAS shows a seasonal patternPrecipitation BIAS is associated with the surface temperatures and total precipitable water [ABSTRACT FROM AUTHOR]

Quantifying tree defoliation by insects over large areas is a major challenge in forest management, but it is essential in ecosystem assessments of disturbance and resistance against herbivory. However, the trajectory from leaf‐flush to insect defoliation to refoliation in broadleaf trees is highly variable. Its tracking requires high temporal‐ and spatial‐resolution data, particularly in fragmented forests.In a unique replicated field experiment manipulating gypsy moth Lymantria dispar densities in mixed‐oak forests, we examined the utility of publicly accessible satellite‐borne radar (Sentinel‐1) to track the fine‐scale temporal trajectory of defoliation. The ratio of backscatter intensity between two polarizations from radar data of the growing season constituted a canopy development index (CDI) and a normalized CDI (NCDI), which were validated by optical (Sentinel‐2) and terrestrial laser scanning (TLS) data as well by intensive caterpillar sampling from canopy fogging.The CDI and NCDI strongly correlated with optical and TLS data (Spearman's ρ = 0.79 and 0.84, respectively). The ΔNCDIDefoliation(A‐C) significantly explained caterpillar abundance (R2 = 0.52). The NCDI at critical timesteps and ΔNCDI related to defoliation and refoliation well discriminated between heavily and lightly defoliated forests.We demonstrate that the high spatial and temporal resolution and the cloud independence of Sentinel‐1 radar potentially enable spatially unrestricted measurements of the highly dynamic canopy herbivory. This can help monitor insect pests, improve the prediction of outbreaks and facilitate the monitoring of forest disturbance, one of the high priority Essential Biodiversity Variables, in the near future. [ABSTRACT FROM AUTHOR]

nd is a software framework for the analysis of n-dimensional datacubes. Such data structures are common in Earth observation where the data are often multivariate on a spatio-temporal grid. The Python library xarray already exists to represent such data sets. This software builds on xarray by providing an interface with other parts of the Python ecosystem, such as scikit-learn and rasterio. Most of the functionality is accessible through an added accessor xarray.Dataset.nd. The software is maintained on GitHub and releases are published on the Python Package Index (PyPI) and Zenodo. [ABSTRACT FROM AUTHOR]

The primary objective of these studies was to quantitatively assess the ground deformation velocities and rates and their natural and man-made controlling factors as the potential risks along the seismically active 70 km section of buried Baku-Tbilisi-Ceyhan Oil, South Caucasus Gas, Western Route Oil and South Caucasus Pipeline Expansion Gas pipelines in Azerbaijan using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique. PS-InSAR analysis showed that the continuous subsidence was prevailing in the kilometer range of 13-70 of pipelines crossing two active seismic faults. The ground uplift deformations were observed in the pipeline kilometer range of 0-13. The minimum and maximum vertical ground movement velocities were observed to be −21.3 mm/y and 14.1 mm/y along 70 km section of pipelines with 250 m buffer zone. Both of these sites were observed at the range of seismic faults. The spatial distribution of sites with ground deformation velocity less than −15 mm/y and more than 15 mm/y was diverse and random all along 70 km of pipelines without any cumulative spatial patterns. Based on the lower mean, variation and standard deviation of pixel values, the seismic fault in the kilometer range of 21-31 revealed its higher vulnerability to subsidence processes rather than the Seismic Fault in the kilometer range of 46-54. The ground deformation velocities within the range of Seismic Fault KP21-31 revealed the minimum and maximum values of −19.74 mm/y and 14.1 mm/y, respectively whereas at the Seismic Fault KP46-54, the minimum and maximum values were −17.07 mm/y and 9.29 mm/y, respectively. Encouraging level of agreement with the regression coefficients of 0.92 and 0.96 for known subsiding sites at KP28 + 500 and KP52 + 750 and 0.97 and 0.96 for known uplifting sites at KP04 + 900 and KP35 + 050 respectively was observed between the high-precision GPS and PS-InSAR measurements. 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. The prediction of the potential subsidence or uplift locations based on the field visual verifications holds a lot of uncertainties without broad and detailed scale airborne and satellite space observation technologies. The justification of the budget for the geotechnical maintenance activities along long-range oil and gas pipelines requires sophisticated prioritization and planning of the remediation sites and clear quantitative and qualitative risk assessment proving the activeness of these sites and effectiveness of the remediation measures. This means that the PS-InSAR – based approach outlined in this paper is a significant improvement over current ground-based monitoring practices or can significantly contribute them in the initial phase of risk assessment and prioritization.

