Search Results (1,025)

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Accurate recognition and segmentation of seafloor topographic units is of great significance for marine surveying and engineering applications. Efficient segmentation of multibeam bathymetric point clouds typically requires projecting them into two-dimensional images. However, segmentation methods based on single-modality images suffer from incomplete information representation and insufficient model adaptability, which often lead to blurred boundaries, false positives, and missed detections, thereby limiting segmentation accuracy. To address these challenges, this study proposes a seafloor topography recognition and segmentation method based on YOLO11n-seg with bimodal image feature fusion, from the perspectives of image generation and model optimization, aiming to improve segmentation accuracy and robustness. First, an early fusion strategy for bimodal images is adopted. Two types of images generated from point clouds via continuous curvature tension spline interpolation are concatenated at the input level, fusing local texture details with absolute water depth information, thereby enhancing the model’s ability to perceive topographic features. Second, a lightweight Efficient Channel Attention (ECA) module is embedded after the Spatial Pyramid Pooling-Fast (SPPF) module of the backbone network. This module adaptively calibrates channel weights, reinforcing the contribution of the grayscale channel to the final segmentation decision. Finally, a weighted BCE-Dice joint loss function is constructed to mitigate class imbalance between flat seabed and topographic regions, while also optimizing boundary segmentation accuracy. Experimental results on a self-constructed multibeam image dataset demonstrate that the proposed method achieves an mAP@50 of 92.8%, representing an absolute improvement of 7.6 percentage points over the baseline model. Notably, the model has only 2.84 M parameters, maintaining a lightweight profile. Full article

The increasing demand for sustainable, affordable smart city infrastructure has heightened the need for low-cost near-real-time water quality monitoring systems. In this study, we propose Water-QI, a low-cost Internet of Things (IoT)-based environmental monitoring platform that combines budget-friendly sensors with deep learning for water quality index (WQI) assessment and forecasting. The sensing platform measures five key physicochemical parameters, namely temperature, total dissolved solids (TDS), pH, turbidity, and electrical conductivity, enabling continuous multi-parameter monitoring in urban water environments. To model temporal variations in water quality under both cloud-based and edge-oriented deployment scenarios, we evaluate multiple gated recurrent unit (GRU) architectures with different widths and depths. Experiments are conducted at two temporal resolutions, hourly and minute-level, in order to examine the trade-off between predictive accuracy and edge computational latencies. In the hourly scenario, the single-layer GRU with 64 units achieved the best overall balance, reaching a validation RMSE of 0.0281 and a test $R^2$ of 0.9820, while deeper stacked GRU models degraded performance substantially. In the minute-resolution scenario, shallow wider GRU models produced the best results, with the single-layer GRU with 512 units attaining the lowest validation RMSE (0.025548) and the 256-unit variant achieving nearly identical accuracy with much lower inference cost. The results show that increasing the GRU model length can yield improvements at high temporal granularity, whereas increasing the GRU layer depth consistently harms convergence and generalization. Overall, the findings indicate that shallow GRU architectures provide the most practical solution for accurate, low-cost, and scalable water quality forecasting. In particular, the 64-unit GRU is the most suitable choice for hourly periodic interval operation, while the 256-unit GRU offers the best edge computational speed and accuracy trade-off for minute-level near-real-time inference on resource-constrained devices. Full article

Water quality monitoring remains a critical challenge for achieving Sustainable Development Goal 6, particularly in rural and resource-constrained environments where conventional laboratory-based methods are costly and slow. This study presents the development and field validation of an Artificial Intelligence of Things (AIoT) ecosystem for intelligent, low-cost, and real-time water quality assessment using edge computing and generative artificial intelligence. The system integrates a laboratory-developed multiparameter probe measuring temperature, pH, dissolved oxygen, and electrical conductivity with a mobile application and a cloud-based backend. Field validation was conducted in riverine environments in the municipality of Pueblo Bello (Cesar, Colombia), where the system was deployed for in situ data acquisition and real-time inference. A supervised Artificial Neural Network (ANN) was trained to classify water quality based on a Water Quality Index (WQI) ground truth derived from a public dataset, employing KNN-based missing data imputation, interquartile range outlier filtering, stratified balancing, and grid search hyperparameter optimization. The best-performing model achieved 85.1% accuracy and an AUC of 0.87 using only four physical parameters and was successfully deployed in TensorFlow Lite format on both the embedded probe and the mobile application with sub-millisecond inference time. Integration with a generative AI backend provides contextual natural-language interpretations of measurements. These results demonstrate that reduced-parameter edge AI systems can provide reliable environmental diagnostics while enhancing accessibility and citizen engagement for participatory water monitoring. Full article

