Search Results (9,334)

The Water and Sediment Regulation Scheme (WSRS), implemented since 2002, has been essential for controlling water flow and mitigating sediment siltation in the lower Yellow River. However, WSRS was suspended for the first time in 2016 and 2017 due to extremely low water flow. The rapid floodwater discharge over roughly 20 days conducted by WSRS strongly impacts total suspended solids (TSS) distribution in the Yellow River Estuary (YRE). This study employs high-frequency Sentinel-3 OLCI satellite imagery to investigate intraday TSS variations in the YRE under new water-sediment regulation conditions from 2016 to 2023. TSS concentrations were generally low during the 2016 and 2017 flood seasons, but increased markedly after WSRS resumed in 2018. Peak TSS values occurred in July or August, sometimes extending into September and October during autumn floods. A moderately strong positive correlation was observed between TSS concentrations at the river mouth and sediment load at Lijin Station during the flood seasons. The 2018 WSRS event generated an extensive river plume, with average TSS concentrations at the river mouth exceeding 400 g·m⁻³. From 2018 to 2023, TSS concentrations exhibited a declining trend during flood seasons, attributed to reduced sediment discharge and ongoing sediment accretion in the Yellow River Delta. Our findings highlight Sentinel-3 OLCI as a powerful tool to resolve WSRS-driven sediment dynamics, offering critical guidance for estuarine management. Full article

The 400 mm isohyet in China serves as a critical geographical demarcation of dry and wet regions. Amidst intensifying global warming, this climatic boundary has undergone notable shifts, with significant implications for China’s agriculture, water resources, and ecosystems. This study integrates meteorological station data, the China Gridded Daily Precipitation dataset (CN05.1), and Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (GPM IMERG) satellite observations to assess the spatiotemporal distribution of precipitation across mainland China and analyze the migration trend of the 400 mm isohyet. Furthermore, utilizing outputs from five models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), we projected future trends of China’s annual mean precipitation and the 400 mm isohyet’s migration under three Shared Socioeconomic Pathways (SSPs: low, medium, and high radiative forcing scenarios) until the end of this century (2100). Results reveal that from 2001 to 2017, the 400 mm isohyet exhibited a prominent northwestward migration trend. This trend is projected to continue in the future. These findings provide a crucial reference for understanding the spatial distribution and changing dynamics of precipitation patterns in China, offering vital decision support for land resource planning and water resource management. Full article

Ionospheric scintillation represents a disturbance phenomenon induced by irregular electron density variations, predominantly occurring in equatorial, auroral, and polar regions, thereby posing significant threats to Global Navigation Satellite Systems (GNSS) performance. Polar regions in particular confront distinctive challenges, including the sparse deployment of dedicated ionospheric scintillation monitoring receiver (ISMR) equipment, the limited availability of strong scintillation samples, severely imbalanced training datasets, and the insufficient sensitivity of conventional Deep Neural Networks (DNNs) to intense scintillation events. To address these challenges, this study proposes a modeling framework that integrates residual neural networks (ResNet) with the Synthetic Minority Over-sampling Technique for Regression with Gaussian Noise (SMOGN). The proposed model incorporates multi-source disturbance features to accurately estimate phase scintillation indices (${\sigma }_{\phi }$) in polar regions. The methodology was implemented and validated across multiple polar observation stations in Canada. Shapley Additive Explanations (SHAP) interpretability analysis reveals that the rate of total electron content index (ROTI) features contribute up to 64.09% of the predictive weight. The experimental results demonstrate a substantial performance enhancement compared with conventional DNN models, with root mean square error (RMSE) values ranging from 0.0078 to 0.038 for daytime samples in 2024, and an average coefficient of determination (R²) consistently exceeding 0.89. The coefficient of determination for the Pseudo-Random Noise (PRN) path estimation results can reach 0.91. The model has good estimation results at different latitudes and is able to accurately capture the distribution characteristics of the local strong scintillation structures and their evolution patterns. Full article

To address the limitations in positioning accuracy and the risk of integrity degradation in Satellite-Based Augmentation Systems (SBAS) user-end after applying augmentation information, this study proposes a positioning algorithm integrating an improved IGG III robust estimation method. By using integrity information from SBAS, this method improves protection level calculations and better adjusts observed weights by adding new factors to the weight function model. This improvement allows for better discrimination between reliable and anomalous measurements, thereby enhancing positioning accuracy, reducing integrity risks, and improving availability. Experimental results show that, compared to conventional SBAS user positioning, the proposed method achieves notable performance improvements across various scenarios. In static environments, it reduces horizontal integrity risk by up to 6.7%, increases availability by up to 6.6%, and improves positioning accuracy by up to 71.3%. In urban vehicular environments, horizontal integrity risk is reduced by 0.5%, availability is increased by 0.5%, and accuracy improves by up to 58.7%. In Unmanned Aerial Vehicle flight scenarios, horizontal integrity risk is reduced by 2.8%, availability increases by 2.8%, and accuracy improves by up to 50.38%. In all scenarios, vertical integrity risk is completely eliminated and availability improves slightly. Additionally, compared to the conventional IGG III estimator, the improved method offers more effective control over weight adjustment during solution estimation, thereby avoiding excessive down-weighting and mitigating overbounding of protection levels. These results demonstrate the potential of the proposed method to improve the performance and reliability of SBAS user-end under both static and dynamic conditions. Full article

