Search Results (1,857)

Salt marshes, as one of the most productive ecosystems on earth, have experienced fragmentation, degradation, and losses due to the impacts of climate change and human overexploitation. Accurate monitoring of vegetation distribution and composition is crucial for salt marsh protection. However, achieving accurate mapping has posed a challenge. Leveraging the high spatiotemporal resolution of the Sentinel series data, this study developed a method for high-accuracy mapping based on monthly changes across the vegetation life cycle, utilizing the random forest algorithm. This method was applied to identify Phragmites australis, Suaeda salsa, Spartina alterniflora, and the mixed-vegetation zones of Tamarix chinensis in the Yellow River Delta, and to analyze the key features of the model. The results indicate that: (1) integrating Sentinel-1 and Sentinel-2 satellite data achieved superior mapping accuracy (OA = 90.7%) compared to using either satellite individually; (2) the inclusion of SAR data significantly enhanced the classification accuracy within the mixed-vegetation zone, with “SAR_divi” in July emerging as the pivotal distinguishing feature; and (3) the overall extent of salt marsh vegetation in the Yellow River Delta remained relatively stable from 2018 to 2022, with the largest area recorded in 2020 (265.69 km²). These results demonstrate the robustness of integrating Sentinel-1 and Sentinel-2 features for mapping salt marsh, particularly in complex mixed-vegetation zones. Such insights offer valuable guidance for the conservation and management of salt marsh ecosystems. Full article

Due to the limited availability of high-quality optical images during the rice growth period in the Lingnan region of China, effectively monitoring the rice planting situation has been a challenge. In this study, we utilized multi-temporal Sentinel-1 data to develop a method for rapidly extracting the range of rice fields using a threshold segmentation approach and employed a U-Net deep learning model to delineate the distribution of rice fields. Spatio-temporal changes in rice distribution in Leizhou City, Guangdong Province, China, from 2017 to 2021 were analyzed. The results revealed that by analyzing SAR-intensive time series data, we were able to determine the backscattering coefficient of typical crops in Leizhou and use the threshold segmentation method to identify rice labels in SAR-intensive time series images. Furthermore, we extracted the distribution range of early and late rice in Leizhou City from 2017 to 2021 using a U-Net model with a minimum relative error accuracy of 3.56%. Our analysis indicated an increasing trend in both overall rice planting area and early rice planting area, accounting for 44.74% of early rice and over 50% of late rice planting area in 2021. Double-cropping rice cultivation was predominantly concentrated in the Nandu River basin, while single-cropping areas were primarily distributed along rivers and irrigation facilities. Examination of the traditional double-cropping areas in Fucheng Town from 2017 to 2021 demonstrated that over 86.94% had at least one instance of double cropping while more than 74% had at least four instances, which suggested that there is high continuity and stability within the pattern of rice cultivation practices observed throughout Leizhou City. Full article

Landslide risks in open-pit mine areas are heightened by artificial slope modifications necessary for mining operations, endangering human life and property. On 22 February 2023, a catastrophic landslide occurred at the Xinjing Open-Pit Coal Mine in Inner Mongolia, China, resulting in 53 fatalities and economic losses totaling 28.7 million USD. Investigating the pre-, co-, and post-failure deformation processes and exploring the potential driving mechanisms are crucial to preventing similar tragedies. In this study, we used multi-source optical and radar images alongside satellite geodetic methods to analyze the event. The results revealed pre-failure acceleration at the slope toe, large-scale southward displacement during collapse, and ongoing deformation across the mine area due to mining operations and waste accumulation. The collapse was primarily triggered by an excessively steep, non-compliant artificial slope design and continuous excavation at the slope’s base. Furthermore, our experiments indicated that the commonly used Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) significantly underestimated landslide deformation due to the maximum detectable deformation gradient (MDDG) limitation. In contrast, the high-spatial-resolution Fucheng-1 provided more accurate monitoring results with a higher MDDG. This underscores the importance of carefully assessing the MDDG when employing InSAR techniques to monitor rapid deformation in mining areas. Full article

Tree species are important factors affecting the carbon sequestration capacity of forests and maintaining the stability of ecosystems, but trees are widely distributed spatially and located in complex environments, and there is a lack of large-scale regional tree species classification models for remote sensing imagery. Therefore, many studies aim to solve this problem by combining multivariate remote sensing data and proposing a machine learning model for forest tree species classification. However, satellite-based laser systems find it difficult to meet the needs of regional forest species classification characters, due to their unique footprint sampling method, and SAR data limit the accuracy of species classification, due to the problem of information blending in backscatter coefficients. In this work, we combined Sentinel-1 and Sentinel-2 data to construct a machine learning tree classification model based on optical features, vegetation spectral features, and PolSAR polarization observation features, and propose a forest tree classification feature selection method featuring the Hilbert–Huang transform for the problem of mixed information on the surface of SAR data. The PSO-RF method was used to classify forest species, including four temperate broadleaf forests, namely, aspen (Populus L.), maple (Acer), peach tree (Prunus persica), and apricot tree (Prunus armeniaca L.), and two coniferous forests, namely, Chinese pine (Pinus tabuliformis Carrière) and Mongolian pine (Pinus sylvestris var. mongolica Litv.). In this study, some experiments were conducted using two Sentinel-1 images, four Sentinel-2 images, and 550 measured forest survey sample data points pertaining to the forested area of Fuxin District, Liaoning Province, China. The results show that the fusion model constructed in this study has high accuracy, with a Kappa coefficient of 0.94 and an overall classification accuracy of 95.1%. In addition, this study shows that PolSAR data can play an important role in forest tree species classification. In addition, by applying the Hilbert–Huang transform to PolSAR data, other feature information that interferes with the perceived vertical structure of forests can be suppressed to a certain extent, and its role in the classification of forest species, combined with PolSAR, should not be ignored. Full article

Faults, as unique geological structures, disrupt the mechanical connections between rock masses. During coal mining, faults in the overlying strata can disturb the original stress balance, leading to fault activation and altering the typical subsidence patterns. This can result in abnormal ground deformation and significant damage to surface structures, posing a serious geological hazard in mining areas. This study examines the influence of a known fault (F13 fault) on ground subsidence in the Wannian Mine of the Fengfeng Mining Area. We utilized 12 Sentinel-1A images and applied SBAS-InSAR, StaMPS-InSAR, and DS-InSAR time-series InSAR methods, alongside the D-InSAR method, to investigate surface deformations caused by the F13 fault. The monitoring accuracy of these methods was evaluated using leveling measurements from 28 surface movement observation stations. In addition, the density of effective monitoring points and the relative strengths and limitations of the three time-series methods were compared. The findings indicate that, in low deformation areas, DS-InSAR has a monitoring accuracy of 7.7 mm, StaMPS-InSAR has a monitoring accuracy of 16.4 mm, and SBAS-InSAR has an accuracy of 19.3 mm. Full article

Urban growth is increasingly shifting from horizontal expansion to vertical development, resulting in skylines dominated by high-rise buildings. The post-construction operations and maintenance of these buildings are critical, requiring regular structural health monitoring (SHM) to proactively identify and address potential safety concerns. Interferometric synthetic aperture radar (InSAR) has proven effective for monitoring building safety, but most studies rely on high-resolution synthetic aperture radar (SAR) images. The high cost and limited coverage of these images restrict their use for large-scale monitoring. Sentinel-1 medium-resolution SAR images, which are freely available and offer broad coverage, make large-scale SHM more feasible. However, studies on the use of Sentinel-1 SAR images for structural health monitoring, especially at large spatial scales, remain limited. To address this gap, in this study, Sentinel-1 SAR images and PS-InSAR technology are proposed for performing a comprehensive structural safety assessment of super high-rise buildings in the Guangzhou–Foshan Metropolitan Area (GFMA) and for analyzing the influencing factors. Our assessment shows that while the overall structural safety of these buildings is satisfactory, certain areas, including Pearl River New Town, central Huadu district in Guangzhou, and southeastern Shunde district in Foshan, exhibit suboptimal safety conditions. We verified these findings using GNSS data and on-site investigations, confirming that Sentinel-1 SAR imagery offers reliable accuracy for monitoring building structural health. Furthermore, we identified factors such as settlement in soft soil layers, the construction of surrounding (underground) infrastructure, and building aging, which could potentially impact building structural safety. The results demonstrate that Sentinel-1 SAR images provide a reliable, rapid, and cost-effective method for the large-scale monitoring of building stability, enhancing our understanding of the underlying mechanisms and informing strategies to prevent potential safety crises, and also ensuring the sustainable development of society. Full article

The paper addresses the spatiotemporal variability of anthropogenic film pollution (AFP) in Avacha Gulf near the Kamchatka Peninsula based on satellite synthetic-aperture radar (SAR) imagery. Coastal waters of the study area are subject to significant anthropogenic impacts associated with intensive marine traffic, as well as the flow of household and industrial wastewater from factories located on the coast. A quantitative approach to the registration and quantitative analysis of spatiotemporal AFP distributions was applied. This approach is based on the processing of long-term time series of SAR imagery, taking into account inhomogeneous observation coverage and changing hydrometeorological conditions of different regions of water areas in various time periods. In total, 318 cases of AFP were detected in 2014–2023 in Avacha Gulf, covering 332 km² of the total area (~3% of the water area) based on the 1134 processed radar Sentinel-1A/B scenes. The average value of AFP exposure, e, was about 93 ppm, evidencing the high level of AFP in the studied water area (comparable to areas of the Black Sea with intensive marine traffic, for which e was previously determined to be between ~90 and ~130 ppm). An interannual positive trend was revealed, indicating that over the 10-year period under study, the exposure of the waters of Avacha Bay (the most polluted part of Avacha Gulf) to AFP increased ~3-fold. An analysis of AFP spatial distributions and marine traffic maps indicates that this type of activity is a significant source of anthropogenic film pollution in Avacha Gulf (including Avacha Bay). It was shown that the generated quantitative information products using the introduced AFP exposure concept can be interpreted and used, for example, for making management decisions. Full article

The Pamir is located on the northwestern margin of the Tibetan Plateau, which is an area of intense continental deformation and part of the famous India–Himalaya collision zone. The dominant structural deformation in the eastern Pamir is characterized by a 250 km long east–west extensional fault system, known as the Kongur Shan extensional system (KSES), which has developed a series of faults with different orientations and characteristics, resulting in highly complex structural deformation and lacking sufficient geodetic constraints. We collected Sentinel-1 SAR data from December 2016 to March 2023, obtained high-resolution ascending and descending LOS velocities and 3D deformation fields, and combined them with GPS data to constrain the current motion characteristics of the northeastern Pamirs for the first time. Based on the two-dimensional screw dislocation model and using the Bayesian Markov chain Monte Carlo (MCMC) inversion method, the kinematic parameters of the fault were calculated, revealing the fault kinematic characteristics in this region. Our results demonstrate that the present-day deformation of the KSES is dominated by nearly E–W extension, with maximum extensional motion concentrated in its central segment, reaching peak extension rates of ~7.59 mm/yr corresponding to the Kongur Shan. The right-lateral Muji fault at the northern end exhibits equivalent rates of extensional motion with a relatively shallow locking depth. The strike-slip rate along the Muji fault gradually increases from west to east, ranging approximately between 4 and 6 mm/yr, significantly influenced by the eastern normal fault. The Tahman fault (TKF) at the southernmost end of the KSES shows an extension rate of ~1.5 mm/yr accompanied by minor strike-slip motion. The Kashi anticline is approaching stability, while the Mushi anticline along the eastern Pamir frontal thrust (PFT) remains active with continuous uplift at ~2 mm/yr, indicating that deformation along the Tarim Basin–Tian Shan boundary has propagated southward from the South Tian Shan thrust (STST). Overall, this study demonstrates the effectiveness of integrated InSAR and GPS data in constraining contemporary deformation patterns along the northeastern Pamir margin, contributing to our understanding of the region’s tectonic characteristics. Full article

