Search Results (2,404)

This manuscript focuses on the process of measuring the angular positions of radio sources using radio engineering systems. This study aims to improve the accuracy of measuring the angular positions of sources that radiate functionally determined signals and to expand the range of the unambiguous operation angles for multi-antenna radio direction finders. To achieve this goal, the following tasks were addressed: (1) defining the models of signals, noise, and their statistical characteristics, (2) developing the theoretical foundations of statistical optimization methods for measuring the angular positions of radio sources in multi-antenna radio direction finders, (3) optimizing the structures of radio direction finders with different configurations, (4) analyzing the accuracy and range of the unambiguous measurement angles in the developed methods, and (5) conducting experimental measurements to confirm the main results. The methods used are based on the statistical theory of optimization for remote sensing and radar systems. For the specified type of signals, given by functionally deterministic models, a likelihood function was constructed, and its maxima were determined for different multi-antenna direction finder configurations. The results of statistical synthesis were verified through simulation modeling and experiments. The primary approach to improving measurement accuracy and expanding the range of unambiguous angles involves combining antennas with different spatial characteristics and optimally integrating classical radio direction-finding methods. The following results were obtained: (1) theoretical studies and simulation modeling confirmed the existence of a contradiction between high resolution and the width of the range of the unambiguous measurements in two-antenna radio direction finders, (2) an improved signal processing method was developed for a four-antenna radio direction finder with a pair of high-gain and a pair of low-gain antennas, and (3) to achieve maximum direction-finding accuracy within the unambiguous measurement range, a new signal processing method was synthesized for a six-element radio receiver, combining processing in two amplitude direction finders and one phase direction finder. This work provides a foundation for further theoretical studies, highlights the specifics of combining engineering measurements in direction-finding systems, and offers examples of rapid verification of new methods through computer modeling and experimental measurements. Full article

Guangdong Province, home to 21 cities and a permanent population of 127.06 million people, boasts the largest provincial economy in China, contributing 11.76% to the national GDP in 2023. However, it is prone to geological hazards due to its geological conditions, extreme weather, and extensive human activities. Geohazards not only endanger lives but also hinder regional economic development. Monitoring surface deformation regularly can promptly detect geological hazards and allow for effective mitigation strategies. Traditional ground subsidence monitoring methods are insufficient for comprehensive surveys and rapid monitoring of geological hazards in the whole province. Interferometric Synthetic Aperture Radar (InSAR) technology using satellite images can achieve wide-area geohazard monitoring. However, current geological hazard monitoring in Guangdong Province based on InSAR technology lacks regional analysis and statistics of surface deformation across the entire province. Furthermore, such monitoring fails to analyze the spatial–temporal characteristics of surface deformation and disaster evolution mechanisms by considering the local geological features. To address these issues, current work utilizes Sentinel-1A/B satellite data covering Guangdong Province from 2015 to 2022 to obtain the wide-area surface deformation in the whole province using the multi-temporal (MT) InSAR technology. Based on the deformation results, a wide-area deformation region automatic identification method is used to identify the surface deformation regions and count the deformation area in each city of Guangdong Province. By analyzing the results, we obtained the following findings: (1) Using the automatic identification algorithm we identified 2394 deformation regions. (2) Surface subsidence is concentrated in the delta regions and reclamation areas; over a 4 cm/year subsidence rate is observed in the hilly regions of northern Guangdong, particularly in mining areas. (3) Surface deformation is closely related to geological structures and human activities. (4) Sentinel-1 satellite C-band imagery is highly effective for wide-area geological hazard monitoring, but has limitations in monitoring small-area geological hazards. In the future, combining the high-spatial–temporal-resolution L-band imagery from the NISAR satellite with Sentinel-1 imagery will allow for comprehensive monitoring and early warning of geological hazards, achieving multiple geometric and platform perspectives for geological hazard monitoring and management in Guangdong Province. The findings of this study have significant reference value for the monitoring and management of geological disasters in Guangdong Province. Full article

