Search Results (1,365)

The occurrence of marine heatwaves (MHWs) and phytoplankton blooms is accelerating under climate change, yet the frequency and drivers of their compound co-occurrence remain poorly understood. Using coastal-optimized satellite observations from 2003–2020, we mapped global compound MHW–phytoplankton bloom (MHW-PB) events across coastal large marine ecosystems and quantified their spatiotemporal trends and environmental predictors. Compound events are increasing at 4.8% yr⁻¹, driven primarily by a 6.5% yr⁻¹ rise in MHW frequency; a temporal shuffle test confirms this trend falls below random co-occurrence expectation, indicating biological suppression actively constrains compound event growth. The compound independence factor (CIF) reveals latitudinal heterogeneity: low-latitude upwelling systems show MHW–PB mutual exclusivity, while high-latitude and eutrophic coastal regions show positive co-occurrence tendency. Interpretable machine learning further shows that nutrient availability dominates bloom responses at low latitudes whereas light dominates at high latitudes, with MHW intensity exhibiting nutrient-dependent non-linear associations with bloom probability. Paradoxically, compound frequency accelerates nearly twice as fast in low latitudes (6.1% yr⁻¹) as in high latitudes (3.5% yr⁻¹), driven by rapid tropical MHW acceleration. These diverging regimes signal dual ecological risks: trophic mismatches in upwelling systems and escalating hypoxia and harmful algal bloom hazards in eutrophic coastal waters. Full article

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry (BDS-R) data acquired from the Fengyun-3E (FY-3E) satellite, this study introduces a classification approach that integrates multi-dimensional sea ice information. A comprehensive feature set was constructed by integrating the Spectral Entropy (SE) of the Normalized Integrated Delay Waveform (NIDW) First-order Differential Curve to characterize the oscillatory complexity of the trailing edge power decay process as a scattering dynamic property, the Root Mean Square height (RMS) to characterize the attenuation magnitude of scattering intensity arising from surface roughness and related factors as a scattering intensity attenuation property, and salinity (S) and L-band brightness temperature (TB) data from SMOS to describe dielectric and radiative properties. These novel features are combined with traditional GNSS-R features. After selecting the optimal feature set via an ablation study, the features were used to train a Random Forest (RF) classifier for sea ice classification. Validated against Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea ice type products, the proposed method yielded an overall accuracy of 93.86% and a Kappa coefficient of 0.8061. The integration of multi-dimensional features notably improved the identification of Multi-Year Ice (MYI), achieving a Recall of 85.11% and an F1-score of 84.43%. These results indicate that the proposed multi-dimensional feature set provides an effective solution for GNSS-R-based sea ice classification. Full article

The inversion of wind and wave parameters in high-frequency ocean remote sensing radar relies heavily on the sea echo Doppler power spectrum. However, the accuracy of parameter inversion is often compromised by radio frequency interference (RFI), which distorts the Doppler spectral power distribution. Existing RFI suppression algorithms primarily focus on enhancing the signal-to-interference-plus-noise ratio post-mitigation, while insufficient attention has been paid to the spectral power fluctuations induced by these suppression processes. To address this issue, this study proposes a narrowband RFI suppression scheme that combines inter-pulse phase agility (IPA) with orthogonal projection (OP). An optimized aperiodic sequence is used to modulate the inter-pulse phases of the transmitted waveform, thus uniformly dispersing the sea echo power across the entire Doppler spectrum. Spatial OP is then applied to suppress RFI stripes on the range-Doppler spectrum, a process in which only the sea echo samples masked by the RFI stripes are affected. Finally, phase compensation restores the sea echo coherence and disperses residual RFI power uniformly into the Doppler domain, minimizing its localized impact. Simulations and semi-synthetic tests involving real-world interference verify that the proposed scheme effectively suppresses RFI while alleviating spectral distortion in the sea-echo Doppler spectrum. Full article