nd is a software framework for the analysis of n-dimensional datacubes. Such data structures are common in Earth observation where the data are often multivariate on a spatio-temporal grid. The Python library xarray already exists to represent such data sets. This software builds on xarray by providing an interface with other parts of the Python ecosystem, such as scikit-learn and rasterio. Most of the functionality is accessible through an added accessor xarray.Dataset.nd. The software is maintained on GitHub and releases are published on the Python Package Index (PyPI) and Zenodo.

Near‐term ecological forecasting has the potential to mitigate negative impacts of human modifications on wildlife by directing efficient action through relevant and timely predictions. We used the U.S. avian migration system to highlight ecological forecasting applications for aeroconservation. We used millions of observations from 143 weather surveillance radars to construct and evaluate a migration forecasting system for nocturnal bird migration over the contiguous United States. We identified the number of nights of mitigation required to reduce the risk of aerial hazards to 50% of avian migrants passing a given area in spring and autumn based on dynamic forecasts of migration activity. We also investigated an alternative approach, that is, employing a fixed conservation strategy based on time windows that historically capture 50% of migratory passage. In practice, during both spring and autumn, dynamic forecasts required fewer action nights compared with fixed window selection at all locations (spring: mean of 7.3 more alert days; fall: mean of 12.8 more alert days). This pattern resulted in part from the pulsed nature of bird migration captured in the radar data, where the majority (54.3%) of birds move on 10% of a migration season's nights. Our results highlight the benefits of near‐term ecological forecasting and the potential advantages of dynamic mitigation strategies over static ones, especially in the face of increasing risks to migrating birds from light pollution, wind energy infrastructure, and collisions with structures. [ABSTRACT FROM AUTHOR]

The cross-validation of radars in a network is important in making consistent retrievals across the domain and assuring the product quality. During the Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations (RELAMPAGO) field campaign, two C-band radars, namely the Colorado State University C-band Hydrological Instrument for Volumetric Observations (CSU-CHIVO) and the C-band Scanning ARM Precipitation Radar (CSAPR-2), were deployed near the Sierras de Cordoba in Argentina, a region known for having some of the most intense severe weather in the world. In addition to these two radars, the operational radar of the Cordoba City, the Radar Meteorologico Argentino 1 (RMA-1), adds another instrument to the RELAMPAGO network. This study presents an intercomparison between the RELAMPAGO C-band radars using the GPM spaceborne radar as a common reference. A method to bring ground-based radars into better agreement is also proposed. Moreover, the attenuation correction for the C-band radar is studied in the context of intercomparing two radars. The attenuation coefficients are computed for the RELAMPAGO domain using the local disdrometers deployed during the campaign. After the attenuation correction, CSU-CHIVO, CSAPR-2, and RMA-1 compare well with GPM-DPR with a high correlation and bias less than 1 dB. [ABSTRACT FROM AUTHOR]

The use of neural network (NN) models for remote sensing (RS) retrieval of landscape biophysical and biochemical properties has become popular in the last decade. Recently, the emergence of "big data" that can be generated from remotely sensed data and innovative machine learning (ML) approaches have provided a platform for novel analytical approaches. Specifically, the advent of deep learning (DL) frameworks developed from traditional neural networks (TNN) offer unprecedented opportunities to improve the accuracy of SOC retrievals from remotely sensed imagery. This review highlights the use of TNN models and their evolution into DL architectures in remote sensing of SOC estimation. The review also highlights the application of DL, with a specific focus on its development and adoption in remote sensing of SOC mapping. The review concludes by highlighting future opportunities for the use of DL frameworks for the retrieval of SOC from remotely sensed data. [ABSTRACT FROM AUTHOR]

