Search Results (167)

One of the persistent challenges in multispectral image analysis is the interference caused by dense cloud cover and its resulting shadows, which can significantly obscure surface features. This becomes especially problematic when attempting to monitor surface changes over time using satellite imagery, such as from Landsat-8. In this study, rather than simply masking visual obstructions, we aimed to investigate the role and influence of clouds within the spectral data itself. To achieve this, we employed Independent Component Analysis (ICA), a statistical method capable of decomposing mixed signals into independent source components. By applying ICA to selected Landsat-8 bands and analyzing each component individually, we assessed the extent to which cloud signatures are entangled with surface data. This process revealed that clouds contribute to multiple ICA components simultaneously, indicating their broad spectral influence. With this influence on multiple wavebands, we managed to configure a set of components that could perfectly delineate the extent and location of clouds. Moreover, because Landsat-8 lacks cloud-penetrating wavebands, such as those in the microwave range (e.g., SAR), the surface information beneath dense cloud cover is not captured at all, making it physically impossible for ICA to recover what is not sensed in the first place. Despite these limitations, ICA proved effective in isolating and delineating cloud structures, allowing us to selectively suppress them in reconstructed images. Additionally, the technique successfully highlighted features such as water bodies, vegetation, and color-based land cover differences. These findings suggest that while ICA is a powerful tool for signal separation and cloud-related artifact suppression, its performance is ultimately constrained by the spectral and spatial properties of the input data. Future improvements could be realized by integrating data from complementary sensors—especially those operating in cloud-penetrating wavelengths—or by using higher spectral resolution imagery with narrower bands. Full article

A timely and accurate distribution of crop types and cropping patterns provides a crucial reference for the management of agriculture and food security. However, accurately mapping crop types and cropping patterns in subtropical hilly and mountainous areas often face challenges such as mixed pixels resulted from fragmented patches and difficulty in obtaining optical satellites due to a frequently cloudy and rainy climate. Here we propose a crop type and cropping pattern mapping framework in subtropical hilly and mountainous areas, considering multiple sources of satellites (i.e., Landsat 8/9, Sentinel-2, and Sentinel-1 images and GF 1/2/7). To develop this framework, six types of variables from multi-sources data were applied in a random forest classifier to map major summer crop types (singe-cropped rice and double-cropped rice) and winter crop types (rapeseed). Multi-scale segmentation methods were applied to improve the boundaries of the classified results. The results show the following: (1) Each type of satellite data has at least one variable selected as an important feature for both winter and summer crop type classification. Apart from the endmember variables, the other five extracted variable types are selected by the RF classifier for both winter and summer crop classifications. (2) SAR data can capture the key information of summer crops when optical data is limited, and the addition of SAR data can significantly improve the accuracy as to summer crop types. (3) The overall accuracy (OA) of both summer and winter crop type mapping exceeded 95%, with clear and relatively accurate cropland boundaries. Area evaluation showed a small bias in terms of the classified area of rapeseed, single-cropped rice, and double-cropped rice from statistical records. (4) Further visual examination of the spatial distribution showed a better performance of the classified crop types compared to three existing products. The results suggest that the proposed method has great potential in accurately mapping crop types in a complex subtropical planting environment. Full article

Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper proposes an improved multi-feature association method that integrates satellite remote sensing and AIS data, with a focus on oriented bounding box course estimation, to improve the detection of dark ships and enhance maritime surveillance. Firstly, the oriented bounding box object detection model (YOLOv11n-OBB) is trained to break through the limitations of horizontal bounding box orientation representation. Secondly, by integrating position, dimensions (length and width), and course characteristics, we devise a joint cost function to evaluate the combined significance of multiple features. Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. Finally, by integrating data from Gaofen-6 (optical) and Gaofen-3B (SAR) satellites, a day-and-night collaborative monitoring framework is constructed to address the blind spots of single-sensor monitoring during night-time or adverse weather conditions. Our results indicate that the detection model demonstrates a high average precision (AP50) of 0.986 on the optical dataset and 0.903 on the SAR dataset. The association accuracy of the multi-feature association algorithm is 91.74% in optical image and AIS data matching, and 91.33% in SAR image and AIS data matching. The association rate reaches 96.03% (optical) and 74.24% (SAR), respectively. This study provides an efficient technical tool for maritime safety regulation through multi-source data fusion and algorithm innovation. Full article

