Remote Sensing

19 pages, 2737 KiB

Open AccessArticle

Semi-Automated Mapping of Pockmarks from MBES Data Using Geomorphometry and Machine Learning-Driven Optimization

by Vasileios Giannakopoulos, Peter Feldens and Elias Fakiris

Remote Sens. 2025, 17(16), 2917; https://doi.org/10.3390/rs17162917 - 21 Aug 2025

Accurate mapping of seafloor morphological features, such as pockmarks, is essential for marine spatial planning, geological hazard assessment, and environmental monitoring. Traditional manual delineation methods are often subjective and inefficient when applied to large, high-resolution bathymetric datasets. This study presents a semi-automated workflow [...] Read more.

Accurate mapping of seafloor morphological features, such as pockmarks, is essential for marine spatial planning, geological hazard assessment, and environmental monitoring. Traditional manual delineation methods are often subjective and inefficient when applied to large, high-resolution bathymetric datasets. This study presents a semi-automated workflow based on the CoMMa (Confined Morphologies Mapping) toolbox to classify pockmarks in Flensburg Fjord, Germany–Denmark. Initial detection employed the Bathymetric Position Index (BPI) with intentionally permissive parameters to ensure high recall of morphologically diverse features. Morphometric descriptors were then extracted and used to train a Random Forest classifier, enabling noise reduction and refinement of overinclusive delineations. Validation against expert-derived mappings showed that the model achieved an overall classification accuracy of 86.16%, demonstrating strong performance across the validation area. These findings highlight how integrating a GIS-based geomorphometry toolbox with machine learning yields a reproducible, objective, and scalable approach to seabed mapping, supporting decision-making processes and advancing standardized methodologies in marine geomorphology. Full article

(This article belongs to the Special Issue Underwater Remote Sensing: Status, New Challenges and Opportunities)

20 pages, 17297 KiB

Open AccessArticle

A GRU-Enhanced Kolmogorov–Arnold Network Model for Sea Surface Temperature Prediction Derived from Satellite Altimetry Product in South China Sea

by Rumiao Sun, Zhengkai Huang, Xuechen Liang, Siyu Zhu and Huilin Li

Remote Sens. 2025, 17(16), 2916; https://doi.org/10.3390/rs17162916 - 21 Aug 2025

Abstract

High-precision Sea Surface Temperature (SST) prediction is critical for understanding ocean–atmosphere interactions and climate anomaly monitoring. We propose GRU_EKAN, a novel hybrid model where Gated Recurrent Units (GRUs) capture temporal dependencies and the Enhanced Kolmogorov–Arnold Network (EKAN) models complex feature interactions between SST [...] Read more.

High-precision Sea Surface Temperature (SST) prediction is critical for understanding ocean–atmosphere interactions and climate anomaly monitoring. We propose GRU_EKAN, a novel hybrid model where Gated Recurrent Units (GRUs) capture temporal dependencies and the Enhanced Kolmogorov–Arnold Network (EKAN) models complex feature interactions between SST and multivariate ocean predictors. This study integrates GRU with EKAN, using B-spline-parameterized activation functions to model high-dimensional nonlinear relationships between multiple ocean variables (including sea water potential temperature at the sea floor, ocean mixed layer thickness defined by sigma theta, sea water salinity, current velocities, and sea surface height) and SST. L2 regularization addresses multicollinearity among predictors. Experiments were conducted at 25 South China Sea sites using 2011–2021 CMEMS data. The results show that GRU_EKAN achieves a superior mean R² of 0.85, outperforming LSTM_EKAN, GRU, and LSTM by 5%, 25%, and 23%, respectively. Its average RMSE (0.90 °C), MAE (0.76 °C), and MSE (0.80 °C²) represent reductions of 31.3%, 27.0%, and 53.2% compared to GRU. The model also exhibits exceptional stability and minimal Weighted Quality Evaluation Index (WQE) fluctuation. During the 2019–2020 temperature anomaly events, GRU_EKAN predictions aligned closest with observations and captured abrupt trend shifts earliest. This model provides a robust tool for high-precision SST forecasting in the South China Sea, supporting marine heatwave warnings. Full article

(This article belongs to the Special Issue Recent Progress in Understanding Global Sea Level Rise Using Space and Earth Observations)

26 pages, 6758 KiB

Open AccessArticle

Spatial–Temporal Characteristics and Drivers of Summer Extreme Precipitation in the Poyang Lake City Group (PLCG) from 1971 to 2022

by Hua Liu, Ziqing Zhang and Bo Liu

Remote Sens. 2025, 17(16), 2915; https://doi.org/10.3390/rs17162915 - 21 Aug 2025

Abstract

Global warming has intensified the hydrological cycle, resulting in more frequent extreme precipitation events and altered spatiotemporal precipitation patterns in urban areas, thereby increasing the risk of urban flooding and threatening socio-economic and ecological security. This study investigates the characteristics of summer extreme [...] Read more.

