Search Results (419)

Recent advances in high-fidelity modeling, numerical computing, and data science have spurred interest in model-data integration for nuclear reactor applications. While machine learning often prioritizes data-driven predictions, this study focuses on data assimilation (DA) to synergize physical models with measured data, aiming to enhance predictive accuracy and reduce uncertainties. We implemented deterministic DA methods—Kalman filter (KF) and three-dimensional variational (3D-VAR)—in a one-dimensional single-phase natural circulation loop and extended 3D-VAR to RELAP5, a system code for two-phase loop analysis. Unlike surrogate-based or model-reduction strategies, our approach leverages full-model propagation without relying on computationally intensive sampling. The results demonstrate that KF and 3D-VAR exhibit robustness against varied noise types, intensities, and distributions, achieving significant uncertainty reduction in state variables and parameter estimation. The framework’s adaptability is further validated under oceanic conditions, suggesting its potential to augment baseline models beyond conventional extrapolation boundaries. These findings highlight DA’s capacity to improve model calibration, safety margin quantification, and reactor field reconstruction. By integrating high-fidelity simulations with real-world data corrections, the study establishes a scalable pathway to enhance the reliability of nuclear system predictions, emphasizing DA’s role in bridging theoretical models and operational demands without compromising computational efficiency. Full article

Ship weather routing is heavily dependent on weather forecasts. However, the predictive nature of meteorological models introduces an unavoidable level of uncertainty which, if not accounted for, can compromise navigational safety, operational efficiency, and environmental impact. This study examines the temporal degradation of forecast accuracy across certain oceanographic and atmospheric variables, using a six-month dataset for the area of North Atlantic provided by the National Oceanic and Atmospheric Administration (NOAA). The analysis reveals distinct variable-specific uncertainty trends with wind speed forecasts exhibiting significant temporal fluctuation (RMSE increasing from 0.5 to 4.0 m/s), while significant wave height forecasts degrade in a more stable and predictable pattern (from 0.2 to 0.9 m). Confidence intervals also exhibit non-monotonic evolution, narrowing by up to 15% between 96–120-h lead times. To address these dynamics, a Python-based framework combines distribution-based modeling with calibrated confidence intervals to generate uncertainty bounds that evolve with forecast lead time (R² = 0.87–0.93). This allows uncertainty to be quantified not as a static estimate, but as a function sensitive to both variable type and prediction horizon. When integrated into routing algorithms, such representations allow for route planning strategies that are not only more reflective of real-world meteorological limitations but also more robust to evolving weather conditions, demonstrated by a 3–7% increase in travel time in exchange for improved safety margins across eight test cases. Full article

The traditional method for the establishment of the green laser underwater transmission model is purely based on the laser transmission mechanism in waterbodies, while neglecting a few exterior conditions. This paper proposes a novel method to establish the underwater transmission model from a maximum measurement depth perspective by refining the dynamic relationship between the effective received power P_A and the background noise power P_B. Different from the traditional empirical model of fixed P_A/P_B, this method combines the sensor, flight, and environmental parameters of airborne LiDAR (ALB) to achieve the dynamic calibration of P_A and P_B. In particular, the empirical relationship between the maximum underwater measurement depth and the laser attenuation coefficient, coupled parameters, etc., is considered. The established model is verified by different types of experiments. The experimental results discovered that the errors are approximately 0.86 m and 1.28, under the same water conditions, when compared to the existing models. The validation experiments demonstrated that the errors for the maximum depth prediction were 0.38 m (indoor tank), 1.58 m (indoor swimming pool), 0.44 m (Li River, Guangxi), and 1.20 m (Beibu Gulf, Pacific Ocean). The experimental results demonstrated that the established model enables us to widely predict the maximum water depth measurable using an airborne LiDAR under different environmental conditions. Full article