Flood monitoring in dry tropical basins, such as the Tumbes River (Peru), faces critical challenges due to persistent cloud cover that restricts the operability of optical sensors during extreme events, coupled with the operational gap between satellite products and conventional hydrological monitoring. To overcome these limitations, this research developed a comprehensive methodological framework in Google Earth Engine that unifies automated image thresholding and Sentinel-1 SAR time series analysis for flood detection and the estimation of early warning thresholds. The Bmax Otsu and Edge Otsu algorithms were evaluated, previously calibrated using high-resolution imagery (PlanetScope) as reference data, topographically constrained by the HAND (Height Above the Nearest Drainage) model, and validated against established change detection algorithms. The analysis of seven hydrological events between 2017 and 2024 confirmed the statistical superiority of Bmax Otsu; although both methods achieved high overall accuracy (Bmax 95.8% versus Edge 95.7%), Bmax Otsu outperformed Edge Otsu in spatial consistency (Kappa 66.1% vs. 63.7%; IoU 45.6% vs. 45.0%). Based on this, a time series analysis was applied to discriminate permanent water bodies and isolate flood dynamics. Subsequently, the functional discharge–impact response was evaluated by linking the instantaneous flood extent captured by the SAR overpasses to their corresponding peak discharges. Validated against official INDECI damage reports, it was determined that significant impacts begin at an activation threshold of 743.49 m³/s (151 flooded ha, 157 affected inhabitants) and scale linearly up to extreme peak events of 1629.02 m³/s, compromising 1234 agricultural ha and 749 inhabitants. This methodology provides a validated, low-cost tool to translate SAR observations into critical thresholds for early warning systems in data-scarce regions. Full article

Conventional Interferometric Synthetic Aperture Radar (InSAR)-based sub-canopy topography retrieval models often suffer from insufficient characterization of scattering mechanisms, strong nonlinearity, and poor parameter convergence. To address these issues, this study proposes an improved Interferometric Water Cloud Model (IWCM) that integrates Fractional Vegetation Cover (FVC) to retrieve sub-canopy topography. The proposed method accounts for both volume and ground scattering and introduces FVC as a constraint to improve the model’s physical realism. In addition, this study utilizes InSAR observations derived from TanDEM-X dual-baseline data, which enhance the information content of the measurements by providing multiple independent interferometric observations. A two-step nonlinear least squares optimization strategy is further employed to enhance the convergence of model parameter estimation. The proposed method was validated in the forested region of Genhe City, Inner Mongolia. Airborne LiDAR-derived surface elevation data were used for assessment. The results indicate that, compared with the original InSAR-derived Digital Elevation Model (DEM), the accuracy of the retrieved sub-canopy topography improves by 39.04%. Furthermore, compared with the previously proposed Normalized Difference Vegetation Index (NDVI)-based method, under their respective optimal initial extinction coefficient conditions (${\mu }_0$), an additional accuracy improvement of 11.69% is achieved. These results demonstrate that the proposed method effectively reduces the influence of the forest canopy on interferometric phase observations and improves the capability of sub-canopy topography reconstruction in complex forest environments. The method also provides a new approach for dual-baseline and multi-baseline InSAR-based sub-canopy topography retrieval. Full article

The field of intelligent transportation on inland waterways is experiencing rapid growth, driven by the global pursuit of enhanced waterway safety, operational efficiency, and environmental sustainability. In real-world autonomous operation scenarios of unmanned surface vehicles (USVs), image-based 2D object detection methods are insufficient to meet the demands of 3D environmental modeling and accurate perception of dynamic objects. Existing 3D perception systems for USVs depend heavily on precise sensor calibration. However, projection offsets between point clouds and images—caused by water surface fluctuations and complex outdoor environments—hinder the practical deployment of these methods. To address these limitations, we propose a weak calibration multi-modal 3D object detection algorithm based on cross-view fusion, termed RCF-Free (Radar-Camera Fusion, Free from precise calibration). Inspired by autonomous driving solutions, we design a Triple-Path Cross-View Fusion module that achieves high-quality cross-view feature fusion without requiring accurate calibration parameters, while simultaneously detecting complete bird’s-eye view (BEV) bounding boxes. We further enhance the spatial layout comprehension of the visual branch through a Mobile Self-Attention Module (MAM) and effectively encode sparse point cloud features in BEV space using a dedicated BEV-Point feature encoder. Additionally, we reconstruct and introduce two water-related 3D object detection datasets, FloW-BEV and WaterScenes-BEV. Experimental results demonstrate that RCF-Free achieves $\mathrm{m}\mathrm{A}{\mathrm{P}}_{\mathrm{B}\mathrm{E}\mathrm{V}}50$ scores of 60.5% and 69.3% on the FloW-BEV and WaterScenes-BEV datasets, respectively, showing the effectiveness in water surface object detection. Moreover, on the DAIR-V2X-I dataset for autonomous driving scenarios, the model attains $\mathrm{m}\mathrm{A}{\mathrm{P}}_{3\mathrm{D}}50$ scores of 73.3%, 61.2%, and 61.2% across three task difficulty levels, illustrating strong cross-domain generalization capability. Full article