Global warming has intensified the hydrological cycle, resulting in more frequent extreme precipitation events and altered spatiotemporal precipitation patterns in urban areas, thereby increasing the risk of urban flooding and threatening socio-economic and ecological security. This study investigates the characteristics of summer extreme precipitation in the Poyang Lake City Group (PLCG) from 1971 to 2022, utilizing the China Daily Precipitation Dataset and NCEP/NCAR reanalysis data. Nine extreme precipitation indices were examined through linear trend analysis, Mann–Kendall tests, wavelet transforms, and correlation methods to quantify trends, periodicity, and atmospheric drivers. The key findings include: (1) All indices exhibited increasing trends, with RX1Day and R95p exhibiting significant rises (p < 0.05). PRCPTOT, R20, and SDII also increased, indicating heightened precipitation intensity and frequency. (2) R50, RX1Day, and SDII demonstrated east-high-to-west-low spatial gradients, whereas PRCPTOT and R20 peaked in the eastern and western PLCG. More than over 88% of stations recorded rising trends in PRCPTOT and R95p. (3) Abrupt changes occurred during 1993–2009 for PRCPTOT, R50, and SDII. Wavelet analysis revealed dominant periodicities of 26–39 years, linked to atmospheric oscillations. (4) Strong subtropical highs, moisture convergence, and negative OLR anomalies were closely associated with extreme precipitation. Warmer SSTs in the eastern equatorial Pacific amplified precipitation in preceding seasons. This study provides a scientific basis for flood prevention and climate adaptation in the PLCG and highlighting the region’s vulnerability to monsoonal shifts under global warming. Full article

(This article belongs to the Special Issue Advanced in Space-Air-Ground-Sea Integrated Remote Sensing: Intelligent Processing and Applications)

20 pages, 3795 KiB

Open AccessArticle

Leaf Area Index Estimation of Grassland Based on UAV-Borne Hyperspectral Data and Multiple Machine Learning Models in Hulun Lake Basin

by Dazhou Wu, Saru Bao, Yi Tong, Yifan Fan, Lu Lu, Songtao Liu, Wenjing Li, Mengyong Xue, Bingshuai Cao, Quan Li, Muha Cha, Qian Zhang and Nan Shan

Remote Sens. 2025, 17(16), 2914; https://doi.org/10.3390/rs17162914 - 21 Aug 2025

Abstract

Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments [...] Read more.

Leaf area index (LAI) is a crucial parameter reflecting the crown structure of the grassland. Accurately obtaining LAI is of great significance for estimating carbon sinks in grassland ecosystems. However, spectral noise interference and pronounced spatial heterogeneity within vegetation canopies constitute significant impediments to achieving high-precision LAI retrieval. This study used hyperspectral sensor mounted on an unmanned aerial vehicle (UAV) to estimate LAI in a typical grassland, Hulun Lake Basin. Multiple machine learning (ML) models were constructed to reveal a relationship between hyperspectral data and grassland LAI using two input datasets, namely spectral transformations and vegetation indices (VIs), while SHAP (SHapley Additive ExPlanation) interpretability analysis was further employed to identify high-contribution features in the ML models. The analysis revealed that grassland LAI has good correlations with the original spectrum at 550 nm and 750 nm–1000 nm, first and second derivatives at 506 nm–574 nm, 649 nm–784 nm, and vegetation indices including the triangular vegetation index (TVI), enhanced vegetation index 2 (EVI2), and soil-adjusted vegetation index (SAVI). In the models using spectral transformations and VIs, the random forest (RF) models outperformed other models (testing R² = 0.89/0.88, RMSE = 0.20/0.21, and RRMSE = 27.34%/28.98%). The prediction error of the random forest model exhibited a positive correlation with measured LAI magnitude but demonstrated an inverse relationship with quadrat-level species richness, quantified by Margalef’s richness index (MRI). We also found that at the quadrat level, the spectral response curve pattern is influenced by attributes within the quadrat, like dominant species and vegetation cover, and that LAI has positive relationship with quadrat vegetation cover. The LAI inversion results in this study were also compared to main LAI products, showing a good correlation (r = 0.71). This study successfully established a high-fidelity inversion framework for hyperspectral-derived LAI estimation in mid-to-high latitude grasslands of the Hulun Lake Basin, supporting the spatial refinement of continental-scale carbon sink models at a regional scale. Full article

(This article belongs to the Section Ecological Remote Sensing)

► Show Figures

Figure 1

21 pages, 12036 KiB

Open AccessArticle

Temporal Analysis of Reservoirs, Lakes, and Rivers in the Euphrates–Tigris Basin from Multi-Sensor Data Between 2018 and 2022

by Omer Gokberk Narin, Roderik Lindenbergh and Saygin Abdikan

Remote Sens. 2025, 17(16), 2913; https://doi.org/10.3390/rs17162913 - 21 Aug 2025

Abstract

Monitoring freshwater resources is essential for assessing the impacts of drought, water management and global warming. Spaceborne LiDAR altimeters allow researchers to obtain water height information, while water area and precipitation data can be obtained using different satellite systems. In our study, we [...] Read more.