Irrigation areas in arid regions are vital production areas for grain and cash crops worldwide. Grasping the temporal and spatial evolution of planting configurations across several years is crucial for effective regional agricultural and resource management. In view of problems such as insufficient optical images caused by cloudy weather in arid regions and the unclear spatiotemporal evolution patterns of the planting structures in irrigation areas over the years, in this study, we took the Santun River Irrigation Area, a typical arid region in Xinjiang, China, as an example. By leveraging long time-series remote sensing images from Sentinel-1 and Sentinel-2, the spectral, index, texture, and polarization features of the ground objects in the study area were extracted. When analyzing the index characteristics, we considered several widely used global vegetation indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), and Global Environment Monitoring Index (GEMI). Additionally, we integrated the vertical–vertical and vertical–horizontal polarization data obtained from synthetic aperture radar (SAR) satellite systems. Machine learning algorithms, including the random forest algorithm (RF), Classification and Regression Trees (CART), and Support Vector Machines (SVM), were employed for planting structure classification. The optimal classification model selected was subjected to inter-annual transfer to obtain the planting structures over multiple years. The research findings are as follows: (1) The RF classification algorithm outperforms CART and SVM algorithms in terms of classification accuracy, achieving an overall accuracy (OA) of 0.84 and a kappa coefficient of 0.805. (2) The cropland area classified by the RF algorithm exhibited a high degree of consistency with statistical yearbook data (R² = 0.82–0.91). Significant differences are observed in the estimated planting areas of cotton, maize, tomatoes, and wheat, while differences in other crops are not statistically significant. (3) From 2019 to 2024, cotton remained the dominant crop, although its proportional area fluctuated considerably, while the areas of maize and wheat tended to remain stable, and those of tomato and melon showed relatively minor changes. Overall, the region demonstrates a cotton-dominated, stable cropping structure for other crops. The newly developed framework exhibits exceptional precision in categorization while maintaining impressive adaptability, offering crucial insights for optimizing agricultural operations and sustainable resource allocation in irrigation-dependent arid zones. Full article

Floods caused by extreme weather events critically impact human and natural systems. Remote sensing can be a very useful tool in mapping these impacts. However, processing and analyzing satellite imagery covering extensive periods is computationally intensive and time-consuming, especially when data from different sensors need to be integrated, hampering its operational use. To address this issue, the present study focuses on mapping flooded areas and analyzing the impacts of the 2023 Storm Daniel flood in the Thessaly region (Greece), utilizing Earth Observation and GIS methods. The study uses multiple Sentinel-1, Sentinel-2, and Landsat 8/9 satellite images based on backscatter histogram statistics thresholding for SAR and Modified Normalized Difference Water Index (MNDWI) for multispectral images to delineate the extent of flooded areas triggered by the 2023 Storm Daniel in Thessaly region (Greece). Cloud computing on the Google Earth Engine (GEE) platform is utilized to process satellite image acquisitions and track floodwater evolution dynamics until the complete drainage of the area, making the process significantly faster. The study examines the usability and transferability of the approach to evaluate flood impact through land cover, linear infrastructure, buildings, and population-related geospatial datasets. The results highlight the vital role of the proposed approach of integrating remote sensing and geospatial analysis for effective emergency response, disaster management, and recovery planning. Full article