Soil moisture is an important component of the hydrologic cycle and ecosystem functioning, and it has a significant impact on agricultural production, climate change and natural disasters. Despite the availability of machine-learning techniques for estimating soil moisture from high-resolution remote-sensing imagery, including synthetic aperture radar (SAR) data and optical remote sensing, comprehensive comparative studies of these techniques remain limited. This paper addresses this gap by systematically comparing the performance of four tree-based ensemble-learning models (random forest (RF), extreme gradient boosting (XGBoost), light gradient-boosting machine (LightGBM), and category boosting (CatBoost)) and three deep-learning models (deep neural network (DNN), convolutional neural network (CNN), and gated recurrent unit (GRU)) in terms of soil moisture estimation. Additionally, we introduce and evaluate the effectiveness of four different stacking methods for model fusion, an approach that is relatively novel in this context. Moreover, Sentinel-1 C-band dual-polarization SAR and Sentinel-2 multispectral data, as well as NASADEM and geographical code and temporal code features, are used as input variables to retrieve the soil moisture in the ShanDian River Basin in China. Our findings reveal that the tree-based ensemble-learning models outperform the deep-learning models, with LightGBM being the best individual model, while the stacking approach can further enhance the accuracy and robustness of soil moisture estimation. Moreover, the stacking all boosting classes ensemble-learning model (SABM), which integrates only boosting-type models, demonstrates superior accuracy and robustness in soil moisture estimation. The SHAP value analysis reveals that ensemble learning can utilize more complex features than deep learning. This study provides an effective method for retrieving soil moisture using machine-learning and high-resolution remote-sensing data, demonstrating the application value of SAR data and high-resolution optical remote-sensing data in soil moisture monitoring. Full article

Monitoring slow-moving landslides is a crucial task for socioeconomic risk prevention and/or mitigation. Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is an advanced remote sensing method for monitoring ground deformation. In this research, PS-InSAR time series derived from COSMO-SkyMed (descending orbit) and Sentinel-1 (ascending orbit) are analyzed for a region in Central Apennines in Italy. The sequential turning point detection method (STPD) is implemented to detect the trend turning dates and their directions in the PS-InSAR time series within areas of interest susceptible to landslides. The monthly maps of significant turning points and their directions for years 2018, 2019, 2020, and 2021 are produced and classified for four Italian administrative regions, namely, Marche, Umbria, Abruzzo, and Lazio. Monthly global precipitation measurement (GPM) images at $0.1^{\circ }\times 0.1^{\circ }$ spatial resolution and four local precipitation time series are also analyzed by STPD to investigate when the precipitation rate has changed and how they might have reactivated slow-moving landslides. Generally, a strong correlation ($r\ge 0.7$) is observed between GPM (satellite-based) and local precipitation (station-based) with similar STPD results. Marche and Abruzzo (the coastal regions) have an insignificant precipitation rate while Umbria and Lazio have a significant increase in precipitation from 2017 to 2023. The coastal regions also exhibit relatively lower precipitation amounts. The results indicate a strong correlation between the trend turning dates of the accumulated precipitation and displacement time series, especially for Lazio during summer and fall 2020, where relatively more significant precipitation rate of change is observed. The findings of this study may guide stakeholders and responsible authorities for risk management and mitigating damage to infrastructures. Full article