Urban areas face an imminent risk of collapse due to structural deficiencies and gradual ground subsidence. Therefore, monitoring surface movements is crucial for detecting abnormal behavior, implementing timely preventive measures, and minimizing the detrimental effects of this phenomenon in residential regions. In this context, interferometric synthetic aperture radar (InSAR) has emerged as a highly effective technique for monitoring slow and long-term ground hazards and surface motions. The first goal of this study is to explore the potential applications of persistent scatterer interferometry (PSI) and small baseline subset (SBAS) algorithms in collapse hotspot detection, utilizing a dataset consisting of 144 Sentinel-1 images. The experimental results from three areas with a history of collapses demonstrate that the SBAS algorithm outperforms PSI in uncovering behavior patterns indicative of collapse and accurately pinpointing collapse points near real collapse sites. In the second phase, this research incorporated an additional dataset of 36 TerraSAR-X images alongside the Sentinel-1 data to compare results based on radar images with different spatial resolutions in the C and X bands. The findings reveal a strong correlation between the TerraSAR-X and Sentinel-1 time series. Notably, the analysis of the TerraSAR-X time series for one study area identified additional collapse-prone points near the accident site, attributed to the higher spatial resolution of these data. By leveraging the capabilities of InSAR and advanced algorithms, like SBAS, this study highlights the potential to identify areas at risk of collapse, enabling the implementation of preventive measures and reducing potential harm to residential communities. Full article

Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada. Full article

With the accumulation of synthetic aperture radar (SAR) altimetry data and advancements in retracking algorithms, the improved along-track spatial resolution and signal-to-noise ratio have significantly enhanced the availability and precision of sea surface height (SSH) measurements, particularly in challenging environments such as coastal areas, ocean currents, and polar regions. These improvements have refined the accuracy and reliability of mean sea surface (MSS) models, which in turn have enhanced the precision of mean dynamic topography (MDT) and geostrophic current models. However, in-depth research is required to quantify the specific contributions of SAR altimetry to these critical regions and their impacts on the MSS, MDT, and geostrophic currents. Given that DTU21MSS (Technical University of Denmark MSS 2021) incorporates a substantial amount of SAR altimetry data, this study utilized independent Sentinel-3A altimetric observations to evaluate the signal improvements of DTU21MSS compared with DTU15MSS, with a focus on its performance in polar, coastal, and current regions. In addition, a least-squares-based approach was employed to assess the impact of the improved MSS model on the deduced MDT and geostrophic current signals. The numerical results revealed that DTU21MSS achieved an accuracy improvement of ~8% within 20 km offshore compared with DTU15MSS. In the polar regions within 100 km offshore, DTU21MSS exhibited a maximum signal enhancement of ~0.1 m, with overall improvements of 10–20%. The DTU21MSS-derived MDT solution demonstrates better consistency with validation data, reducing the standard deviation of misfits from 0.058 m to 0.054 m. Signal enhancements of maximumly 0.1 m were observed in the polar regions and the Mediterranean/Red Sea. Furthermore, improvements in the MSS and its error information could directly enhance the deduced MDT models, highlighting its foundational role in precise oceanographic modeling. Full article

Respiratory pathogen coinfections pose significant challenges to global public health, particularly regarding the intersecting epidemics of COVID-19 and influenza. This study investigated the incidences of respiratory infectious pathogens in this unique context. We collected throat swab samples from 308 patients with a fever from outpatient and emergency departments at sentinel surveillance hospitals in Xiamen, southeast of China, between April and May 2023, testing for SARS-CoV-2 and 26 other respiratory pathogens. The coinfection rate of the XBB SARS-CoV-2 variant with other respiratory pathogens was higher than that observed during the Alpha and Delta phases. Among patients with influenza, bacterial coinfections were more prevalent. Only 0.65% (2/308) of the patients were concurrently infected with both COVID-19 and influenza. Age-stratified analysis showed a clear pattern, with a higher incidence of coinfections in children under 18 years of age. These findings highlight the need for the timely detection of respiratory pathogen coinfections and for the implementation of appropriate interventions, crucial for reducing disease burden during intersecting respiratory epidemics. Full article

