Remote Sensing

26 pages, 2072 KB

Open AccessArticle

Evaluation of ALOS-2/PALSAR-2 L-band SAR Polarimetric Parameters for Water-Level Estimation in Irrigated Rice Paddy Fields

by Dandy Aditya Novresiandi, Khalifah Insan Nur Rahmi, Hilda Ayu Pratikasiwi, Rendi Handika, Masnita Indriani Oktavia, Anisa Rarasati, Parwati Sofan, Rahmat Arief, Muhammad Rokhis Khomarudin, Shinichi Sobue, Kei Oyoshi, Go Segami and Pegah Hashemvand Khiabani

Remote Sens. 2026, 18(9), 1313; https://doi.org/10.3390/rs18091313 (registering DOI) - 24 Apr 2026

Abstract

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric [...] Read more.

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications. Full article

(This article belongs to the Special Issue CH4Rice Project: Assessment of Methane Emission from Rice Paddies and Water Management Using Remote Sensing Technology)

27 pages, 6458 KB

Open AccessArticle

Arctic Sea Ice Type Classification Using a Multi-Dimensional Feature Set Derived from FY-3E GNSS-R and SMOS

by Yuan Hu, Xingjie Chen, Weimin Huang and Wei Liu

Remote Sens. 2026, 18(9), 1312; https://doi.org/10.3390/rs18091312 (registering DOI) - 24 Apr 2026

Abstract

Sea ice classification is of fundamental importance for polar monitoring and global climate research. Global Navigation Satellite System Reflectometry (GNSS-R) has emerged as a frontier technology in polar remote sensing due to its high spatiotemporal resolution and cost-effectiveness. Based on BeiDou System Reflectometry [...] Read more.

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

30 pages, 3811 KB

Open AccessArticle

FA-CTNet: A Geometry-Aware Deep Learning Approach for Tree Species Classification from LiDAR Point Clouds

by Shengchao Sha, Qianhui Liu, Yan Zhang and Ting Yun

Remote Sens. 2026, 18(9), 1311; https://doi.org/10.3390/rs18091311 - 24 Apr 2026

Abstract

Accurate identification of tree species is important for forest management, biodiversity studies, and precision forestry. Near-range LiDAR point clouds provide detailed three-dimensional information about individual trees. However, the complex structure of the point clouds and the unbalanced distribution of species make automatic classification [...] Read more.

Accurate identification of tree species is important for forest management, biodiversity studies, and precision forestry. Near-range LiDAR point clouds provide detailed three-dimensional information about individual trees. However, the complex structure of the point clouds and the unbalanced distribution of species make automatic classification difficult. To address these issues, this study presents a Transformer model with geometric enhancement. The model combines local geometric features and global attention to improve species recognition in forest environments. It uses geometric information with biological meaning, including point cloud normals, local density, vertical structure, and growth direction. A focal loss with class balance is also introduced to reduce the impact of species distributions with long tails. Experiments on the ForSpecial20K dataset show that the proposed method performs better than representative models based on convolution, graph methods, and Transformer architectures. It achieves higher overall accuracy (78.20%), higher mean class accuracy (73.48%), and a higher Macro-F1 score (73.21%). Results from confusion matrices and visual analysis of similar species further verify the effectiveness of the geometric features and the loss design. These results suggest that modeling structural information of forests helps improve robustness and generalization. The proposed method offers a practical solution for tree-level species mapping, fusion of LiDAR data from multiple sources, and fine-scale forest inventory. It also shows the value of combining high-resolution LiDAR data with deep learning for forestry applications. Full article

(This article belongs to the Section Forest Remote Sensing)

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24 pages, 1653 KB

Open AccessArticle

Early Detection of Spatiotemporal Stabilization in Open-Pit Mine Waste Dumps via Time-Series InSAR Coherence

by Yueming Sun, Yanjie Tang, Zhibin Li and Yanling Zhao

Remote Sens. 2026, 18(9), 1310; https://doi.org/10.3390/rs18091310 - 24 Apr 2026

Abstract

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to [...] Read more.