To address the recent increase in wildfire severity and incidence, as well as the subsequent financial and physical costs, forest managers and wildland firefighting agencies rely on remotely sensed products for better decision-making and mitigation efforts. To address the remote sensing needs of these agencies, which include high spatial resolution, immunity to atmospheric and solar illumination effects, and day/night capabilities, the use of synthetic aperture radar (SAR) is under investigation for application in current and upcoming systems for all phases of a wildfire. Focusing on the active phase, a method for monitoring wildfire activity is presented based on changes in the radar vegetation index (RVI). L-band backscatter measurements from NASA/JPL’s UAVSAR instrument are used to obtain RVI images on multiple dates during the 2020 Bobcat (located in Southern CA, USA) and Hennessey (located in Northern CA, USA) fires and the 2021 Caldor (located in the Sierra Nevada region of CA, USA) fire. Changes in the RVI between measurement dates of a single fire are then compared to indicators of fire activity such as ancillary GIS-based burn extent perimeters and the Landsat 8-based difference normalized burn ratio (dNBR). An RVI-based wildfire “burn” detector/index is then developed by thresholding the RVI change. A combination of the receiver operating characteristic (ROC) curves and F1 scores for this detector are used to derive change detection thresholds at varying spatial resolutions. Six repeat-track UAVSAR lines over the 2020 fires are used to determine appropriate threshold values, and the performance is subsequently investigated for the 2021 Caldor fire. The results show good performance for the Bobcat and Hennessey fires at 100 m resolution, with optimum probability of detections of 67.89% and 71.98%, F1 scores of 0.6865 and 0.7309, and Matthews correlation coefficients of 0.5863 and 0.6207, respectively, with an overall increase in performance for all metrics as spatial resolution becomes coarser. The results for pixels identified as “burned” compare well with other fire indicators such as soil burn severity, known progression maps, and post-fire agency publications. Good performance is also observed for the Caldor fire where the percentage of pixels identified as burned within the known fire perimeters ranges from 37.87% at ~5 m resolution to 88.02% at 500 m resolution, with a general increase in performance as spatial resolution increases. All detections for Caldor show dense collections of burned pixels within the known perimeters, while pixels identified as burned that lie outside of the know perimeters have a sparse spatial distribution similar to noise that decreases as spatial resolution is degraded. The Caldor results also align well with other fire indicators such as soil burn severity and vegetation disturbance. Full article

In this study, we tackle the task of road segmentation from Synthetic Aperture Radar (SAR) imagery, which is vital for remote sensing applications including urban planning and disaster management. Despite its significance, SAR-based road segmentation is hindered by the scarcity of high-resolution, annotated SAR datasets and the distinct characteristics of SAR imagery, which differ significantly from more commonly used electro-optical (EO) imagery. To overcome these challenges, we introduce a multi-source data approach, creating the HybridSAR Road Dataset (HSRD). This dataset includes the SpaceNet 6 Road (SN6R) dataset, derived from high-resolution SAR images and OSM road data, as well as the DG-SAR and SN3-SAR datasets, synthesized from existing EO datasets. We adapt an off-the-shelf road segmentation network from the optical to the SAR domain through an enhanced training framework that integrates both real and synthetic data. Our results demonstrate that the HybridSAR Road Dataset and the adapted network significantly enhance the accuracy and robustness of SAR road segmentation, paving the way for future advancements in remote sensing. Full article

Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set. Full article

Rainfall information with high spatial and temporal resolution are essential in various fields. Heavy rainfall in a short period can cause problems and disasters that result in loss of life and damage to property. Conversely, the absence of rain for an extended period can also have negative social and economic impacts. Data accuracy, wide spatial coverage, and high temporal resolution are challenges in obtaining rainfall information in Indonesia. This article presents information on data sources and methods for measuring rainfall and reviews the latest research regarding statistical algorithms and machine learning to estimate rainfall in Indonesia. Rainfall information in Indonesia was obtained from several sources. Firstly, the method of direct rainfall measurement conducted with both manual and automatic rain gauges was reviewed; however, this data source provided minimal results, with uneven spatial density. Secondly, the application of remote sensing estimation using both radar and weather satellites was reviewed. The estimated rainfall results obtained using remote sensing showed more comprehensive spatial coverage and higher temporal resolution. Finally, we reviewed rainfall products obtained from model calculations, using both statistical and machine learning by integrating measurement and remote sensing data. The results of the review demonstrated that rainfall estimation products applied in remote sensing using machine learning models have the potential to produce more accurate spatial and temporal data. However, the validation of rainfall data from direct measurements is required first. This research’s contribution can provide practitioners and researchers in Indonesia and the surrounding region with information on problems, challenges, and recommendations for optimizing rainfall measurement products using appropriate adaptive technology. Full article

