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Keywords = depth imagery

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31 pages, 20437 KiB  
Article
Satellite-Derived Bathymetry Using Sentinel-2 and Airborne Hyperspectral Data: A Deep Learning Approach with Adaptive Interpolation
by Seung-Jun Lee, Han-Saem Kim, Hong-Sik Yun and Sang-Hoon Lee
Remote Sens. 2025, 17(15), 2594; https://doi.org/10.3390/rs17152594 - 25 Jul 2025
Viewed by 308
Abstract
Accurate coastal bathymetry is critical for navigation, environmental monitoring, and marine resource management. This study presents a deep learning-based approach that fuses Sentinel-2 multispectral imagery with airborne hyperspectral-derived reference data to generate high-resolution satellite-derived bathymetry (SDB). To address the spatial resolution mismatch between [...] Read more.
Accurate coastal bathymetry is critical for navigation, environmental monitoring, and marine resource management. This study presents a deep learning-based approach that fuses Sentinel-2 multispectral imagery with airborne hyperspectral-derived reference data to generate high-resolution satellite-derived bathymetry (SDB). To address the spatial resolution mismatch between Sentinel-2 (10 m) and LiDAR reference data (1 m), three interpolation methods—Inverse Distance Weighting (IDW), Natural Neighbor (NN), and Spline—were employed to resample spectral reflectance data to a 1 m grid. Two spectral input configurations were evaluated: the log-ratio of Bands 2 and 3, and raw RGB composite reflectance (Bands 2, 3, and 4). A Fully Convolutional Neural Network (FCNN) was trained under each configuration and validated using LiDAR-based depth. The RGB + NN combination yielded the best performance, achieving an RMSE of 1.2320 m, MAE of 0.9381 m, bias of +0.0315 m, and R2 of 0.6261, while the log-ratio + IDW configuration showed lower accuracy. Visual and statistical analyses confirmed the advantage of the RGB + NN approach in preserving spatial continuity and spectral-depth relationships. This study demonstrates that both interpolation strategy and input configuration critically affect SDB model accuracy and generalizability. The integration of spatially adaptive interpolation with airborne hyperspectral reference data represents a scalable and efficient solution for high-resolution coastal bathymetry mapping. Full article
(This article belongs to the Section Ocean Remote Sensing)
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28 pages, 7404 KiB  
Article
SR-YOLO: Spatial-to-Depth Enhanced Multi-Scale Attention Network for Small Target Detection in UAV Aerial Imagery
by Shasha Zhao, He Chen, Di Zhang, Yiyao Tao, Xiangnan Feng and Dengyin Zhang
Remote Sens. 2025, 17(14), 2441; https://doi.org/10.3390/rs17142441 - 14 Jul 2025
Viewed by 376
Abstract
The detection of aerial imagery captured by Unmanned Aerial Vehicles (UAVs) is widely employed across various domains, including engineering construction, traffic regulation, and precision agriculture. However, aerial images are typically characterized by numerous small targets, significant occlusion issues, and densely clustered targets, rendering [...] Read more.
The detection of aerial imagery captured by Unmanned Aerial Vehicles (UAVs) is widely employed across various domains, including engineering construction, traffic regulation, and precision agriculture. However, aerial images are typically characterized by numerous small targets, significant occlusion issues, and densely clustered targets, rendering traditional detection algorithms largely ineffective for such imagery. This work proposes a small target detection algorithm, SR-YOLO. It is specifically tailored to address these challenges in UAV-captured aerial images. First, the Space-to-Depth layer and Receptive Field Attention Convolution are combined, and the SR-Conv module is designed to replace the Conv module within the original backbone network. This hybrid module extracts more fine-grained information about small target features by converting image spatial information into depth information and the attention of the network to targets of different scales. Second, a small target detection layer and a bidirectional feature pyramid network mechanism are introduced to enhance the neck network, thereby strengthening the feature extraction and fusion capabilities for small targets. Finally, the model’s detection performance for small targets is improved by utilizing the Normalized Wasserstein Distance loss function to optimize the Complete Intersection over Union loss function. Empirical results demonstrate that the SR-YOLO algorithm significantly enhances the precision of small target detection in UAV aerial images. Ablation experiments and comparative experiments are conducted on the VisDrone2019 and RSOD datasets. Compared to the baseline algorithm YOLOv8s, our SR-YOLO algorithm has improved mAP@0.5 by 6.3% and 3.5% and mAP@0.5:0.95 by 3.8% and 2.3% on the datasets VisDrone2019 and RSOD, respectively. It also achieves superior detection results compared to other mainstream target detection methods. Full article
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23 pages, 4237 KiB  
Article
Debris-Flow Erosion Volume Estimation Using a Single High-Resolution Optical Satellite Image
by Peng Zhang, Shang Wang, Guangyao Zhou, Yueze Zheng, Kexin Li and Luyan Ji
Remote Sens. 2025, 17(14), 2413; https://doi.org/10.3390/rs17142413 - 12 Jul 2025
Viewed by 311
Abstract
Debris flows pose significant risks to mountainous regions, and quick, accurate volume estimation is crucial for hazard assessment and post-disaster response. Traditional volume estimation methods, such as ground surveys and aerial photogrammetry, are often limited by cost, accessibility, and timeliness. While remote sensing [...] Read more.