Accurately monitoring the surface stabilization of waste dumps in open-pit coal mines is critical for hazard prevention and ecological reclamation. In arid and semi-arid regions, traditional optical remote sensing vegetation indices suffer from a systematic “response lag” in assessing physical stability due to the slow establishment of pioneer vegetation. To overcome this biological limitation, this study proposes a quantitative spatiotemporal monitoring framework based on time-series Interferometric Synthetic Aperture Radar (InSAR) coherence to detect early-stage geotechnical stabilization. Using Sentinel-1 imagery of the Balongtu coal mine, a sliding-window detection algorithm was developed to capture the physical transition of surface electromagnetic scattering mechanisms from active disturbance to stable consolidation. The main findings are as follows: (1) Statistical analysis identified a critical geophysical coherence threshold of 0.15, which effectively and objectively distinguishes active dumping disturbance zones from structurally stable areas. (2) The spatiotemporal evolution dynamics of the completed dump areas from 2017 to 2023 were successfully characterized, revealing that 87.6% of the open-pit areas achieved physical stabilization within three years post-mining, with a spatial distribution highly consistent with the objective operational rule of “mining first, dumping later”. (3) Accuracy assessment using 700 spatiotemporally balanced validation points—derived through strict visual interpretation of high-resolution optical imagery—demonstrated high algorithm reliability, achieving overall accuracies (OA) of 87.57% and 90.43% at half-yearly and annual monitoring intervals, respectively. By decoupling physical surface stabilization from optical greenness, this study provides a timely abiotic precursor indicator, offering scientific, quantitative decision support for precision ecological zoning and accelerated land turnover approval in mining areas. Full article

(This article belongs to the Special Issue Advances in Geological Hazard Characterization and Assessment: Merging Remote Sensing with Direct Surveys)

19 pages, 11015 KB

Open AccessArticle

Analysis of Influencing Factors on Phytoplankton Primary Productivity Across Ice-Free and Ice-Covered Seasons Through Remote Sensing and Optical Parameter Correction

by Haifeng Yu, Yongfeng Ren, Yuhan Gao, Biao Sun and Xiaohong Shi

Remote Sens. 2026, 18(9), 1309; https://doi.org/10.3390/rs18091309 - 24 Apr 2026

Abstract

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an [...] Read more.

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PP_eu by incorporating optical parameters such as the ice surface refractive index and the extinction coefficient of the ice layer into the vertical generalized production model (VGPM). This approach overcomes the challenges associated with remote sensing-based estimation of PP_eu during ice-covered periods. The results indicate that the annual carbon sequestration of the WLSHL is 1.72 × 10⁴ t C, with an average annual PP_eu of 316.96 mg C·m⁻²·d⁻¹. In addition to the indicators that are directly involved in the estimation of PP_eu, the environmental factors that affect PP_eu include water temperature (WT), ice thickness (IT), snow, water depth (D), total dissolved solids (TDSs), salinity (S), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and oxidation–reduction potential (ORP). The PP_eu in the ice period is found to be only 17% lower than that in the ice-free period. However, the PP_eu during the ice period is considerably higher than that during the ice + snow period. The findings indicate that the impact of freezing on PP_eu during the winter is relatively limited, whereas the influence of snowfall is more pronounced. In order to mitigate the elevated PP_eu and the occurrence of algal blooms during the summer, the intensity of underwater radiation can be regulated on a periodic basis. To optimize the function of the carbon sink in winter lakes, the PP_eu can be enhanced through initiatives such as water replenishment prior to freezing and snow removal following freezing. Full article

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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25 pages, 5717 KB

Open AccessArticle

An End-to-End Foundation Model-Based Framework for Robust LAI Retrieval Under Cloud Cover

by Xiangfeng Gu, Wenyuan Li and Shikang Guan

Remote Sens. 2026, 18(9), 1308; https://doi.org/10.3390/rs18091308 - 24 Apr 2026

Abstract

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions [...] Read more.

Leaf Area Index is a crucial biophysical variable, and its accurate estimation is essential for understanding vegetation dynamics. However, cloud cover significantly restricts optical remote sensing, hindering the generation of spatially continuous Leaf Area Index products. Remote sensing foundation models offer novel solutions to this challenge. This study presents an end-to-end framework based on the fine-tuned Prithvi foundation model for direct LAI retrieval from cloud-contaminated 30 m Harmonized Landsat and Sentinel-2 imagery. By mapping inputs directly to Hi-GLASS reference labels, the proposed architecture processes cloud contamination and vegetation signals simultaneously and circumvents the error propagation inherent in cascaded retrieval pipelines. Results demonstrate that the end-to-end LAI retrieval model significantly outperforms cascaded variants, achieving a superior R² (0.78) and lower RMSE (0.57). Furthermore, predictive accuracy exhibits a distinct U-shaped trajectory relative to the temporal mean cloud fraction, reaching an inflection point at 50–60% occlusion, which highlights the model’s implicit regularization capacity under severe atmospheric interference. This work establishes that direct feature learning with foundation models offers a more robust and streamlined pathway for generating continuous biophysical products from imperfect optical observations, prioritizing quantitative fidelity over artificial perceptual sharpness. Full article