In 2014 a catastrophic ice storm occurred in the forests of Börzsöny Mts., Hungary. In this study we analyzed the potential of Synthetic Aperture Radar (SAR) data, complemented, and compared with optical imagery, in mapping this event. Great emphasis was put on reference data: three types of field-based reference datasets were used and the damaged patches were delineated manually based on the visual interpretation of pre- and post-event orthophotos. Four classifications with different set-ups were carried out by applying the eXtreme Gradient Boosting method. Combinations of radar backscatter coefficients, polarimetric descriptors, interferometric coherence, and optical data variables were tested. All classifications were suitable for identifying uprooted trees properly (1–11% underestimation), but none of them could detect crown loss accurately (55–58% overestimation), based on the validation of the most damaged area. Proper differentiation of healthy forests with various levels of canopy closure in the reference data seems crucial for accurate canopy loss estimation. In the case of methods using only Sentinel-1 imagery, interferometric coherence together with polarimetric descriptors provided the best results (OA: 65.7%). This setup can be useful for immediate uproot damage detection for planning salvage logging if a natural disturbance happens outside the vegetation period. [ABSTRACT FROM AUTHOR]

The first step to accurately measure global snowfall is to separate rainfall from snowfall correctly (i.e., precipitation phase discrimination). This study first evaluates the phase discrimination performance in four remote sensing datasets, including observations from ground radar, spaceborne radars, and spaceborne radiometer, relative to ground observations. Results show that the snowfall discrimination accuracy varies greatly among these datasets ranging from 42% to 96%, dependent on whether and how the temperature information are considered. For example, over half of the snowfall from the Global Precipitation Measurement Mission (GPM) spaceborne radar is actually rainfall at the surface since it detects snowfall in the air without considering the temperature information close to the surface. Second, we evaluate the discrimination performance using the temperature information from four reanalysis datasets. It is found that MERRA2 temperature close to the surface is colder than the other three datasets, leading to more rainfall being misclassified as snowfall. Plain Language Summary: Satellite remote sensing provides the only means of measuring rainfall/snowfall on the global scale. Misclassifying the precipitation phase (i.e., rainfall as snowfall, or vice versa) could lead to the estimated precipitation rate being one order of magnitude smaller or larger. Our results reveal that the snowfall discrimination accuracy varies greatly among four remote sensing datasets ranging from 42% to 96%. For example, over half of the snowfall from the state‐of‐the‐art precipitation product based on the Global Precipitation Measurement radar is rainfall at the surface without considering the temperature information close to surface. Additionally, the temperature discrepancy among different reanalysis datasets also greatly affects precipitation phase discrimination. Our results show that MERRA2 temperature close to the surface is colder than the other three major datasets, leading to more rainfall pixels being misclassified as snowfall pixels. Key Points: Snowfall determination accuracy varies greatly among four remote sensing datasets ranging from 42% to 96%More than half of the snowfall indicated by Global Precipitation Measurement Mission (GPM) dual frequency precipitation radar (DPR) is rainfall on the groundMERRA2 temperature close to the surface is noticeably colder than observed, leading to more rainfall being classified as snowfall [ABSTRACT FROM AUTHOR]

Thermal modeling of different transmission lines (TLs) based on resonant scatterers is presented. The coplanar strip (CPS) and microstrip (MS) TLs are used to model resonators and to introduce the thermal dependence. A reflectometry approach is employed to validate the model by detecting the scatterers’ resonance frequency and comparing it with analytical expressions. The observable shift in the resonance frequency of the scatterers with temperature variations is due to the thermal expansion of metals and temperature dependence of the substrate permittivity. Since all the measurements are done remotely with no direct line of sight, it is shown how such a reflectometry approach can be used for remote temperature sensing using a passive label composed of resonators. Unlike previous works in this domain where the thermal dependence is considered empirically, the introduced model is used to take into account all thermal effects affecting the resonant scatterers allowing to link rigorously the variations of the measured resonance frequency with the temperature without any lookup table. Temperature sensing using very simple TLs based on resonant scatterers was demonstrated in a real environment. A temperature error of less than 3 °C is obtained. Once the temperature has been determined, it is possible to go back to the TL parameters, such as the effective permittivity and the physical length. [ABSTRACT FROM AUTHOR]