Digital Surface Models (DSMs) have a wide range of applications, including the spatial and temporal analysis of human habitation. Traditionally, DSMs are generated by rasterizing Light Detection and Ranging (LiDAR) point clouds. While LiDAR provides high-resolution details, the acquisition of required data is logistically challenging and costly, leading to limited spatial coverage and temporal frequency. Satellite imagery, such as Synthetic Aperture Radar (SAR), contains information on surface height variations in the scene within the reflected signal. Transforming satellite imagery data into a global DSM is challenging but would be of great value if those challenges were overcome. This study explores the application of a U-Net architecture to generate DSMs by coupling Sentinel-1 SAR and Sentinel-2 optical imagery. The model is trained on surface height data from multiple U.S. cities to produce a normalized DSM (NDSM) and assess its ability to generalize inferences for cities outside the training dataset. The analysis of the results shows that the model performs moderately well when inferring test cities but its performance remains well below that of the training cities. Further examination, through the comparison of height distributions and cross-sectional analysis, reveals that estimation bias is influenced by the input image resolution and the presence of geometric distortion within the SAR image. These findings highlight the need for refinement in preprocessing techniques as well as advanced training approaches and model architecture that can better handle the complexities of urban landscapes encoded in satellite imagery. Full article

Accurate near-surface wind speed and direction measurements are crucial for validating atmospheric models, especially for the purpose of adequately assessing the interactions between the surface and wind, which in turn results in characteristic vertical profiles. Coastal regions pose unique challenges due to the discontinuity between land and sea and the complex interplay of atmospheric stability, topography, and boundary/layer dynamics. This study focuses on a unique database of wind profiles collected over several years at a World Meteorological Organization—Global Atmosphere Watch (WMO/GAW) coastal site in the southern Italian region of Calabria (Lamezia Terme, code: LMT). By leveraging remote sensing technologies, including wind lidar combined with in situ measurements, this work comprehensively analyzes wind circulation at low altitudes in the narrowest point of the entire Italian peninsula. Seasonal, daily, and hourly wind profiles at multiple heights are analyzed, highlighting the patterns and variations induced by land–sea interactions. A case study integrating Synthetic Aperture Radar (SAR) satellite images and in situ observations demonstrates the importance of multi-sensor approaches in capturing wind dynamics and validating model simulations. Data analyses demonstrate the occurrence of extreme events during the winter and spring seasons, linked to synoptic flows; fall seasons have variable patterns, while during the summer, low-speed winds and breeze regimes tend to prevail. The prevailing circulation is of a westerly nature, in accordance with other studies on large-scale flows. Full article

This study investigates vertical soil movement, a subsidence phenomenon affecting infrastructure and communities in the Emilia-Romagna region (Italy). Building upon previous research—initially based on leveling and GNSS observations and later expanded with interferometric synthetic aperture radar (InSAR)—this study focuses on recent data from 2016 to 2021. A key innovation is the use of dual-geometry ascending and descending acquisitions to derive the vertical and the east–west movement components, a technique not previously applied at a regional scale in this area. The integration of advanced geodetic techniques involved processing 1208 Sentinel-1 satellite images with the SqueeSAR^® algorithm and analyzing data from 28 GNSS permanent stations using the precise point positioning (PPP) methodology. By calibrating the InSAR data with GNSS measurements, we generated a comprehensive subsidence map for the study period, identifying trends and anomalies. The analysis produced 13.5 million measurement points, calibrated and validated using multiple GNSS stations. The final dataset, processed through geostatistical methods, provided a high-resolution (100-m) regional subsidence map covering nearly 11,000 square kilometers. Finally, the vertical soil movement map for 2016–2021 was developed, featuring isokinetic curves with an interval of 2.5 mm/year. The results underscore the value of integrating these geodetic techniques for effective environmental monitoring in subsidence-prone areas. Furthermore, comparisons with previous subsidence maps reveal the evolution of soil movement in Emilia-Romagna, reinforcing the importance of these maps as essential tools for precise subsidence monitoring. Full article