(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)

25 pages, 5193 KB

Open AccessArticle

Scenario-Adaptive Visibility Level Retrieval via Multi-Source Synergy: Enhancing Physical Traceability and Scene Decoupling Within a Tree-Routed TabPFN Framework

by Chuhan Lu, Shanwen Luo and Zhiyuan Han

Remote Sens. 2026, 18(9), 1307; https://doi.org/10.3390/rs18091307 - 24 Apr 2026

Abstract

Accurate retrieval of visibility grades is critical for transportation safety. Due to the highly complex meteorological backgrounds, traditional global deep learning models frequently struggle with limited physical traceability and feature heterogeneity. To address these challenges by enhance physical traceability and reduces heterogeneity, this [...] Read more.

Accurate retrieval of visibility grades is critical for transportation safety. Due to the highly complex meteorological backgrounds, traditional global deep learning models frequently struggle with limited physical traceability and feature heterogeneity. To address these challenges by enhance physical traceability and reduces heterogeneity, this study proposes a scenario-adaptive visibility retrieval framework based on multi-source synergy, namely TabPFN-ExtraTrees (TabPFN-ET), targeting major transportation routes in Anhui Province, China. Fusing Fengyun-4 (FY-4A/4B) satellite multispectral observations with ground meteorological data, this framework utilizes the divide-and-conquer routing mechanism of ExtraTrees to decouple the complex, heterogeneous feature space into highly homogeneous sub-scenarios. Subsequently, the TabPFN model conducts high-precision inference within each specific subspace. Evaluations on a class-balanced benchmark demonstrate that TabPFN-ET achieves an Overall Accuracy of 0.681, outperforming baseline models such as SAINT across various metrics. Furthermore, this paper conducts a physically consistent analysis of the framework. Feature importance and node profiling corroborate its physical consistency: the FY-4 upper-level water vapor channel (Channel 09) and near-surface humidity act as the macroscopic atmospheric stability and microscopic thermodynamic constraints, respectively, driving the model’s scene decoupling and inference. Cross-regional tests in Jiangsu provide preliminary indications of context-specific transferability. Full article

31 pages, 2149 KB

Open AccessArticle

ATCFNet: A Lightweight Cross-Level Attention-Guided High-Resolution Remote Sensing Image Change Detection Network

by Dongxu Li, Peng Chu, Chen Yang, Zhen Wang and Chuanjin Dai

Remote Sens. 2026, 18(9), 1306; https://doi.org/10.3390/rs18091306 - 24 Apr 2026

Abstract

Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving [...] Read more.

Remote sensing change detection (RSCD), a fundamental task in Earth observation, aims to automatically identify land-cover changes (e.g., building construction, vegetation degradation) by comparing multitemporal satellite or aerial images of the same region. With the explosive growth of high-resolution remote sensing data, achieving real-time accurate change detection on edge computing devices (e.g., drone-embedded chips, satellite on-board processors) has become an urgent challenge—existing deep learning methods, despite high accuracy, are hindered by massive parameters and computational costs that preclude deployment on resource-constrained embedded hardware. To address this, we focus on lightweight (i.e., low parameter count and low computational cost) RSCD network design, targeting three critical bottlenecks: blurred boundaries of changed regions, missed detection of small objects, and insufficient computational efficiency. We propose ATCFNet (Adjacent-Temporal Cross Fusion Network), featuring a three-step progressive feature optimization strategy: (1) the Adjacent Feature Aggregation Module (AFAM) enhances shallow geometric details via lateral three-stage fusion to compensate for lightweight backbones; (2) the Temporal Attention Cross Module (TACM) integrates cross-level feature propagation and Convolutional Block Attention Module (CBAM) for collaborative optimization of high-level semantics and low-level details; and (3) the Efficient Guidance Module (EGM) establishes long-range dependencies using shared change priors and lightweight self-attention to suppress internal voids in changed regions. Experiments on three public datasets (LEVIR-CD, HRCUS, SYSU-ChangeDet) demonstrate that ATCFNet achieves state-of-the-art accuracy with merely 3.71 million (M) parameters and 3.0 billion (G) floating-point operations (FLOPs)—F1-scores of 91.46%, 77.05%, and 83.53%, significantly outperforming 18 existing methods in most indicators. Notably, it excels in edge integrity (avoiding jagged blurring at change boundaries) and small-target detection in high-resolution urban scenes. This study provides an efficient and reliable lightweight solution for edge computing scenarios such as real-time drone inspection and satellite on-board intelligent processing. Full article

