Search Results (1,682)

Grazing lands are crucial for agricultural productivity, ecological stability, and carbon sequestration, underscoring the importance of monitoring vegetation biomass for the effective management of these ecosystems. Remote sensing data, including optical vegetation indices (VIs) like the Normalized Difference Vegetation Index (NDVI), are widely used to monitor vegetation dynamics due to their simplicity and high sensitivity. In contrast, radar-based VIs, such as the Polarimetric Radar Vegetation Index (PRVI), offer additional advantages, including all-weather imaging capabilities, a wider saturation range, and sensitivity to the vegetation structure information. This study introduces an enhanced form of the PRVI, termed the Normalized PRVI (NPRVI), which is calibrated to a 0 to 1 range, constraining the minimum value to reduce the background effects. The calibration and range factor were derived from statistical analysis of PRVI components across vegetated regions in the Contiguous United States (CONUS), using dual-polarization C-band Sentinel-1 and L-band ALOS-PALSAR data on the Google Earth Engine (GEE) platform. Machine learning models using NPRVI and NDVI demonstrated their complementarity with annual herbaceous biomass data from the Rangeland Analysis Platform. The results showed that the Random Forest Model outperformed the other machine learning models tested, achieving R² ≈ 0.51 and MAE ≈ 498 kg/ha (relative MAE ≈ 32.1%). Integrating NPRVI with NDVI improved biomass estimation accuracy by approximately 10% compared to using NDVI alone, highlighting the added value of incorporating radar-based vegetation indices. NPRVI may enhance the monitoring of grazing lands with relatively low biomass compared to other vegetation types, while also demonstrating applicability across a broad range of biomass levels and in diverse vegetation covers. Full article

The Fucheng-1 (FC-1) satellite has successfully transitioned from its initial operational phase and is now undergoing a detailed performance assessment for time-series deformation monitoring. This study evaluates the surface deformation monitoring capabilities of the newly launched FC-1 satellite using the interferometric synthetic aperture radar (InSAR) technique, particularly in urban applications. By analyzing the observation data from 20 FC-1 scenes and 20 Sentinel-1 scenes, deformation velocity maps of a university in Mianyang city were obtained using persistent scatterer interferometry (PSI) and distributed scatterer interferometry (DSI) techniques. The results show that thanks to the high resolution of 3 × 3 m of the FC-1 satellite, significantly more PS points and DS points were detected than those detected by Sentinel-1, by 13.4 times and 17.9 times, respectively. The distribution of the major deformation areas detected by both satellites in the velocity maps is generally consistent. FC-1 performs better than Sentinel-1 in monitoring densely structured and vegetation-covered areas. Its deformation monitoring capability at the millimeter level was further validated through comparison with leveling measurements, with average errors and root mean square errors of 1.761 mm and 2.172 mm, respectively. Its high-resolution and high-precision interferometry capabilities make it particularly promising in the commercial remote sensing market. Full article

This article introduces a small microwave remote sensing satellite weighing 310 kg, operating in low earth orbit (LEO). It is equipped with an X-band synthetic aperture radar (SAR) antenna, capable of a maximum imaging resolution of 0.6 m. To achieve the objectives of lower cost, reduced weight, minimized power consumption, and enhanced temperature stability, an optimized thermal design method tailored for satellites has been developed, with a particular focus on SAR antennas. The thermal control method of the antenna is closely integrated with structural design, simplifying the thermal design and its assembly process, reducing the resource consumption of thermal control systems. The distribution of thermal interface material (TIM) in the antenna assembly has been carefully calculated, achieving a zero-consumption thermal design for the SAR antenna. And the temperature difference of the entire antennas when powered on and powered off would not exceed 17 °C, meeting the specification requirements. In addition, to ensure the accuracy of antenna pointing, the support plate of antennas requires stable temperature. The layout of the heaters on the board has been optimized, reducing the use of heaters by 30% while ensuring that the temperature variation of the support board remains within 5 °C. Then, an on-orbit thermal simulation analysis of the satellite was conducted to refine the design and verification. Finally, the thermal test of the SAR satellite under vacuum conditions was conducted, involving operating the high-power antenna, verifying that the peak temperature of T/RM is below 29 °C, the temperature fluctuation amplitude during a single imaging task is 10 °C, and the lowest temperature point of the support plate is 16 °C. The results of the thermal simulation and test are highly consistent, verifying the correctness and effectiveness of the thermal design. Full article