High-precision hyperspectral remote sensing reflectance measurement of water bodies serves as the fundamental technical basis for accurately retrieving spatiotemporal distribution characteristics of water quality parameters, providing critical data support for dynamic monitoring of aquatic ecosystems and pollution source tracing. To address the critical issue of water surface glint interference significantly affecting measurement accuracy in aquatic remote sensing, this study innovatively developed a novel sensor system based on multi-field-of-view Kohler-polarization technology. The system incorporates three Kohler illumination lenses with exceptional surface uniformity exceeding 98.2%, effectively eliminating measurement errors caused by water surface brightness inhomogeneity. By integrating three core technologies—multi-field polarization measurement, skylight blocking, and high-precision radiometric calibration—into a single spectral measurement unit, the system achieves radiation measurement accuracy better than 3%, overcoming the limitations of traditional single-method glint suppression approaches. A glint removal efficiency (GRE) calculation model was established based on a skylight-blocked approach (SBA) and dual-band power function fitting to systematically evaluate glint suppression performance. Experimental results show that the system achieves GRE values of 93.1%, 84.9%, and 78.1% at ±3°, ±7°, and ±12° field-of-view angles, respectively, demonstrating that the ±3° configuration provides a 9.2% performance improvement over the ±7° configuration. Comparative analysis with dual-band power-law fitting reveals a GRE difference of 2.1% (93.1% vs. 95.2%) at ±3° field-of-view, while maintaining excellent consistency (ΔGRE < 3.2%) and goodness-of-fit (R² > 0.96) across all configurations. Shipborne experiments verified the system’s advantages in glint suppression (9.2%~15% improvement) and data reliability. This research provides crucial technical support for developing an integrated water remote sensing reflectance monitoring system combining in situ measurements, UAV platforms, and satellite observations, significantly enhancing the accuracy and reliability of ocean color remote sensing data. Full article

The ocean is one of the world’s largest anthropogenic carbon dioxide (CO₂) sinks, but closing the carbon budget is logistically difficult and expensive, and uncertainties in carbon fluxes and reservoirs remain. One specific challenge is that measuring the CO₂ flux at the air–sea interface usually requires costly sensors or analyzers (USD > 30,000), which can limit observational capacity. Our group has developed and validated a low-cost ΔpCO₂ system, able to measure both pCO₂^water and pCO₂^air, for USD ~1400 to combat this limitation. The device is equipped with Internet of Things (IoT) capabilities and built around a USD ~100 pCO₂ K30 sensor at its core. Our Sensor for the Exchange of Atmospheric CO₂ with Water (SEACOW) may be placed in an observational network with traditional pCO₂ sensors or ∆pCO₂ sensors to extend the spatial coverage and resolution of monitoring systems. After calibration, the SEACOW reports atmospheric pCO₂ measurements that are within 2–3% of the measurements made with a calibrated LI-COR LI-850. We also demonstrate the SEACOW’s ability to capture diel pCO₂ cycling in seagrass, provide recommendations for SEACOW field deployments, and provide additional technical specifications for the SEACOW and for the K30 itself (e.g., air- and water-side 99.3% response time; 5.7 and 29.6 min, respectively). Full article

Methane hydrate is a crystalline compound in which methane is trapped within a water lattice under high-pressure, low-temperature conditions. Its presence in oceanic and permafrost sediments makes it a promising alternative energy source, but also a potential contributor to climate change. Accurate in situ detection remains a major challenge due to hydrate’s dispersed occurrence and the limitations of conventional geophysical methods. This study investigates the feasibility of using the associated alpha particle (AAP) technique for the direct detection of methane hydrate. A series of laboratory measurements was conducted on sand-based samples with varying levels of methane hydrate simulant. Using a 14 MeV neutron generator and a LaBr₃ gamma detector, the 4.44 MeV carbon peak was monitored and calibrated against volumetric hydrate saturation. The minimum detection limit (MDL) was experimentally determined to be $(67\pm 25)\%$. Although the result is subject to high uncertainty, it provides a preliminary benchmark for evaluating the method’s sensitivity and highlights the potential of AAP-based gamma spectroscopy for in situ detection, especially when supported by higher neutron flux in future applications. Full article

Sea surface temperature (SST) is crucial for weather forecasting, climate modeling, and environmental monitoring. This study proposes a novel prediction model that achieves a 60-day forecast with a root mean square error (RMSE) consistently below 0.9 °C. The model combines the nonlinear feature extraction of a deep belief network (DBN) with the high-precision regression of support vector regression (SVR), enhanced by spatiotemporal secondary calibration (SSC) to better capture SST variation patterns. Using satellite-derived remote sensing data, the DBN-SVR model outperforms baseline methods in both the Indian Ocean and North Pacific regions, demonstrating strong applicability across diverse marine environments. This work advances long-term SST prediction capabilities, providing a reliable foundation for extended-range marine forecasts. Full article