Global Navigation Satellite System Reflectometry (GNSS-R) provides a promising approach for wind speed retrieval over inland waters, with relevance to wind energy assessment and lake–atmosphere exchange studies. Existing GNSS-R wind retrieval methods are well established for open oceans but face major challenges over inland waters, where coherent scattering dominates and traditional ocean models produce large systematic biases. Unlike open oceans, inland waters are dominated by coherent scattering due to limited fetch, resulting in Delay-Doppler Maps (DDM) with highly concentrated energy and minimal spreading. These characteristics render conventional ocean-based retrieval models—built on incoherent scattering assumptions—often inadequate. To overcome this, we develop a lightweight convolutional neural network (CNN) tailored to the coherent regime, using raw CYGNSS DDM as input for end-to-end wind speed regression. Cross-seasonal validation over Lake Victoria and Lake Hongze shows that the model robustly captures wind-driven spatiotemporal patterns aligned with ERA5. Notably, ERA5 reanalysis winds exhibit uncertainties over inland waters, with a root mean square error (RMSE) of 1.5–2.5 m/s against in situ buoys. The model yields a low RMSE (<0.7 m/s) in reconstructing ERA5-resolved wind patterns. This work extends GNSS-R to inland waters, offering a lightweight, deployable remote sensing solution for wind energy and lake–atmosphere research. Full article

Accurate tropical cyclone (TC) intensity forecasting remains challenging due to the strong nonlinearity of intensity evolution and the rapid structural changes associated with storm development. In this work, we propose TC-QFormer, an interval-based probabilistic framework for 24 h TC intensity forecasting that combines transformer-based spatiotemporal modeling with scalar conditioning. Specifically, we adapt the PredFormer video prediction model for multi-horizon scalar regression and introduce a lightweight Scalar–Image Fusion Block to incorporate historical intensity information into the visual representations. A two-stage training strategy is adopted, in which the model is first pretrained for deterministic median prediction and subsequently fine-tuned to directly predict multiple conditional quantiles using the pinball loss. Experiments are conducted on the TCIR dataset using geostationary infrared and water vapor satellite imagery together with aligned historical intensity records. The proposed method is evaluated against representative recurrent and non-recurrent baselines, including ConvLSTM, PredRNN, and SimVP. Results indicate that the proposed framework achieves improved deterministic accuracy and produces well-calibrated 80% prediction intervals, particularly at longer forecast lead times and during rapidly evolving intensity regimes. These findings suggest that combining transformer-based spatiotemporal modeling with scalar–image conditioning provides an effective and interpretable approach for probabilistic TC intensity forecasting. Full article

To achieve high-precision Ulva prolifera semantic segmentation from remote sensing imagery and address issues such as boundary fragmentation, contour dilation, and missed segmentation of scattered patches under complex marine backgrounds, this paper proposes an improved SegFormer-based network termed ECAB-SegFormer. The proposed method enhances near-infrared feature representation and boundary perception by embedding an Efficient Channel Attention (ECA) module into shallow features and introducing a boundary supervision branch. Experimental results on the HYU dataset demonstrate that the proposed method achieves consistent improvements over classical baseline models and further outperforms several representative modern strong segmentation baselines. Compared with advanced methods such as DeepLabV3+, Swin-Unet, and Gated-SCNN, the proposed model achieves maximum improvements of 2.77%, 5.80%, and 4.26(pixel) in mIoU, BFScore, and Hausdorff Distance (HD), respectively, while also obtaining superior Precision and F1 Scores. These results demonstrate significant advantages in both regional segmentation accuracy and boundary localization quality, validating the effectiveness, robustness, and practical potential of the proposed method for Ulva prolifera semantic segmentation in remote sensing applications. Full article