High-flying insects that exploit tropospheric winds can disperse over far greater distances in a single generation than species restricted to below-canopy flight. However, the ecological consequences of such long-range dispersal remain poorly understood. For example, high-altitude dispersal may facilitate more rapid range shifts in these species and reduce their sensitivity to habitat fragmentation, in contrast to low-flying insects that rely more on terrestrial patch networks. Previous studies have primarily used surface-level variables with limited spatial coverage to explore dispersal timing and movement. In this study, we introduce a novel application of niche modelling to insect aeroecology by examining the relationship between a comprehensive set of atmospheric conditions and high-flying insect activity in the troposphere, as detected by weather surveillance radars (WSRs). We reveal correlations between large-scale dispersal events and atmospheric conditions, identifying key variables that influence dispersal behaviour. By incorporating high-altitude atmospheric conditions into niche models, we achieve significantly higher predictive accuracy compared with models based solely on surface-level conditions. Key predictive factors include the proportion of arable land, altitude, temperature, and relative humidity. Full article

Preprocessing Synthetic Aperture Radar (SAR) data is a crucial initial stage in leveraging SAR data for remote sensing applications. Terrain correction, both radiometric and geometric, and the detection of layover/shadow areas hold significant importance when SAR data are collected over mountainous regions. This study aims at investigating the impact of the Digital Elevation Model (DEM) used for terrain correction (radiometric and geometric) and for mapping layover/shadow areas on windthrow detection using COSMO SkyMed SAR images. The terrain correction was done using a radiometric and geometric terrain correction algorithm. Specifically, we evaluated five different DEMs: (i–ii) a digital terrain model and a digital surface model derived from airborne LiDAR flights; (iii) the ALOS Global Digital Surface Model; (iv) the Copernicus global DEM; and (v) the Shuttle Radar Topography Mission (SRTM) DEM. All five DEMs were resampled at 2 m and 30 m pixel spacing, obtaining a total of 10 DEMs. The terrain-corrected COSMO SkyMed SAR images were employed for windthrow detection in a forested area in the north of Italy. The findings revealed significant variations in windthrow detection across the ten corrections. The detailed LiDAR-derived terrain model (i.e., DTM at 2 m pixel spacing) emerged as the optimal choice for both pixel spacings considered. Full article

Apart from headwind changes, crosswind changes may be hazardous to aircraft operation. This paper presents two cases of recently observed crosswind changes from the range height indicator scans of ground-based remote sensing meteorological equipment, namely an X-band microwave radar and a short-range LIDAR. Both instruments have a range resolution down to around 30 m, allowing the study of fine-scale details of the vertical profiles of cross-mountain airflow at the Hong Kong International Airport. Rapidly evolving winds have been observed by the equipment in tropical cyclone situations, revealing high levels of turbulence and vertically propagating waves. The eddy dissipation rate derived from radar spectrum width indicated severe turbulence, with values exceeding 0.5 m^2/3 s⁻¹. In order to study the feasibility of predicting such disturbed airflow, a mesoscale meteorological model and a computational fluid dynamics model with high spatial resolution are used in this paper. It is found that the mesoscale meteorological model alone is sufficient to capture some rapidly evolving airflow features, including the turbulence level, the waves, and the rapidly changing wind speeds. However, the presence of reverse flow could only be reproduced with the use of a building-resolving computational fluid dynamics model. This paper aims at providing a reference for airports to consider the feasibility of performing high-resolution numerical simulations of rapidly evolving airflow to alert the pilots in advance for airports in complex terrains and the setup of buildings. Full article