Debris flows pose significant risks to mountainous regions, and quick, accurate volume estimation is crucial for hazard assessment and post-disaster response. Traditional volume estimation methods, such as ground surveys and aerial photogrammetry, are often limited by cost, accessibility, and timeliness. While remote sensing offers wide coverage, existing optical and Synthetic Aperture Radar (SAR)-based techniques face challenges in direct volume estimation due to resolution constraints and rapid terrain changes. This study proposes a Super-Resolution Shape from Shading (SRSFS) approach enhanced by a Non-local Piecewise-smooth albedo Constraint (NPC), hereafter referred to as NPC SRSFS, to estimate debris-flow erosion volume using single high-resolution optical satellite imagery. By integrating publicly available global Digital Elevation Model (DEM) data as prior terrain reference, the method enables accurate post-disaster topography reconstruction from a single optical image, thereby reducing reliance on stereo imagery. The NPC constraint improves the robustness of albedo estimation under heterogeneous surface conditions, enhancing depth recovery accuracy. The methodology is evaluated using Gaofen-6 satellite imagery, with quantitative comparisons to aerial Light Detection and Ranging (LiDAR) data. Results show that the proposed method achieves reliable terrain reconstruction and erosion volume estimates, with accuracy comparable to airborne LiDAR. This study demonstrates the potential of NPC SRSFS as a rapid, cost-effective alternative for post-disaster debris-flow assessment. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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18 pages, 10338 KiB  
Article
Visual Geolocalization for Aerial Vehicles via Fusion of Satellite Remote Sensing Imagery and Its Relative Depth Information
by Maoan Zhou, Dongfang Yang, Jieyu Liu, Weibo Xu, Xiong Qiu and Yongfei Li
Remote Sens. 2025, 17(13), 2291; https://doi.org/10.3390/rs17132291 - 4 Jul 2025
Viewed by 337
Abstract
Visual geolocalization for aerial vehicles based on an analysis of Earth observation imagery is an effective method in GNSS-denied environments. However, existing methods for geographic location estimation have limitations: one relies on high-precision geodetic elevation data, which is costly, and the other assumes [...] Read more.
Visual geolocalization for aerial vehicles based on an analysis of Earth observation imagery is an effective method in GNSS-denied environments. However, existing methods for geographic location estimation have limitations: one relies on high-precision geodetic elevation data, which is costly, and the other assumes a flat ground surface, ignoring elevation differences. This paper presents a novel aerial vehicle geolocalization method. It integrates 2D information and relative depth information, which are both from Earth observation images. Firstly, the aerial and reference remote sensing satellite images are fed into a feature-matching network to extract pixel-level feature-matching pairs. Then, a depth estimation network is used to estimate the relative depth of the satellite remote sensing image, thereby obtaining the relative depth information of the ground area within the field of view of the aerial image. Finally, high-confidence matching pairs with similar depth and uniform distribution are selected to estimate the geographic location of the aerial vehicle. Experimental results demonstrate that the proposed method outperforms existing ones in terms of geolocalization accuracy and stability. It eliminates reliance on elevation data or planar assumptions, thus providing a more adaptable and robust solution for aerial vehicle geolocalization in GNSS-denied environments. Full article
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15 pages, 5107 KiB  
Article
Spatiotemporal Evolution and Influencing Factors of Aerosol Optical Depth in Zhejiang Province: Insights from Land Use Dynamics and Transportation Networks Based on Remote Sensing
by Qi Wang, Ben Wang, Wanlin Kong, Jiali Wu, Zhifeng Yu, Xiwen Wu and Xiaohong Yuan
Sustainability 2025, 17(13), 6126; https://doi.org/10.3390/su17136126 - 3 Jul 2025
Viewed by 294
Abstract
Aerosol optical depth (AOD) serves as a critical indicator for atmospheric aerosol monitoring and air quality assessment, and quantifies the radiative attenuation caused by airborne particulate matter. This study uses MODIS remote sensing imagery together with land use transition datasets (2000–2020) and road [...] Read more.