Ocean fronts play a vital role in modulating climate variability, driving material transport, and maintaining the stability of marine ecosystems. Therefore, accurate identification of ocean fronts is of great significance for marine environmental monitoring and resource management. This study focuses on the Northwestern Pacific region and conducts a systematic comparison between two representative deep learning models—U-Net and Mask R-CNN—for automated ocean front detection. The objective is to evaluate the adaptability and strengths of different network architectures in handling multi-scale features, complex background conditions, and boundary delineation, thereby providing a theoretical basis for model selection and application-specific deployment. Experimental results show that U-Net achieves superior spatial consistency in large-scale frontal segmentation, with an IoU of 0.81 and a Dice coefficient of 0.76, while maintaining relatively high computational efficiency. In contrast, Mask R-CNN demonstrates stronger boundary modeling capabilities in detecting small-scale fronts and handling heterogeneous backgrounds, achieving an IoU of 0.78 and a Dice score of 0.73, though at the cost of increased computational demand. Overall, U-Net is more suitable for broad-scale automatic detection of ocean fronts, whereas Mask R-CNN exhibits greater potential in complex scene recognition. Integrating the structural advantages of both models holds promise for further enhancing the stability and accuracy of frontal detection, thereby offering robust technical support for ocean remote sensing analysis and environmental forecasting. Full article

Three-dimensional (3D) glacier velocities capture the full dynamic behavior of ice masses. For marine-terminating glaciers, acquiring 3D velocity fields is particularly critical for quantifying ice discharge into the ocean, assessing the stability of floating ice tongues, and constraining ice–ocean interactions that govern submarine melting, calving processes, and freshwater fluxes to the ocean. To further investigate glacier dynamics and elucidate ice–ocean interaction mechanisms, this study analyzed the 3D velocity of the Petermann Glacier throughout 2021 using long-term Sentinel-1 synthetic aperture radar (SAR) observations. First, two-dimensional velocity time series were derived from ascending and descending SAR images, and the glacier’s 3D velocity components were reconstructed based on the geometric relationships between the two viewing geometries. The estimated 3D velocities were then used as prior constraints, and glacier motion was treated as a continuously evolving state variable within a Kalman filtering framework. Multi-track, asynchronous remote sensing observations were integrated into a unified system to obtain a stable and temporally continuous 3D velocity field. Finally, statistical analyses of the 3D velocity time series were conducted to characterize spatiotemporal variations, seasonal patterns, and topographic influences on glacier motion, thereby providing quantitative insights into the dynamic coupling between glacier and ocean. Full article

Seasonal hypoxia in bottom waters of the Bohai Sea poses an escalating threat to marine ecosystems, yet monitoring it via satellite remote sensing continues to be challenging due to the inaccessibility of bottom layers. However, surface bio-optical signals do not instantaneously reflect variation in bottom-water dissolved oxygen (DO); instead, a distinct temporal lag exists between surface biological activity and its influence on bottom DO. Leveraging this insight, an inversion framework was established, integrating multi-source remote sensing data with decision tree-based machine learning models to estimate bottom-water DO concentration. We evaluated multiple lag intervals for satellite-derived bio-optical variables and adopted a 14-day lag as representative of the delayed impact of surface processes on bottom DO. An optimized feature set selected via a genetic algorithm (GA) was used to train the XGBoost model, which achieved high predictive performance (R² = 0.86, RMSE = 0.79 mg/L, MAPE = 8.89%). Interpretability analysis identified the sea surface temperature as the dominant driver of bottom-water DO variation in the Bohai Sea. The framework successfully reproduced the spatiotemporal variability in bottom DO from 2022 to 2024 in the Bohai Sea and captured the locations of summer hypoxic zones. Further analysis demonstrated that incorporating physically based bottom-layer variables substantially enhances model accuracy (R² = 0.89, RMSE = 0.68 mg/L, MAPE = 7.85%), underscoring their critical role in regulating bottom-water DO concentrations. Building on the established inversion framework and integrating extended in situ and satellite observations, we reconstruct the long-term temporal distribution of bottom DO in the Bohai Sea from 2014 to 2025, revealing the considerable potential of satellite data for monitoring bottom-water DO conditions in coastal seas. Full article