Monitoring freshwater resources is essential for assessing the impacts of drought, water management and global warming. Spaceborne LiDAR altimeters allow researchers to obtain water height information, while water area and precipitation data can be obtained using different satellite systems. In our study, we examined 5 years (2018–2022) of data concerning the Euphrates–Tigris Basin (ETB), one of the most important freshwater resources of the Middle East, and the water bodies of both the ETB and the largest lake of Türkiye, Lake Van. A multi-sensor study aimed to detect and monitor water levels and water areas in the water scarcity basin. The ATL13 product of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was used to determine water levels, while the normalized difference water index was applied to the Sentinel-2 optical imaging satellite to monitor the water area. Variations in both water level and area may be related to the time series of precipitation data from the ECMWF Reanalysis v5 (ERA5) product. In addition, our results were compared with global HydroWeb water level data. Consequently, it was observed that the water levels in the region decreased by 5–6 m in many reservoirs after 2019. It is noteworthy that there was a decrease of approximately 14 m in the water level and 684 km² in the water area between July 2019 and July 2022 in Lake Therthar. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Water Management)

► Show Figures

Figure 1

22 pages, 9182 KiB

Open AccessArticle

Sensor Synergy in Bathymetric Mapping: Integrating Optical, LiDAR, and Echosounder Data Using Machine Learning

by Emre Gülher and Ugur Alganci

Remote Sens. 2025, 17(16), 2912; https://doi.org/10.3390/rs17162912 - 21 Aug 2025

Abstract

Bathymetry, the measurement of water depth and underwater terrain, is vital for scientific, commercial, and environmental applications. Traditional methods like shipborne echosounders are costly and inefficient in shallow waters due to limited spatial coverage and accessibility. Emerging technologies such as satellite imagery, drones, [...] Read more.

Bathymetry, the measurement of water depth and underwater terrain, is vital for scientific, commercial, and environmental applications. Traditional methods like shipborne echosounders are costly and inefficient in shallow waters due to limited spatial coverage and accessibility. Emerging technologies such as satellite imagery, drones, and spaceborne LiDAR offer cost-effective and efficient alternatives. This research explores integrating multi-sensor datasets to enhance bathymetric mapping in coastal and inland waters by leveraging each sensor’s strengths. The goal is to improve spatial coverage, resolution, and accuracy over traditional methods using data fusion and machine learning. Gülbahçe Bay in İzmir, Turkey, serves as the study area. Bathymetric modeling uses Sentinel-2, Göktürk-1, and aerial imagery with varying resolutions and sensor characteristics. Model calibration evaluates independent and integrated use of single-beam echosounder (SBE) and satellite-based LiDAR (ICESat-2) during training. After preprocessing, Random Forest and Extreme Gradient Boosting algorithms are applied for bathymetric inference. Results are assessed using accuracy metrics and IHO CATZOC standards, achieving A1 level for 0–10 m, A2/B for 0–15 m, and C level for 0–20 m depth intervals. Full article

(This article belongs to the Section Environmental Remote Sensing)

► Show Figures

Figure 1

21 pages, 4703 KiB

Open AccessArticle

A Web-Based National-Scale Coastal Tidal Flat Extraction System Using Multi-Algorithm Integration on AI Earth Platform

by Shiqi Shen, Qianqian Su, Hui Lei, Zhifeng Yu, Pengyu Cheng, Wenxuan Gu and Bin Zhou

Remote Sens. 2025, 17(16), 2911; https://doi.org/10.3390/rs17162911 - 21 Aug 2025

Abstract

As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and [...] Read more.

As coastal tidal flats—ecosystems of high ecological significance and socio-economic value—face accelerating degradation driven by climate change and intensified anthropogenic disturbances, there is an urgent need for efficient, automated, and scalable monitoring solutions. Traditional monitoring approaches are constrained by high implementation costs and limited spatial coverage, whereas remote sensing—particularly multispectral satellite imagery such as Sentinel-2—has emerged as a primary and widely adopted tool for large-scale environmental observation. Building upon recent advancements in cloud computing and WebGIS technologies, this study presents a web-based, interactive tidal flat extraction system implemented on Alibaba’s AI Earth platform. The system integrates multiple water indices (NDWI, mNDWI, and IWI) with a machine learning algorithm (Random Forest), and is deployed through a user-friendly interface developed using Vue.js and Leaflet, enabling flexible parameter configuration and real-time visualization of extraction results. Its front-end/back-end decoupled architecture enables non-programming users to conduct large-scale tidal flat mapping, thereby substantially lowering the technical barriers to coastal tidal flat monitoring and management in China. Full article

► Show Figures

Figure 1

28 pages, 50179 KiB

Open AccessArticle

Single Image to Semantic BIM: Domain-Adapted 3D Reconstruction and Annotations via Multi-Task Deep Learning

by Serdar Erişen, Mansour Mehranfar and André Borrmann

Remote Sens. 2025, 17(16), 2910; https://doi.org/10.3390/rs17162910 - 21 Aug 2025

Abstract

The digitization and semantic enrichment of built environments traditionally rely on costly and labor-intensive processes, which hinder scalability, adaptability, and real-time deployment in real-world applications. This research presents a novel, fully automated approach that transforms single RGB images directly into semantically enriched, Building [...] Read more.

