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Remote Sens., Volume 17, Issue 18 (September-2 2025) – 6 articles

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19 pages, 4504 KB

Open AccessArticle

Assessing the Performance of GNSS-IR for Sea Level Monitoring During Hurricane-Induced Storm Surges

by Runtao Zhang, Kai Liu, Xue Wang, Zhao Li, Tao Xie, Qusen Chen and Xin Chang

Remote Sens. 2025, 17(18), 3132; https://doi.org/10.3390/rs17183132 - 9 Sep 2025

Abstract

With the intensification of extreme climate change, hurricanes are becoming increasingly frequent, and coastal regions are often impacted by hurricane-induced storm surges. While GNSS-IR (Global Navigation Satellite System–Interferometric Reflectometry) has been widely used for sea level monitoring, its application in extreme weather events such as storm surges remains limited. This study focuses on GNSS-IR-based storm surge monitoring and investigates six hurricane events using data from two GNSS stations (CALC and FLCK) located in the Gulf of Mexico. The monitoring accuracy and effectiveness are systematically evaluated. Results indicate that GNSS-IR achieves a sea level accuracy of approximately 7 cm under non-storm surge conditions. Compared with the FLCK station, the CALC station has a wider field of water reflection and higher precision observation results. This further confirms that an open environment is a prerequisite for ensuring the accuracy of GNSS-IR measurements. However, accuracy degrades significantly during storm surges, reaching only a decimeter-level precision. Multi-GNSS observations notably improve temporal resolution, with valid observation periods covering 83% to 97% of the total time, compared with only 40% to 60% for single-system observations. Moreover, dynamic sea level variations are closely correlated with hurricane trajectories, which affects GNSS-IR measurement accuracy to some extent. The GPS L2 band is particularly sensitive, likely due to the complex surface-reflected condition caused by hurricanes. Despite reduced accuracy during storm surges, GNSS-IR remains capable of capturing dynamic sea level changes effectively, demonstrating its potential as a valuable supplement to the existing observation networks for extreme weather monitoring. Full article

(This article belongs to the Special Issue Advanced Multi-GNSS Positioning and Its Applications in Geoscience)

19 pages, 10212 KB

Open AccessArticle

Data-Driven Prediction of Deep-Sea Near-Seabed Currents: A Comparative Analysis of Machine Learning Algorithms

by Hairong Bao, Zhixiong Yao, Dongfeng Xu, Jun Wang, Chenghao Yang, Nuan Liu and Yuntian Pang

Remote Sens. 2025, 17(18), 3131; https://doi.org/10.3390/rs17183131 - 9 Sep 2025

Abstract

Deep-sea mining has garnered significant global attention, and accurate prediction of ocean currents plays a critical role in optimizing the design of sediment plume monitoring networks associated with mining activities. Using near-seabed mooring data from the Western Pacific M2 block (Beijing Pioneer polymetallic nodule Exploration Area, BPEA), this study trained four machine learning models—LSTM, XGBoost, ARIMA, and SVR—on current velocity to generate 96 h forecasts. Key findings include the following: LSTM and ARIMA models outperformed XGBoost and SVR in near-seabed current prediction. 1 h ahead forecasts substantially improved accuracy over rolling predictions (an iterative process where predicted values are treated as observed values for subsequent prediction steps), reducing zonal current (east–west component) RMSE from 2.395 cm/s to 1.120 cm/s and meridional current (north–south component) RMSE from 2.024 cm/s to 1.224 cm/s. For practical deployment, 3 h ahead forecasts achieved a zonal current RMSE of 1.412 cm/s. Full article

(This article belongs to the Special Issue Linking Marine Heatwaves/Cold-Spells, Eddies, and Sea Surface Features)

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29 pages, 2172 KB

Open AccessArticle

A Decision-Support Framework for Evaluating Riverine Sediment Influence on U.S. Tidal Wetlands

by Joanne N. Halls, Scott H. Ensign and Erin K. Peck

Remote Sens. 2025, 17(18), 3130; https://doi.org/10.3390/rs17183130 - 9 Sep 2025

Abstract

Tidal wetlands are essential for coastal resilience, biodiversity, and carbon storage; yet, many are increasingly vulnerable to sea-level rise due to insufficient sediment supply. This study presents a national-scale, GIS-based model that quantifies riverine inorganic sediment contributions to tidal wetland accretion across over 700,000 coastal catchments in the contiguous United States. By integrating datasets from USGS, NOAA, and USFWS, the model calculates sediment yield, thickness, and accretion balance, enabling comparison with current sea-level rise projections. Results reveal significant regional disparities: the Northeast and Midwest exhibit higher sediment accumulation, while the Pacific and Southeast show widespread sediment deficits. Spatial statistical analyses identified clusters of high and low sediment supply, highlighting areas of resilience and vulnerability. A total of 93 field sites confirmed the model’s ability to distinguish between riverine-dominated and mixed-source sedimentation regimes. These findings underscore the importance of riverine sediment in sustaining wetland elevation and inform where non-riverine sources may be critical. The model’s outputs have been shared with coastal planners and stakeholders to support local decision-making, conservation prioritization, and adaptation strategies. This work demonstrates both the challenges and fruitfulness of harmonizing disparate national datasets into a unified framework for assessing wetland vulnerability and provides a scalable tool for guiding coastal resilience planning in the face of accelerating sea-level rise. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal, Wetland, and Intertidal Zones)