Deep neural networks (DNNs) have achieved great success in various computer vision tasks. However, they are susceptible to artificially designed adversarial perturbations, which limit their deployment in security-critical applications. In this paper, we propose a gradual adversarial training (GAT) method for remote sensing image segmentation. Our method incorporates a domain-adaptive mechanism that dynamically modulates input data, effectively reducing adversarial perturbations. GAT not only improves segmentation accuracy on clean images but also significantly enhances robustness against adversarial attacks, all without necessitating changes to the network architecture. The experimental results demonstrate that GAT consistently outperforms conventional standard adversarial training (SAT), showing increased resilience to adversarial attacks of varying intensities on both optical and Synthetic Aperture Radar (SAR) images. Compared to the SAT defense method, GAT achieves a notable defense performance improvement of 1% to 12%. Full article

Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1–SC6, combining 5 m resolution data, and SC7–SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy. Full article

Predicting crop yield throughout its development cycle is crucial for planning storage, processing, and distribution. Optical remote sensing has been used for yield prediction but has limitations, such as cloud interference and only capturing canopy-level data. Synthetic Aperture Radar (SAR) complements optical data by capturing information even in cloudy conditions and providing additional plant insights. This study aimed to explore the correlation of SAR variables with soybean yield at different crop stages, testing if SAR data enhances predictions compared to optical data alone. Data from three growing seasons were collected from an area of 106 hectares, using eight SAR variables (Alpha, Entropy, DPSVI, RFDI, Pol, RVI, VH, and VV) and four speckle noise filters. The Random Forest algorithm was applied, combining SAR variables with the EVI optical index. Although none of the SAR variables showed strong correlations with yield (r < |0.35|), predictions improved when SAR data were included. The best performance was achieved using DPSVI with the Boxcar filter, combined with EVI during the maturation stage (with EVI:RMSE = 0.43, 0.49, and 0.60, respectively, for each season; while EVI + DPSVI:RMSE = 0.39, 0.49, and 0.42). Despite improving predictions, the computational demands of SAR processing must be considered, especially when optical data are limited due to cloud cover. Full article

Snow avalanches, one of the most severe natural hazards in mountainous regions, pose significant risks to human lives, infrastructure, and ecosystems. As climate change accelerates shifts in snowfall and temperature patterns, it is increasingly important to improve our ability to monitor and predict avalanches. This review explores the use of remote sensing technologies in understanding key geomorphological, geobotanical, and meteorological factors that contribute to avalanche formation. The primary objective is to assess how remote sensing can enhance avalanche risk assessment and monitoring systems. A systematic literature review was conducted, focusing on studies published between 2010 and 2025. The analysis involved screening relevant studies on remote sensing, avalanche dynamics, and data processing techniques. Key data sources included satellite platforms such as Sentinel-1, Sentinel-2, TerraSAR-X, and Landsat-8, combined with machine learning, data fusion, and change detection algorithms to process and interpret the data. The review found that remote sensing significantly improves avalanche monitoring by providing continuous, large-scale coverage of snowpack stability and terrain features. Optical and radar imagery enable the detection of crucial parameters like snow cover, slope, and vegetation that influence avalanche risks. However, challenges such as limitations in spatial and temporal resolution and real-time monitoring were identified. Emerging technologies, including microsatellites and hyperspectral imaging, offer potential solutions to these issues. The practical implications of these findings underscore the importance of integrating remote sensing data with ground-based observations for more robust avalanche forecasting. Enhanced real-time monitoring and data fusion techniques will improve disaster management, allowing for quicker response times and more effective policymaking to mitigate risks in avalanche-prone regions. Full article