The digitization and semantic enrichment of built environments traditionally rely on costly and labor-intensive processes, which hinder scalability, adaptability, and real-time deployment in real-world applications. This research presents a novel, fully automated approach that transforms single RGB images directly into semantically enriched, Building Information Modeling (BIM)-compatible 3D representations via an innovative domain adaptation and multi-task learning pipeline. The proposed method simultaneously leverages depth estimation and semantic segmentation from single-image inputs, using high-capacity 2D neural networks, thereby enabling accurate 3D mesh reconstruction and semantic labeling without manual annotation or specialized sensors. The developed pipeline segments and reconstructs both common architectural elements and previously unrepresented object classes, such as stairs, balustrades, railings, people, and furniture items, expanding the coverage of existing 3D indoor datasets. Experimental evaluations demonstrate remarkable reconstruction precision, with an RMSE as low as 0.02 and a per-point semantic accuracy of 81.89% on the TUM CMS Indoor Point Clouds dataset. The resulting 3D models are directly exportable to BIM, OBJ, and CAD formats, supporting a wide range of applications including digital documentation, asset management, and digital twins. By achieving high accuracy and semantic richness with minimal input, the proposed framework offers a scalable, efficient, and automated solution for the rapid digitization of complex built environments, addressing critical limitations in traditional scan-to-BIM workflows and setting new performance standards for future research in the field. Full article

(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))

► Show Figures

Figure 1

27 pages, 24146 KiB

Open AccessArticle

Large-Scale Flood Detection and Mapping in the Yangtze River Basin (2016–2021) Using Convolutional Neural Networks with Sentinel-1 SAR Images

by Xuan Wu, Zhijie Zhang, Wanchang Zhang, Bangsheng An, Zhenghao Li, Rui Li and Qunli Chen

Remote Sens. 2025, 17(16), 2909; https://doi.org/10.3390/rs17162909 - 21 Aug 2025

Abstract

Synthetic Aperture Radar (SAR) technology offers unparalleled advantages by delivering high-quality images under all-weather conditions, enabling effective flood monitoring. This capability provides massive remote sensing data for flood mapping, while recent rapid advances in deep learning (DL) offer methodologies for large-scale flood mapping. [...] Read more.

Synthetic Aperture Radar (SAR) technology offers unparalleled advantages by delivering high-quality images under all-weather conditions, enabling effective flood monitoring. This capability provides massive remote sensing data for flood mapping, while recent rapid advances in deep learning (DL) offer methodologies for large-scale flood mapping. However, the full potential of deep learning in large-scale flood monitoring utilizing remote sensing data remains largely untapped, necessitating further exploration of both data and methodologies. This paper presents an innovative approach that harnesses convolutional neural networks (CNNs) with Sentinel-1 SAR images for large-scale inundation detection and dynamic flood monitoring in the Yangtze River Basin (YRB). An efficient CNN model entitled FloodsNet was constructed based on multi-scale feature extraction and reuse. The study compiled 16 flood events comprising 32 Sentinel-1 images for CNN training, validation, inundation detection, and flood mapping. A semi-automatic inundation detection approach was developed to generate representative flood samples with labels, resulting in a total of 5296 labeled flood samples. The proposed model FloodsNet achieves 1–2% higher F1-score than the other five DL models on this dataset. Experimental inundation detection in the YRB from 2016 to 2021 and dynamic flood monitoring in the Dongting and Poyang Lakes corroborated the scheme’s outstanding performance through various validation procedures. This study marks the first application of deep learning with SAR images for large-scale flood monitoring in the YRB, providing a valuable reference for future research in flood disaster studies. This study explores the potential of SAR imagery and deep learning in large-scale flood monitoring across the Yangtze River Basin, providing a valuable reference for future research in flood disaster studies. Full article

► Show Figures

Figure 1

27 pages, 4926 KiB

Open AccessArticle

Integrating Multi-Temporal Landsat and Sentinel Data for Enhanced Oil Palm Plantation Mapping and Age Estimation in Malaysia

by Caihui Li, Bangqian Chen, Xincheng Wang, Meilina Ong-Abdullah, Zhixiang Wu, Guoyu Lan, Kamil Azmi Tohiran, Bettycopa Amit, Hongyan Lai, Guizhen Wang, Ting Yun and Weili Kou

Remote Sens. 2025, 17(16), 2908; https://doi.org/10.3390/rs17162908 - 20 Aug 2025

Abstract

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including [...] Read more.

Mapping the oil palm (Elaeis guineensis), the globally leading oil-bearing crop and a crucial industrial commodity, is of vital importance for food security and raw material supply. However, existing remote sensing approaches for oil palm mapping present several methodological challenges including temporal resolution constraints, suboptimal feature parameterization, and limitations in age structure assessment. This study addresses these gaps by systematically optimizing temporal, spatial, and textural parameters for enhanced oil palm mapping and age structure analysis through integration of Landsat 4/5/7/8/9, Sentinel-2 multispectral, and Sentinel-1 radar data (LSMR). Analysis of oil palm distribution and dynamics in Malaysia revealed several key insights: (1) Methodological optimization: The integrated LSMR approach achieved 94% classification accuracy through optimal parameter configuration (3-month temporal interval, 3-pixel median filter, and 3 × 3 GLCM window), significantly outperforming conventional single-sensor approaches. (2) Age estimation capabilities: The adapted LandTrendr algorithm enabled precise estimation of the plantation establishment year with an RMSE of 1.14 years, effectively overcoming saturation effects that limit traditional regression-based methods. (3) Regional expansion patterns: West Malaysia exhibits continued plantation expansion, particularly in Johor and Pahang states, while East Malaysia shows significant contraction in Sarawak (3.34 × 10⁵ hectares decline from 2019–2023), with both regions now converging toward similar topographic preferences (100–120 m elevation, 6–7° slopes). (4) Age structure concerns: Analysis identified a critical “replanting gap” with 13.3% of plantations exceeding their 25-year optimal lifespan and declining proportions of young plantations (from 60% to 47%) over the past five years. These findings provide crucial insights for sustainable land management strategies, offering policymakers an evidence-based framework to balance economic productivity with environmental conservation while addressing the identified replanting gap in one of the world’s most important agricultural commodities. Full article