Traditional single-channel Synthetic Aperture Radar (SAR) cannot achieve high-resolution and wide-swath (HRWS) imaging due to the constraint of the minimum antenna area. Distributed HRWS SAR can realize HRWS imaging and also possesses the resolution ability in the height dimension by arranging multiple satellites in the elevation direction. Nevertheless, due to the excessively high pulse repetition frequency (PRF) of the distributed SAR system, range ambiguity will occur in large detection scenarios. When directly performing 3D-imaging processing on SAR images with range ambiguity, both focused point clouds and blurred point clouds will exist simultaneously in the generated 3D point clouds, which affects the quality of the generated 3D-imaging point clouds. To address this problem, this paper proposes a 3D blur suppression method for HRWS blurred images, which estimates and eliminates defocused point clouds based on focused targets. The echoes with range ambiguity are focused in the near area and the far area, respectively. Then, through image registration, amplitude and phase correction, and height-direction focusing, the point clouds in the near area and the far area are obtained. The strongest points in the two sets of point clouds are iteratively selected to estimate and eliminate the defocused point clouds in the other set of point clouds until all the ambiguity is eliminated. Simulation experiments based on airborne measured data verified the capability to achieve HRWS 3D blur suppression of this method. Full article

Monitoring forest health has become essential due to increasing pressures caused by climate change and dust events, particularly in semi-arid regions. This study investigates the impact of dust events on forest vegetation in Paphos forest in Cyprus, which is a semi-arid area prone to frequent dust storms. Using multispectral and radar satellite data from Sentinel-1 and Landsat series, vegetation responses to eight documented dust events between 2015 and 2019 were analysed, employing BFAST (Breaks For Additive Season and Trend) algorithms to detect abrupt changes in vegetation indices and radar backscatter. The outcomes showed that radar data were particularly effective in identifying only the most significant dust events (PM10 > 100 μg/m³, PM2.5 > 30 μg/m³), indicating that SAR (Synthetic Aperture Radar) is more responsive to pronounced dust deposition, where backscatter changes reflect more substantial vegetation stress. Conversely, optical data were sensitive to a wider range of events, capturing responses even at lower dust concentrations (PM10 > 50 μg/m³, PM2.5 > 20 μg/m³) and detecting minor vegetation stress through indices like SAVI, EVI, and AVI. The analysis highlighted that successful detection relies on multiple factors beyond sensor type, such as rainfall timing and imagery availability close to the dust events. This study highlights the importance of an integrated remote sensing approach for effective forest health monitoring in regions prone to dust events. Full article

Ecotones, i.e., transition zones between habitats, are important landscape features, yet they are often ignored in landscape monitoring. This study addresses the challenge of delineating ecotones at multiple scales by integrating multisource remote sensing data, including ultra-high-resolution RGB images, LiDAR data from UAVs, and satellite data. We first developed a fine-resolution landcover map of three plots in Yunnan, China, with accurate delineation of ecotones using orthoimages and canopy height data derived from UAV-LiDAR. These maps were subsequently used as the training set for four machine learning models, from which the most effective model was selected as an upscaling model. The satellite data, encompassing Synthetic Aperture Radar (SAR; Sentinel-1), multispectral imagery (Sentinel-2), and topographic data, functioned as explanatory variables. The Random Forest model performed the best among the four models (kappa coefficient = 0.78), with the red band, shortwave infrared band, and vegetation red edge band as the most significant spectral variables. Using this RF model, we compared landscape patterns between 2017 and 2023 to test the model’s ability to quantify ecotone dynamics. We found an increase in ecotone over this period that can be attributed to an expansion of 0.287 km² (1.1%). In sum, this study demonstrates the effectiveness of combining UAV and satellite data for precise, large-scale ecotone detection. This can enhance our understanding of the dynamic relationship between ecological processes and landscape pattern evolution. Full article

Underground coal mining induces surface subsidence, which in turn impacts the stability of slopes in mountainous regions. However, research that investigates the coupling relationship between surface subsidence in mountainous regions and the occurrence of multiple surface hazards is scarce. Taking a coal mine in southwestern China as a case study, a detailed catalog of the surface hazards in the study area was created based on multi-temporal satellite imagery interpretation and Unmanned aerial vehicle (UAV) surveys. Using interferometric synthetic aperture radar (InSAR) technology and the logistic subsidence prediction method, this study investigated the evolution of surface subsidence induced by underground mining activities and its impact on the triggering of multiple surface hazards. We found that the study area experienced various types of surface hazards, including subsidence, landslides, debris flows, sinkholes, and ground fissures, due to the effects of underground mining activities. The InSAR monitoring results showed that the maximum subsidence at the back edge of the slope terrace was 98.2 mm, with the most severe deformation occurring at the mid-slope of the mountain, where the maximum subsidence reached 139.8 mm. The surface subsidence process followed an S-shaped curve, comprising the stages of initial subsidence, accelerated subsidence, and residual subsidence. Additionally, the subsidence continued even after coal mining operations concluded. Predictions derived from the logistic model indicate that the duration of residual surface subsidence in the study area is approximately 1 to 2 years. This study aimed to provide a scientific foundation for elucidating the temporal and spatial variation patterns of subsidence induced by underground coal mining in mountainous regions and its impact on the formation of multiple surface hazards. Full article