There has been significant research devoted to the development of distributed microwave wireless systems in recent years. The progression from large, single-platform wireless systems to collections of smaller, coordinated systems on separate platforms enables significant benefits for radar, remote sensing, communications, and other applications. The ultimate level of coordination between platforms is at the wavelength level, where separate platforms operate as a coherent distributed system. Wireless coherent distributed systems operate in essence as distributed phased arrays, and the signal gains that can be achieved scale proportionally to the number of transmitters squared multiplied by the number of receivers, providing potentially dramatic increases in wireless system capabilities enabled by increasing the number of nodes in the array. Coordinating the operations of nodes in a distributed array requires accurate control of the relative electrical states of the nodes. The basic challenge is the synchronization and stability of the relative phases of the signals transmitted or received. Generally, such control requires wireless frequency synchronization, phase calibration, and time alignment. For radar operations, phase control also requires high-accuracy knowledge of the relative positions of the nodes in the array to support beamforming. Various technologies have been developed in recent years to address the coordination challenges for closed-loop applications, such as distributed communications, and more recently, there has been growing interest in new technologies for open-loop applications, such as radar and remote sensing. This article presents an overview of distributed phased arrays, the principal challenges involved in their coordination, and recent research progress addressing these challenges. [ABSTRACT FROM AUTHOR]

This study evaluated the effectiveness of various remote sensing (RS) data (Sentinel-1, Sentinel-2, and Landsat 8) in the early recognition of irrigated areas in a densely cultivated area of central Italy. The study was based on crop data collected on more than 2000 plots in 2016 and 2017, characterized by quite different climatic conditions. The different RS data sources were used both alone and combined and with precipitation to define corresponding random forest (RF) classifiers whose overall accuracy (OA) was assessed by gradually increasing the number of available features from the beginning of the irrigation season. All tested RF classifiers reach stable OAs (OA 0.9) after 7-8 weeks from the start of the irrigation season. The performance of the radar indexes slightly improves when used in combination with precipitation data, but three weeks of features are required to obtain OA above 80%. The optical indices alone (Sentinel-2 and Landsat 8) reach OA ?85% in the first week of observation. However, they are ineffective in cloudy conditions or when rainfed and irrigated fields have similar vigour. The most effective and robust indices are those based on combined sources (radar, optical, and meteorological), allowing OAs of about 92% and 96% at the beginning and in the middle of the irrigation season, respectively. [ABSTRACT FROM AUTHOR]

Ground-based remote sensing of atmospheric parameters is often limited to single station observations by vertical profiles at a certain geographic location. This is a limiting factor for investigating gravity wave dynamics as the spatial information is often missing, e.g., horizontal wavelength, propagation direction or intrinsic frequency. In this study, we present a new retrieval algorithm for multistatic meteor radar networks to obtain tomographic 3-D wind fields within a pre-defined domain area. The algorithm is part of the Agile Software for Gravity wAve Regional Dynamics (ASGARD) and called 3D-Var, and based on the optimal estimation technique and Bayesian statistics. The performance of the 3D-Var retrieval is demonstrated using two meteor radar networks: the Nordic Meteor Radar Cluster and the Chilean Observation Network De Meteor Radars (CONDOR). The optimal estimation implementation provide statistically sound solutions and diagnostics from the averaging kernels and measurement response. We present initial scientific results such as body forces of breaking gravity waves leading to two counter-rotating vortices and horizontal wavelength spectra indicating a transition between the rotational k-3 and divergent k-5/3 mode at scales of 80–120 km. In addition, we performed a keogram analysis over extended periods to reflect the latitudinal and temporal impact of a minor sudden stratospheric warming in December 2019. Finally, we demonstrate the applicability of the 3D-Var algorithm to perform large-scale retrievals to derive meteorological wind maps covering a latitude region from Svalbard, north of the European Arctic mainland, to central Norway. [ABSTRACT FROM AUTHOR]