(This article belongs to the Special Issue Dynamic Monitoring of Forest Resources Based on Multi-Source Remote Sensing Data)

► Show Figures

Figure 1

24 pages, 3562 KiB

Open AccessArticle

Geographically Weighted Quantile Machine Learning for Probabilistic Soil Moisture Prediction from Spatially Resolved Remote Sensing

by Bader Oulaid, Paul Harris, Ellen Maas, Ireoluwa Akinlolu Fakeye and Chris Baker

Remote Sens. 2025, 17(16), 2907; https://doi.org/10.3390/rs17162907 - 20 Aug 2025

Abstract

This study proposes a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The framework incorporates satellite radar backscatter, meteorological re-analysis, and topographic variables, applied across 15 SM stations [...] Read more.

This study proposes a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The framework incorporates satellite radar backscatter, meteorological re-analysis, and topographic variables, applied across 15 SM stations and six land use systems at the North Wyke Farm Platform, southwest England, UK. GWQML was implemented using Gaussian and Tricube spatial kernels across a range of kernel bandwidths (500–1500 m). Model performance was evaluated using both in-sample and Leave-One-Land-Use-Out validation schemes, and a global quantile machine learning model (QML) without spatial weighting served as the benchmark. GWQML achieved R² values up to 0.85 and prediction interval coverage probabilities up to 0.9, with intermediate kernel bandwidths (750–1250 m) offering the best balance between accuracy and uncertainty calibration. Spatial autocorrelation analysis using Moran’s I revealed a lower residual clustering under GWQML relative to the benchmark model, which suggests improved handling of local spatial variation. This study represents one of the first applications of geographically weighted kernel functions in a quantile machine learning framework for daily soil moisture prediction. The approach implicitly captures spatially varying relationships while delivering calibrated uncertainty estimates for scalable SM monitoring across heterogenous agricultural landscapes. Full article

► Show Figures

Figure 1

18 pages, 3031 KiB

Open AccessArticle

Post-Sunrise Ionospheric Irregularities in Southeast Asia During the Geomagnetic Storm on 19–20 April 2024

by Prayitno Abadi, Ihsan Naufal Muafiry, Teguh Nugraha Pratama, Angga Yolanda Putra, Agri Faturahman, Noersomadi, Edy Maryadi, Febrylian Fahmi Chabibi, Umar Ali Ahmad, Guozhu Li, Wenjie Sun, Haiyong Xie, Yuichi Otsuka, Septi Perwitasari and Punyawi Jamjareegulgran

Remote Sens. 2025, 17(16), 2906; https://doi.org/10.3390/rs17162906 - 20 Aug 2025

Abstract

We present new insights into post-sunrise ionospheric irregularities in Southeast Asia during the intense geomagnetic storm of 19–20 April 2024. By utilizing Total Electron Content (TEC) and Rate of TEC Change Index (ROTI) maps, along with ionosondes, we identified the emergence of post-sunset [...] Read more.

We present new insights into post-sunrise ionospheric irregularities in Southeast Asia during the intense geomagnetic storm of 19–20 April 2024. By utilizing Total Electron Content (TEC) and Rate of TEC Change Index (ROTI) maps, along with ionosondes, we identified the emergence of post-sunset Equatorial Plasma Bubbles (EPBs)—plasma depletion structures and irregularities—in western Southeast Asia on 19 April. These EPBs moved eastward, and the irregularities dissipated before midnight after the EPBs covered approximately 10° of longitude. Interestingly, plasma density depletion structures persisted and turned westward after midnight until post-sunrise the following day. Concurrently, an increase in F-region height from midnight to sunrise, possibly induced by the storm’s electric field, facilitated the regeneration of irregularities in the residual plasma depletions during the post-sunrise period. The significant increase in F-region height was particularly pronounced in western Southeast Asia. As a result, post-sunrise irregularities expanded their latitudinal structure while propagating westward. These findings suggest that areas with decayed plasma depletion structures from post-sunset EPBs that last past midnight could be sites for creating post-sunrise irregularities during geomagnetic storms. The storm-induced electric fields produce EPBs and ionospheric irregularities at longitudes where the surviving plasma depletion structures of post-sunset EPBs are present. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

► Show Figures

Figure 1

21 pages, 8656 KiB

Open AccessArticle

Dynamic Monitoring of Soil Salinization in Oasis Regions Using Spatiotemporal Fusion Algorithms

by Jinjie Wang, Annan Zeng, Jianli Ding and Shaofeng Qin

Remote Sens. 2025, 17(16), 2905; https://doi.org/10.3390/rs17162905 - 20 Aug 2025

Abstract

Accurate dynamic monitoring of soil salinization in arid oasis regions is crucial for sustainable regional development. Remote sensing is widely used for large-scale, long-term monitoring, but its effectiveness is often limited by image quality and spatiotemporal resolution. Spatiotemporal fusion algorithms, due to their [...] Read more.