25 pages, 18797 KB

Open AccessArticle

AEFusion: Adaptive Enhanced Fusion of Visible and Infrared Images for Night Vision

by Xiaozhu Wang, Chenglong Zhang, Jianming Hu, Qin Wen, Guifeng Zhang and Min Huang

Remote Sens. 2025, 17(18), 3129; https://doi.org/10.3390/rs17183129 - 9 Sep 2025

Abstract

Under night vision conditions, visible-spectrum images often fail to capture background details. Conventional visible and infrared fusion methods generally overlay thermal signatures without preserving latent features in low-visibility regions. This paper proposes a novel deep learning-based fusion algorithm to enhance visual perception in night driving scenarios. Firstly, a local adaptive enhancement algorithm corrects underexposed and overexposed regions in visible images, thereby preventing oversaturation during brightness adjustment. Secondly, ResNet152 extracts hierarchical feature maps from enhanced visible and infrared inputs. Max pooling and average pooling operations preserve critical features and distinct information across these feature maps. Finally, Linear Discriminant Analysis (LDA) reduces dimensionality and decorrelates features. We reconstruct the fused image by the weighted integration of the source images. The experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods in both objective metrics and subjective visual assessments. Full article

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18 pages, 4791 KB

Open AccessArticle

A Machine-Learning-Based Cloud Detection and Cloud-Top Thermodynamic Phase Algorithm over the Arctic Using FY3D/MERSI-II

by Caixia Yu, Xiuqing Hu, Yanyu Lu, Wenyu Wu and Dong Liu

Remote Sens. 2025, 17(18), 3128; https://doi.org/10.3390/rs17183128 - 9 Sep 2025

Abstract

The Arctic, characterized by extensive ice and snow cover with persistent low solar elevation angles and prolonged polar nights, poses significant challenges for conventional spectral threshold methods in cloud detection and cloud-top thermodynamic phase classification. The study addressed these limitations by combining active and passive remote sensing and developing a machine learning framework for cloud detection and cloud-top thermodynamic phase classification. Utilizing the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) cloud product from 2021 as the truth reference, the model was trained with spatiotemporally collocated datasets from FY3D/MERSI-II (Medium Resolution Spectral Imager-II) and CALIOP. The AdaBoost (Adaptive Boosting) machine learning algorithm was employed to construct the model, with considerations for six distinct Arctic surface types to enhance its performance. The accuracy test results showed that the cloud detection model achieved an accuracy of 0.92, and the cloud recognition model achieved an accuracy of 0.93. The inversion performance of the final model was then rigorously evaluated using a completely independent dataset collected in July 2022. Our findings demonstrated that our model results align well with results from CALIOP, and the detection and identification outcomes across various surface scenarios show high consistency with the actual situations displayed in false-color images. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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10 pages, 10494 KB

Open AccessCommunication

Detection and Analysis of Airport Tailwind Events Triggered by Frontal Activity

by Yue Liu, Yixiang Chen, Jinlong Yuan, Zhekai Li, Fangzhi Wei, Tianwen Wei, Jiadong Hu and Haiyun Xia

Remote Sens. 2025, 17(18), 3127; https://doi.org/10.3390/rs17183127 - 9 Sep 2025

Abstract

Excessive tailwind, threatening the safety of aircraft takeoff and landing, is one of the prominent research topics in the field of aviation meteorology. This paper analyzes the causes of tailwinds at Beijing Daxing International Airport (BDIA), based on coherent Doppler wind lidar (CDWL) and ERA5 reanalysis data. CDWL with high spatiotemporal resolution is utilized to detect variations in the low-level wind field in the vicinity of airport areas. ERA5 reanalysis data are employed to investigate the distribution characteristics of meteorological elements such as wind fields, pressure, and temperature in the Beijing surrounding regions. The study of two typical tailwind events reveals that frontal activity, through the combined effects of pressure gradient adjustment and topographic constraints from the Taihang Mountains, drives the development of low-level southerly jets. It serves as the key mechanism triggering excessive tailwind. By integrating CDWL and ERA5 data for local and regional analysis, this study contributes to enhancing understanding of tailwind causal mechanisms and provides critical support for aviation meteorological disaster early warning. Full article

(This article belongs to the Special Issue Remote Sensing for High Impact Weather and Extremes (2nd Edition))

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