Rice is a staple crop for over half the world’s population, and accurate, timely information on its planted area and production is crucial for food security and agricultural policy, particularly in developing nations like Sri Lanka. However, reliable rice monitoring in regions like Sri Lanka faces significant challenges due to frequent cloud cover and the fragmented nature of smallholder farms. This research introduces a novel, cost-effective method for mapping rice-planted area and yield at field scales in Sri Lanka using optical satellite data. The rice-planted fields were identified and mapped using a phenologically tuned image classification algorithm that highlights rice presence by observing water occurrence during transplanting and vegetation activity during subsequent crop growth. To estimate yields, a random forest regression model was trained at the district level by incorporating a satellite-derived chlorophyll index and environmental variables and subsequently applied at the field level. The approach has enabled the creation of two decades (2000–2022) of reliable, field-scale rice area and yield estimates, achieving map accuracies between 70% and over 90% and yield estimates with less than 20% error. These highly granular results, which are not available through traditional surveys, show a strong correlation with government statistics. They also demonstrate the advantages of a rule-based, phenology-driven classification over purely statistical machine learning models for long-term consistency in dynamic agricultural environments. This work highlights the significant potential of remote sensing to provide accurate and detailed insights into rice cultivation, supporting policy decisions and enhancing food security in Sri Lanka and other cloud-prone regions. Full article

Polar environmental research requires advanced detection methods to understand rapid changes in these regions. Unmanned aerial vehicles (UAVs) bridge the gap between satellite remote sensing and in situ ice-based buoy measurements, offering improved spatiotemporal resolution and operational efficiency. However, their widespread use in polar regions remains limited due to insufficient endurance capabilities. To address this problem, this paper presents a new monitoring system, the so-called UAV and Ice-based buoy cross-domain observation system (UBCOS). Particularly, the ice-based buoy integrates a Real-Time Kinematic (RTK) base station, a contact-based charging system, and an Iridium communication system, providing UAVs with centimeter-level positioning correction, low-temperature charging support, and remote data transmission capabilities. UAVs equipped with pod-mounted cameras capture imagery of sea ice surface characteristics within a 4 km radius of the buoy. Field tests conducted in the Arctic in 2024 demonstrate that the system achieved expected performance in both monitoring task execution and data collection, validating its practicality and reliability for polar sea ice monitoring. Full article

In complicated scenarios, due to the low precision of float solutions and poor reliability of fixed solutions, it is challenging to achieve a balance between accuracy and reliability of the integer least squares (ILS) estimation. To address this dilemma, the best integer equivariant (BIE) estimation, which makes a weighted sum of all possible candidates, has recently been attached great importance. The BIE solution approaches the float solution at a low ILS success rate, maintaining positioning reliability. As the success rate increases, it converges to the fixed solution, facilitating high-precision positioning. Furthermore, the posterior variance of BIE estimation provides the capability of reliability evaluation. However, in environments with a limited number or a deficient configuration of available satellites, there is a sharp decline in the strength of the GNSS precise positioning model. In this case, the exactness of weight allocation for integer candidates in BIE estimation will be severely compromised by unmodeled errors. When the ambiguity is incorrectly fixed, the wrongly determined optimal candidate is probably assigned an excessively high weight. Therefore, the BIE solution in a weak GNSS model always exhibits a significant positioning error consistent with the fixed solution. Moreover, the posterior variance of BIE estimation approximately resembles that of a fixed solution, losing error warning ability. Consequently, the BIE estimation may exhibit lower reliability compared to the ILS estimation employing a validation test with a loose acceptance threshold. To improve the positioning performance in weak GNSS models, a BIE ambiguity resolution (AR) method based on Tikhonov regularization is proposed in this paper. The method introduces Tikhonov regularization into the least squares (LS) estimation and the ILS ambiguity search, mitigating the serious impact of unmodeled errors on the BIE estimation under weak observation conditions. Meanwhile, the regularization factors are appropriately selected by utilizing an optimized approach established on the L-curve method. Simulation experiments and field tests have demonstrated that the method can significantly enhance the positioning accuracy and reliability in weak GNSS models. Compared to the traditional BIE estimation, the proposed method achieved accuracy improvements of 73.6% and 69.3% in the field tests with 10 km and 18 km baselines, respectively. Full article