This study examines the impact of rockfalls and floods on road and rail traffic in the Jiului Gorge, Romania, a critical transportation corridor. Using Sentinel-1 radar imagery processed through ESA SNAP and ArcGIS Pro, alongside traffic detection facilitated by YOLO models, we assessed susceptibility to both rockfalls and floods. The primary aim was to enhance public safety for traffic participants by providing accurate hazard mapping. Our study focuses on the area from Bumbești-Jiu to Petroșani, traversing the Southern Carpathians. The results demonstrate the utility of integrating remote sensing with machine learning to improve hazard management and inform more effective traffic planning. These findings contribute to safer, more resilient infrastructure in areas vulnerable to natural hazards. Full article

Remote sensing (RS) and Geographic Information Systems (GISs) provide significant opportunities for monitoring and managing natural resources across various temporal, spectral, and spatial resolutions. There is a critical need for natural resource managers to understand the expanding capabilities of image sources, analysis techniques, and in situ validation methods. This article reviews key image analysis tools in natural resource management, highlighting their unique strengths across diverse applications such as agriculture, forestry, water resources, soil management, and natural hazard monitoring. Google Earth Engine (GEE), a cloud-based platform introduced in 2010, stands out for its vast geospatial data catalog and scalability, making it ideal for global-scale analysis and algorithm development. ENVI, known for advanced multi- and hyperspectral image processing, excels in vegetation monitoring, environmental analysis, and feature extraction. ERDAS IMAGINE specializes in radar data analysis and LiDAR processing, offering robust classification and terrain analysis capabilities. Global Mapper is recognized for its versatility, supporting over 300 data formats and excelling in 3D visualization and point cloud processing, especially in UAV applications. eCognition leverages object-based image analysis (OBIA) to enhance classification accuracy by grouping pixels into meaningful objects, making it effective in environmental monitoring and urban planning. Lastly, QGIS integrates these remote sensing tools with powerful spatial analysis functions, supporting decision-making in sustainable resource management. Together, these tools when paired with in situ data provide comprehensive solutions for managing and analyzing natural resources across scales. Full article

Coherent S-band radar is a remote sensing observation device with high spatial-temporal resolution and can be used to achieve deterministic sea wave reconstruction and prediction (DSWRP) technology. However, coherent S-band radar can observe nonlinear details of the sea surface due to its high resolution, which makes the propagation operator matrix an ill-conditioned overdetermined matrix. To solve this problem, this paper proposes a DSWRP scheme using condition number regularized least squares (CN-RLS) for coherent S-band radar. First, the space-time velocity information was obtained from the radar echo. Second, the CN-RLS method solved the phase-resolved model coefficients. Finally, the deterministic wave field was predicted according to the solved model coefficients. The proposed scheme was verified by simulation data and the real radar dataset observed by the coherent S-band wave-measuring radar onboard the ship XIANGYANGHONG-18 in the East China Sea in April 2024. The predicted wave elevation of the proposed method was compared with the wave elevation observed based on the X-band wave-measuring radar, and the root mean square error (RMSE) and correlation coefficient (CC) were 0.22 m and 0.76, respectively, which show that the proposed method could effectively implement the DSWRP technology. Full article

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture. Full article

Surface mining in the southeastern Gobi Desert has significant environmental impacts, primarily due to the creation of large coal piles that are highly susceptible to aeolian processes. Using spaceborne remote sensing and numerical simulations, we investigated erosional processes and their environmental impacts. Our primary tool was Interferometric Synthetic Aperture Radar (InSAR) data from Sentinel-1 imagery collected between 2017 and 2022. We analyzed these data using phase angle information from the Small Baseline InSAR time series framework. The time series analyses revealed intensive aeolian erosion in the coal piles, represented as thin deformation patterns along the potential pathways of aerodynamic transportation. Further analysis of multispectral data, combined with correlations between wind patterns and trajectory simulations, highlighted the detrimental impact of coal dust on the surrounding environment and the mechanism of aeolian erosion. The lack of mitigation measures, such as water spray, appeared to exacerbate erosion and dust generation. This study demonstrates the feasibility of using publicly available remote sensing data to monitor coal mining activities and their environmental hazards. Our findings contribute to a better understanding of coal dust generation processes in surface mining operations as well as the aeolian erosion mechanism in desert environments. Full article