Forward-looking multichannel synthetic aperture radar (FLMC-SAR) is an important tool for modern remote sensing applications, which has the capability to reconstruct the high-resolution image of the front area. However, due to the azimuth-variant characteristics of the motion errors over a long aperture, FLMC-SAR data processing is usually a challenging task, especially when involving the motion compensation (MOCO) coupled with Doppler ambiguity resolving. To accomplish an accurate MOCO for FLMC-SAR, a novel parametric azimuth-variant MOCO approach is proposed in this article. Aiming at the coupling problem of MOCO and Doppler ambiguity resolving over the full aperture, we can decouple them through the subaperture division. As a full synthetic aperture is decomposed into several subapertures, the high-order motion errors of the full aperture can be decomposed into the first-order motion errors of the subaperture. On this basis, the mismatch of the space–time spectrum caused by the motion errors can be solved by spectral estimation, yielding Doppler ambiguity resolving for each subaperture. Meanwhile, the azimuth-variant characteristic of motion errors in FLMC-SAR system is characterized by a parametric angle-dependent quadratic phase error (QPE) model. The motion parameters are estimated by a joint multichannel angle estimation-based signal quadratic decomposition method. Immediately, the MOCO for ambiguous targets with different motion errors can be processed separately to improve the imaging performance. Experimental results based on both simulated and real data demonstrate that the proposed method is suitable for FLMC-SAR system. [ABSTRACT FROM AUTHOR]

A large number of initialised false tracks in clutter environments will cause a heavy burden on the detection and track‐processing system. The quantity of initialised false tracks in clutter used to be estimated by experience or simulation, which lacks a fundamental explanation in theory. In order to estimate the amount of linear initialised false tracks with analytical expressions, statistical estimators are proposed based on prior knowledge of the clutter in a two‐dimensional scenario. Meanwhile, the differential Hough transform is presented to verify the estimators. To be specific, firstly, a one‐dimensional scenario is illustrated to present the statistical estimators of the number of initialised linear and equal‐length false tracks, which is based on statistical estimation theory. Secondly, regarding the clutter map as a uniform distributed binary image, the estimators described at first are extended to a two‐dimensional situation. Finally, to verify the validity of the proposed estimators, a differential Hough transform algorithm is utilised to obtain the number of linear and equal‐length tracks in the simulation. The results show that the estimators proposed herein are reasonable in a certain clutter environment. [ABSTRACT FROM AUTHOR]

A multistatic passive radar network originally presented in Ptak et al. for long‐distance aircraft tracking is sensitive to track initialisation errors. When these errors exceed 50 km, target tracking often diverges. Initialisation can be improved by even coarse directional information. The impact of binary directional information was studied, which allows flights to be assigned to start from east or west of the multistatic network. Substantial improvement to the performance of flight tracking is observed on reference data based on Automatic Dependent Surveillance–Broadcast information. [ABSTRACT FROM AUTHOR]

Sparse recovery (SR) based space‐time adaptive processing (STAP) methods have received much attention recently due to their dramatically reduced requirements of training samples. However, most of the existing SR‐STAP algorithms suffer from the off‐grid effect induced by the discretisation of angle‐Doppler plane. To eliminate this effect and improve the performance of clutter suppression, a novel gridless mixed‐norm minimisation (MNM) based STAP algorithm is proposed. Unlike previous grid‐based SR‐STAP methods, the clutter covariance matrix (CCM) is estimated in continuous domain by solving a semidefinite programme (SDP), which is established by utilising the properties of the CCM and the compact formulation of MNM. Furthermore, to reduce the computational burden of solving this SDP, a fast iterative algorithm via the framework of the alternating direction method of multipliers (ADMM) is also derived, where the unknown parameters are iteratively updated with closed‐form expressions. Simulation results show that our proposed algorithm achieves better performance of clutter suppression and target detection with lower computational complexity. [ABSTRACT FROM AUTHOR]