Heat-related morbidity and mortality are increasing due to climate change, emphasizing the need to identify vulnerable areas and people exposed to extreme temperatures. To improve heat stress impact assessment, we developed a replicable machine learning model that integrates remote sensing, ground station, and geospatial data to estimate daily air temperature at a spatial resolution of 100 m × 100 m across the region of Tuscany, Italy. Using a two-stage approach, we first imputed missing land surface temperature data from MODIS using gradient-boosted trees and spatio-temporal predictors. Then, we modeled daily maximum and minimum air temperatures by incorporating monitoring station observations, satellite-derived data (MODIS, Landsat 8), topography, land cover, meteorological variables (ERA5-land), and vegetation indices (NDVI). The model achieved high predictive accuracy, with R² values of 0.95 for Tmax and 0.92 for Tmin, and root mean square errors (RMSE) of 1.95 °C and 1.96 °C, respectively. It effectively captured both temporal (R²: 0.95; 0.94) and spatial (R²: 0.92; 0.72) temperature variations, allowing for the creation of high-resolution maps. These results highlight the potential of integrating Earth Observation and machine learning to generate high-resolution temperature maps, offering valuable insights for urban planning, climate adaptation, and epidemiological studies on heat-related health effects. Full article

Coastal heritage assets are crucial to a society’s history and must be preserved sustainably, despite their vulnerability to both natural and anthropogenic hazards. Their monitoring is challenging due to the interrelated nature of these attributes. While expert observations and on-site measurements are employed, they cover limited areas over time, whereas remote sensing can assess larger regions more regularly. This study examines the impacts of climate change on Old Town North, a conservation area within Southampton Harbour, UK, designated as “heritage at risk” by Historic England in 2024. Particular focus is given to temperature and moisture variations, which may accelerate material decay and heighten risks. Using a multidisciplinary approach, the study uses satellite data to extract land surface temperatures, monitor coastal changes, and identify vulnerable risk zones. Results show that the conservation area faces multiple pressures, including moisture deficiency, urban sprawl, and increased surface temperatures, that together could hasten the deterioration of heritage assets. Full article

The Haiyang-2B (HY-2B) satellite, launched on 25 October 2018, carries both active and passive microwave sensors, including a scanning microwave Radiometer (SMR), to deliver high-precision, all-weather global observations. Sea surface temperature (SST) is among its key products. We evaluated the HY-2B SMR Level-4A (L4A) SST (25 km resolution) over the North Pacific (0–60°N, 120°E–100°W) for the period 1 October 2023 to 31 March 2025 using the extended triple collocation (ETC) and dual-pairing methods. These comparisons were made against the Remote Sensing System (RSS) microwave and infrared (MWIR) fused SST product and the National Oceanic and Atmospheric Administration (NOAA) in situ SST Quality Monitor (iQuam) observations. Relative to iQuam, HY-2B SST has a mean bias of –0.002 °C and a root mean square error (RMSE) of 0.279 °C. Compared to the MWIR product, the mean bias is 0.009 °C with an RMSE of 0.270 °C, indicating high accuracy. ETC yields an equivalent standard deviation (ESD) of 0.163 °C for HY-2B, compared to 0.157 °C for iQuam and 0.196 °C for MWIR. Platform-specific ESDs are lowest for drifters (0.124 °C) and tropical moored buoys (0.088 °C) and highest for ship and coastal moored buoys (both 0.238 °C). Both the HY-2B and MWIR products exhibit increasing ESD and RMSE toward higher latitudes, primarily driven by stronger winds, higher columnar water vapor, and elevated cloud liquid water. Overall, HY-2B SST performs reliably under most conditions, but incurs larger errors under extreme environments. This analysis provides a robust basis for its application and future refinement. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

Accurate precipitation data are essential for hydrological modeling, climate studies, and water resource management. Indeed, there is an increasing focus on understanding shifts in precipitation events to monitor the risks of floods and droughts, as well as to ensure sustainable water resource management. This study compares four reanalysis and satellite precipitation products (ERA5-Land, CHIRPS, PERSIANN, and TerraClimate) with ground data from 2003 to 2022. Among the datasets evaluated, ERA5-Land has the best performance (overall) in reproducing ground data, with a minimal mean bias error (MBE) of 1.91 mm, the highest correlation coefficient (R² = 0.93), and the most favorable Nash–Sutcliffe efficiency (NSE = 0.93). In contrast, CHIRPS, PERSIANN, and TerraClimate significantly underestimate precipitation as compared to ground data. The categorical metrics also highlight ERA5-Land’s superior performance in identifying wet months. Spatial analysis shows that ERA5-Land and other datasets generally exhibit agreement regarding precipitation patterns. However, PERSIANN displays notable variances, particularly in northern regions, where it overestimates precipitation. To investigate possible changes in precipitation patterns, a longer period (1983–2022) is selected for trend analysis based on gridded precipitation products. Sen’s slope analysis does not reveal any significant annual precipitation trend. In autumn, the PERSIANN dataset indicates a significant increasing trend of +1.81 mm/year, which is also confirmed by ERA5-Land (+2.68 mm/year) and CHIRPS (+1.34 mm/year), although without statistical significance. The findings emphasize the need for more sophisticated satellite algorithms and integration with ground observations to improve precipitation accuracy. Full article

This study investigates the impact of assimilating FY-3D Microwave Radiation Imager (MWRI) radiance data into the Weather Research and Forecasting (WRF) model, utilizing a 3D-Var data assimilation system, on the forecast accuracy of Typhoon Muifa (2022). The research focuses on the selection of data from different channels, land/ocean coverage, and orbits of the MWRI, along with the synergistic assimilation strategy with MWHS-2 data. Ten assimilation experiments were conducted, starting from 0600 UTC on 14 September 2022, covering a 42 h forecast period. The results show that after assimilating the microwave radiometer data, the brightness temperature deviation in the ocean area was significantly reduced compared to the simulation without data assimilation. This led to an improvement in the accuracy of typhoon track and intensity predictions, particularly for predictions beyond 24 h. Furthermore, the assimilation of land data and single-orbit data (particularly from the western orbit) further enhanced forecast accuracy, while the joint assimilation of MWHS-2 and MWRI data yielded additional error reductions. These findings underscore the potential of satellite data assimilation in improving typhoon forecasting and highlight the need for optimal land observation and channel selection techniques. Full article

A multidisciplinary investigation was conducted in southwestern Sicily, near the seismically active Belice Valley, based on the analysis of morphostructural features. These were observed as open fractures between 2014 and 2017; they were subsequently filled anthropogenically and then reactivated during a seismic swarm in 2019. We generated a seismic event distribution map to analyze the location, magnitude, and depth of earthquakes. This analysis, combined with multitemporal satellite imagery, allowed us to investigate the spatial and temporal relationship between seismic activity and fracture evolution. To investigate the spatial variation in thickness of the superficial cover and to assess the depth to the underlying bedrock or stiffer substratum, 45 Horizontal-to-Vertical Spectral Ratio (HVSR) ambient noise measurements were conducted. This method, which analyzes the resonance frequency of the ground, produced maps of the amplitude, frequency, and vulnerability index of the ground (K_g). By inverting the HVSR curves, constrained by Multichannel Analysis of Surface Waves (MASW) results, a subsurface model was created aimed at supporting the structural interpretation by highlighting variations in sediment thickness potentially associated with fault-controlled subsidence or deformation zones. The surface investigation revealed depressed elliptical deformation zones, where mainly sands outcrop. Grain-size and morphoscopic analyses of sediment samples helped understand the processes generating these shapes and predict future surface deformation. These elliptical shapes recall the liquefaction process. To investigate the potential presence of subsurface fluids that could have contributed to this process, Electrical Resistivity Tomography (ERT) was performed. The combination of the maps revealed a correlation between seismic activity and surface deformation, and the fractures observed were interpreted as inherited tectonic and/or geomorphological structures. Full article

Achaea lienardi is a polyphagous moth occurring in sub-Saharan Africa. It is a fruit-sucking moth, causing secondary damage to fruit such as citrus and peaches, while the larval stage can cause significant tree defoliation, including in several indigenous trees, wattle, Eucalyptus, and castor oil plants, amongst others. In February and March of 2025, a massive outbreak of the caterpillars was observed in the Hluhluwe-iMfolozi Park in South Africa, feeding primarily on Tamboti trees (Spirostachys africana). Satellite imagery from the previous five years was examined, but no similar large defoliation events were observed during this period. Climate data for the last five years (September 2019–March 2025) were collated and examined to determine the conditions supporting the outbreak. Above average winter rainfall, early spring rains, sustained rains, and high humidity in January and February, with warm nighttime temperatures, likely acted in concert to create favourable conditions for the caterpillar outbreak. This outbreak coincided with historic outbreaks of the African armyworm (Spodoptera exempta) in the summer rainfall areas of South Africa where precipitation, temperature, solar radiation, and humidity were found to be critical factors affecting armyworm outbreaks. Further research is required to determine specific criteria to enable predictions of future outbreaks. Full article