Accurate dynamic monitoring of soil salinization in arid oasis regions is crucial for sustainable regional development. Remote sensing is widely used for large-scale, long-term monitoring, but its effectiveness is often limited by image quality and spatiotemporal resolution. Spatiotemporal fusion algorithms, due to their low cost and accessibility, are frequently applied to generate missing images. However, the applicability of these fused images for soil salinization inversion, the impact of different fusion strategies on image quality, and the potential for using multiple fused images to improve model accuracy remain unclear. This study evaluates the performance of three typical spatiotemporal fusion algorithms on raw spectral bands and compares two fusion strategies: fusion-then-index (FI) and index-then-fusion (IF), for two vegetation indices (NDVI and EVI) and two salinity indices (SI and SI2) related to soil salinization. Additionally, the inclusion of multiple fused images during the sampling period is examined for its effect on model accuracy. The results show that (1) spatiotemporal fusion images are suitable for soil salinization inversion, with accuracy depending on image quality; (2) for vegetation indices (NDVI and EVI), the IF strategy yields better results, while for salinity indices (SI and SI2), the FI strategy is more effective; and (3) combining multi-year and multiple fused images significantly improves model accuracy, though using fused images as auxiliary datasets or variables does not further enhance accuracy. These findings provide valuable insights for large-scale, long-term monitoring of soil salinization in arid regions. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Condition Assessment and Degradation Drivers Monitoring)

► Show Figures

Figure 1

19 pages, 5092 KiB

Open AccessArticle

Estimating Position, Diameter at Breast Height, and Total Height of Eucalyptus Trees Using Portable Laser Scanning

by Milena Duarte Machado, Gilson Fernandes da Silva, André Quintão de Almeida, Adriano Ribeiro de Mendonça, Rorai Pereira Martins-Neto and Marcos Benedito Schimalski

Remote Sens. 2025, 17(16), 2904; https://doi.org/10.3390/rs17162904 - 20 Aug 2025

Abstract

Forest management planning depends on accurately collecting information on available resources, gathered by forest inventories. However, due to the extent of the planted areas in the world, collecting information traditionally has become challenging. Terrestrial light detection and ranging (LiDAR) has emerged as a [...] Read more.

Forest management planning depends on accurately collecting information on available resources, gathered by forest inventories. However, due to the extent of the planted areas in the world, collecting information traditionally has become challenging. Terrestrial light detection and ranging (LiDAR) has emerged as a promising tool to enhance forest inventory. However, selecting the optimal 3D point cloud density for accurately estimating tree attributes remains an open question. The objective of this study was to evaluate the accuracy of different point densities (points per square meter) in point clouds obtained through portable laser scanning combined with simultaneous localization and mapping (PLS-SLAM). The study aimed to identify tree positions and estimate the diameter at breast height (DBH) and total height (H) of 71 trees in a eucalyptus plantation in Brazil. We also tested a semi-automatic method for estimating total height. Point clouds with densities greater than 100 points/m² enabled the detection of over 88.7% of individual trees. The root mean square error (RMSE) of the best DBH measurement was 1.6 cm (RMSE = 5.9%) and the best H measurement (semi-automatic method) was 1.2 m (RMSE = 4.2%) for the point cloud with 36,000 points/m². When measuring the total heights of the largest trees (H > 31.4 m) using LiDAR, the values were always underestimated considering a reference value, and their measurements were significantly different (p-value < 0.05 by the t-test). For point clouds with a density of 36,000 points/m², the automated DBH and total tree height estimations yielded RMSEs of 5.9% and 14.4%, with biases of 4.8% and −1.4%, respectively. When using point clouds of 10 points/m², RMSE values increased to 18.8% for DBH and 28.4% for total tree height, while the bias was 6.2% and 18.4%, respectively. Additionally, total tree height estimations obtained via a semi-automatic method resulted in a lower RMSE of 4.2% and a bias of 1.5%. These findings indicate that point clouds acquired through PLS-SLAM with densities exceeding 100 points/m² are suitable for automated DBH estimation in the studied plantation. Despite the increased processing time required, the semi-automatic method is recommended for total tree height estimation due to its superior accuracy. Full article

(This article belongs to the Section Forest Remote Sensing)

► Show Figures

Figure 1

21 pages, 10281 KiB

Open AccessArticle

Identifying Forest Drought Sensitivity Drivers in China Under Lagged and Accumulative Effects via XGBoost-SHAP

by Ze Xue, Simeng Diao, Fuxiao Yang, Long Fei, Wenjuan Wang, Lantong Fang and Yan Liu

Remote Sens. 2025, 17(16), 2903; https://doi.org/10.3390/rs17162903 - 20 Aug 2025

Abstract

Drought, a complex and frequent natural hazard in the context of global change, poses a major threat to key forest ecosystems in the carbon cycle. However, current research lacks a systematic and quantitative analysis of the multi-factor drivers of drought sensitivity based on [...] Read more.