Aerosol optical depth (AOD) serves as a critical indicator for atmospheric aerosol monitoring and air quality assessment, and quantifies the radiative attenuation caused by airborne particulate matter. This study uses MODIS remote sensing imagery together with land use transition datasets (2000–2020) and road network density metrics (2014–2020), to investigate the spatiotemporal evolution of AOD in Zhejiang Province and its synergistic correlations with urbanization patterns and transportation infrastructure. By integrating MODIS_1KM AOD product, grid-based road network density mapping, land use dynamic degree modeling, and transfer matrix analysis, this study systematically evaluates the interdependencies among aerosol loading, impervious surface expansion, and transportation network intensification. The results indicate that during the study period (2000–2020), the provincial AOD level shows a significant declining trend, with obvious spatial heterogeneity: the AOD values in eastern coastal industrial zones and urban agglomerations continue to increase, with lower values dominating southwestern forested highlands. Meanwhile, statistical analyses confirm highly positive correlations between AOD, impervious surface coverage, and road network density, emphasizing the dominant role of anthropogenic activities in aerosol accumulation. These findings provide actionable insights for enhancing land-use zoning, minimizing vehicular emissions, and developing spatially targeted air quality management strategies in rapidly urbanizing regions. This study provides a solid scientific foundation for advancing environmental sustainability by supporting policy development that balances urban expansion and air quality. It contributes to building more sustainable and resilient cities in Zhejiang Province. Full article
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18 pages, 13604 KiB  
Essay
Scenario Simulation of Glacier Collapse in the Amnye Machen Mountains, Qinghai–Tibetan Plateau
by Jia Li, Junhui Wu, Xuyan Ma, Dongwei Zhou, Long Li, Le Lv, Lei Guo, Lingshuai Kong and Jiahao Dian
Geosciences 2025, 15(7), 254; https://doi.org/10.3390/geosciences15070254 - 3 Jul 2025
Viewed by 351
Abstract
Simulating potential glacier collapses can provide crucial support for local disaster prevention and mitigation efforts. The Xiaomagou Glacier in the Amnye Machen Mountains, Qinghai–Tibetan Plateau, has experienced five collapses in the past two decades. Field investigation and remote sensing observations indicate that the [...] Read more.