In radar High-Resolution Range Profile (HRRP) target recognition, the targets of interest are always non-cooperative, posing a significant challenge in acquiring sufficient samples. This limitation results in the prevalent issue of limited sample availability. To mitigate this problem, researchers have sought to integrate handcrafted features into deep neural networks, thereby augmenting the information content. Nevertheless, existing methodologies for fusing handcrafted and deep features often resort to simplistic addition or concatenation approaches, which fail to fully capitalize on the complementary strengths of both feature types. To address these shortcomings, this paper introduces a novel radar HRRP feature fusion technique grounded in the Feature Weight Assignment Generative Adversarial Network (FWA-GAN) framework. This method leverages the generative adversarial network architecture to facilitate feature fusion in an innovative manner. Specifically, it employs the Feature Weight Assignment Model (FWA) to adaptively assign attention weights to both handcrafted and deep features. This approach enables a more efficient utilization and seamless integration of both feature modalities, thereby enhancing the overall recognition performance under conditions of limited sample availability. As a result, the recognition rate increases by over 4% compared to other state-of-the-art methods on both the simulation and experimental datasets. Full article

The beat signal obtained from frequency-modulated continuous-wave (FMCW) radar is a waveform that is corrupted by noise and requires filtering out interference components for frequency calibration. Traditional FFT methods are affected by the fence effect and spectral leakage, leading to a reduction in frequency estimation accuracy. Therefore, an improved double-spectrum-line interpolation frequency estimation algorithm is proposed in this paper, utilizing spectral refinement and phase interpolation. Firstly, the post-FFT spectral signal is refined to narrow the frequency search range and enhance frequency resolution, thereby separating the noise signal. Then, a frequency deviation factor is defined based on the relationship between adjacent phase angles. Finally, the signal’s phase angles are interpolated using the frequency deviation factor to estimate the frequency of the beat signal. Experimental results demonstrate that the proposed algorithm reduces the impact of quantization on the frequency distribution and increases the signal’s noise resistance. The proposed algorithm has a higher accuracy and lower standard deviation compared to the recently proposed algorithm. Full article

Synthetic-Aperture Radar (SAR) has emerged as an important tool for monitoring tropical cyclones (TCs) due to its high spatial resolution and cloud-penetrating capability. Recent advancements in SAR technology have led to smaller and lighter satellites, yet few studies have evaluated their effectiveness in TC monitoring. This paper employs an algorithm for automatic TC center location, involving three stages: coarse estimation from a whole SAR image; precise estimation from a sub-SAR image; and final identification of the center using the lowest Normalized Radar Cross-Section (NRCS) value within a smaller sub-SAR image. Using three wide-swath miniaturized SAR images of TC Noru (2022), and TCs Doksuri and Koinu (2023), the algorithm’s accuracy was validated by comparing estimated TC center positions with visually located data. For TC Noru, the distances for the three stages were 21.42 km, 14.39 km, and 8.19 km; for TC Doksuri—14.36 km, 20.48 km, and 17.10 km; and for TC Koinu—47.82 km, 31.59 km, and 5.42 km. The results demonstrate the potential of miniaturized SAR in TC monitoring. Full article