(This article belongs to the Special Issue Foundation Model-Based Multi-Modal Data Fusion in Remote Sensing)

32 pages, 32650 KB

Open AccessArticle

Snow-Covered Filter-Enhanced Canopy Surface Points: A Lightweight and Efficient Framework for Individual Tree Segmentation from LiDAR Data

by Bin Wang, Guangqing Xie, Ning Li, Ertao Gao, Guoqing Zhou, Cheng Wang and Haoyu Wang

Remote Sens. 2026, 18(9), 1305; https://doi.org/10.3390/rs18091305 (registering DOI) - 24 Apr 2026

Abstract

As fundamental units of forest ecosystems, individual trees provide essential structural characteristics for forest resource assessment. However, existing LiDAR-based individual tree segmentation methods are often limited by a trade-off between information preservation and computational efficiency. This study proposes a novel framework for individual [...] Read more.

As fundamental units of forest ecosystems, individual trees provide essential structural characteristics for forest resource assessment. However, existing LiDAR-based individual tree segmentation methods are often limited by a trade-off between information preservation and computational efficiency. This study proposes a novel framework for individual tree segmentation from LiDAR data based on canopy surface points (CSP), aiming to balance this trade-off. The framework introduces a Snow-Covered Filter (SCF) that simulates snow deposition to extract surface points from the point cloud. After removing ground points from these surface points, the resulting CSP retains the core 3D structure of the canopy while significantly reducing data volume. We validate the proposed framework on four multi-platform datasets using four algorithms that represent the evolution of individual tree segmentation methods: Dalponte2016, K-means, Li2012, and SegmentAnyTree. The results demonstrate that: (a) the SCF effectively extracts surface points, with an average F1-score of 0.703; (b) segmentation using CSP achieves accuracy comparable to that obtained using all points or raster data (mean ΔF = 0.027), with the primary gap observed for SegmentAnyTree (maximum F-score reduction of 0.259); (c) the framework offers substantial efficiency gains: >40% point reduction, ~38.4% average runtime reduction (maximum saving ~4660 s), and lower memory consumption. By providing a lightweight yet structurally rich data representation, this work presents an innovative and efficient approach to individual tree segmentation, with promising potential for large-scale forest resource management. Full article

20 pages, 6049 KB

Open AccessArticle

Under Construction Reclamation Airport Deformation Monitoring Using Sequential Multi-Polarization Time-Series InSAR

by Xiaying Wang, Yuexin Lu, Dongping Zhao, Shuangcheng Zhang, Yantian Xu, Shouzhou Gu, Jiaxing Fu and Ruiyi Wei

Remote Sens. 2026, 18(9), 1304; https://doi.org/10.3390/rs18091304 - 24 Apr 2026

Abstract

Monitoring surface deformation at reclaimed airports under construction is crucial for ensuring construction safety. However, significant variations in surface scattering characteristics cause severe decorrelation, limiting the effectiveness of conventional single-polarization Interferometric Synthetic Aperture Radar (InSAR). To address the issue of insufficient coherent pixels, [...] Read more.

Monitoring surface deformation at reclaimed airports under construction is crucial for ensuring construction safety. However, significant variations in surface scattering characteristics cause severe decorrelation, limiting the effectiveness of conventional single-polarization Interferometric Synthetic Aperture Radar (InSAR). To address the issue of insufficient coherent pixels, we propose a dual-polarization sequential InSAR technique and compare its performance with traditional Persistent Scatterer Interferometry (PSI) and Distributed Scatterer Interferometry (DSI) at the Dalian Jinzhou Bay International Airport (DJBIA). Using 89 Sentinel-1A dual-polarization (VV-VH) images (August 2022 to October 2025), the results demonstrate that VV and VH polarizations exhibit significant spatial complementarity, highlighting the necessity of multi-polarization data. Further, to address the issue of long-term changes in scattering characteristics, we applied the Sequential Estimation and Total Power-Enhanced Expectation Maximization Inversion (SETP-EMI) method, which dynamically integrates dual-polarization information and performs adaptive phase optimization. This approach significantly enhances monitoring capability in low-coherence areas of the airport under construction, effectively suppressing phase noise, improving interferogram quality, and yielding a more complete and reliable deformation field. Overall, this study systematically validates the SETP-EMI method with dual-polarization information for deformation monitoring at reclaimed airports under construction, providing technical support for engineering safety control and research on reclamation subsidence mechanisms. Full article