Simulating potential glacier collapses can provide crucial support for local disaster prevention and mitigation efforts. The Xiaomagou Glacier in the Amnye Machen Mountains, Qinghai–Tibetan Plateau, has experienced five collapses in the past two decades. Field investigation and remote sensing observations indicate that the topography and bedrock characteristics of the Qushi’an No. 22 Glacier, which is 3.5 km south of the Xiaomagou Glacier, are similar to those of the Xiaomagou Glacier. More importantly, the mass movement of the Qushi’an No. 22 Glacier since 2018 closely resembles that of the Xiaomagou Glacier exhibited before its previous collapses. Therefore, in the context of rising temperatures, it is possible that the Qushi’an No. 22 Glacier will collapse in the near future. Based on remote sensing imagery and the glacier’s surface elevation changes, we reconstructed the 2004 collapse process of the Xiaomagou Glacier via numerical simulation. The key parameters of the mass flow model were optimized based on the actual deposition area of the 2004 collapse. The model with optimized parameters was then used to simulate the potential Qushi’an No. 22 Glacier collapse. Two collapse scenarios were set for the Qushi’an No. 22 Glacier. In Scenario 1, the lower half of the tongue detaches; in Scenario 2, the whole tongue detaches. Simulation results show that, in Scenario 1, the maximum mass flow depth is 72 m, the maximum mass flow speed is 51.6 m/s, and the deposition area is 5.40 × 106 km2; in Scenario 2, the maximum mass flow depth is 75 m, the maximum mass flow speed is 59.7 m/s, and the deposition area is 6.32 × 106 km2. In both scenarios, the deposition area is much larger than that of the Xiaomagou Glacier 2004 collapse, which had a deposition area of 2.21 × 106 km2. The simulation results suggest that the Qushi’an No. 22 Glacier collapse could devastate the pastures and township roads lying in front of the glacier, seriously affecting local transportation and livestock farming; furthermore, it may deposit in the Qinglong River, forming a large, dammed lake. At present, the Qushi’an No. 22 Glacier remains in an unstable state. It is crucial to strengthen monitoring of its surface morphology, flow speed, and elevation. Full article
(This article belongs to the Section Cryosphere)
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20 pages, 3340 KiB  
Article
Infrared Monocular Depth Estimation Based on Radiation Field Gradient Guidance and Semantic Priors in HSV Space
by Rihua Hao, Chao Xu and Chonghao Zhong
Sensors 2025, 25(13), 4022; https://doi.org/10.3390/s25134022 - 27 Jun 2025
Viewed by 400
Abstract
Monocular depth estimation (MDE) has emerged as a powerful technique for extracting scene depth from a single image, particularly in the context of computational imaging. Conventional MDE methods based on RGB images often degrade under varying illuminations. To overcome this, an end-to-end framework [...] Read more.
Monocular depth estimation (MDE) has emerged as a powerful technique for extracting scene depth from a single image, particularly in the context of computational imaging. Conventional MDE methods based on RGB images often degrade under varying illuminations. To overcome this, an end-to-end framework is developed that leverages the illumination-invariant properties of infrared images for accurate depth estimation. Specifically, a multi-task UNet architecture was designed to perform gradient extraction, semantic segmentation, and texture reconstruction from infrared RAW images. To strengthen structural learning, a Radiation Field Gradient Guidance (RGG) module was incorporated, enabling edge-aware attention mechanisms. The gradients, semantics, and textures were mapped to the Saturation (S), Hue (H), and Value (V) channels in the HSV color space, subsequently converted into an RGB format for input into the depth estimation network. Additionally, a sky mask loss was introduced during training to mitigate the influence of ambiguous sky regions. Experimental validation on a custom infrared dataset demonstrated high accuracy, achieving a δ1 of 0.976. These results confirm that integrating radiation field gradient guidance and semantic priors in HSV space significantly enhances depth estimation performance for infrared imagery. Full article
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16 pages, 921 KiB  
Article
Aiding Depth Perception in Initial Drone Training: Evidence from Camera-Assisted Distance Estimation
by John Murray, Steven Richardson, Keith Joiner and Graham Wild
Technologies 2025, 13(7), 267; https://doi.org/10.3390/technologies13070267 - 24 Jun 2025
Viewed by 487
Abstract
Remotely Piloted Aircraft (RPA) pilots frequently experience difficulties with depth perception, particularly when estimating distances between the drone and environmental obstacles. This study evaluates whether the use of onboard camera imagery can improve exocentric distance estimation accuracy among ab initio drone pilots operating [...] Read more.
Remotely Piloted Aircraft (RPA) pilots frequently experience difficulties with depth perception, particularly when estimating distances between the drone and environmental obstacles. This study evaluates whether the use of onboard camera imagery can improve exocentric distance estimation accuracy among ab initio drone pilots operating under visual line-of-sight (VLOS) conditions. Two groups of undergraduate students performed distance estimation tasks at 20 and 50 m. One group used direct observation only to estimate the exocentric distance between the drone and an obstacle. The second group, as well as direct observation, had access to a live video feed from the drone’s onboard camera via a ground control station. At 20 m, there was no statistically significant difference in estimation accuracy between the groups. However, at 50 m, the camera-assisted group demonstrated significantly improved accuracy in distance estimation and reduced variance in estimation error. These findings suggest that a ubiquitous and low-cost technology, originally intended for imaging, can offer measurable benefits for depth perception at greater operational distances. The inclusion of camera-assisted perception training during early-stage licensing may enhance safety and spatial judgement in RPAS operations. Full article
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19 pages, 3119 KiB  
Article
Retrieval of Internal Solitary Wave Parameters and Analysis of Their Spatial Variability in the Northern South China Sea Based on Continuous Satellite Imagery
by Kexiao Lu, Tao Xu, Cun Jia, Xu Chen and Xiao He
Remote Sens. 2025, 17(13), 2159; https://doi.org/10.3390/rs17132159 - 24 Jun 2025
Viewed by 389
Abstract
The remote sensing inversion of internal solitary waves (ISWs) enables the retrieval of ISW parameters and facilitates the analysis of their spatial variability. In this study, we utilize continuous optical imagery from the FY-4B satellite to extract real-time ISW propagation speeds throughout their [...] Read more.