Existing validation of mean wind speed estimates via reflectometry from global navigation systems of satellites (GNSS-R)—has been largely limited in spatial coverage to equatorial buoys or tropical cyclone events near continental United States. Two alternative validation techniques are presented for the Cyclone GNSS (CYGNSS) mission using surface-based observations along coasts and coral reefs instead of buoys, and triple collocation analysis (TCA) instead of a 1:1 gridded comparison for tropical cyclone (TC) events. For the surface-based analysis, Fully Developed Seas (FDS) v3.2 and NOAA v1.2 were compared to anemometer data provided by the Australian Bureau of Meteorology across the Australia and Pacific regions. Overall, the products performed similarly to previous studies with NOAA having higher correlations and lower errors than FDS, though FDS performed better than NOAA over the Australian dataset for high wind speed events. TCA was used to validate NOAA v1.2 and Merged v3.2 datasets with other satellite remotely sensed products from the Soil Moisture Active Passive (SMAP) mission and Synthetic Aperture Radar (SAR). Both additive and multiplicative error models for TCA were applied. The performance overall was similar between the two products, with NOAA producing higher errors. NOAA performed better than Merged for mean winds above 17 m/s as the large temporal averaging reduced sensitivity to high winds. For SMAP winds above 17 m/s, NOAA’s average bias (−2.1 m/s) was significantly smaller than the average bias in Merged (−4.4 m/s). Future ideas for rapid intensification detection and constellation design are discussed. Full article

Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land. Full article

Ground subsidence in urban areas is mainly due to natural or anthropogenic activities, and it seriously threatens the healthy and sustainable development of the city and the security of individuals’ lives and assets. Shanghai is a megacity of China, and it has a long history of ground subsidence due to the overexploitation of groundwater and urban expansion. Time Series Synthetic Aperture Radar Interferometry (TS-InSAR) is a highly effective and widely used approach for monitoring urban ground deformation. However, it is difficult to obtain long-term (such as over 10 years) deformation results using single-platform SAR satellite in general. To acquire long-term surface deformation monitoring results, it is necessary to integrate data from multi-platform SAR satellites. Furthermore, the deformations are the result of multiple factors that are superimposed, and relevant studies that quantitatively separate the contributions from different driving factors to subsidence are rare. Moreover, the time series cumulative deformation results of massive measurement points also bring difficulties to the deformation interpretation. In this study, we have proposed a long-term surface deformation monitoring and quantitative interpretation method that integrates multi-platform TS-InSAR, PCA, and K-means clustering. SAR images from three SAR datasets, i.e., 19 L-band ALOS-1 PALSAR, 22 C-band ENVISAT ASAR, and 20 C-band Sentinel-1A, were used to retrieve annual deformation rates and time series deformations in Shanghai from 2007 to 2018. The monitoring results indicate that there is serious uneven settlement in Shanghai, with a spatial pattern of stability in the northwest and settlement in the southeast of the study area. Then, we selected Pudong International Airport as the area of interest and quantitatively analyzed the driving factors of land subsidence in this area by using PCA results, combining groundwater exploitation and groundwater level change, precipitation, temperature, and engineering geological and human activities. Finally, the study area was divided into four sub-regions with similar time series deformation patterns using the K-means clustering. This study helps to understand the spatiotemporal evolution of surface deformation and its driving factors in Shanghai, and provides a scientific basis for the formulation and implementation of precise prevention and control strategies for land subsidence disasters, and it can also provide reference for monitoring in other urban areas. Full article