(This article belongs to the Special Issue Advances in Multi-GNSS Technology and Applications (2nd Edition))

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37 pages, 14671 KB

Open AccessArticle

A Landsat-Based Framework for Long-Term Mapping of Topsoil Sand Content in Croplands

by Hongjie Wang, Kun Shang, Weichao Sun, Yisong Xie and Chenchao Xiao

Remote Sens. 2026, 18(9), 1303; https://doi.org/10.3390/rs18091303 - 24 Apr 2026

Abstract

Topsoil sand content (TSC) is a critical indicator of soil degradation in black soil regions, yet its long-term dynamics remain poorly quantified. To address this, we developed an automated Landsat-based framework on Google Earth Engine (GEE) for mapping cropland TSC across the Northeast [...] Read more.

Topsoil sand content (TSC) is a critical indicator of soil degradation in black soil regions, yet its long-term dynamics remain poorly quantified. To address this, we developed an automated Landsat-based framework on Google Earth Engine (GEE) for mapping cropland TSC across the Northeast China Black Soil Region (NCBSR) from 1984 to 2023. The methodology integrates a hierarchical bare-soil extraction strategy using the Normalized Difference Bare Soil Index (NDBSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Tillage Index (NDTI) with a Random Forest (RF) model optimized by three-band spectral indices and a “prediction-first” compositing workflow. Results demonstrate that the bare-soil extraction achieved an overall accuracy of 96%, while the TSC retrieval model maintained robust performance with a coefficient of determination (R²) of 0.80 and a root mean square error (RMSE) of 9.68%, together with satisfactory temporal transferability. Long-term mapping revealed a significant biphasic evolutionary trajectory: 23.4% of croplands experienced soil coarsening predominantly before 2000, followed by a partial reversal and stabilization in later decades. This framework provides a high-resolution, multi-decadal baseline for monitoring soil physical degradation and supports sustainable agricultural management in global black soil regions. Full article

(This article belongs to the Special Issue Multi-Source Remote Sensing for Spatiotemporal Monitoring of Soil Quality and Degradation)

22 pages, 5503 KB

Open AccessArticle

On the Concurrence of the Atmospheric and Marine Heatwaves in the Red Sea

by Mostafa Morsy, Bayoumy Mohamed, Hazem Nagy, Ahmad E. Samman, Abdallah Abdaldym and Hassan Aboelkhair

Remote Sens. 2026, 18(9), 1302; https://doi.org/10.3390/rs18091302 - 24 Apr 2026

Abstract

Atmospheric heatwaves (AHWs) and marine heatwaves (MHWs) are intensifying under climate change, yet their coupled behavior in the Red Sea remains insufficiently quantified. This study investigates the spatial and temporal characteristics of AHWs, MHWs, and their concurrent occurrence across the Red Sea from [...] Read more.

Atmospheric heatwaves (AHWs) and marine heatwaves (MHWs) are intensifying under climate change, yet their coupled behavior in the Red Sea remains insufficiently quantified. This study investigates the spatial and temporal characteristics of AHWs, MHWs, and their concurrent occurrence across the Red Sea from 1990 to 2024 using ERA5 surface air temperature (SAT) and NOAA OISST v2.1 satellite-derived sea surface temperature (SST). Remote-sensing daily satellite-derived Level-4 (L4) OISST products were used in this study to enable spatially complete and temporally consistent detection of MHWs in this narrow, semi-enclosed basin despite contamination and coastal sampling constraints. Both SAT and SST exhibit statistically significant warming trends (p < 0.05), with basin mean increases of 0.40 ± 0.07 °C/decade and 0.31 ± 0.05 °C/decade, respectively. The strongest warming was observed in the central and northern Red Sea. This warming is accompanied by significant increases in the frequency and duration of AHWs, MHWs, and their concurrent AHW-MHW events, particularly after 2010, indicating a shift toward more frequent heatwave conditions. AHWs occur more frequently than MHWs across the Red Sea, whereas MHWs exhibit long duration, particularly in the northern Red Sea, where annual durations exceed 45–50 days/year. Concurrent AHW-MHW events account for about 66% of MHWs in the Red Sea, and their characteristics show a significant increasing trend across the entire basin. These findings identify the Red Sea as a regional hotspot of increasing concurrent heatwave events and highlight the importance of satellite-based monitoring for assessing evolving climate risks in semi-enclosed basins. Full article