The remote sensing inversion of internal solitary waves (ISWs) enables the retrieval of ISW parameters and facilitates the analysis of their spatial variability. In this study, we utilize continuous optical imagery from the FY-4B satellite to extract real-time ISW propagation speeds throughout their evolution from generation to shoaling. ISW parameters are retrieved in the northern South China Sea based on the quantitative relationship between sea surface current divergence and ISW surface features in optical imagery. The inversion method employs a fully nonlinear equation with continuous stratification to account for the strongly nonlinear nature of ISWs and uses the propagation speed extracted from continuous imagery as a constraint to determine a unique solution. The results show that as ISWs propagate from deep to shallow waters in the northern South China Sea, their statistically averaged amplitude initially increases and then decreases, while their propagation speed continuously decreases with decreasing depth. The inversion results are consistent with previous in situ observations. Furthermore, a three-day consecutive remote sensing tracking analysis of the same ISW revealed that the spatial variation in its parameters aligned well with the abovementioned statistical results. The findings provide an effective inversion approach and supporting datasets for extensive ISW monitoring. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Ocean and Coastal Environment Monitoring)
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19 pages, 26591 KiB  
Article
Hand Washing Gesture Recognition Using Synthetic Dataset
by Rüstem Özakar and Eyüp Gedikli
J. Imaging 2025, 11(7), 208; https://doi.org/10.3390/jimaging11070208 - 22 Jun 2025
Cited by 1 | Viewed by 469
Abstract
Hand hygiene is paramount for public health, especially in critical sectors like healthcare and the food industry. Ensuring compliance with recommended hand washing gestures is vital, necessitating autonomous evaluation systems leveraging machine learning techniques. However, the scarcity of comprehensive datasets poses a significant [...] Read more.
Hand hygiene is paramount for public health, especially in critical sectors like healthcare and the food industry. Ensuring compliance with recommended hand washing gestures is vital, necessitating autonomous evaluation systems leveraging machine learning techniques. However, the scarcity of comprehensive datasets poses a significant challenge. This study addresses this issue by presenting an open synthetic hand washing dataset, created using 3D computer-generated imagery, comprising 96,000 frames (equivalent to 64 min of footage), encompassing eight gestures performed by four characters in four diverse environments. This synthetic dataset includes RGB images, depth/isolated depth images and hand mask images. Using this dataset, four neural network models, Inception-V3, Yolo-8n, Yolo-8n segmentation and PointNet, were trained for gesture classification. The models were subsequently evaluated on a large real-world hand washing dataset, demonstrating successful classification accuracies of 56.9% for Inception-V3, 76.3% for Yolo-8n and 79.3% for Yolo-8n segmentation. These findings underscore the effectiveness of synthetic data in training machine learning models for hand washing gesture recognition. Full article
(This article belongs to the Section Computer Vision and Pattern Recognition)
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22 pages, 11790 KiB  
Article
Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China
by Zhonghe Zhao, Yuyang Li, Kun Liu, Chunsheng Wu, Bowei Yu, Gaohuan Liu and Youxiao Wang
Remote Sens. 2025, 17(13), 2130; https://doi.org/10.3390/rs17132130 - 21 Jun 2025
Viewed by 465
Abstract
Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such [...] Read more.
Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies—such as high spatiotemporal resolution optical, radar, and thermal infrared sensors—has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0–5 cm, 5–10 cm, 10–15 cm, 15–20 cm). After vegetation suppression, the coefficient of determination (R2) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical–radar–thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning. Full article
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27 pages, 1354 KiB  
Review
High-Resolution Global Land Cover Maps and Their Assessment Strategies
by Qiongjie Xu, Vasil Yordanov, Lorenzo Bruzzone and Maria Antonia Brovelli
ISPRS Int. J. Geo-Inf. 2025, 14(6), 235; https://doi.org/10.3390/ijgi14060235 - 18 Jun 2025
Cited by 1 | Viewed by 1758
Abstract
Global High-Resolution Land Cover Maps (GHRLCs), characterized by spatial resolutions higher than 30 m per pixel, have become essential tools for environmental monitoring, urban planning, and climate modeling. Over the past two decades, new GHRLCs have emerged, offering increasingly detailed and timely representations [...] Read more.
Global High-Resolution Land Cover Maps (GHRLCs), characterized by spatial resolutions higher than 30 m per pixel, have become essential tools for environmental monitoring, urban planning, and climate modeling. Over the past two decades, new GHRLCs have emerged, offering increasingly detailed and timely representations of Earth’s surface. This review provides an in-depth analysis of recent developments by examining the data sources, methodologies, and validation techniques utilized in 19 global binary and multi-class land cover products. The evolution of GHRLC production techniques is analyzed, starting from the use of singular source input data, such as multi-temporal remotely sensed optical imagery, to the integration of satellite radar and other geospatial data. The article highlights significant advances in data pre-processing and processing, showcasing a shift from classical methods to modern approaches, including machine learning (ML) and deep learning techniques (e.g., neural networks and transformers), and their direct application on powerful cloud-computing platforms. A comprehensive analysis of the temporal dimension of land cover products, where available, is conducted, highlighting a shift from decadal intervals to production intervals of less than a month. This review also addresses the ongoing challenge of land cover legend harmonization, a topic that remains crucial for ensuring consistency and comparability across datasets. Validation remains another critical aspect of GHRLC production. The methods used to assess map accuracy and reliability, including statistical techniques and visual inspections, are briefly discussed. The validation approaches adopted in recent studies are summarized, with an emphasis on their importance in maintaining data integrity and addressing emerging needs, such as the development of common validation datasets. Ultimately, this review aims to provide a comprehensive overview of the current state and future directions of GHRLC production and validation, highlighting the advancements that have shaped this rapidly evolving field. Full article
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24 pages, 7069 KiB  
Article
AI-Driven Time Series Forecasting of Coastal Water Quality Using Sentinel-2 Imagery: A Case Study in the Gulf of Thailand
by Arsanchai Sukkuea, Pensiri Akkajit, Korakot Suwannarat, Punnawit Foithong, Nasrin Afsarimanesh and Md Eshrat E. Alahi
Water 2025, 17(12), 1798; https://doi.org/10.3390/w17121798 - 16 Jun 2025
Cited by 1 | Viewed by 1957
Abstract
The accurate prediction of water quality parameters is essential for effective pollution control and resource management. This study presents a hybrid AI-remote sensing framework for forecasting water quality in the Gulf of Thailand, which combines Sentinel-2 imagery with Support Vector Machine (SVM) and [...] Read more.
The accurate prediction of water quality parameters is essential for effective pollution control and resource management. This study presents a hybrid AI-remote sensing framework for forecasting water quality in the Gulf of Thailand, which combines Sentinel-2 imagery with Support Vector Machine (SVM) and Autoregressive Integrated Moving Average (ARIMA) models. Our approach achieves a 5.4× increase in data coverage over traditional methods, demonstrating the effectiveness of machine learning in environmental monitoring. Predictive accuracy was evaluated across Support Vector Machine (SVM), ARIMA, and Amazon Forecast models. Results indicate that SVM, optimised through RBF kernel and grid search, outperforms other models for Chlorophyll-a (RMSE: 1.8), while ARIMA exhibits superior performance for Secchi Depth (RMSE: 0.2) and Trophic State Index (RMSE: 0.8). The study also introduces Aqua Sight, a web-based visualisation tool built on Google Earth Engine, enabling stakeholders to access real-time water quality forecasts. These findings highlight the potential of integrating satellite-derived data with machine learning to enhance early warning systems and support environmental decision making in coastal ecosystems. Full article
(This article belongs to the Special Issue Monitoring and Modelling of Contaminants in Water Environment)
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15 pages, 4479 KiB  
Article
Hue Angle-Based Remote Sensing of Secchi Disk Depth Using Sentinel-3 OLCI in the Coastal Waters of Qinhuangdao, China
by Yongwei Huo, Sufang Zhao, Zhongjie Yuan, Xiang Wang and Lin Wang
J. Mar. Sci. Eng. 2025, 13(6), 1149; https://doi.org/10.3390/jmse13061149 - 10 Jun 2025
Viewed by 388
Abstract
Seawater transparency provides critical insight into marine ecological dynamics and serves as a foundational indicator for fisheries management, environmental monitoring, and coastal resource development. Among various indicators, the Secchi disk depth (SDD) is widely used to quantify seawater transparency in marine environmental monitoring. [...] Read more.
Seawater transparency provides critical insight into marine ecological dynamics and serves as a foundational indicator for fisheries management, environmental monitoring, and coastal resource development. Among various indicators, the Secchi disk depth (SDD) is widely used to quantify seawater transparency in marine environmental monitoring. This study develops a remote sensing inversion model for estimating the SDD in the coastal waters of Qinhuangdao, utilizing Sentinel-3 OLCI satellite imagery and in situ measurements. The model is based on the CIE hue angle and demonstrates high accuracy (R2 = 0.93, MAPE = 7.88%, RMSE = 0.25 m), outperforming traditional single-band, band-ratio, and multi-band approaches. Using the proposed model, we analyzed the monthly and interannual variations of SDD in Qinhuangdao’s coastal waters from 2018 to 2024. The results reveal a clear seasonal pattern, with SDD values generally increasing and then decreasing throughout the year, primarily driven by the East Asian monsoon and other natural factors. Notably, the average annual SDD in 2018 was significantly lower than in subsequent years (2019–2024), which is closely associated with comprehensive water management and pollution reduction initiatives in the Bohai Sea region. These findings highlight marked improvements in the coastal marine environment and underscore the benefits of China’s ecological civilization strategy, particularly the principle that “lucid waters and lush mountains are invaluable assets.” Full article
(This article belongs to the Special Issue Remote Sensing for Maritime Monitoring and Ship Surveillance)
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28 pages, 6433 KiB  
Article
Quantifying Thermal Spatiotemporal Signatures and Identifying Hidden Mining-Induced Fissures with Various Burial Depths via UAV Infrared Thermometry
by Duo Xu, Yi-Xin Zhao, Kang-Ning Zhang, Chun-Wei Ling and Peng Li
Remote Sens. 2025, 17(12), 1992; https://doi.org/10.3390/rs17121992 - 9 Jun 2025
Viewed by 270
Abstract
Hidden mining-induced fissures connected to a goaf may induce spontaneous combustion of abandoned coal, threatening safe coal mining operation and ecological and environmental protection. To identify hidden mining-induced fissures rapidly, accurately and in a timely manner, a novel method involving infrared remote sensing [...] Read more.
Hidden mining-induced fissures connected to a goaf may induce spontaneous combustion of abandoned coal, threatening safe coal mining operation and ecological and environmental protection. To identify hidden mining-induced fissures rapidly, accurately and in a timely manner, a novel method involving infrared remote sensing via an unmanned aerial vehicle (UAV) was proposed. Hidden mining-induced fissures above working face No. 52605 of the Daliuta coal mine were continuously monitored using this method. Field experiments revealed that hidden mining-induced fissures could be effectively identified via infrared technology. The diurnal variation in the hidden mining-induced fissure temperature was cosinusoidal. The temperature of the hidden mining-induced fissures was highly correlated with burial depth, and the burial depths of the identified hidden mining-induced fissures differed at various times. The temperature differences among hidden mining-induced fissures, aeolian sands and vegetation varied with time and burial depth. The temperature difference variation between in situ hidden mining-induced fissures and aeolian sand matches that between hidden mining-induced fissures at a 20 cm burial depth and sand. In situ hidden mining-induced fissures could be identified from 1:00 to 5:00 a.m. and from 11:00 a.m. to 7:00 p.m. under the studied conditions. Full article
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