This paper describes the benefits of using a microwave radiative transfer model (RTM) to improve the inter-satellite radiometric calibration (XCAL) between two independent satellite microwave radiometers. Because this work was sponsored by the NASA Global Precipitation Mission, the emphasis of this paper is on radiometer channels that are used for atmospheric precipitation retrievals; however, this technique is applicable for microwave remote sensing in general, over a wide range of satellite remote-sensing applications. An XCAL example is presented for the NASA Global Precipitation Mission, whereby the GPM Microwave Imager is used to calibrate another microwave radiometer (TROPICS) within the GPM constellation of satellites. This approach involves intercomparing near-simultaneous measured brightness temperatures from these radiometers viewing a common homogeneous ocean scene. The double difference between observed and theoretical brightness temperature, derived using a radiative transfer model, is used to establish a radiometric calibration offset or bias. On-orbit comparisons are presented for two different approaches, namely, with and without the aid of the RTM. The results demonstrate significant improvements in the XCAL biases derived when using the RTM, and this is especially beneficial when one radiometer produces anomalous brightness temperatures. Full article

With the gradual application of 3D reconstruction technology in underwater scenes, the design of vision-based reconstruction models has become an important research direction for human ocean exploration and development. The underwater laser triangulation method is the most commonly used approach, yet it misses details during the reconstruction of sparse point clouds, which do not meet the requirements of practical applications. On the other hand, existing underwater photometric stereo methods can accurately reconstruct local details of target objects, but they require relative stillness to be maintained between the camera and the target, which is practically difficult to achieve in underwater imaging environments. In this paper, we propose an underwater target reconstruction algorithm that combines laser triangulation and multispectral photometric stereo (MPS) to address the aforementioned practical problems in underwater 3D reconstruction.This algorithm can obtain more comprehensive 3D surface data of underwater objects through mobile measurement. At the same time, we propose to optimize the laser place calibration and laser line separation processes, further improving the reconstruction performance of underwater laser triangulation and multispectral photometric stereo. The experimental results show that our method achieves higher-precision and higher-density 3D reconstruction than current state-of-the-art methods. Full article

The development of underwater high-precision navigation technology is of great significance for the application of autonomous underwater vehicles (AUVs). Traditional long baseline (LBL) positioning systems require pre-deployment and the calibration of multiple beacons, which consumes valuable time and manpower. In contrast, the range-only single-beacon (ROSB) positioning technology can help autonomous underwater vehicles (AUVs) obtain accurate position information by deploying only one beacon. This method greatly reduces the time and workload of deploying beacons, showing high application potential and cost ratio. Given the operational constraints of AUV open-ocean navigation with single-beacon weak observations and absence of valid a priori positioning data in calibration zones, a multi-sensor underwater virtual beacon localization framework was established, proposing a differential Chan–Gauss–Newton (DCGN) methodology for submerged vehicles. Based on inertial navigation, the method uses the distance measurement information from a single beacon and observations from multiple sensors, such as the Doppler velocity log (DVL) and pressure sensor, to obtain accurate position estimates by discriminating the initial position of multiple hypotheses. A simulation experiment and lake test show that the proposed method not only significantly improves the positioning accuracy and convergence speed, but also shows high reliability. Full article

As part of the Copernicus program of the European Union (EU), the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) are currently operating the Sentinel-3 mission that consists of a constellation of two unites A and B (S3A, S3B). Each unit carries on board an Ocean and Land Colour Instrument (OLCI) that is acquiring moderate-spatial-resolution optical imagery. This article provides a description of the Level-1B radiometry product validation activities of the constellation Sentinel-3A and Sentinel-3B after six years in orbit. Several vicarious calibration methods have been applied independently by three expert groups and the results are compared over different surface target types. All methods agree on the good radiometric performance of both instruments. Although OLCI-A shows brighter Top-of-Atmosphere (TOA) radiance than OLCI-B by about 1–2%, both sensors exhibit very good stability and good image quality. The results are analyzed and discussed to propose a set of vicarious gain coefficients that could be used to align OLCI-A with OLCI-B radiometry time-series. Finally, recommendations for future missions are suggested. Full article

Remote sensing data play a crucial role in capturing and evaluating eutrophication, providing a comprehensive view of spatial and temporal variations in water quality parameters. Chlorophyll-a concentration time series analysis aids in understanding the current trophic state of coastal waters and tracking changes over time, enabling the evaluation of water bodies’ trophic status. This research presents a novel and replicable methodology able to derive accurate phenological patterns using remote sensing data. The methodology proposed uses the two-decade MODIS-Aqua surface reflectance dataset, analyzing data from 30-point stations and calculating chlorophyll-a concentrations from NASA’s Ocean Color algorithm. Then, a correction process is implemented through a robust, simple statistical analysis by applying LOESS smoothing to detect and remove outliers from the extensive dataset. Different scenarios are reviewed and compared with field data to calibrate the proposed methodology accurately. The results demonstrate the methodology’s capacity to produce consistent chlorophyll-a time series and to present phenological patterns that can effectively identify key indicators and trends, resulting in valuable insights into the coastal body’s trophic state. The case study of the Ambracian Gulf is characterized as hypertrophic since algal bloom during August reaches up to 5 mg/m³, while the replicate case study of Aitoliko shows algal bloom reaching up to 2.5 mg/m³. Finally, the proposed methodology successfully identifies the positive chlorophyll-a climate tendencies of the two selected Greek water bodies. This study highlights the value of integrating statistical methods with remote sensing data for accurate, long-term monitoring of water quality in aquatic ecosystems. Full article