The target classification of unknown classes using radar sensor data is discussed. The neural network‐based classifier shows high classification accuracy for the learned class targets. However, there is a risk of false decision for the untrained class target owing to an overconfidence problem. The output confidence of the classifier is calibrated using the deep convolutional neural network ensemble structure to propose a method to set the proper threshold for output confidence to decide unknown class targets. When using the proposed method, the accuracy of the learned target is maintained similar to that of the existing single neural network‐based classifier, whereas the unknown class target is better identified. Further analysis verifies the effectiveness of the proposed method using commercial automotive radar. The proposed method can classify learned targets with an accuracy of 95% and distinguish unknown class targets with an accuracy of at least 85%. Based on the interaction with other sensors, individual sensors need to make reserved decisions about uncertain information. It is expected that the proposed ensemble network will be efficient in designing the classifier to perform target classification including unknown class decision. [ABSTRACT FROM AUTHOR]

Airborne multiple‐input multiple‐output (MIMO) radar can transmit orthogonal waveforms to increase the range of the detection target. However, in a multipath environment, airborne MIMO radar also has energy radiated to the target through multipath directions, which makes the multipath phenomenon of airborne MIMO radar complicated. Because the direct path signal and the multipath signals of the airborne radar are at different range gates, the characteristics of data between the direct path signal and the multipath signals of the airborne MIMO radar can be used to propose a method for estimating the reflection coefficient amplitude (RCA). Considering that the target echoes data at different range gates can be used to estimate the elevation angle of the target, a direction of arrival (DOA) estimation method using data fusion at low signal‐to‐noise ratio (SNR) is proposed. Moreover, at high SNR, the signal path corresponding to the maximum target echo power is selected for DOA estimation based on the estimated multipath RCA. The proposed method makes full use of all target echo data of airborne MIMO radar in a multipath environment and is suitable for practical applications. Finally, simulation experiments verify that the proposed method can effectively improve the DOA estimation accuracy. [ABSTRACT FROM AUTHOR]

To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target‐free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non‐homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi‐channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm. [ABSTRACT FROM AUTHOR]

Waveforms used in multiple‐input multiple‐output (MIMO) systems must be considered in such a way that individual transceiver (T‐R) pairs can be separated from the composite received signal. An algorithm called variational mode decomposition of coded beat‐frequency shifted signals (VMD‐CBFSS) is presented for waveform diversity. First, transmitters are divided into several groups. A small shift is applied to the carrier frequency of the transmitters of different groups. Also, signals within each group are encoded by assigning a binary phase in a time block. On the receiver side, by using a multiresolution analysis technique, the combined signal is decomposed into its main frequency components and then the composite signals of each group are decoded to retrieve each T‐R pair. VMD‐CBFSS provides an efficient solution in terms of overall transmitting time and sampling rate in addition to robustness against channel noise. Moreover, the algorithm is employed in a near‐field MIMO millimetre‐wave imaging system, and results are presented in the form of numerical simulations. [ABSTRACT FROM AUTHOR]

A micro‐Doppler frequency estimation and association algorithm of the space target based on the time‐varying autoregressive (TVAR) model is proposed herein. The calculation time of the existing algorithms grows significantly with the problem size, and micro‐Doppler frequencies of different scattering centres are associated incorrectly where the frequencies intersect. The echo of the space target is modelled by the TVAR model, then the TVAR coefficients and instantaneous frequencies are estimated through the fast sparse Bayes learning (SBL) algorithm, finally, the micro‐Doppler frequencies of different scattering centres are associated in the frequency–frequency change rate domain. The effectiveness and rapidity of the proposed algorithm is demonstrated by simulation results. [ABSTRACT FROM AUTHOR]

