Remote Sensing

22 pages, 23389 KB

Open AccessArticle

A Novel Laser Detection Method Against Soot Interference Based on Pulse-Width Comparison

by Yuran Tan, Bingting Zha, Zhen Zheng, Jiaqi Li, Rui He and Junyang Weng

Remote Sens. 2025, 17(17), 3013; https://doi.org/10.3390/rs17173013 - 29 Aug 2025

Traditional pulsed laser detection systems predominantly utilize threshold-based decision methods that rely on the peak voltage of echo signals for target recognition. However, in environments with soot (smoke and dust) interference, strong backscattering effects lead to severe signal distortion, resulting in false alarms [...] Read more.

Traditional pulsed laser detection systems predominantly utilize threshold-based decision methods that rely on the peak voltage of echo signals for target recognition. However, in environments with soot (smoke and dust) interference, strong backscattering effects lead to severe signal distortion, resulting in false alarms or missed detections, thereby significantly degrading recognition accuracy. To overcome this limitation, through comprehensive simulations and experimental trials, we propose a novel pulse-width comparison method that utilizes the characteristic broadening of echoes induced by soot particles. The method exploits the differences in pulse width between target and soot-induced echoes and achieves effective interference suppression. The target-detection accuracy increases from 71.25% to 93.75% in environments with different concentrations (0–350 mg/m

^{3}

), corresponding to a 31.58% relative improvement. This approach leverages pulse-width differences between soot-induced and target echoes to enhance anti-interference capability in dusty environments. Full article

28 pages, 3199 KB

Open AccessReview

Assessing the Suitability of Available Global Forest Maps as Reference Tools for EUDR-Compliant Deforestation Monitoring

by Juliana Freitas Beyer, Margret Köthke and Melvin Lippe

Remote Sens. 2025, 17(17), 3012; https://doi.org/10.3390/rs17173012 - 29 Aug 2025

Abstract

Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cocoa, coffee, palm oil, rubber, soy, and timber are deforestation-free after 31 December 2020. Earth observation (EO) [...] Read more.

Deforestation monitoring is critical to support compliance with regulatory frameworks such as the EU Deforestation Regulation (EUDR), which requires that products containing or derived from beef, cocoa, coffee, palm oil, rubber, soy, and timber are deforestation-free after 31 December 2020. Earth observation (EO) offers a means to assess deforestation, yet map-based verification remains technically limited and uncertain. This study addresses the lack of a systematic assessment of global Forest/Non-Forest (FNF), Tree Cover/Non-Tree Cover (TC/NTC) and Land Use/Land Cover (LULC) datasets by identifying and evaluating 21 publicly available global forest/tree cover reference maps for their alignment with EUDR criteria. This goes beyond merely treating these datasets as simply “fit” or “not fit” for the purpose of the EUDR, but rather aims to assess how well each dataset meets the needs compared to others, acknowledging strengths, weaknesses, and trade-offs. The 21 datasets are reviewed based on EUDR-related parameters (temporal proximity, spatial resolution, and forest definition) as well as accuracy metrics. From this broader review, eight datasets are shortlisted based on their alignment with key regulatory requirements. However, most datasets fail to fully meet all EUDR requirements, particularly forest definitions, with only two datasets satisfying all indicators. Notably, all datasets are unable to distinguish forests from other non-forest, tree-based systems. Reported accuracy metrics reveal a general overestimation of forest areas, while canopy height-based maps tend to underestimate tree cover, potentially excluding forested regions. Regional comparisons show more consistent estimates in South America, while Europe and North America display greater variability. These findings support informed decision-making by companies and policymakers for selecting suitable datasets, while also highlighting conflicts and challenges associated with the use of global forest/tree cover maps for regulatory compliance. Full article

(This article belongs to the Special Issue Using Earth Observation Data to Monitor Deforestation and Forest Degradation to Ensure Compliance with Regulatory Frameworks)

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19 pages, 54218 KB

Open AccessArticle

Estimation of Forest Stock Volume in Complex Terrain Using Spaceborne Lidar

by Yiran Zhang, Qingtai Shu, Xiao Zhang, Zeyu Li and Lianjin Fu

Remote Sens. 2025, 17(17), 3011; https://doi.org/10.3390/rs17173011 - 29 Aug 2025

Abstract

In forest remote sensing monitoring of complex terrain, spaceborne lidar data has become a key technology for obtaining large-scale forest structure parameters due to its uniquethree-dimensional observation capabilities. However, in complex terrain conditions, there are still many challenges for spaceborne lidar. Particularly in [...] Read more.

In forest remote sensing monitoring of complex terrain, spaceborne lidar data has become a key technology for obtaining large-scale forest structure parameters due to its uniquethree-dimensional observation capabilities. However, in complex terrain conditions, there are still many challenges for spaceborne lidar. Particularly in mountainous forest areas with significant topographic relief, overcoming the limitations imposed by complex terrain conditions to achieve high-precision forest stock volume estimation has emerged as one of the most challenging and cutting-edge research areas in vegetation remote sensing. Objective: This study aims to explore the feasibility and methods of forest stock volume estimation using spaceborne lidar data ICESat-2/ATL08 in complex terrain and to compare the effectiveness of three machine learning regression models for this purpose. Method: Based on the ATL08 product from ICESat-2/ATLAS data, a sequential Gaussian conditional simulation was used for spatial interpolation of forest areas in Jingdong Yi Autonomous County, Pu’er City, Yunnan Province. XGBoost, LightGBM, and Random Forest methods were then employed to develop stock volume models, and their estimation capabilities were analyzed and compared. Results: (1) Among the 57 ICESat-2/ATLAS footprint parameters extracted, 13 were retained for interpolation after analysis and screening. (2) Based on sequential Gaussian conditional simulation, three parameters demonstrating lower interpolation accuracy were eliminated, with the remaining ten parameters allocated for inversion model development. (3) In terms of inversion model accuracy, XGBoost outperformed LightGBM and Random Forest, achieving an R² of 0.89 and an rRMSE of 10.5912. The average forest stock volume derived from the inversion was 141.00 m³/hm². Conclusions: Overall, large-area forest stock volume estimation through spaceborne Lidar inversion using ICESat-2/ATLAS photon-counting footprints proved feasible for mountainous environments with complex terrain. The XGBoost method demonstrates strong forest stock volume inversion capabilities. This study provides a case study for investigating forest structure parameters in complex mountainous terrain using spaceborne lidar ICESat-2/ATLAS data. Full article

(This article belongs to the Topic Forest Productivity, Carbon Dynamics and Eco-Environmental Response: Potential, Development and Challenges)

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25 pages, 4747 KB

Open AccessArticle

Land Cover Change as a Critical Driver of Vegetation Restoration in Water-Scarce Northern China

by Lihua Lan, Zhenbo Wang and Fei He

Remote Sens. 2025, 17(17), 3010; https://doi.org/10.3390/rs17173010 - 29 Aug 2025

Abstract

The implementation of vegetation restoration projects in the Three-North Sandstorm Region has provided a critical opportunity for ecological rehabilitation. A thorough exploration of the mutual feedback mechanisms between land use and vegetation dynamics holds significant guiding value for ecological restoration practices. This study [...] Read more.

The implementation of vegetation restoration projects in the Three-North Sandstorm Region has provided a critical opportunity for ecological rehabilitation. A thorough exploration of the mutual feedback mechanisms between land use and vegetation dynamics holds significant guiding value for ecological restoration practices. This study innovatively integrates the Hurst index, trend analysis, and significance testing to systematically analyze the characteristics of vegetation dynamics and their responses to land-use changes from 1990 to 2020. By constructing a multidimensional evaluation system, including the comprehensive land-use index (L) and land-use change rate (R), this research is the first to quantitatively reveal the relationship between land-use characteristics and the Normalized Difference Vegetation Index (NDVI) across different desertification regions. Key findings include: (1) Vegetation in the study area exhibited a significant improvement trend, with 92.12% of regions showing a Hurst index > 0.5, indicating strong persistence in vegetation recovery. (2) Land-use transitions revealed a consistent increase in construction land across all sandy regions, accompanied by the conversion of unused land to grassland. (3) The fluctuation of R gradually narrowed from 1990 to 2020, while the L demonstrated a significant upward trend. (4) The R generally exerted a negative influence on NDVI, whereas the L exhibited a threshold effect. Specifically, NDVI increased with rising L when L < 215 (e.g., MWSD, KBQD regions), but declined when L exceeded 215 (e.g., SNS region). This suggests that frequent land-use changes hinder vegetation growth, while moderate increases in land-use intensity initially promote biomass accumulation—until a critical threshold is surpassed, beyond which further intensification exerts adverse effects. One-way ANOVA and significance testing further demonstrated that climate predominantly drove NDVI increases in cropland, forest, shrubland, and grassland, whereas human activities played a decisive role in vegetation growth within unused or sparsely vegetated areas. This study not only quantitatively identifies critical thresholds for land-use impacts on vegetation but also provides a scientific foundation for targeted ecological restoration strategies, offering practical insights for reconciling ecological conservation with land-use demands. Full article

26 pages, 8278 KB

Open AccessArticle

Radiative Forcing and Albedo Dynamics in the Yellow River Basin: Trends, Variability, and Land-Cover Effects

by Long He, Qianrui Xi, Mei Sun, Hu Zhang, Junqin Xie and Lei Cui

Remote Sens. 2025, 17(17), 3009; https://doi.org/10.3390/rs17173009 - 29 Aug 2025

Abstract

Climate change results from disruptions in Earth’s radiation energy balance. Radiative forcing is the dominant factor of climate change. Yet, most studies have focused on radiative effects within the calculated actual albedo, usually overlooking the angle effect of regions with large-scale and highly [...] Read more.

Climate change results from disruptions in Earth’s radiation energy balance. Radiative forcing is the dominant factor of climate change. Yet, most studies have focused on radiative effects within the calculated actual albedo, usually overlooking the angle effect of regions with large-scale and highly varied terrain. This study produced the actual albedo databases by using albedo retrieval look-up tables. And then we investigated the spatiotemporal variations in land surface albedo and its corresponding radiative effects in the Yellow River Basin from 2000 to 2022 using MODIS-derived reflectance data. We employed time-series, trend, and anomaly detection analyses alongside surface downward shortwave radiation measurements to quantify the radiative forcing induced by land-cover changes. Our key findings reveal that (i) the basin’s average surface albedo was 0.171, with observed values ranging from 0.058 to 0.289; the highest variability was noted in the Loess Plateau during winter—primarily due to snowfall and low temperatures; (ii) a notable declining trend in the annual average albedo was observed in conjunction with rising temperatures, with annual values fluctuating between 0.165 and 0.184 and monthly averages spanning 0.1595 to 0.1853; (iii) land-cover transitions exerted distinct radiative forcing effects: conversions from grassland, shrubland, and wetland to water bodies produced forcings of 2.657, 2.280, and 2.007 W/m², respectively, while shifts between barren land and cropland generated forcings of 4.315 and 2.696 W/m². In contrast, transitions from cropland to shrubland and from grassland to shrubland resulted in minimal forcing, and changes from impervious surfaces and forested areas to other cover types yielded negative forcing, thereby exerting a net cooling effect. These findings not only deepen our understanding of the interplay between land-cover transitions and radiative forcing within the Yellow River Basin but also offer robust scientific support for regional climate adaptation, ecological planning, and sustainable land use management. Full article

(This article belongs to the Special Issue Time-Series Mapping and Analysis of Land Surface Parameters and Changes Using Remote Sensing Data)

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26 pages, 7561 KB

Open AccessArticle

Satellite Optical Target Edge Detection Based on Knowledge Distillation

by Ying Meng, Luping Zhang, Yan Zhang, Moufa Hu, Fei Zhao and Xinglin Shen

Remote Sens. 2025, 17(17), 3008; https://doi.org/10.3390/rs17173008 - 29 Aug 2025

Abstract

Edge detection of space targets is vital in aerospace applications, such as satellite monitoring and analysis, yet it faces challenges due to diverse target shapes and complex backgrounds. While deep learning-based edge detection methods dominate due to their powerful feature representation capabilities, they [...] Read more.

Edge detection of space targets is vital in aerospace applications, such as satellite monitoring and analysis, yet it faces challenges due to diverse target shapes and complex backgrounds. While deep learning-based edge detection methods dominate due to their powerful feature representation capabilities, they often suffer from large parameter sizes and lack explicit geometric prior constraints for space targets. This paper proposes a novel edge detection method for satellite targets based on knowledge distillation, namely STED-KD. Firstly, a multi-stage distillation strategy is proposed to guide a lightweight, fully convolutional network with fewer parameters to learn key features and decision boundaries from a complex teacher model, achieving model efficiency. Next, a shape prior guidance module is integrated into the student branch, incorporating geometric shape information through shape prior model construction, similarity metric calculation, and feature reconstruction, enhancing adaptability to space targets and improving detection accuracy. Additionally, a curvature-guided edge loss function is designed to ensure continuous and complete edges, minimizing local discontinuities. Experimental results on the UESD space target dataset demonstrate superior performance, with ODS, OIS, and AP scores of 0.659, 0.715, and 0.596, respectively. On the BSDS500, STED-KD achieves ODS, OIS, and AP scores of 0.818, 0.829, and 0.850, respectively, demonstrating strong competitiveness and further confirming its stability. Full article

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29 pages, 15321 KB

Open AccessArticle

Ground-Based Evaluation of Hourly Surface Ozone in China Using CAM-Chem Model Simulations and Himawari-8 Satellite Estimates

by Peng Zhou, Jieming Chou, Li Dan, Jing Peng, Fuqiang Yang, Kai Li, Younong Li, Fugang Li and Hong Wang

Remote Sens. 2025, 17(17), 3007; https://doi.org/10.3390/rs17173007 - 29 Aug 2025

Abstract

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating [...] Read more.

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating current hourly surface ozone estimation methods. Therefore, this study collaboratively evaluated the performance of chemical transport model simulations and satellite-based estimates of hourly surface ozone concentrations over mainland China in 2019. Using data from 3185 ground monitoring stations operated by the Ministry of Ecology and Environment, as well as six independent observation sites in Hong Kong, Xianghe, Nam Co, Akedala, Longfengshan, and Waliguan, this study found that both datasets exhibited systematic biases and lacked spatiotemporal consistency. The Community Atmosphere Model with Chemistry simulation results exhibited an average relative bias of 23.17%, generally overestimated ozone concentrations in high-altitude regions, but outperformed the satellite-based estimates at the independent sites, while consistently underestimating ozone concentrations in densely populated urban areas. In contrast, the satellite-based estimates performed better in regions with dense monitoring sites, with mean biases typically within 10% of observations, but their accuracy was limited in remote areas due to sparse ground-based calibration. It is particularly noteworthy that both datasets showed deficiencies in capturing extremely high-value events, nighttime ozone variations, and dynamic transport processes, underscoring challenges in the representation of photochemical processes in the model and in the design of satellite estimation algorithms. The results highlight the importance of optimizing model parameterization schemes, improving satellite estimation algorithms, and integrating multi-source data to enhance the accuracy and stability of hourly ozone estimates. This study provides multi-scale quantitative insights into the relative strengths and limitations of different ozone estimation methods, laying a solid scientific foundation for future data integration, regional air quality management, and policy development. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Observation of Aerosol Properties and Assessment of Their Effects)

19 pages, 3020 KB

Open AccessArticle

Prediction of Sandstorm Moving Path in Mongolian Plateau Based on CNN-BiLSTM

by Daoting Zhang, Wala Du, Shan Yu, Zhimin Hong, Dashtseren Avirmed, Mingyue Li and Yu’ang He

Remote Sens. 2025, 17(17), 3006; https://doi.org/10.3390/rs17173006 - 29 Aug 2025

Abstract

The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature [...] Read more.

The frequent occurrence of sandstorms on the Mongolian Plateau has become a critical factor influencing the stability of regional ecosystems and social activities. In this study, a deep learning framework was developed for predicting sandstorm paths on the Mongolian Plateau. A spatio-temporal feature dataset was established using remote sensing imagery and meteorological observations. Spatial features were extracted through a convolutional neural network (CNN), while the temporal evolution of sandstorms was modeled using a bidirectional long short-term memory (BiLSTM) network. A random forest algorithm was employed to assess the relative importance of meteorological and geographical factors. The results indicate that the proposed CNN-BiLSTM model achieved strong performance at prediction intervals of 1, 6, 12, 18, and 24 h, with overall accuracy, F1-score, and AUC all exceeding 0.80. The 24 h prediction yielded the best results, with evaluation metrics of 0.861, 0.878, and 0.898, respectively. Compared with the individual CNN and BiLSTM models, the CNN-BiLSTM model demonstrated superior performance. The findings suggest that the model provides high predictive accuracy and stability across different time steps, thereby offering strong support for dust storm path prediction on the Mongolian Plateau and contributing to the reduction of disaster-related risks and losses. Full article

(This article belongs to the Section Ecological Remote Sensing)

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19 pages, 13244 KB

Open AccessArticle

MWR-Net: An Edge-Oriented Lightweight Framework for Image Restoration in Single-Lens Infrared Computational Imaging

by Xuanyu Qian, Xuquan Wang, Yujie Xing, Guishuo Yang, Xiong Dun, Zhanshan Wang and Xinbin Cheng

Remote Sens. 2025, 17(17), 3005; https://doi.org/10.3390/rs17173005 - 29 Aug 2025

Abstract

Infrared video imaging is an cornerstone technology for environmental perception, particularly in drone-based remote sensing applications such as disaster assessment and infrastructure inspection. Conventional systems, however, rely on bulky optical architectures that limit deployment on lightweight aerial platforms. Computational imaging offers a promising [...] Read more.

Infrared video imaging is an cornerstone technology for environmental perception, particularly in drone-based remote sensing applications such as disaster assessment and infrastructure inspection. Conventional systems, however, rely on bulky optical architectures that limit deployment on lightweight aerial platforms. Computational imaging offers a promising alternative by integrating optical encoding with algorithmic reconstruction, enabling compact hardware while maintaining imaging performance comparable to sophisticated multi-lens systems. Nonetheless, achieving real-time video-rate computational image restoration on resource-constrained unmanned aerial vehicles (UAVs) remains a critical challenge. To address this, we propose Mobile Wavelet Restoration-Net (MWR-Net), a lightweight deep learning framework tailored for real-time infrared image restoration. Built on a MobileNetV4 backbone, MWR-Net leverages depthwise separable convolutions and an optimized downsampling scheme to minimize parameters and computational overhead. A novel wavelet-domain loss enhances high-frequency detail recovery, while the modulation transfer function (MTF) is adopted as an optics-aware evaluation metric. With only 666.37 K parameters and 6.17 G MACs, MWR-Net achieves a PSNR of 37.10 dB and an SSIM of 0.964 on a custom dataset, outperforming a pruned U-Net baseline. Deployed on an RK3588 chip, it runs at 42 FPS. These results demonstrate MWR-Net’s potential as an efficient and practical solution for UAV-based infrared sensing applications. Full article

(This article belongs to the Special Issue Advances in Remote Sensing Video Data Processing: Theories, Technologies and Applications)

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18 pages, 55646 KB

Open AccessArticle

Physics-Constrained Deterministic Sea Wave Reconstruction Methodology Based on X-Band Coherent Radar

by Jingjun Li, Can Zhao, Xuewen Ma, Jihao Fan, Guangbiao Wang, Limin Huang and Yukang Li

Remote Sens. 2025, 17(17), 3004; https://doi.org/10.3390/rs17173004 - 29 Aug 2025

Abstract

Deterministic sea wave reconstruction techniques are critical for enhancing maritime safety and disaster warnings. Coherent radar remote sensing captures sea surface velocity information to enable more precise wave reconstruction. Existing difference matrix methods address rank-deficient systems through artificial boundary processing, which distorts local [...] Read more.

Deterministic sea wave reconstruction techniques are critical for enhancing maritime safety and disaster warnings. Coherent radar remote sensing captures sea surface velocity information to enable more precise wave reconstruction. Existing difference matrix methods address rank-deficient systems through artificial boundary processing, which distorts local hydrodynamic characteristics and propagates errors to global features, thereby limiting the accuracy and stability of reconstructions. To resolve this limitation, this study proposes a physics-constrained deterministic wave reconstruction methodology. We introduce the Data-Anchored Projection model for the differential matrix, extracting hydrodynamic constraints directly from radar backscatter data. This approach achieves stable solutions for rank-deficient systems without artificial boundaries. The model’s performance was rigorously validated through both simulated and real-sea experiments. The simulation results demonstrate a minimum 13% accuracy improvement over conventional methods and high stability under various sea states and at different range resolutions. In a real-sea trial under sea states 3 to 5, reconstruction errors remained below 10%, with consistent stability observed across varying sea states. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))

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26 pages, 30091 KB

Open AccessArticle

Crop Mapping Using kNDVI-Enhanced Features from Sentinel Imagery and Hierarchical Feature Optimization Approach in GEE

by Yanan Liu, Ai Zhang, Xingtao Zhao, Yichen Wang, Yuetong Hao and Pingbo Hu

Remote Sens. 2025, 17(17), 3003; https://doi.org/10.3390/rs17173003 - 29 Aug 2025

Abstract

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the [...] Read more.

Accurate crop mapping is vital for monitoring agricultural resources, food security, and ecosystem sustainability. Advances in high-resolution sensing technologies now enable precise, large-scale crop mapping, improving agricultural management and decision-making. However, in scenarios where balancing precision and computational resources is important, obtaining the optimal feature combination (especially newly proposed features) and strategies from the rich feature sets contained in multi-source remote sensing imagery remains one of the challenges. In this paper, we propose a hierarchical feature optimization method, incorporating a newly reported vegetation feature, for mapping crop types by combining the Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical imagery within the Google Earth Engine (GEE) platform. The method first calculates spectral features, texture features, polarization features, vegetation index features, and crop phenological features, with a particular focus on infrared band features and the newly developed Kernel Normalized Difference Vegetation Index (kNDVI). These 126 features are then selected to construct 15 crop type mapping models based on different feature combinations and a random forest (RF) classifier. Feature selection was performed using the feature correlation analysis and random forest recursive feature elimination (RF-RFE) to identify the optimal subset. The experiment was conducted in the Linhe region, covering an area of 2333 km². The resulting 10 m crop map, generated by the optimal model (Model 15) with 34 key features, demonstrated that integrating multi-source features significantly enhances mapping accuracy. The model achieved an overall accuracy of 90.10% across five crop types (corn, wheat, sunflower, soybean, and beet), outperforming other representative feature optimization methods, Relief-F (87.50%) and CFS (89.60%). The study underscores the importance of feature optimization and reduction of redundant features while also showcasing the effectiveness of red edge and infrared features, as well as the kNDVI, in mapping crop type. Full article

(This article belongs to the Special Issue GeoAI and EO Big Data Driven Advances in Earth Environmental Science)

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21 pages, 11278 KB

Open AccessArticle

Thin Sea Ice Thickness Prediction Using Multivariate Radar-Physical Features and Machine Learning Algorithms

by Mehran Dadjoo and Dustin Isleifson

Remote Sens. 2025, 17(17), 3002; https://doi.org/10.3390/rs17173002 - 29 Aug 2025

Abstract

Climate change in the Arctic is causing significant declines in sea ice extent and thickness. This study investigated lab-grownsea ice thickness using Linear Regression and three Machine Learning algorithms: Decision Tree, Random Forest, and Fully Connected Neural Network. To comprehensively track thin sea [...] Read more.

Climate change in the Arctic is causing significant declines in sea ice extent and thickness. This study investigated lab-grownsea ice thickness using Linear Regression and three Machine Learning algorithms: Decision Tree, Random Forest, and Fully Connected Neural Network. To comprehensively track thin sea ice growth using various parameters, a combination of up to 13 radar and physical parameters including surface-based C-band NRCS values in VV, HH, and HV polarizations, air temperature, surface temperature, Cumulative Freezing Degree Moments, humidity, wind speed, surface cover salinity, ice surface salinity, bulk ice salinity, frost flower height and snow depth were input to the four multivariate models in two time series datasets. The results showed that Random Forest was the superior model, with =0.01 cm, for thicknesses of 1–8 cm and 27–47 cm. Using the Permutation Importance method, the role of the employed parameters in the thickness prediction process were ranked and showed that the key parameters were Cumulative Freezing Degree Moment, salinity parameters (surface cover, ice surface, and bulk ice salinities), and C-band co-polarized radar backscattering. The results of this study enhance thickness prediction capacity and accuracy, while providing insights for future research and real-time sea ice thickness prediction in Arctic regions. Full article

(This article belongs to the Special Issue Recent Advances in Sea Ice Research Using Satellite Data (Second Edition))

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23 pages, 261573 KB

Open AccessArticle

A Continuous Low-Rank Tensor Approach for Removing Clouds from Optical Remote Sensing Images

by Dong-Lin Sun, Teng-Yu Ji, Siying Li and Zirui Song

Remote Sens. 2025, 17(17), 3001; https://doi.org/10.3390/rs17173001 - 28 Aug 2025

Abstract

Optical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem using low-rank and sparse priors within a discrete [...] Read more.

Optical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem using low-rank and sparse priors within a discrete representation framework. However, these approaches typically rely on manually designed regularization terms, which fail to accurately capture the complex geostructural patterns in remote sensing imagery. In response to this issue, we develop a continuous blind cloud removal model. Specifically, the cloud-free component is represented using a continuous tensor function that integrates implicit neural representations with low-rank tensor decomposition. This representation enables the model to capture both global correlations and local smoothness. Furthermore, a band-wise sparsity constraint is employed to represent the cloud component. To preserve the information in regions not covered by clouds during reconstruction, a box constraint is incorporated. In this constraint, cloud detection is performed using an adaptive thresholding strategy, and a morphological erosion function is employed to ensure accurate detection of cloud boundaries. To efficiently handle the developed model, we formulate an alternating minimization algorithm that decouples the optimization into three interpretable subproblems: cloud-free reconstruction, cloud component estimation, and cloud detection. Our extensive evaluations on both synthetic and real-world data reveal that the proposed method performs competitively against state-of-the-art cloud removal methods. Full article

(This article belongs to the Special Issue Knowledge-Driven and/or Data-Driven Methods for Remote Sensing Image Processing)

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22 pages, 17501 KB

Open AccessArticle

Fusing BDS and Dihedral Corner Reflectors for High-Precision 3D Deformation Measurement: A Case Study in the Jinsha River Reservoir Area

by Zhiyong Qi, Yanpian Mao, Zhengyang Tang, Tao Li, Rongxin Fang, You Mou, Xuhuang Du and Zongyi Peng

Remote Sens. 2025, 17(17), 3000; https://doi.org/10.3390/rs17173000 - 28 Aug 2025

Abstract

In mountainous canyon regions, BeiDou Navigation Satellite System (BDS)/Global Navigation Satellite System (GNSS) receivers are susceptible to multireflection and tropospheric factors, which frequently reduce the accuracy in monitoring vertical deformation monitoring under short-baseline methods. This limitation hinders the application of BDS/GNSS in high-precision [...] Read more.

In mountainous canyon regions, BeiDou Navigation Satellite System (BDS)/Global Navigation Satellite System (GNSS) receivers are susceptible to multireflection and tropospheric factors, which frequently reduce the accuracy in monitoring vertical deformation monitoring under short-baseline methods. This limitation hinders the application of BDS/GNSS in high-precision monitoring scenarios in those cases. To address this issue, this study proposes a three-dimensional (3D) deformation measurement method that integrates BDS/GNSS positioning with dihedral corner reflectors (CRs). By incorporating high-precision horizontal positioning results obtained from BDS/GNSS into the radar line-of-sight (LOS) correction process and utilizing ascending and descending Synthetic Aperture Radar (SAR) data for joint monitoring, the method achieves millimeter-level- accuracy in measuring vertical deformation at corner reflector sites. At the same time, it enhances the 3D positioning accuracy of BDS/GNSS to the 1 mm level under short-baseline configurations. Based on monitoring stations deployed at the Jinsha River dam site, the proposed deformation fusion monitoring method was validated using high-resolution SAR imagery from Germany’s TerraSAR-X (TSX) satellite. Simulated horizontal and vertical displacements were introduced at the stations. The results demonstrate that BDS/GNSS achieves better than 1 mm horizontal monitoring accuracy and a vertical accuracy of around 5 mm. Interferometric SAR (InSAR) CRs achieve approximately 2 mm in horizontal accuracy and 1 mm in vertical accuracy. The integrated method yields a 3D deformation monitoring accuracy better than 1 mm. This paper’s results show high potential for achieving high-precision deformation observations by fusing BDS/GNSS and dihedral CRs, offering promising prospects for deformation monitoring in reservoir canyon regions. Full article

(This article belongs to the Special Issue Applications of Radar Remote Sensing in Earth Observation)

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25 pages, 16586 KB

Open AccessArticle

Novel Extension of Full-Polarimetric Bistatic Scattering Modeling of Canonical Scatterers for Radar Recognition

by Wenjie Deng, Wei Yang, Yue Song, Sifan Su, Shiwen Lei, Yongpin Chen and Haoquan Hu

Remote Sens. 2025, 17(17), 2999; https://doi.org/10.3390/rs17172999 - 28 Aug 2025

Abstract

In radar target recognition, the canonical scatterer model (CSM) serves as an effective alternative to the scatterer center model (SCM) for efficiently characterizing electromagnetic (EM) scattering properties of complex targets. Based on physical optics (PO) and the stationary phase method (SPM), this paper [...] Read more.

In radar target recognition, the canonical scatterer model (CSM) serves as an effective alternative to the scatterer center model (SCM) for efficiently characterizing electromagnetic (EM) scattering properties of complex targets. Based on physical optics (PO) and the stationary phase method (SPM), this paper analytically derives the novel extension of the CSM for six canonical scatterers: plate, dihedral, trihedral, cylinder, cone, and sphere. The proposed polarization-dependent framework isolates the polarimetric response from CSMs’ intrinsic geometries, reducing the full-polarimetric matrix to an explicit function exclusively governed by bistatic radar spatial configurations. Experimental validation demonstrates mean relative percentage errors (MRPEs) in radar cross section (RCS) of 0.3%, 2%, 2.6%, 3%, 6%, and 7%. This model constitutes a foundational prototype for scattering dictionaries addressing both forward and inverse EM scattering problems, possessing significant practical utility in radar target recognition and image interpretation. Full article

(This article belongs to the Special Issue Radar Sensing and Intelligent Recognitions: Algorithms, Data and Advanced Applications)

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22 pages, 4922 KB

Open AccessArticle

PDE-Guided Diverse Feature Learning for SAR Rotated Ship Detection

by Mingjin Zhang, Zhongkai Yang, Jie Guo and Yunsong Li

Remote Sens. 2025, 17(17), 2998; https://doi.org/10.3390/rs17172998 - 28 Aug 2025

Abstract

Detecting ships in Synthetic Aperture Radar (SAR) images poses a complex challenge, with recent progress primarily attributed to the development of rotated detectors. However, existing methods often neglect the crucial influence of inherent characteristics in SAR images, such as common speckle noise. Moreover, [...] Read more.

Detecting ships in Synthetic Aperture Radar (SAR) images poses a complex challenge, with recent progress primarily attributed to the development of rotated detectors. However, existing methods often neglect the crucial influence of inherent characteristics in SAR images, such as common speckle noise. Moreover, a notable gap exists in modeling diverse features, particularly the fusion of rotational and high-frequency features. To address these challenges, this paper introduces a high-accuracy detector called PRDet, which builds on two key innovations: partial differential equation (PDE)-Guided Wavelet Transform (PGWT) and Diverse Feature Learning Block (DFLB). The PGWT enhances high-frequency features, such as edges and textures, while eliminating speckle noise by optimizing wavelet transform with PDE, leveraging the ability of PDE to model local variations and preserve structural details. The DFLB, with strong expressive capability, extracts and fuses multi-form ship features through three branches, enabling more accurate ship localization. Extensive experimental evaluations on the publicly available RSSDD and SRSDD-V1.0 benchmarks demonstrate PRDet’s superiority over other SAR rotated ship detectors. For example, on the RSSDD dataset, PRDet achieves an offshore precision of 0.938 and an mAP of 0.908, confirming its effectiveness for practical maritime surveillance applications. Full article

(This article belongs to the Special Issue Synthetic Aperture Radar (SAR) Image Object Detection and Information Extraction: Methods and Applications (Second Edition))

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27 pages, 5066 KB

Open AccessArticle

DSM-Seg: A CNN-RWKV Hybrid Framework for Forward-Looking Sonar Image Segmentation in Deep-Sea Mining

by Xinran Liu, Jianmin Yang, Enhua Zhang, Wenhao Xu and Changyu Lu

Remote Sens. 2025, 17(17), 2997; https://doi.org/10.3390/rs17172997 - 28 Aug 2025

Abstract

Accurate and real-time environmental perception is essential for the safe and efficient execution of deep-sea mining operations. Semantic segmentation of forward-looking sonar (FLS) images plays a pivotal role in enabling environmental awareness for deep-sea mining vehicles (DSMVs), but remains challenging due to strong [...] Read more.

Accurate and real-time environmental perception is essential for the safe and efficient execution of deep-sea mining operations. Semantic segmentation of forward-looking sonar (FLS) images plays a pivotal role in enabling environmental awareness for deep-sea mining vehicles (DSMVs), but remains challenging due to strong acoustic noise, blurred object boundaries, and long-range semantic dependencies. To address these issues, this study proposes DSM-Seg, a novel hybrid segmentation architecture combining Convolutional Neural Networks (CNNs) and Receptance Weighted Key-Value (RWKV) modeling. The architecture integrates a Physical Prior-Based Semantic Guidance Module (PSGM), which utilizes sonar-specific physical priors to produce high-confidence semantic guidance maps, thereby enhancing the delineation of target boundaries. In addition, a RWKV-Based Global Fusion with Semantic Constraints (RGFSC) module is introduced to suppress cross-regional interference in long-range dependency modeling and achieve the effective fusion of local and global semantic information. Extensive experiments on both a self-collected seabed terrain dataset and a public marine debris dataset demonstrate that DSM-Seg significantly improves segmentation accuracy under complex conditions while satisfying real-time performance requirements. These results highlight the potential of the proposed method to support intelligent environmental perception in DSMV applications. Full article

(This article belongs to the Special Issue Ocean Remote Sensing Based on Radar, Sonar and Optical Techniques (Second Edition))

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22 pages, 6851 KB

Open AccessArticle

The Interaction Between Vegetation Change and Land–Atmosphere Heat Exchange on the Tibetan Plateau

by Chengqi Gong, Xiaohua Dong, Yaoming Ma, Dan Yu, Chong Wei, Tao Peng, Min An and Bob Su

Remote Sens. 2025, 17(17), 2996; https://doi.org/10.3390/rs17172996 - 28 Aug 2025

Abstract

Vegetation–heat flux feedbacks have a great influence on ecosystems, but the interaction between them is still unclear. This is particularly critical in ecologically fragile areas, where plant growth is especially sensitive to land–atmosphere interactions that help plants withstand environmental pressures. To the causal [...] Read more.

Vegetation–heat flux feedbacks have a great influence on ecosystems, but the interaction between them is still unclear. This is particularly critical in ecologically fragile areas, where plant growth is especially sensitive to land–atmosphere interactions that help plants withstand environmental pressures. To the causal relationship between vegetation and heat flux under different topographies on the Tibetan Plateau, we improved the Granger causality model to handle nonstationary scenarios, enabling us to uncover previously unknown interaction patterns between unstable vegetation change and heat fluxes. Further sensitivity analysis was performed to assess the strength of causal influences. The results showed that the sensible heat (SH) and latent heat (LH) fluxes were increasing at rates of 0.28 W·m⁻²·decade⁻¹ and 0.105 W·m⁻²·decade⁻¹, respectively. The interaction between them on vegetation change depends on terrains, at low elevations below 3000 m and high elevations of 5000–6000 m, SH and LH jointly regulate vegetation growth of shady and gentle to moderate slopes, predominantly involving dense grasslands, but the influence of SH is stronger. While at middle elevations of 3000–5000 m and on steep slopes, LH and vegetation of all types interact to form an intensive local energy cycle. Conversely, vegetation change also influences heat flux. Below 6000 m (excluding the 2000–3000 m), vegetation only regulates LH, and this influence appears largely independent of terrain, contributing to energy redistribution and water cycle maintenance in these regions. These interactions suggest that vegetation plays a central role in shaping energy distribution on the plateau, maintaining the water cycle, and regulating climate in alpine regions by regulating heat flux. Full article

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33 pages, 13230 KB

Open AccessArticle

Harmonization of Gaofen-1/WFV Imagery with the HLS Dataset Using Conditional Generative Adversarial Networks

by Haseeb Ur Rehman, Guanhua Zhou, Franz Pablo Antezana Lopez and Hongzhi Jiang

Remote Sens. 2025, 17(17), 2995; https://doi.org/10.3390/rs17172995 - 28 Aug 2025

Abstract

The harmonized multi-sensor satellite data assists users by providing seamless analysis-ready data with enhanced temporal resolution. The Harmonized Landsat Sentinel (HLS) product has gained popularity due to the seamless integration of Landsat OLI and Sentinel-2 MSI, achieving a temporal resolution of 2.8 to [...] Read more.

The harmonized multi-sensor satellite data assists users by providing seamless analysis-ready data with enhanced temporal resolution. The Harmonized Landsat Sentinel (HLS) product has gained popularity due to the seamless integration of Landsat OLI and Sentinel-2 MSI, achieving a temporal resolution of 2.8 to 3.5 days. However, applications that require monitoring intervals of less than three days or cloudy data can limit the usage of HLS data. Gaofen-1 (GF-1) Wide Field of View (WFV) data provides the capacity further to enhance the data availability by harmonization with HLS. In this study, GF-1/WFV data is harmonized with HLS by employing deep learning-based conditional Generative Adversarial Networks (cGANs). The harmonized WFV data with HLS provides an average temporal resolution of 1.5 days (ranging from 1.2 to 1.7 days), whereas the temporal resolution of HLS varies from 2.8 to 3.5 days. This enhanced temporal resolution will benefit applications that require frequent monitoring. Various processes are employed in HLS to achieve seamless products from the Operational Land Imager (OLI) and Multispectral Imager (MSI). This study applies 6S atmospheric correction to obtain GF-1/WFV surface reflectance data, employs MFC cloud masking, resamples the data to 30 m, and performs geographical correction using AROP relative to HLS data, to align preprocessing with HLS workflows. Harmonization is achieved without using BRDF normalization and bandpass adjustment like in the HLS workflows; instead, cGAN learns cross-sensor reflectance mapping by utilizing a U-Net generator and a patchGAN discriminator. The harmonized GF-1/WFV data were compared to the reference HLS data using various quality indices, including SSIM, MBE, and RMSD, across 126 cloud-free validation tiles covering various land covers and seasons. Band-wise scatter plots, histograms, and visual image color quality were compared. All these indices, including the Sobel filter, histograms, and visual comparisons, indicated that the proposed method has effectively reduced the spectral discrepancies between the GF-1/WFV and HLS data. Full article

(This article belongs to the Special Issue Advances in Multimodal Remote Sensing Data: Processing, Fusion and Applications)

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