The articles presented in this Special Issue epitomize the convergence of cutting-edge sensor technologies, innovative data processing techniques, and advanced algorithmic approaches in ocean remote sensing. Through studies ranging from sensor calibration and data fusion to the application of deep learning and transformer models, the research showcased here pushes the boundaries of what can be achieved in ocean observation. A recurring theme among these contributions is the importance of integrating data from multiple sources and employing state-of-the-art computational methods. Deep learning and the transformer architecture highlight a paradigm shift in remote sensing data analysis. These advanced techniques help extract complex features from high-dimensional datasets and can process large amounts of data quickly and automatically. Furthermore, research focusing on spatiotemporal dynamics and environmental monitoring highlights the critical role of remote sensing in addressing global challenges. By capturing the dynamic interactions between atmospheric, oceanic, and terrestrial processes, these studies provide important insights into the drivers of climate and environmental change. This information is valuable for developing predictive models and informing policy decisions related to climate change mitigation and adaptation. Full article

The hydrogeological study of the Rharb coastal basin, located in the semi-arid northwest region of Morocco, focuses on its two aquifers: the Plio-Quaternary aquifer characterized by high-quality water with salt concentrations ranging from 0.4 to 2 g/L, and the Upper Quaternary aquifer, with lower water quality (2 to 6 g/L). The deep aquifer is overexploited for agricultural purposes. This overexploitation has led to declining piezometric levels and the worsening of the oceanic intrusion phenomenon. The study aims to develop a numerical model for a period of 15 years, from 1992/93 to 2006/07 for monitoring groundwater quantity and quality, considering recharge, exploitation, and basin characteristics. A hydrodynamic model based on storage coefficient calibration identifies overexploitation for irrigation, increasing from 93 Mm³ in 1993 to 170 Mm³ in 2007, as the primary driver of declining water levels. A hydrodispersive model highlights higher salt concentrations in the shallow aquifer (up to 6 g/L), high nitrate concentrations due to human activity, and pinpoints areas of seawater intrusion approximately 500 m from the shoreline. Although the deeper aquifer remains relatively preserved, negative hydraulic balances from −15.4 Mm³ in 1993 to −36.6 Mm³ in 2007 indicate an impending critical period. Full article

Deforestation and agricultural practices, such as livestock farming, disrupt biogeochemical cycles, contribute to climate change, and can lead to serious environmental problems. Understanding the water cycle and changes in discharge patterns at the watershed scale is essential to tracking how deforestation affects the flow to downstream water bodies and the ocean. The Amazon basin, which provides about 15–20% of the freshwater flowing into the oceans, is one of the most important river systems in the world. Despite this, it is increasingly suffering from anthropogenic pressure, mainly from converting rainforests to agricultural and livestock areas, which can drive global warming and ecosystem instability. In this study, we applied a calibrated Soil and Water Assessment Tool (SWAT) model to the Jari River Watershed, a part of the Brazilian Amazon, to assess the combined effects of deforestation and climate change on water resources between 2020 and 2050. The model was calibrated and validated using observed streamflow. The results show an NS of 0.85 and 0.89, PBIAS of −9.5 and −0.6, p-factor of 0.84 and 0.93, and r-factor of 0.84 and 0.78, for periods of calibration and validation, respectively, indicating a strong model performance. We analyzed four scenarios that examined different levels of deforestation and climate change. Our results suggest that deforestation and climate change could increase surface runoff by 18 mm, while groundwater recharge could vary between declines of −20 mm and increases of 120 mm. These changes could amplify streamflow variability, affect its dynamics, intensify flood risks, and reduce water availability during dry periods, leading to significant risks for the hydrology of Amazonian watersheds and human water supply. This, in turn, could profoundly impact the region’s megadiverse flora and fauna, which directly depend on balanced streamflow in the watersheds. Full article