(This article belongs to the Special Issue Enhanced Satellite Perspectives of Sea Surface Temperature and Air-Sea Interaction)

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20 pages, 10477 KB

Open AccessArticle

Enhancing PM_2.5 Forecasting via the Integration of Lidar and Radiosonde Vertical Structures

by Siying Chen, Daoming Li, Weishen Wang, He Chen, Pan Guo, Yurong Jiang, Xian Yang, Yangcheng Ma, Yuhao Jin and Yingjie Shu

Remote Sens. 2026, 18(9), 1301; https://doi.org/10.3390/rs18091301 - 24 Apr 2026

Abstract

Accurate forecasting of near-surface PM_2.5 concentrations remains challenging due to the complex coupling between atmospheric vertical structure, thermodynamic stability, and pollutant accumulation processes. Most existing surface-based statistical and deep learning approaches struggle to represent the three-dimensional state of the atmosphere, which limits [...] Read more.

Accurate forecasting of near-surface PM_2.5 concentrations remains challenging due to the complex coupling between atmospheric vertical structure, thermodynamic stability, and pollutant accumulation processes. Most existing surface-based statistical and deep learning approaches struggle to represent the three-dimensional state of the atmosphere, which limits their robustness under complex meteorological conditions. In this study, we propose a multi-source spatiotemporal learning framework(MST-Net) to enhance PM_2.5 forecasting accuracy by integrating vertically resolved atmospheric information from lidar and radiosonde observations. The proposed approach incorporates vertical profile features together with surface measurements to provide complementary information on atmospheric vertical structure and its temporal evolution. Experimental results demonstrate that MST-Net consistently outperforms conventional time-series models across multiple forecast horizons. Notably, at extended lead times (12–24 h), the proposed framework exhibits enhanced stability and slower error growth. For 24 h forecasts, MST-Net reduces RMSE by approximately 13% and MAE by about 19%. These results indicate that leveraging multi-source vertical atmospheric information can effectively improve the reliability of urban air quality forecasting. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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25 pages, 2086 KB

Open AccessArticle

Estimating Canopy Structure Parameters and Leaf Nitrogen in Olive Orchards Using UAV Imagery Across Two Agro-Ecological Zones in Tunisia

by Marius Hobart, Olfa Boussadia, Amel Ben Hamouda, Antje Giebel, Pierre Ellssel, Cornelia Weltzien and Michael Schirrmann

Remote Sens. 2026, 18(9), 1300; https://doi.org/10.3390/rs18091300 - 24 Apr 2026

Abstract

Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This [...] Read more.

Optimizing olive orchard management requires timely, per-tree data to enhance productivity and sustainability. Unoccupied aerial vehicle (UAV)-based red, green, and blue (RGB) imagery offers a low-cost solution for acquiring high-resolution spatiotemporal insights for orchard management, which are not yet common in Tunisia. This study monitored tree structural parameters, leaf area index (LAI), and leaf nitrogen content (%N DW) in two Tunisian olive orchards during 2022 and 2023. UAV-derived imagery was photogrammetrically processed into 3D point clouds and analyzed using an automated approach. Target variables of the automated approach included tree-wise estimates of height, projected crown area, and crown volume, as well as raster cell counts of the canopy cloud and spectral indices such as the normalized green-red difference index (NGRDI) and green leaf index (GLI). In addition, the estimated parameters per tree were used to model LAI and leaf nitrogen content. Analyses were conducted separately for trees represented by a high and a low number of points in the dense point cloud. Outcomes were compared to reference data collected in the field on dates close to the UAV flights. The findings showed strong relationships for the projected crown area (R² = 0.82 and 0.91) and tree height (R² = 0.89 and 0.88) when compared to reference values. Linear regression models for LAI (R² = 0.73 and 0.68) and crown volume (R² = 0.85 and 0.91) estimation also show strong relationships. However, leaf nitrogen estimation was not feasible from RGB spectral index values, as it showed a weak relationship (R² = 0.34). A dataset with multispectral imagery could overcome this limitation but would increase costs, making it less suitable for the low-budget approach required in price-sensitive farming contexts, particularly in low-income regions. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Biophysical and Biochemical Parameters Retrieval)

18 pages, 2207 KB

Open AccessArticle

Investigation Methods of Large-Scale Milltailings Debris Flow Based on InSAR Deformation Monitoring and UAV Topographic Survey: Correlation and Comparison

by Han Zhang, Wei Wang, Juan Du, Zhan Zhang, Junhu Chen, Jingzhou Yang and Bo Chai

Remote Sens. 2026, 18(9), 1299; https://doi.org/10.3390/rs18091299 - 24 Apr 2026

Abstract

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² [...] Read more.