This study addresses a signal processing technique in a search and rescue system. An international disaster rescue communications support programme such as COSPAS‐ search and rescue satellite‐aided tracking (SARSAT) estimates the position of a beacon based on time difference of arrival (TDOA) and frequency difference of arrival (FDOA) techniques. To backtrack signals from encrypted beacons without knowing the encryption codes, the cross ambiguity function (CAF) is proposed to estimate the TDOA and FDOA, in which high sampling rates are needed to increase the positioning accuracy of existing CAF algorithms, which entail high calculation loads. To overcome this, an interpolation method is proposed for peaks of Fourier transform capable of maintaining high accuracy at low calculation loads after a well‐performed initial acquisition. The numerical simulation shows an improved estimation accuracy of the proposed algorithm compared with existing peak detection methods. [ABSTRACT FROM AUTHOR]

Deep‐learning‐based radar imaging is developed with distributed frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radars in which a deep‐learning approach based on the convolutional neural network (CNN) is proposed to achieve radar images robust to adverse circumstances. Differently from the existing deep‐learning methods applied to radar object recognition, the deramped radar signal is exploited as the input of the proposed deep CNN (DCNN) without any processing related to the spectrogram transform and the subspace decomposition. To effectively train the proposed DCNN, the received signal is reformulated in terms of the reflection gain values in the (azimuth, range) patches in the image region of interest such that the output vector of the DCNN is composed of the reflection gain values in the associated patches. Furthermore, to overcome the limitations on the amount of training data and training time, the transfer learning approach is effectively applied to the distributed FMCW MIMO radar imaging. The proposed radar imaging is assessed with synthetic simulation data. Specifically, by transferring the pretrained DCNN model for a given reference radar to other distributed radars, the distributed radars can save about 52.4 % in training time compared with a DCNN having the same architecture but without transfer learning. [ABSTRACT FROM AUTHOR]

Due to its low hardware complexity, small volume and simple structure, time‐division‐multiplexing multiple‐input‐multiple‐output (TDM MIMO) radar has been widely applied in automotive applications. The transmitting antennas of TDM MIMO automotive radar are usually switched according to a sequential‐TDM pattern. However, moving targets can introduce a motion‐induced phase into the sequential‐TDM pattern, which is coupled to the spatial phase, resulting in errors in velocity and azimuth estimation. Herein, a random‐TDM pattern with the matrix completion (MC)‐based estimation method is proposed to address these issues. In the proposed method, the random‐TDM pattern, which means randomly activating the transmitting antenna instead of sequentially, can decouple the linear temporal‐spatial coupling phase, avoiding the coupling problem in velocity and azimuth estimation. Then, by reshaping the echo data into a matrix form of sparse sampling, the inexact augmented Lagrange multiplier algorithm is adopted to recover this matrix, solving the underdetermined estimation problem caused by sparse sampling. Finally, the high‐accuracy velocity and azimuth can be jointly estimated by applying the estimation of signal parameters via rotational invariance technique algorithm to the recovered full sampling data. Moreover, compared with the compressed sensing‐based method, the proposed method overcomes the grid mismatch problem. The results of comparative simulations and real‐data experiments demonstrate the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Spaceborne synthetic aperture radar (SAR) with digital beam‐forming (DBF) in elevation uses very sharp time‐variant antenna beams to improve the signal‐to‐noise ratio (SNR) and range ambiguity‐to‐signal ratio. In acquisition scenarios of imprecisely known Earth topography, however, antenna pointing might be mismatched with surface geometry. To handle this issue, it is suggested that preprocessing is applied to DBF to determine the angle of the incident echo signal on the planar antenna array. The authors derive a method that estimates the direction of arrival on the acquired raw data with high accuracy in real time. The method is based on the matrix pencil, which is a common approach in mathematics for solving linear equation systems. As a key innovation, this paper describes further improvements and adaptions to SAR scenarios and provides a possible concept for practical implementation. For proof of concept, a series of different simulation tasks is performed. [ABSTRACT FROM AUTHOR]

The issue of aircraft tracking using passive sensors with poor measurement precision and a variable update rate is investigated. First, the need for a time‐dependent Markov chain transition matrix for the Interacting Multiple Model (IMM) is thoroughly evaluated using simulation and real data from an experimental multilateration system. Three different methods for computing the time‐dependent matrix are compared, showing their superiority for highly aperiodic sensors. Second, the Variable Structure Interacting Multiple Model (VS‐IMM) for passive sensors with poor geometry is proposed, in which the mode set update is based on local dilution of precision or distance root mean square error. The performance of the estimator is compared with the IMM using real data. The VS‐IMM results in the best performance for areas of both high and low measurement precision. [ABSTRACT FROM AUTHOR]

The optimal implementation for a biologically inspired coupling structure to overcome the limitation of short baseline direction‐finding is determined. This approach is inspired by the Ormia ochracea, a parasitoid insect living in North America. It can locate the crickets' call accurately with the very small distance between its ears far beyond the accuracy of an interferometer with the same baseline. This outstanding performance depends on the mechanical coupling in its auditory system. The first research focus is on the mechanism of the coupling structure, considering not only the amplification on phase difference but also the effect on output power, which leads to the performance improvement in comparison with the traditional method. Then, the biologically inspired coupling structure is optimised to achieve the best direction‐finding performance in crickets' sound frequency, reducing the estimation error by 75% when the signal incident at boresight. To implement the actual coupling structure with optimal direction‐finding performance, both the analogue circuit and the digital filter implementation are discussed, and the latter attains the theoretical optimal performance. Finally, a direction‐finding system prototype is carried out to verify the advantage of digitally implemented coupling structure, and the measurement result approximates the corresponding Cramér–Rao lower bound. [ABSTRACT FROM AUTHOR]

Two‐dimensional (2‐D) transmit arrays offer many advantages over traditional over‐the‐horizon radar (OTHR) arrays, including the capability to steer and mitigate auroral clutter, a clutter type prevalent in polar operation, in all azimuth and elevation directions. The approaches of previous studies on OTHR 2‐D transmit arrays are considered suboptimal, but it can be shown that the 2‐D array is an effective OTHR transmitter when operated in an appropriate manner. A reference system derived from publicly available information on an operational OTHR array is used as a benchmark to compare the performance of various 2‐D OTHR transmit systems with respect to effective isotropic radiated power on target, sidelobe levels, and elevation selectivity. [ABSTRACT FROM AUTHOR]

A strong windthrow affected many forests in Slovenia and northern Italy on the night of the 29th and the 30th of October, 2018. In this paper, we propose a new methodology for windthrow identification, comprising two new indexes, PCA-CI for evaluating change detection and PCA-WI for the identification of windthrows by analysing the time series of C-SAR Sentinel-1 data. The proposed methodology, unlike the previous studies on the identification of windthrows, does not subtract the values of dual and single polarised VH-VV data combinations, but proposes a new approach with a multivariate transformation of dimensionality reduction of the time series of the dual and single polarised data in two different time frames.

Herein, a semiempirical model is proposed to remove the atmospheric phase screen (APS) that occurs during ground-based synthetic aperture radar (GB-SAR) monitoring in steep mountainous areas with extreme weather conditions. The proposed method is based on a model-based statistical technique, which combines the topographical information and the estimated phase of interferograms. A 3-D geographical model was designed to investigate the effect of topographical irregularities, such as elevation, slope, and their correlation with the APS. The observed phases were then modeled according to the altitude and range of the 3-D topographical structure seen by the radar. A two-stage semiempirical algorithm is proposed to compensate for the APS in the spatial domain. Herein, the temporal changes in meteorological parameters, such as the atmospheric temperature, pressure, or humidity, were not considered for phase correction, drastically reducing the model background information and providing faster data processing for real-time GB-SAR monitoring. The proposed model was applied to the mountainous environment of a road reconstruction site in Minami-Aso, Kumamoto, Japan, where large-scale landslides were triggered after the Kumamoto earthquake in April 2016.