Drought, a complex and frequent natural hazard in the context of global change, poses a major threat to key forest ecosystems in the carbon cycle. However, current research lacks a systematic and quantitative analysis of the multi-factor drivers of drought sensitivity based on lagged and accumulative effects. To address this gap, a drought sensitivity model was established by integrating both lagged and accumulative effects derived from long-term remote sensing datasets. To leverage both predictive power and interpretability, the XGBoost–SHAP framework was employed to model nonlinear associations and identify the threshold effects of driving factors. In addition, the Geodetector model was applied to examine spatially explicit interactions among multiple drivers, thereby uncovering the coupling effects that jointly shape forest drought sensitivity across China. The results reveal the following: (1) Drought had lagged and accumulative effects on 99.52% and 95.55% of forest GPP, with evergreen broadleaf forest showing the strongest effects and deciduous needleleaf forest the weakest. (2) Evergreen needleleaf forests have the highest proportion of extremely high drought sensitivity (16.94%), while deciduous needleleaf forests have the least (1.02%), and the drought sensitivity index declined in 67.12% of forests over decades. (3) Temperature and precipitation are the primary drivers of drought sensitivity, with clear threshold effects. Evergreen forests are mainly driven by climatic factors, while forest age is a key driver in deciduous needleleaf forests. (4) Interactive effects among multiple factors significantly amplify spatial variations in drought sensitivity, with water–heat coupling dominating in evergreen forests and structure–climate interactions prevailing in deciduous forests. Full article

► Show Figures

Figure 1

30 pages, 21184 KiB

Open AccessArticle

FSTC-DiMP: Advanced Feature Processing and Spatio-Temporal Consistency for Anti-UAV Tracking

by Desen Bu, Bing Ding, Xiaozhong Tong, Bei Sun, Xiaoyong Sun, Runze Guo and Shaojing Su

Remote Sens. 2025, 17(16), 2902; https://doi.org/10.3390/rs17162902 - 20 Aug 2025

Abstract

The widespread application of UAV technology has brought significant security concerns that cannot be ignored, driving considerable attention to anti-unmanned aerial vehicle (UAV) tracking technologies. Anti-UAV tracking faces challenges, including target entry into and exit from the field of view, thermal crossover, and [...] Read more.

The widespread application of UAV technology has brought significant security concerns that cannot be ignored, driving considerable attention to anti-unmanned aerial vehicle (UAV) tracking technologies. Anti-UAV tracking faces challenges, including target entry into and exit from the field of view, thermal crossover, and interference from similar objects, where Siamese network trackers exhibit notable limitations in anti-UAV tracking. To address these issues, we propose FSTC-DiMP, an anti-UAV tracking algorithm. To better handle feature extraction in low-Signal-to-Clutter-Ratio (SCR) images and expand receptive fields, we introduce the Large Selective Kernel (LSK) attention mechanism, achieving a balance between local feature focus and global information integration. A spatio-temporal consistency-guided re-detection mechanism is designed to mitigate tracking failures caused by target entry into and exit from the field of view or similar-object interference through spatio-temporal relationship analysis. Additionally, a background augmentation module has been developed to more efficiently utilise initial frame information, effectively capturing the semantic features of both targets and their surrounding environments. Experimental results on the AntiUAV410 and AntiUAV600 datasets demonstrate that FSTC-DiMP achieves significant performance improvements in anti-UAV tracking tasks, validating the algorithm’s strong robustness and adaptability to complex environments. Full article

(This article belongs to the Special Issue Recent Advances in Infrared Target Detection)

► Show Figures

Figure 1

25 pages, 6683 KiB

Open AccessArticle

Integrating Remote Sensing and Ecological Modeling to Assess Marine Habitat Suitability for Endangered Chinese Sturgeon

by Shuhui Cao, Yingchao Dang, Xuan Ban, Qi Feng, Yadong Zhou, Jiahuan Luo, Jiazhi Zhu and Fei Xiao

Remote Sens. 2025, 17(16), 2901; https://doi.org/10.3390/rs17162901 - 20 Aug 2025

Abstract

The Chinese sturgeon (Acipenser sinensis), a critically endangered anadromous fish species, spends over 90% of its life cycle in marine habitats, yet research on its marine ecology and habitat requirements is limited due to sparse data. To address this, we integrated [...] Read more.