Soil moisture data with both a fine spatial scale and a short global repeat period would benefit many hydrologic and climatic applications. Since the radar transmitter malfunctioned on NASA’s Soil Moisture Active Passive (SMAP) in 2015, SMAP soil moisture has been downscaled using numerous alternative fine-resolution data. In this paper, we describe the creation and validation of a new downscaled 3 km soil moisture dataset, which is the culmination of previous work. We downscaled SMAP enhanced 9 km brightness temperatures by merging them with L-band Cyclone Global Navigation Satellite System (CYGNSS) reflectivity data, using a modified version of the SMAP active–passive brightness temperature algorithm. We then calculated 3 km SMAP/CYGNSS soil moisture using the resulting 3 km SMAP/CYGNSS brightness temperatures and the SMAP single-channel vertically polarized soil moisture algorithm (SCA-V). To remedy the sparse daily coverage of CYGNSS data at a 3 km spatial resolution, we used spatially interpolated CYGNSS data to downscale SMAP soil moisture. 3 km interpolated SMAP/CYGNSS soil moisture matches the SMAP repeat period of ~2–3 days, providing a soil moisture dataset with both a fine spatial scale and a short repeat period. 3 km interpolated SMAP/CYGNSS soil moisture, upscaled to 9 km, has an average correlation of 0.82 and an average unbiased root mean square difference (ubRMSD) of 0.035 cm³/cm³ using all SMAP 9 km core validation sites (CVSs) within ±38° latitude. The observed (not interpolated) SMAP/CYGNSS soil moisture did not perform as well at the SMAP 9 km CVSs, with an average correlation of 0.68 and an average ubRMSD of 0.048 cm³/cm³. A sensitivity analysis shows that CYGNSS reflectivity is likely responsible for most of the uncertainty in downscaled SMAP/CYGNSS soil moisture. The success of 3 km SMAP/CYGNSS soil moisture demonstrates that Global Navigation Satellite System–Reflectometry (GNSS-R) observations are effective for downscaling soil moisture. Full article

Cemented subsoils, commonly referred to as caliche, pose a regular challenge for agricultural development in arid and semi-arid regions like coastal southern Peru. These subsurface features restrict root penetration, limit water infiltration and hinder essential soil processes, ultimately reducing crop yields and agricultural productivity. Accurate and efficient mapping of caliche is important for optimizing land-use planning and implementing sustainable agricultural practices. This study presents the application of near-surface geophysical techniques for mapping caliche deposits in the context of agricultural development at the future Majes II site in the Arequipa region of southern Peru. Specifically, we employed high-frequency ground-penetrating radar (GPR) and frequency-domain electromagnetics (FDEM) at a testbed on the Majes II site to evaluate their ability to delineate the extent, thickness, and depth of caliche within the local geology. GPR offers high-resolution imaging, effectively capturing sharp contrasts between caliche and surrounding materials, providing detailed information on the thickness (approximately 0.4 m) and the depth (up to 1.5 m) of the caliche layers. FDEM provides valuable insights into the presence of caliche at a faster rate of data acquisition and processing, enabling rapid assessment of the extent of caliche deposits, although with the tradeoff of lower resolution and depth information. We demonstrate that these two geophysical methods can be used separately or in an integrated manner for collaborative interpretation at the Majes II site to inform land management decisions, including identifying areas with favorable conditions for crop production and implementing targeted interventions to mitigate the adverse effects of caliche on agricultural productivity. Full article

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional $\omega$K imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the $Group$-$Nstream$ processing model, and the $Fthread$-$Group$-$Nstream$ processing model. The $Fthread$-$Group$-$Nstream$ processing model utilizes $Fthread$, $Group$, and $Nstream$ for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm’s performance in dynamic parallelism and alleviating the issue of serial execution within the $Group$-$Nstream$ processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring. Full article

Located in the south of the Iberian Peninsula, the Doñana salt marshes occupy around half of Doñana National Park and are currently considered among the most important wetlands worldwide due to the importance of their ecosystem. In this research work, using a novel patented procedure, the effects of climate change on the study area between 2009 and 2020 were evaluated. For this reason, DEMs were downloaded from the 30-meter Shuttle Radar Topography Mission (SRTM). Furthermore, to check the depth of the flooded area, 792 satellite images (L5 TM, L7 ETM+, and L8 OLI) with a resolution of 30 m were analyzed. The results show how the combined use of geomatic techniques, such as radar, optical, and geographic information system (GIS) data, along with regression models and iterative processes, plays a key role in the prediction and analysis of the flooded area volume in the Doñana salt marshes. Another significant contribution of this work is the development of a new remote sensing index. In conclusion, given that the study area depends on its aquifers’ status, it would be advisable to implement policies aimed at eradicating illegal aquifer extraction, as well as recovery plans to avoid the complete clogging of this salt marsh. Full article