In view of the limitations of existing studies, in which remote sensing extraction of algal blooms is easily affected by cloud interference, and mechanistic models are constrained by excessive parameters and inadequate representation of nonlinear relationships, resulting in limited timeliness and accuracy, this study took Taihu Lake as the study area and constructed a research framework of bloom extraction-scale matching-spatial prediction-scenario response based on Landsat imagery and gridded meteorological data, constructing the relationship between meteorological factors and algal blooms using machine learning methods. First, the Tasseled Cap transformation (TCap) and Floating Algae Index (FAI) were combined to extract the spatial distribution and area of algal blooms, while cloud interference was addressed to improve recognition stability under complex background conditions. Next, the spatial scales of bloom rasters and meteorological factors were unified to build a matched bloom-meteorological dataset. On this basis, a U-Net model driven by multiple meteorological factors was developed to predict remote-sensing-based bloom distribution/extent patterns under three warming scenarios. The results showed that: (1) the combination of TCap and FAI improved the accuracy and efficiency of bloom extraction; FAI was more sensitive but tended to overestimate bloom area, whereas TCap was more stable under cloud interference; (2) the U-Net model achieved an overall accuracy of 95% and a prediction accuracy of 88%; and (3) bloom area increased under all three warming scenarios, and the extent of expansion generally became more pronounced with increasing warming magnitude, although the response was not strictly monotonic across all cases. Based on the seasonal mean bloom-area increase relative to the baseline condition (S0), the warming response was strongest in spring, followed by summer and autumn, and weakest in winter. This study can provide a reference for cyanobacterial bloom early warning and water environment management in Lake Taihu. Full article

The CALIPSO V4.10 5 km cloud-layer product contains a small yet influential fraction of low-confidence and “unknown” cloud-type labels, which constrains its effectiveness in climatological analyses and limits its utility for downstream Earth system applications. To improve the practical usability and completeness of these observations, this study develops a multilayer perceptron (MLP)-based refinement framework using global summer daytime CALIPSO data from 2006–2021. High-confidence cloud samples (76% of the dataset), defined as cases with high Feature Type QA and high Ice/Water Phase QA, were used as the reliable supervision subset to train the MLP model using 11 geolocation-, optical-, and microphysics-related variables, including cloud optical depth, cloud thickness, depolarization ratio, and color ratio. The trained model was subsequently applied to a separately defined low-confidence cloud subset (~5% of the dataset), consisting of cases with high Feature Type QA but low Ice/Water Phase QA, of which over 60% were originally labeled as “unknown”, to generate probabilistic assignments of three cloud types: ice clouds, water clouds, and oriented ice crystals. Evaluation using withheld high-confidence samples indicates a strong level of agreement with operational CALIPSO classifications (~94.99%). Moreover, the refined low-confidence results exhibit physically coherent vertical structural characteristics consistent with established cloud thermodynamic regimes. It is emphasized that the proposed framework does not establish an independent physical truth beyond CALIOP’s measurement capability; instead, it provides a physically consistent and statistically robust approach to improving the completeness and practical usability of CALIPSO cloud-type products for large-scale scientific and modeling applications. Full article

Warm rain events occur when moist air masses containing elevated precipitable water produce high rainfall rates capable of generating local flash floods. Catalonia, located on the northeastern Mediterranean coast of the Iberian Peninsula, is regularly affected by such episodes: approximately 70% of daily precipitation events exceeding 10 mm with fewer than ten cloud-to-ground lightning flashes can be classified as warm rain. The current research aimed to identify the meteorological conditions most conducive to heavy warm rain episodes in Catalonia. These cases are commonly associated with flash flood episodes in the study region. We utilized rain gauges, lightning data, radar, and model fields, combined with radio sounding profiles. First, we identified and characterized warm rain cases, and second, we have selected some relevant cases to characterize the phenomenon. These events occur predominantly along the Catalan coast during the warm season, typically following the passage of a cold front, and are associated with shallow convective clouds producing little or no lightning. However, the key determining factor is a characteristic vertical thermodynamic profile: a moist and saturated lower troposphere with high precipitable water beneath a low- to mid-level thermal inversion, and weak instability concentrated near the surface. Furthermore, local wind convergence plays a principal role in the rainfall pattern. Full article

Fast radiative transfer models are essential for the efficient processing of hyperspectral satellite data in trace gas retrievals, as full multi-stream radiative transfer simulations are computationally demanding. We present FALCAS (Fast Atmospheric Line-by-line Code with Aerosol and Cloud Scattering), a surrogate forward model combining line-by-line radiative transfer with the virtual isotropic scattering layer approximation adopted from FOCAL. FALCAS retains much of the accuracy of full multi-stream calculations while enabling rapid simulations. Previously validated against synthetic spectra from a discrete ordinate radiative transfer model, FALCAS is here applied to real measurements from the TROPOspheric Monitoring Instrument (TROPOMI) to retrieve total column water vapour (TCWV) in the shortwave infrared band around 2.3 μm. Retrieval results are compared to the operational TROPOMI Level-2 TCWV from the CH₄ product. As this comparison is performed against an operational product from the same instrument, it represents an intercomparison rather than an evaluation against an independent reference dataset. FALCAS retrievals show a Pearson correlation coefficient greater than 0.99 with the operational data, and after empirical bias correction, the mean absolute bias across all regions is 1.45 mol m⁻² (0.12% relative) and the mean RMSE is 39.24 mol m⁻² (3.85% relative). These results demonstrate that FALCAS shows strong agreement with the operational TROPOMI Level-2 TCWV product, offering substantial computational advantages for large-scale processing. Full article

During offshore oilfield development, traditional injection–production processes commonly suffer from delayed regulation, low operational efficiency, and heavy reliance on manual intervention. Achieving real-time diagnosis of injection–production anomalies and dynamic optimization under complex geological conditions and harsh marine environments represents a core scientific challenge. This study presents the development and field deployment of an intelligent cable-controlled injection–production integrated management system. The work is positioned as an application- and system-oriented study, focusing on addressing practical challenges in offshore oilfield operations through the integration of established machine learning techniques into a cohesive operational platform. The system employs a cloud-native microservice architecture and integrates nine functional modules, enabling closed-loop management from data acquisition to intelligent decision making. Key methodological contributions include: (1) a weighted ensemble model combining Random Forest and SVM for blockage diagnosis, balancing global feature learning with boundary sample discrimination to achieve 92% diagnostic accuracy; (2) a Bayesian fusion framework that integrates static geological priors with dynamic sensitivity analysis for probabilistic quantification of injector–producer connectivity, achieving 85% identification accuracy with rigorous uncertainty propagation; and (3) a three-stage human–machine collaborative mechanism that substantially reduces anomaly response latency while ensuring field safety. Field application in Bohai oilfields demonstrates that the system shortens the injection–production response cycle by approximately 42%, reduces anomaly response time from over 72 h to less than 2 h (a 97% reduction), decreases water consumption per ton of oil by 27.6%, and increases injection–production uptime by 11.3 percentage points. This study provides an interpretable, extensible, and closed-loop technical solution for intelligent offshore oilfield development, with future directions including digital twin predictive simulation and reinforcement learning for real-time optimization. Full article

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies. Full article

Lake ice monitoring is critical for assessing climate change, but in-situ observations are often limited. Sentinel-1 Synthetic Aperture Radar (SAR) data is a strong method for ice detection because it is not restricted by cloud cover and it is readily available. However, SAR-based classification can be affected by atmospheric and surface-related noise. This study examines the impact of noise on machine learning-based lake ice detection over Lake Śniardwy, Poland, using Sentinel-1 Vertical-Vertical (VV) and Vertical-Horizontal (VH) backscatter data. Binary logistic regression models were trained on scenes with strong class separability between ice and water and then validated on separate low- and high-noise datasets. The models achieved high accuracy under low-noise scenes, reaching up to 96.9%, but performed poorly on high-noise scenes. The results show that wind-related surface roughness and associated atmospheric conditions can significantly reduce classification reliability. Comparison with backscatter from a nearby coniferous forest confirmed that the main disturbances were concentrated over the lake surface. The study highlights the importance of careful scene selection and noise assessment in SAR-based lake ice classification. Full article

Visual perception in Unmanned Surface Vehicles (USVs) suffers from drastic lighting changes and missing texture features. These factors lead to depth scale drift and motion estimation bias. Moreover, existing multi-modal fusion models are computationally complex and unfit for resource-limited edge devices. To address these problems, a lightweight Radar–Vision Fusion (Geo-RVF) algorithm is proposed. To supplement spatial information, point clouds are projected to build sparse depth maps. A probability consistency-based depth correction module is designed to suppress water noise. This helps extract accurate geometric anchors to guide visual depth propagation. Subsequently, a Recurrent Autoregressive Network (RAN) fuses radar and image features in the temporal dimension. This resolves dynamic positional deviations caused by texture degradation and distant small targets. After real-time optimization, Geo-RVF achieves 23 FPS on the Jetson Orin NX. On a collected dataset, the method attains a mean average precision (mAP) 50–90 of 44.2% and a mean intersection over union (mIoU) of 99%, outperforming HybridNets and Achelous. Full article