Accurate three-dimensional (3D) salinity fields are crucial for diagnosing freshwater transport and upper-to-intermediate ocean dynamics, yet subsurface salinity observations remain uneven in space and time, especially away from primary observing corridors. Gridded fields derived from sparse measurements can also suffer from regional biases and over-smoothing, which may blur mesoscale signals in energetic regimes. This study presents an empirical orthogonal function-guided U-shaped Vision Transformer (EOF-UViT) to reconstruct daily 3D salinity in the Northwest Pacific (0–50°N, 100–150°E) from multi-source surface remote-sensing factors. Compared with the U-Net baseline and the Modular Ocean Data Assimilation System (MODAS) reconstruction, EOF-UViT produces more realistic horizontal structures and improved vertical consistency, with the largest gains in dynamically active regions. Comparison with collocated in situ profiles further supports the reconstruction skill (RMSE = 0.094 psu; R² = 0.904), with estimates clustering more tightly around the 1:1 line than MODAS. Overall, EOF-UViT provides an efficient, observation-driven route to spatially coherent 3D salinity fields, supporting applications such as model initialization, assimilation background fields, and basin-scale salinity variability analysis. Full article

The Ieodo plume is a distinctive suspended sediment plume near the Ieodo Ocean Research Station (I-ORS), located in the middle of the northern East China Sea. Because the Ieodo plume exhibits multiple different spatial scales, this study conducted an integrated remote sensing observation using satellites and unmanned aerial vehicles (UAVs) to observe its development and dispersion. Sentinel-2 and Geostationary Ocean Color Imager-II (GOCI-II) data were used to determine the plume’s spatial characteristics, broad-scale behavior, hourly variability, and turbidity characteristics. Also, TPXO model outputs were employed to evaluate the relationship between plume occurrence and tides, together with satellite imagery. Plume was repeatedly observed near the top of the Ieodo Seamount, with an affected extent of 11.4 ± 3.2 km in the east–west direction and 14.3 ± 4.1 km in the north–south direction. Moreover, hourly variations observed using GOCI-II showed that the Ieodo plume rotated clockwise with shifting tidal currents, forming a counterclockwise curved band or a ring-shaped structure. Total suspended solids (TSSs) in the plume reached their maximum when the southward component of the TPXO tidal current was dominant. Based on UAV optical surveys at the I-ORS, fine-scale morphology at the early stage of plume development was revealed, and it was confirmed that the Ieodo plume can occur even when it is not detected by satellite imagery. Furthermore, the u- and v-velocity vectors of the propagating Ieodo plume were derived by applying large-scale particle image velocimetry (LSPIV) to geometrically corrected sequential UAV imagery obtained in I-ORS. Plume speed was greatest near the source during the initial stage (0.81 ± 0.30 m s⁻¹) and gradually decreased to 0.34 ± 0.29 m s⁻¹ over distance. Based on the results above, we propose that the Ieodo plume is primarily generated by a pressure reduction associated with tidally accelerated currents over topography, driven by the Bernoulli effect. This study shows that an integrated satellite and UAV observation framework can effectively monitor rapidly evolving suspended sediment plumes. It can further help improve our understanding of dynamically driven submesoscale marine events. Full article

The ocean archives massive, stable remote sensing datasets, and leveraging these data to achieve intelligent real-time recognition of marine organisms has become a core task in the field of marine remote sensing. However, in complex seabed environments, marine monitoring equipment is often constrained by limited computing power—this creates an urgent demand among oceanographers for detection algorithms with low computational complexity, which can be widely deployed on low-cost, simple marine remote sensing devices. To address this demand, this study proposes a deep learning-based algorithm for lightweight seabed organism detection efficiently (LSOD). This algorithm integrates Mamba and YOLO principles to enable efficient lightweight benthic organism detection. For LSOD’s neck, the original concatenation modules are improved, which efficiently aggregates feature layer information across backbone stages for cross-scale fusion. To further reduce the computational requirements of LSOD, a new detection head module based on group normalization and shared convolution operations is designed. These improvements maintain a reasonable computational load while enhancing the precision of the object detection network. EUDD tests indicate LSOD’s performance: the detection precision achieves 90.6% (sea cucumbers), 91.6% (sea urchins), and 93.5% (scallops). Comparisons with mainstream models confirm its superiority in detecting benthic organisms. This work is expected to provide new insights and approaches for intelligent remote sensing and analysis in marine ranches. Full article

The spatial–temporal impact of the El Niño–Southern Oscillation (ENSO) phenomenon in Peru is characterised by marked regional variability, affecting the economy and general well-being. This study focuses on the Piura region, which is highly sensitive to ENSO events, with the aim of determining the implications for land management and climate adaptation in the Peruvian coastal region, particularly in the context of ENSO events. The objective of the study is to ascertain the correlation between sea surface temperature (SST) anomalies and the Normalised Difference Vegetation Index (NDVI) in the region. The researchers employed a machine learning approach to model and predict monthly NDVI behaviour, incorporating spatial and seasonal variables from the Moderate Resolution Imaging Spectroradiometer (MODIS) during two periods of ENSO occurrence on the Peruvian coast (2017; 2023) and the one-year post-occurrence periods (2018; 2024). The results demonstrated a correlation between NDVI and SST anomalies in coastal provinces such as Sechura and Morropón, indicating sensitivity to oceanic conditions. In contrast, high Andean provinces such as Ayabaca and Huancabamba exhibited more moderate values, indicating a weaker dependence on SST variability. The study also found that the NDVI exhibited a marked monthly variation associated with altitudinal gradients and climatic conditions. This research demonstrates the potential of remote sensing and GIS technologies in capturing climate-sensitive land-use dynamics and provides a framework for operational monitoring and decision support. Full article

Satellite remote sensing has revolutionized the monitoring of atmospheric carbon dioxide (CO₂) concentrations, yet its integration into studies of air–sea CO₂ flux dynamics remains limited. Leveraging high-resolution observations from the Orbiting Carbon Observatory 2 (OCO-2) and Copernicus Marine Environment Monitoring Service (CMEMS), this study investigated the spatiotemporal heterogeneity of atmospheric column-averaged CO₂ (XCO₂) and sea surface partial pressure of CO₂ (pCO₂) between 2015 and 2020. Our analysis reveals pronounced latitudinal gradients, with the Northern Hemisphere exhibiting stronger seasonal XCO₂ variability (5.67 ± 0.42 ppm annual amplitude) compared to the Southern Hemisphere (1.2 ± 0.18 ppm). Notably, the XCO₂ growth rate was marginally higher in the Southern Hemisphere (2.48 ppm yr⁻¹) than the Northern Hemisphere (2.39 ppm yr⁻¹), while coastal regions showed elevated atmospheric CO₂ concentrations, but slower pCO₂ increases relative to the open ocean, suggesting a buffering capacity of marginal seas. Furthermore, we identified distinct seasonal phasing between land and ocean XCO₂, with oceanic signals lagging terrestrial ones by approximately one month. These findings highlight the utility of satellite data in resolving fine-scale air–sea carbon flux dynamics and provide critical insights into how heterogeneous atmospheric CO₂ changes propagate across marine systems. Full article

Synthetic aperture radar (SAR) is widely used in the field of ocean remote sensing. However, SAR images are usually affected by sea spikes, which appear as strong echo and azimuth defocus characteristics. The texture features of ocean waves in SAR images are submerged by sea spikes, making them weak or even invisible. This seriously affects the further applications of SAR technology in ocean remote sensing. To address this issue, an airborne SAR imaging algorithm for ocean waves oriented to sea spike suppression is proposed in this paper. The non-stationary characteristics of sea spikes are taken into account in the proposed algorithm. The SAR echo data is transformed into the time–frequency domain by short-time Fourier transform (STFT). And the echo signals of sea spikes are suppressed in the time–frequency domain. Then, the ocean waves are imaged in focus by applying focus settings. In order to verify the effectiveness of the proposed algorithm, airborne SAR data was processed using the proposed algorithm, including SAR data with completely invisible waves and other data with weakly visible waves under sea spike influence. Through analyzing the ocean wave spectrum and imaging quality, it is confirmed that the proposed algorithm can significantly suppress sea spikes and improve the texture features of ocean waves in SAR images. Full article