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

(This article belongs to the Special Issue Geohazard Mapping, Monitoring and Prediction with Advanced Remote Sensing Techniques)

21 pages, 9621 KB

Open AccessArticle

Insights into Spatial Heterogeneity of Land Subsidence Susceptibility Using InSAR and Explainable Machine Learning

by Min Shi, Xiaoyu Wang, Chenghong Gu, Mingliang Gao, Chaofan Zhou and Huili Gong

Remote Sens. 2026, 18(9), 1298; https://doi.org/10.3390/rs18091298 - 24 Apr 2026

Abstract

Land subsidence (LS) is a widespread geoenvironmental problem driven by both natural processes and human activities. Identifying the main factors controlling LS susceptibility and their spatial contribution patterns is essential for LS management and mitigation. In this study, an interpretable earth observation framework [...] Read more.

Land subsidence (LS) is a widespread geoenvironmental problem driven by both natural processes and human activities. Identifying the main factors controlling LS susceptibility and their spatial contribution patterns is essential for LS management and mitigation. In this study, an interpretable earth observation framework was developed for the North China Plain (NCP) to quantify both spatial and non-spatial contributions of dominant LS drivers. Land displacement was derived from Sentinel-1A SAR images using Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) processing. The displacement map was then combined with nine geoenvironmental variables to construct an LS susceptibility model using the eXtreme Gradient-Boosting (XGBoost) algorithm. The model performed well, with an R² of 0.96, an EVS of 0.96, and an MAE of 2.25 mm/yr. SHapley Additive exPlanations (SHAP) analysis was employed to quantify feature contributions and their effects on LS susceptibility. The results show that a deep groundwater level (DGL) was the dominant factor, followed by elevation and a shallow groundwater level (SGL). The effect of DGL was strongest when it ranged from −75 to 20 m. Elevation showed a clear effect on LS occurrence when values fall between 30 and 50 m. Relatively high subsidence sensitivity was mainly observed in areas where SGL was below −7 m. Interaction effects, particularly those between DGL and elevation and between DGL and SGL, further increased LS susceptibility in specific areas. The highest predicted susceptibility occurred in areas with DGL below −20 m and elevations below 30 m. Higher susceptibility was also identified where DGL was high and SGL ranged between −20 and −10 m, and where DGL was low and SGL ranged from 15 to 20 m. In contrast, factors such as slope and aspect had limited influence at the regional scale. The contributions of the predominant factors show obvious marginal effects and significant spatial heterogeneity to LS susceptibility. The results clarify where and how key factors shape subsidence and can inform targeted mitigation measures and urban planning by local authorities. Full article

(This article belongs to the Special Issue Interpretation and Attribution of Land Subsidence: A Remote Sensing and Machine Learning Perspective)

32 pages, 11567 KB

Open AccessArticle

The DLOD&MCCA Framework for Accurate Mapping of Reservoir Dams in Arid Regions from Remote Sensing Imagery: A Multimodal Fusion and Constraint Approach

by Shu Qian, Qian Shen, Majid Gulayozov, Junli Li, Bingqian Chen, Yakui Shao and Changming Zhu

Remote Sens. 2026, 18(9), 1297; https://doi.org/10.3390/rs18091297 - 24 Apr 2026

Abstract

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that [...] Read more.

Accurate reservoir dam detection in arid regions is challenging because of spectral similarity between dams and surrounding backgrounds, indistinct boundaries, and substantial target-scale variation. To address these issues, this study proposes a deep learning object detection with multi-conditional constraint assistance (DLOD&MCCA) framework that combines a dual deep enhancement YOLO network (DDE-YOLO) with a multi-conditional constraint assistance (MCCA) strategy. In DDE-YOLO, visible (VIS) and near-infrared (NIR) imagery are fused to enhance cross-spectral discrimination, while task-oriented architectural refinements improve the representation of dam targets with diverse scales and structural characteristics. Meanwhile, the MCCA strategy constrains the search space to geographically plausible candidate regions, thereby reducing background interference and improving detection efficiency. Experiments conducted on the self-constructed S2-Dam dataset and the public DIOR dataset show that DDE-YOLO achieves mAP50 values of 92.8% and 76.2%, respectively, outperforming existing state-of-the-art (SOTA) methods. Furthermore, regional-scale dam mapping in Xinjiang achieved an accuracy of over 95%, demonstrating the effectiveness and practical applicability of the proposed framework for large-scale reservoir dam detection in arid environments. Full article

(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery (Second Edition))

27 pages, 6929 KB

Open AccessArticle

Forecasting Sea Surface Cooling During Typhoons Based on Machine Learning

by Ye Zhang, Huiwen Cai and Dan Song

Remote Sens. 2026, 18(9), 1296; https://doi.org/10.3390/rs18091296 - 24 Apr 2026

Abstract

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The [...] Read more.

Sea surface cooling (SSC) induced by typhoons has a significant impact on typhoon intensity and regional air–sea interaction. This study develops a machine learning model based on a multilayer perceptron (MLP) to predict SSC during typhoon passage over the western North Pacific. The model uses pre-typhoon ocean background conditions and ocean states at the typhoon peak moment as inputs, including wind field, sea level anomaly (SLA), mixed layer depth (MLD), and 100 m water temperature. Trained on historical typhoon data and multi-source ocean observations from 2002 to 2018, the model directly predicts SSC during typhoon events from 2019 to 2020. Results show that the model achieves a mean absolute error (MAE) of 0.379 °C, a root mean square error (RMSE) of 0.488 °C, and a bias of 0.087 °C. The model reproduces the typical rightward bias in SSC spatial distribution. Under normal ocean conditions, such as open deep-water areas with moderate stratification and no strong eddy interference, the model performs well, with errors below 0.1 °C at some points. Although some biases exist under complex ocean environments and abrupt changes in typhoon dynamics, the model still captures the overall cooling trend. This study demonstrates the feasibility of machine learning for typhoon–ocean interaction forecasting. The proposed framework can provide technical support for typhoon intensity forecasting, marine disaster warning, and aquaculture risk prevention. Full article

(This article belongs to the Special Issue Advancing Ocean Observation, Analysis, and Forecasting Through AI-Powered Remote Sensing)

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31 pages, 5015 KB

Open AccessArticle

Efficient Adaptation of Vision Foundation Model for High-Resolution Remote Sensing Image Segmentation via Spatial-Frequency Modeling and Sparse Refinement

by Chenlong Ding, Chengyi Shi, Daofang Liu, Zhihao Shi, Xin Lyu, Zhenyu Fang, Xue Liu, Lingqiang Meng, Yiwei Fang, Chengming Zhang and Xin Li

Remote Sens. 2026, 18(9), 1295; https://doi.org/10.3390/rs18091295 - 24 Apr 2026

Abstract

High-resolution remote-sensing semantic segmentation requires models to simultaneously capture global scene semantics and preserve fine-grained local structures. Although satellite-pretrained vision foundation models provide strong transferable representations, the features extracted by a frozen backbone remain insufficiently adapted to dense prediction, particularly for representing high-frequency [...] Read more.

High-resolution remote-sensing semantic segmentation requires models to simultaneously capture global scene semantics and preserve fine-grained local structures. Although satellite-pretrained vision foundation models provide strong transferable representations, the features extracted by a frozen backbone remain insufficiently adapted to dense prediction, particularly for representing high-frequency details and multiscale local patterns. In addition, correcting residual prediction errors with dense full-map refinement introduces substantial computational redundancy, since hard errors are typically concentrated in only a small subset of locations. To address these challenges, we propose ADVMSeg, an efficient remote-sensing semantic segmentation framework built upon a frozen satellite-pretrained DINOv3 backbone. Specifically, we introduce a Spatial-Frequency Adapter (SF-Adapter) to improve backbone-level dense feature adaptation by jointly modeling global frequency responses and multiscale local spatial details in a lightweight bottleneck space. We further design an Adaptive Sparse Refinement (ASR) module after the pixel decoder, which identifies hard regions from coarse predictions via uncertainty and boundary cues, and performs targeted local cross-attention refinement only on selected critical locations. Extensive experiments on GID-15, LoveDA, and ISPRS Potsdam validate the effectiveness of the proposed framework. Under the unified setting, ADVMSeg achieves 63.1% mIoU on GID-15, 63.5% mIoU on LoveDA, and 81.4% mIoU on ISPRS Potsdam. These results validate the effectiveness of jointly improving backbone-level feature adaptation and prediction-stage computation allocation under the evaluated setting of frozen DINOv3, and three representative remote-sensing semantic-segmentation datasets. Full article

(This article belongs to the Special Issue Deep Learning-Driven Hyperspectral Unmixing and Classification Techniques for Remote Sensing Images)

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