High-spatiotemporal-resolution and accurate soil moisture (SM) data are crucial for investigating climate, hydrology, and agriculture. Existing SM products do not yet meet the demands for high spatiotemporal resolution. The objective is to develop and evaluate a retrieval framework to derive SM estimates with high spatial (100 m) and temporal (<3 days) resolution that can be used on a national scale in China. Therefore, this study integrates multi-source data, including optical remote sensing (RS) data from Sentinel-2 and Landsat-7/8/9, synthetic aperture radar (SAR) data from Sentinel-1, and auxiliary data. Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). The integrated framework (IF) considers three feature scenarios (SC1: optical RS + auxiliary data, SC2: SAR + auxiliary data, SC3: optical RS + SAR + auxiliary data), encompassing a total of 33 features. The results are as follows: (1) The correlation coefficients (r) between auxiliary data (such as sand fraction, r = −0.48; silt fraction, r = 0.47; and evapotranspiration, r = −0.42), SAR features (such as the backscatter coefficients for VV-pol (σvv0), r = 0.47), and optical RS features (such as Shortwave Infrared Band 2 (SWIR2) reflectance data from Sentinel-2 and Landsat-7/8/9, r = −0.39) with observed SM are significant. This indicates that multi-source data can provide complementary information for SM monitoring. (2) Compared to XGBoost and LSTM, RFR and EL demonstrate superior overall performance and are the preferred models for SM prediction. Their R² for the training and test sets exceed 0.969 and 0.743, respectively, and their ubRMSE are below 0.022 and 0.063 m³/m³, respectively. (3) The SM prediction accuracy is highest for the scenario of optical + SAR + auxiliary data, followed by SAR + auxiliary data, and finally optical + auxiliary data. (4) With an increasing Normalized Difference Vegetation Index (NDVI) and SM values, the trained models exhibit a general decrease in prediction performance and accuracy. (5) In 2021 and 2022, without considering cloud cover, the IF theoretically achieved an SM revisit time of 1–3 days across 95.01% and 96.53% of China’s area, respectively. However, SC1 was able to achieve a revisit time of 1–3 days over 60.73% of China’s area in 2021 and 69.36% in 2022, while the area covered by SC2 and SC3 at this revisit time accounted for less than 1% of China’s total area. This study validates the effectiveness of combining multi-source RS data with auxiliary data in large-scale SM monitoring and provides new methods for improving SM retrieval accuracy and spatiotemporal coverage. Full article

This study examines the combined use of ground penetrating radar (GPR) and Sentinel-1 synthetic aperture radar (SAR) data for estimating soil moisture in a 25-decare field in Sivas, Türkiye. Soil moisture, vital for sustainable agriculture and ecosystem management, was assessed using in situ measurements, SAR backscatter analysis, and GPR-derived dielectric constants. A novel empirical model adapted from the classical soil moisture index (SSM) was developed for Sentinel-1, while GPR data were processed using the reflected wave method for estimating moisture at 0–10 cm depth. GPR demonstrated a stronger correlation within situ measurements (R² = 74%) than Sentinel-1 (R² = 32%), reflecting its ability to detect localized moisture variations. Sentinel-1 provided broader trends, revealing its utility for large-scale analysis. Combining these techniques overcame individual limitations, offering detailed spatial insights and actionable data for precision agriculture and water management. This integrated approach highlights the complementary strengths of GPR and SAR, enabling accurate soil moisture mapping in heterogeneous conditions. The findings emphasize the value of multi-technique methods for addressing challenges in sustainable resource management, improving irrigation strategies, and mitigating climate impacts. Full article

Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management. Full article