The Chinese sturgeon (Acipenser sinensis), a critically endangered anadromous fish species, spends over 90% of its life cycle in marine habitats, yet research on its marine ecology and habitat requirements is limited due to sparse data. To address this, we integrated satellite remote sensing with ecological modeling to assess spatiotemporal dynamics in marine habitat suitability across China’s continental shelf (2003–2020). Nine key habitat factors were derived from multi-source remote sensing data and inverted transparency algorithms. Species occurrence data were coupled with the Maximum Entropy (MaxEnt) model to evaluate habitat preferences and seasonal shifts. Results revealed distinct environmental preferences: shallow depths (≤20 m), sea surface and bottom temperature (10–30 °C and 10–25 °C), salinity (10–35‰), transparency (0.40–3.00 m), eastward and northward seawater velocity (−0.20–0.15 m/s and −0.20–0.20 m/s), moderate productivity (1000–3000 mg/m²), and zooplankton carbon (0.20–6.00 g/m²). Habitat factor importance varied seasonally—salinity, depth, and net primary productivity dominated in spring; bottom temperature and productivity in summer/autumn; salinity and transparency in winter. Spatially, high-suitability areas peaked in autumn (70% total suitable habitat), concentrating near the Yangtze Estuary, northern Jiangsu coast, and Zhoushan Archipelago. This study emphasizes the need to prioritize these areas for protection and inform proliferation and release schemes for Chinese sturgeon. It also demonstrates the efficacy of remote sensing for mapping essential habitats of migratory megafauna in complex coastal ecosystems and provides actionable insights for targeted conservation strategies. Full article

(This article belongs to the Special Issue Towards Biodiversity Conservation: Remote Sensing Applications in Ecological Modeling)

23 pages, 3505 KiB

Open AccessArticle

Digital Imaging Simulation and Closed-Loop Verification Model of Infrared Payloads in Space-Based Cloud–Sea Scenarios

by Wen Sun, Yejin Li, Fenghong Li and Peng Rao

Remote Sens. 2025, 17(16), 2900; https://doi.org/10.3390/rs17162900 - 20 Aug 2025

Abstract

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability [...] Read more.

Driven by the rising demand for digitalization and intelligent development of infrared payloads, next-generation systems must be developed within compressed timelines. High-precision digital modeling and simulation techniques offer essential data sources but often falter in complex space-based scenarios due to the limited availability of infrared characteristic data, hindering evaluation of the payload effectiveness. To address this, we propose a digital imaging simulation and verification (DISV) model for high-fidelity infrared image generation and closed-loop validation in the context of cloud–sea target detection. Based on on-orbit infrared imagery, we construct a cloud cluster database via morphological operations and generate physically consistent backgrounds through iterative optimization. The DISV model subsequently calculates scene infrared radiation, integrating radiance computations with an electron-count-based imaging model for radiance-to-grayscale conversion. Closed-loop verification via blackbody radiance inversion is performed to confirm the model’s accuracy. The mid-wave infrared (MWIR, 3–5 µm) system achieves mean square errors (RSMEs) < 0.004, peak signal-to-noise ratios (PSNRs) > 49 dB, and a structural similarity index measure (SSIM) > 0.997. The long-wave infrared (LWIR, 8–12 µm) system yields RMSEs < 0.255, PSNRs > 47 dB, and an SSIM > 0.994. Under 20–40% cloud coverage, the target radiance inversion errors remain below 4.81% and 7.30% for the MWIR and LWIR, respectively. The DISV model enables infrared image simulation across multi-domain scenarios, offering vital support for optimizing on-orbit payload performance. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

► Show Figures

Figure 1

29 pages, 6923 KiB

Open AccessArticle

Canadian Wildfire Smoke Episode over Europe in October 2023: Lidar, Sun-Photometer, and Model Characterization of Smoke Layers Observed Above Sofia, Bulgaria

by Tsvetina Evgenieva, Stefan Dosev, Ljuan Gurdev, Liliya Vulkova, Zahari Peshev, Eleonora Toncheva, Lyubomir Popov, Orlin Vankov and Tanja Dreischuh

Remote Sens. 2025, 17(16), 2899; https://doi.org/10.3390/rs17162899 - 20 Aug 2025

Abstract

Massive wildfires release enormous amounts of biomass-burning (BB) aerosols into the atmosphere, which might have a major impact on its thermal and radiative budget, as well as the environment and human health. This work presents the results of a study and characterization of [...] Read more.

Massive wildfires release enormous amounts of biomass-burning (BB) aerosols into the atmosphere, which might have a major impact on its thermal and radiative budget, as well as the environment and human health. This work presents the results of a study and characterization of a long-range transport episode of smoke aerosols from Canadian forest fires towards the entirety of Europe, as observed over Sofia, Bulgaria, in early October 2023. This study makes use of data from combined lidar, ceilometer, and sun-photometer measurements, supported by model and forecast data, meteorological radiosonde profiling, and (re)analyses, together with tracking and mapping of the aerosol air transport. A distinctive feature of the considered episode over Europe is the downward movement of the air masses, entraining smoke aerosols from the continental mid-troposphere down to the near-surface layers. The driving mechanism of the long-range transport of BB aerosols and their spread over Europe is revealed. Optical parameters of the registered aerosols are determined and vertically profiled with a high range resolution by lidar data analysis. A wide set of columnar optical and microphysical aerosol characteristics is also provided by sun-photometer measurements. The results show a dominance of relatively fine modes of dry smoke particles in the submicron size range, with a predominantly low degree of non-sphericity, indicating minimal up-size aging during the BB aerosol transport from Canada to the Sofia region. The average daily aerosol radiative forcing is determined by sun-photometer measurements and briefly discussed. Full article

(This article belongs to the Special Issue Remote Sensing of Atmospheric Vertical Profile—Air Quality, Pollution and Aerosol Optical Properties)

► Show Figures

Figure 1

Journal Description

Remote Sensing

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI