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Volume 17
Issue 11
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Remote Sens., Volume 17, Issue 11 (June-1 2025) – 157 articles

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33 pages, 12604 KiB  
Article
YOLO-SCNet: A Framework for Enhanced Detection of Small Lunar Craters
by Wei Zuo, Xingye Gao, Di Wu, Jiaqian Liu, Xingguo Zeng and Chunlai Li
Remote Sens. 2025, 17(11), 1959; https://doi.org/10.3390/rs17111959 - 5 Jun 2025
Viewed by 298
Abstract
The study of impact craters is crucial for understanding planetary evolution and geological processes, particularly small craters, which are key to reconstructing the lunar impact history. Detecting small craters, with diameters ranging from 0.2 to 2 km, remains a challenge due to the [...] Read more.
The study of impact craters is crucial for understanding planetary evolution and geological processes, particularly small craters, which are key to reconstructing the lunar impact history. Detecting small craters, with diameters ranging from 0.2 to 2 km, remains a challenge due to the power-law distribution of crater sizes and the complex topography of the lunar surface. This work uses high-resolution lunar imagery data from the Chang’E-2 mission, with a 7 m spatial resolution, to develop a deep learning framework for small crater detection, named YOLO-SCNet. The framework combines a high-quality, diversified sample dataset, generated through data augmentation techniques, with YOLO-SCNet, specifically designed for small target detection. Key challenges in lunar crater detection, such as varying lighting conditions and complex terrains, are addressed through the innovative model architecture, which incorporates a small object detection head, dynamic anchor boxes, and multi-scale feature fusion. Experimental results demonstrate that YOLO-SCNet achieves outstanding performance in detecting small craters across different lunar regions, with precision, recall, and F1 scores of 90.2%, 88.7%, and 89.4%, respectively. The framework offers a scalable solution for constructing a global lunar crater catalog (≥0.2 km) and can be extended to other planetary bodies like Mars and Mercury, significantly supporting future planetary exploration and mapping efforts. Full article
(This article belongs to the Special Issue Advanced Applications of Artificial Intelligence in Remote Sensing Image Recognition)
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23 pages, 10258 KiB  
Article
Characterizing Crop Distribution and the Impact on Forest Conservation in Central Africa
by Mohammed S. Ozigis, Serge Wich, Mahsa Abdolshahnejad, Adrià Descals, Zoltan Szantoi, Douglas Sheil and Erik Meijaard
Remote Sens. 2025, 17(11), 1958; https://doi.org/10.3390/rs17111958 - 5 Jun 2025
Viewed by 307
Abstract
While the role of expanding agriculture in deforestation and the loss of other natural ecosystems is well known, the specific drivers in the context of small- and large-scale agriculture remain poorly understood. In this study, we employed satellite data and a deep learning [...] Read more.
While the role of expanding agriculture in deforestation and the loss of other natural ecosystems is well known, the specific drivers in the context of small- and large-scale agriculture remain poorly understood. In this study, we employed satellite data and a deep learning algorithm to map the agricultural landscape of Central Africa (Cameroon, Central Africa Republic, Congo, Democratic Republic of Congo, Equatorial Guinea, and Gabon) into large- (including for plantations and intensively cultivated areas) and small-scale tree crops and non-tree crop cover. This permits the assessment of forest loss between the years 2000 and 2022 as a result of small- and large-scale agriculture. Thematic [user’s] accuracy ranged between 91.2 ± 2.5 percent (large-scale oil palm) and 17.8 ± 3.9 percent (large-scale non-tree crops). Small-scale tree crops achieved relatively low accuracy (63.5 ± 5.9 percent), highlighting the difficulties of reliably mapping crop types at a regional scale. In general, we observed that small-scale agriculture is fifteen times the size of large-scale agriculture, as area estimates of small-scale non-tree crops and small-scale tree crops ranged between 164,823 ± 4224 km2 and 293,249 ± 12,695 km2, respectively. Large-scale non-tree crops and large-scale tree crops ranged between 20,153 ± 1195 km2 and 7436 ± 280 km2, respectively. Small-scale cropping activities represent 12 percent of the total land cover and have led to dramatic encroachment into tropical moist forests in the past two decades in all six countries. We summarized key recommendations to help the forest conservation effort of existing policy frameworks. Full article
(This article belongs to the Special Issue Dynamic Monitoring of Forest Resources Based on Multi-Source Remote Sensing Data)
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28 pages, 6141 KiB  
Article
Detection of DRFM Deception Jamming Based on Diagonal Integral Bispectrum
by Dianxing Sun, Ao Li, Hao Ding and Jifeng Wei
Remote Sens. 2025, 17(11), 1957; https://doi.org/10.3390/rs17111957 - 5 Jun 2025
Viewed by 171
Abstract
The transponder-style deception jamming implemented by Digital Radio Frequency Memory (DRFM) exhibits high similarity to real target radar echoes, while traditional detection methods suffer severe performance degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a DRFM active [...] Read more.
The transponder-style deception jamming implemented by Digital Radio Frequency Memory (DRFM) exhibits high similarity to real target radar echoes, while traditional detection methods suffer severe performance degradation under low signal-to-noise ratio (SNR) conditions. To address this issue, this paper proposes a DRFM active deception jamming detection method based on diagonal integral bispectrum, aiming to overcome the bottleneck of jamming detection under low-SNR conditions. By establishing a harmonic effect signal model for DRFM deception jamming, the cross-term generation mechanism in the bispectrum domain is revealed: the jamming signal generates dense cross-terms due to harmonic distortion, whereas the real target energy exhibits single-peak aggregation. To quantify this difference, the Diagonal Integral Bispectrum Relative Peak Height (DIBRP) is proposed to characterize the energy aggregation of true and false targets in the diagonal integral bispectrum, and the Diagonal Integral Bispectrum Approximate Entropy (DIBAE) is introduced to describe their complexity. A joint detection framework combining the DIBRP-DIBAE dual-feature space and a polynomial kernel support vector machine (SVM) is constructed to achieve active deception jamming detection. The proposed method demonstrates excellent performance under low-SNR conditions. Simulations and experimental results show that the correct detection rate reaches 92% at a jamming-to-signal ratio (JSR) and SNR of 0 dB, validating the effectiveness of the algorithm. Full article
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21 pages, 14200 KiB  
Article
A Re-Identification Framework for Visible and Thermal-Infrared Aerial Remote Sensing Images with Large Differences of Elevation Angles
by Chunhui Zhao, Wenxuan Wang, Yiming Yan, Baoyu Ge, Wei Hou and Fengjiao Gao
Remote Sens. 2025, 17(11), 1956; https://doi.org/10.3390/rs17111956 - 5 Jun 2025
Viewed by 179
Abstract
Visible and thermal-infrared re-identification (VTI-ReID) based on aerial images is a challenging task due to the large range of elevation angles, which exacerbates the modality differences between different modalities. The substantial modality gap makes it challenging for existing methods to extract identity information [...] Read more.
Visible and thermal-infrared re-identification (VTI-ReID) based on aerial images is a challenging task due to the large range of elevation angles, which exacerbates the modality differences between different modalities. The substantial modality gap makes it challenging for existing methods to extract identity information from aerial images captured at wide elevation angles. This limitation significantly reduces VTI-ReID accuracy. This issue is particularly pronounced in elongated targets. To address this issue, a robust framework for extracting identity representation (RIRE) is proposed, specifically designed for VTI-ReID in aerial cross-modality images. This framework adopts a mapping method based on global representation decomposition and local representation aggregation. It effectively extracts features related to identity from aerial images and aligns the global representations of images captured from different angles within the same identity space. This approach enhances the adaptability of the VTI-ReID task to elevation angle differences. To validate the effectiveness of the proposed framework, a dataset group for elongated target VTI-ReID based on unmanned aerial vehicle (UAV)-captured data has been created. Extensive evaluations of the proposed framework on the proposed dataset group indicate that the framework significantly improves the robustness of the extracted identity information for elongated targets in aerial images, thereby enhancing the accuracy of VTI-ReID. Full article
(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)
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28 pages, 12784 KiB  
Article
Nonlinear Interactions and Dynamic Analysis of Ecosystem Resilience and Human Activities in China’s Potential Urban Agglomerations
by Xinyu Wang, Shidong Ge, Yaqiong Xu, László Kollányi and Tian Bai
Remote Sens. 2025, 17(11), 1955; https://doi.org/10.3390/rs17111955 - 5 Jun 2025
Viewed by 193
Abstract
Understanding the nonlinear relationship between human activity intensity (HAI) and ecosystem resilience (ER) is crucial for sustainability, yet underdeveloped areas are often overlooked. This study examines the Xuzhou Urban Agglomeration (XZUA) from 2012 to 2022, creating a framework to assess both ER and [...] Read more.
Understanding the nonlinear relationship between human activity intensity (HAI) and ecosystem resilience (ER) is crucial for sustainability, yet underdeveloped areas are often overlooked. This study examines the Xuzhou Urban Agglomeration (XZUA) from 2012 to 2022, creating a framework to assess both ER and HAI. Both frameworks utilize multi-source datasets, such as remote sensing, statistical yearbooks, and geospatial data. The ER framework uniquely combines dynamic and static indicators, while the HAI framework differentiates explicit and implicit human activity dimensions. We used spatial analysis, the Optimal Parameter Geodetector (OPGD), and Multi-Scale Geographically Weighted Regression (MGWR) to examine the nonlinear spatiotemporal interaction between HAI and ER. Results show the following: (1) ER exhibited a “shock-recovery” pattern with a net decline of 3.202%, while HAI followed a nonlinear “rise-fall” trend with a net decrease of 0.800%. (2) Spatial mismatches between HAI and ER intensified over time. (3) The negative correlation in high-HAI regions remained stable, whereas neighboring low-HAI areas deteriorated, indicating a spillover effect. (4) OPGD identified the change in HAI (Sen’s slope) as the primary driver of ER change (q = 0.512), with the strongest interaction observed between HAI Sen’s slope and precipitation (q = 0.802). (5) Compared to HAI intensity (mean), its temporal variation had a more spatially stable influence on ER. These findings offer insights for ecological management and sustainable planning in underdeveloped regions, highlighting the need for targeted HAI and ER interventions. Full article
(This article belongs to the Special Issue Remote Sensing and Geoinformatics in Sustainable Development)
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24 pages, 2276 KiB  
Article
Key Environmental Drivers of Summer Phytoplankton Size Class Variability and Decadal Trends in the Northern East China Sea
by Jung-Woo Park, Huitae Joo, Hyo Keun Jang, Jae Joong Kang, Joon-Soo Lee and Changsin Kim
Remote Sens. 2025, 17(11), 1954; https://doi.org/10.3390/rs17111954 - 5 Jun 2025
Viewed by 245
Abstract
Phytoplankton size classes (PSC), which categorize phytoplankton into pico- (<2 µm), nano- (2–20 µm), and microphytoplankton (>20 µm), have been widely used to describe functional group responses to environmental variability. Distribution of PSCs heavily influences marine ecosystems and biogeochemical processes. Despite the importance [...] Read more.
Phytoplankton size classes (PSC), which categorize phytoplankton into pico- (<2 µm), nano- (2–20 µm), and microphytoplankton (>20 µm), have been widely used to describe functional group responses to environmental variability. Distribution of PSCs heavily influences marine ecosystems and biogeochemical processes. Despite the importance of PSC distributions, especially in the face of climate change, long-term studies on PSC variability and its driving factors are lacking. This study aimed to identify the key environmental drivers affecting summer PSC variability in the northern East China Sea (NECS) by analyzing 27 years (1998–2024) of satellite-derived data. Statistical analyses using random forest and multiple linear regression models revealed that euphotic depth (Zeu) and suspended particulate matter (SPM) were the primary factors influencing PSC variation; deeper Zeu values favored smaller picophytoplankton, whereas higher SPM concentrations supported larger PSCs. Long-term trend analysis showed a clear shift toward increasing picophytoplankton contributions (+2.4% per year), with corresponding declines in nano- and microphytoplankton levels (2.2% and 0.4% annually, respectively). These long-term changes are hypothesized to result from a persistent decline in SPM concentrations, which modulate light attenuation and nutrient dynamics in the euphotic zone. Marine heat waves intensify these shifts by promoting picophytoplankton dominance through enhanced stratification and reduced nutrient availability. These findings underscore the need for continuous monitoring to inform ecosystem management and predict the impacts of climate change in the NECS. Full article
(This article belongs to the Special Issue Water Quality Monitoring and Quantitative Analysis in Marine and Inland Areas Utilizing Remote Sensing Techniques)
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27 pages, 43277 KiB  
Article
A Hybrid VMD-BO-GRU Method for Landslide Displacement Prediction in the High-Mountain Canyon Area of China
by Bao Liu, Jiahuan Xu, Jiangbo Xi, Chaoying Zhao, Xiaosong Feng, Chaofeng Ren and Haixing Shang
Remote Sens. 2025, 17(11), 1953; https://doi.org/10.3390/rs17111953 - 5 Jun 2025
Viewed by 163
Abstract
Landslides are major geological hazards that pose serious threats to life and property, particularly in the high-mountain canyon regions of Sichuan, Yunnan, and southeastern Tibet. Displacement prediction plays a critical role in disaster prevention and mitigation. In recent years, machine learning methods based [...] Read more.
Landslides are major geological hazards that pose serious threats to life and property, particularly in the high-mountain canyon regions of Sichuan, Yunnan, and southeastern Tibet. Displacement prediction plays a critical role in disaster prevention and mitigation. In recent years, machine learning methods based on InSAR data have achieved significant breakthroughs in landslide forecasting. However, models relying solely on a single data-driven approach may fail to fully capture the complex physical mechanisms of landslides, affecting both the reliability and interpretability of predictions. Therefore, developing effective landslide displacement prediction models is essential. The paper introduces a model designed to forecast the landslide displacement using Variational Mode Decomposition (VMD), Bayesian Optimization (BO), and Gated Recurrent Units (GRU). First, wavelet analysis is employed to identify the trend component in the landslide displacement data. Then, the total displacement is separated into its trend and periodic components through the application of the Variational Mode Decomposition (VMD) technique. A wide range of influencing factors is introduced, and Utilizing Grey Relational Analysis, we evaluate the interplay between contributing factors and all components of landslide displacement, both trend and periodic. Prediction models incorporate the trend and periodic terms, alongside the contributing factors, as input variables. The overall displacement is computed by summing the trend and periodic terms series using the Mianshawan landslide as a case study, experimental studies were conducted with landslide data from January 2019 to December 2022 with a Root Mean Squared Error (RMSE) of 0.402, Mean Absolute Error (MAE) of 0.187, Mean Absolute Percentage Error (MAPE) of 2.05%, and a coefficient of determination (R²) of 0.998. These findings indicate that, compared to traditional methods, our model delivers remarkable improvements in performance, offering higher prediction accuracy and greater reliability in the landslide forecasting task for the Mianshawan area. Full article
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19 pages, 3892 KiB  
Article
Impact of Fengyun-4A Atmospheric Motion Vector Data Assimilation on PM2.5 Simulation
by Kaiqiang Gu, Jinyan Wang, Shixiang Su, Jiangtao Zhu, Yu Zhang, Feifan Bian and Yi Yang
Remote Sens. 2025, 17(11), 1952; https://doi.org/10.3390/rs17111952 - 5 Jun 2025
Viewed by 191
Abstract
PM2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM2.5 simulation. This study simulated a representative PM2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation [...] Read more.
PM2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM2.5 simulation. This study simulated a representative PM2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation of infrared atmospheric motion vector (AMV) data from the Fengyun-4A (FY-4A) satellite. A comprehensive analysis was conducted to examine the meteorological characteristics of the event and their influence on PM2.5 concentration simulations. The results demonstrate that the assimilation of FY-4A infrared AMV data significantly enhanced the simulation performance of meteorological variables, particularly improving the wind field and capturing local and small-scale wind variations. Moreover, PM2.5 concentrations simulated with AMV assimilation showed improved spatial and temporal agreement with ground-based observations, reducing the root mean square error (RMSE) by 8.2% and the mean bias (MB) by 15.2 µg/m3 relative to the control (CTL) experiment. In addition to regional improvements, the assimilation notably enhanced PM2.5 simulation accuracy in severely polluted cities, such as Tangshan and Tianjin. Mechanistic analysis revealed that low wind speeds and weak atmospheric divergence restricted pollutant dispersion, resulting in higher near-surface concentrations. This was exacerbated by cooler nighttime temperatures and a lower planetary boundary layer height (PBLH). These findings underscore the utility of assimilating satellite-derived wind products to enhance regional air quality modeling and forecasting accuracy. This study highlights the potential of FY-4A infrared AMV data in improving regional pollution simulations, offering scientific support for the application of next-generation Chinese geostationary satellite data in numerical air quality forecasting. Full article
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36 pages, 6489 KiB  
Article
Improving SAR Ship Detection Accuracy by Optimizing Polarization Modes: A Study of Generalized Compact Polarimetry (GCP) Performance
by Guo Song, Yunkai Deng, Heng Zhang, Xiuqing Liu and Sheng Chang
Remote Sens. 2025, 17(11), 1951; https://doi.org/10.3390/rs17111951 - 5 Jun 2025
Viewed by 247
Abstract
The debate surrounding the optimal polarimetric modes—compact polarimetry (CP) versus dual polarization (DP)—for PolSAR ship detection persists. This study pioneers a systematic investigation into Generalized Compact Polarimetry (GCP) for this application. By synthesizing and evaluating 143 distinct GCP configurations from fully polarimetric data, [...] Read more.
The debate surrounding the optimal polarimetric modes—compact polarimetry (CP) versus dual polarization (DP)—for PolSAR ship detection persists. This study pioneers a systematic investigation into Generalized Compact Polarimetry (GCP) for this application. By synthesizing and evaluating 143 distinct GCP configurations from fully polarimetric data, this study presents the first comprehensive comparison of their ship detection performance against conventional modes using Target-to-Clutter Ratio (TCR) and deep learning-based accuracy (AP50). Experiments on the FPSD dataset reveal that an optimized GCP mode (e.g., ellipse/orientation: [−10, −5]) consistently outperforms traditional CP and DP modes, yielding TCR gains of 0.2–2.7 dB. This translates to AP50 improvements of 0.5–4.7% (Faster R-CNN) and 0.1–5.5% (RetinaNet) over five common baseline modes. Crucially, this enhancement arises from optimizing the interaction between the polarization mode and target/clutter scattering characteristics rather than algorithmic improvements, supporting the proposed “optimization from the information source” strategy. These findings offer significant implications for future PolSAR system design and operational mode selection. 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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22 pages, 6037 KiB  
Article
Mapping Wheat Stem Sawfly (Cephus cinctus Norton) Infestations in Spring and Winter Wheat Fields via Multiway Modelling of Multitemporal Sentinel 2 Images
by Lochlin S. Ermatinger, Scott L. Powell, Robert K. D. Peterson and David K. Weaver
Remote Sens. 2025, 17(11), 1950; https://doi.org/10.3390/rs17111950 - 5 Jun 2025
Viewed by 241
Abstract
The wheat stem sawfly (WSS, Cephus cinctus Norton) is a major insect pest of wheat (Triticum aestivum L.) in North America. Few management tactics exist, and quantifying their efficacy is confounded by the difficulty in monitoring infestation at the field scale. Accurate [...] Read more.
The wheat stem sawfly (WSS, Cephus cinctus Norton) is a major insect pest of wheat (Triticum aestivum L.) in North America. Few management tactics exist, and quantifying their efficacy is confounded by the difficulty in monitoring infestation at the field scale. Accurate estimates of WSS infestation are cost prohibitive as they rely on comprehensive stem dissection surveys due to the concealed life cycle of the pest. Consolidating the available management tactics into an effective strategy requires inexpensive, spatially explicit estimates of WSS infestation that are compatible with the large field sizes dryland wheat is often sown to. Therefore, we investigated using multitemporal satellite passive remote sensing (RS) to estimate various metrics of WSS infestation collected from field surveys at the subfield scale. To achieve this, we dissected 43,155 individual stems collected from 1158 unique locations across 9 production wheat fields in Montana, USA. The dissected stem samples from each location were then quantified using the following metrics: the proportion of total WSS-infested stems, proportion of stems with more than one node burrowed through (adequate WSS infestations), and proportion of WSS cut stems only. Cloud-free Sentinel 2 images were collected from Google Earth Engine for each field from across the growing season and sparse multiway partial least squares regression was used to produce a model for total WSS infestations, adequate WSS infestations, and WSS cut stems, for each sampled field. Upon comparing the performance of these models, we found that, on average, the metrics for total (R2 = 0.57) and adequate WSS infestations (R2 = 0.57) were more accurately estimated than WSS cut (R2 = 0.34). The results of this study indicate that multitemporal RS can help estimate total and adequate WSS infestations, but more holistic methods of field level sensing should be explored, especially for estimating WSS cutting. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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28 pages, 24251 KiB  
Article
Anthropogenic and Climate-Induced Water Storage Dynamics over the Past Two Decades in the China–Mongolia Arid Region Adjacent to Altai Mountain
by Yingjie Yan, Yuan Su, Hongfei Zhou, Siyu Wang, Linlin Yao and Dashlkham Batmunkh
Remote Sens. 2025, 17(11), 1949; https://doi.org/10.3390/rs17111949 - 4 Jun 2025
Viewed by 277
Abstract
The China–Mongolia arid region adjacent to the Altai Mountain (CMA) has a sensitive ecosystem that relies heavily on both terrestrial water (TWS) and groundwater storage (GWS). However, during the 2003–2016 period, the CMA experienced significant glacier retreat, lake shrinkage, and grassland degradation. To [...] Read more.
The China–Mongolia arid region adjacent to the Altai Mountain (CMA) has a sensitive ecosystem that relies heavily on both terrestrial water (TWS) and groundwater storage (GWS). However, during the 2003–2016 period, the CMA experienced significant glacier retreat, lake shrinkage, and grassland degradation. To illuminate the TWS and GWS dynamics in the CMA and the dominant driving factors, we employed high-resolution (0.1°) GRACE (Gravity Recovery and Climate Experiment) data generated through random forest (RF) combined with residual correction. The downscaled data at a 0.1° resolution illustrate the spatial heterogeneity of TWS and GWS depletion. The highest TWS and GWS decline rates were both on the north slope of the Tianshan River Basin (NTRB) of the Junggar Basin of Northwestern China (JBNWC) (27.96 mm/yr and −32.98 mm/yr, respectively). Human impact played a primary role in TWS decreases in the JBNWC, with a relative contribution rate of 62.22% compared to the climatic contribution (37.78%). A notable shift—from climatic (2002–2010) to anthropogenic factors (2011–2020)—was observed as the primary driver of TWS decline in the Great Lakes Depression region of western Mongolia (GLDWM). To maintain ecological stability and promote sustainable regional development, effective action is urgently required to save essential TWS from further depletion. Full article
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28 pages, 4962 KiB  
Article
YOLO-Ssboat: Super-Small Ship Detection Network for Large-Scale Aerial and Remote Sensing Scenes
by Yiliang Zeng, Xiuhong Wang, Jinlin Zou and Hongtao Wu
Remote Sens. 2025, 17(11), 1948; https://doi.org/10.3390/rs17111948 - 4 Jun 2025
Viewed by 300
Abstract
Enhancing the detection capabilities of marine vessels is crucial for maritime security and intelligence acquisition. However, accurately identifying small ships in complex oceanic environments remains a significant challenge, as these targets are frequently obscured by ocean waves and other disturbances, compromising recognition accuracy [...] Read more.
Enhancing the detection capabilities of marine vessels is crucial for maritime security and intelligence acquisition. However, accurately identifying small ships in complex oceanic environments remains a significant challenge, as these targets are frequently obscured by ocean waves and other disturbances, compromising recognition accuracy and stability. To address this issue, we propose YOLO-ssboat, a novel small-target ship recognition algorithm based on the YOLOv8 framework. YOLO-ssboat integrates the C2f_DCNv3 module to extract fine-grained features of small vessels while mitigating background interference and preserving critical target details. Additionally, it employs a high-resolution feature layer and incorporates a Multi-Scale Weighted Pyramid Network (MSWPN) to enhance feature diversity. The algorithm further leverages an improved multi-attention detection head, Dyhead_v3, to refine the representation of small-target features. To tackle the challenge of wake waves from moving ships obscuring small targets, we introduce a gradient flow mechanism that improves detection efficiency under dynamic conditions. The Tail Wave Detection Method synergistically integrates gradient computation with target detection techniques. Furthermore, adversarial training enhances the network’s robustness and ensures greater stability. Experimental evaluations on the Ship_detection and Vessel datasets demonstrate that YOLO-ssboat outperforms state-of-the-art detection algorithms in both accuracy and stability. Notably, the gradient flow mechanism enriches target feature extraction for moving vessels, thereby improving detection accuracy in wake-disturbed scenarios, while adversarial training further fortifies model resilience. These advancements offer significant implications for the long-range monitoring and detection of maritime vessels, contributing to enhanced situational awareness in expansive oceanic environments. Full article
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23 pages, 5811 KiB  
Article
Multi-Attitude Hybrid Network for Remote Sensing Hyperspectral Images Super-Resolution
by Chi Chen, Yunhan Sun, Xueyan Hu, Ning Zhang, Hao Feng, Zheng Li and Yongcheng Wang
Remote Sens. 2025, 17(11), 1947; https://doi.org/10.3390/rs17111947 - 4 Jun 2025
Viewed by 289
Abstract
Benefiting from the development of deep learning, the super-resolution technology for remote sensing hyperspectral images (HSIs) has achieved impressive progress. However, due to the high coupling of complex components in remote sensing HSIs, it is challenging to achieve a complete characterization of the [...] Read more.
Benefiting from the development of deep learning, the super-resolution technology for remote sensing hyperspectral images (HSIs) has achieved impressive progress. However, due to the high coupling of complex components in remote sensing HSIs, it is challenging to achieve a complete characterization of the internal information, which in turn limits the precise reconstruction of detailed texture and spectral features. Therefore, we propose the multi-attitude hybrid network (MAHN) for extracting and characterizing information from multiple feature spaces. On the one hand, we construct the spectral hypergraph cross-attention module (SHCAM) and the spatial hypergraph self-attention module (SHSAM) based on the high and low-frequency features in the spectral and the spatial domains, respectively, which are used to capture the main structure and detail changes within the image. On the other hand, high-level semantic information in mixed pixels is parsed by spectral mixture analysis, and semantic hypergraph 3D module (SH3M) are constructed based on the abundance of each category to enhance the propagation and reconstruction of semantic information. Furthermore, to mitigate the domain discrepancies among features, we introduce a sensitive bands attention mechanism (SBAM) to enhance the cross-guidance and fusion of multi-domain features. Extensive experiments demonstrate that our method achieves optimal reconstruction results compared to other state-of-the-art algorithms while effectively reducing the computational complexity. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning Applied to Remote Sensing Image Analysis)
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29 pages, 4263 KiB  
Article
An Autofocus Method for Long Synthetic Time and Large Swath Synthetic Aperture Radar Imaging Under Multiple Non-Ideal Factors
by Kaiwen Zhu, Zhen Wang, Zehua Dong, Han Li and Linghao Li
Remote Sens. 2025, 17(11), 1946; https://doi.org/10.3390/rs17111946 - 4 Jun 2025
Viewed by 186
Abstract
Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including [...] Read more.
Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including atmospheric disturbances, orbital perturbations, and antenna vibrations. degrade imaging performance, causing defocusing and ghost targets. Furthermore, the long synthetic time and large imaging swath further enlarge the temporal and spatial variability of these factors and seriously degrade the imaging effect. These inherent challenges make autofocusing indispensable for SAR imaging with a long synthetic time and large swath. In this paper, a novel autofocus method specifically designed to address these non-ideal factors is proposed for SAR imaging with a long synthetic time and large swath. The innovation of the method mainly consists of two parts. The first is the autofocus for multiple non-ideal factors, which is accomplished by an improved phase gradient autofocus (PGA) equipped with amplitude error estimation and discrete windowing. PGA with amplitude error estimation can solve the problem of defocus, and discrete windowing can focus the energy of paired echoes. The second is an error fusion and interpolation method for a long synthetic time and large swath. This method fuses errors among sub-apertures in the long synthetic time and can fulfill autofocus for blocks where strong scatterers are not sufficient in the large swath. The proposed method can effectively achieve SAR focusing with a long synthetic time and large swath, considering spatial and temporal variant non-ideal factors. Point target simulations and distributed target simulations based on real scenarios are conducted to validate the proposed method. Full article
(This article belongs to the Special Issue High Earth Orbit Spaceborne SAR Systems, Technologies, and Applications)
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20 pages, 4039 KiB  
Article
Ionospheric TEC and ROT Analysis with Signal Combinations of QZSS Satellites in the Korean Peninsula
by Byung-Kyu Choi, Dong-Hyo Sohn, Junseok Hong, Jong-Kyun Chung, Kwan-Dong Park, Hyung Keun Lee, Jeongrae Kim and Heon Ho Choi
Remote Sens. 2025, 17(11), 1945; https://doi.org/10.3390/rs17111945 - 4 Jun 2025
Viewed by 249
Abstract
This study investigates the performance of three different signal combinations (L1-L2, L1-L5, and L2-L5) for estimating ionospheric total electron content (TEC) and the rate of TEC (ROT) using Quasi-Zenith Satellite System (QZSS) observations over the Korean Peninsula. GNSS data collected from nine stations [...] Read more.
This study investigates the performance of three different signal combinations (L1-L2, L1-L5, and L2-L5) for estimating ionospheric total electron content (TEC) and the rate of TEC (ROT) using Quasi-Zenith Satellite System (QZSS) observations over the Korean Peninsula. GNSS data collected from nine stations across the Korean Peninsula were analyzed for the period from Day of Year (DOY) 1 to 182 in 2024. Differential Code Bias (DCB) was estimated for QZSS satellites, showing high temporal stability with daily variations within ±0.3 ns. The TEC values derived from three different signal combinations were compared with the CODE Global Ionospheric Map (GIM). Compared to other combinations, the L1-L5 pair shows the closest agreement with the CODE GIM, yielding a mean bias of +0.25 TEC units (TECU) with a root mean square (RMS) of 3.59 TECU. In addition, the ROT analysis over the consecutive six days revealed that the L1-L5 combination consistently exhibited the lowest RMS values of about 0.027 TECU compared to other signal pairs. As a result, we suggest that the L1-L5 combination can provide better performance for QZSS-based ionospheric monitoring and TEC studies. Full article
(This article belongs to the Special Issue Advances in GNSS Remote Sensing for Ionosphere Observation)
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21 pages, 9519 KiB  
Article
Robust Pose Estimation for Noncooperative Spacecraft Under Rapid Inter-Frame Motion: A Two-Stage Point Cloud Registration Approach
by Mingyuan Zhao and Long Xu
Remote Sens. 2025, 17(11), 1944; https://doi.org/10.3390/rs17111944 - 4 Jun 2025
Viewed by 177
Abstract
This paper addresses the challenge of robust pose estimation for spacecraft under rapid inter-frame motion, proposing a two-stage point cloud registration framework. The first stage computes coarse pose estimation by leveraging Fast Point Feature Histogram (FPFH) descriptors with random sample and consensus (RANSAC) [...] Read more.
This paper addresses the challenge of robust pose estimation for spacecraft under rapid inter-frame motion, proposing a two-stage point cloud registration framework. The first stage computes coarse pose estimation by leveraging Fast Point Feature Histogram (FPFH) descriptors with random sample and consensus (RANSAC) for correspondence matching, effectively handling significant positional displacements. The second stage refines the solution through geometry-aware fine registration using raw point cloud data, enhancing precision through a multi-scale iterative ICP-like framework. To validate the approach, we simulate time-of-flight (ToF) sensor measurements by rendering NASA’s public 3D spacecraft models and obtain 3D point clouds by back-projecting the depth measurements to 3D space. Comprehensive experiments demonstrate superior performance over several state-of-the-art methods in both accuracy and robustness under rapid inter-frame motion scenarios. The dual-stage architecture proves effective in maintaining tracking continuity while mitigating error accumulation from fast relative motion, showing promise for autonomous spacecraft proximity operations. Full article
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28 pages, 9711 KiB  
Article
Analyzing the Adversarial Robustness and Interpretability of Deep SAR Classification Models: A Comprehensive Examination of Their Reliability
by Tianrui Chen, Limeng Zhang, Weiwei Guo, Zenghui Zhang and Mihai Datcu
Remote Sens. 2025, 17(11), 1943; https://doi.org/10.3390/rs17111943 - 4 Jun 2025
Viewed by 213
Abstract
Deep neural networks (DNNs) have shown strong performance in synthetic aperture radar (SAR) image classification. However, their “black-box” nature limits interpretability and poses challenges for robustness, which is critical for sensitive applications such as disaster assessment, environmental monitoring, and agricultural insurance. This study [...] Read more.
Deep neural networks (DNNs) have shown strong performance in synthetic aperture radar (SAR) image classification. However, their “black-box” nature limits interpretability and poses challenges for robustness, which is critical for sensitive applications such as disaster assessment, environmental monitoring, and agricultural insurance. This study systematically evaluates the adversarial robustness of five representative DNNs (VGG11/16, ResNet18/101, and A-ConvNet) under a variety of attack and defense settings. Using eXplainable AI (XAI) techniques and attribution-based visualizations, we analyze how adversarial perturbations and adversarial training affect model behavior and decision logic. Our results reveal significant robustness differences across architectures, highlight interpretability limitations, and suggest practical guidelines for building more robust SAR classification systems. We also discuss challenges associated with large-scale, multi-class land use and land cover (LULC) classification under adversarial conditions. Full article
(This article belongs to the Special Issue Advancing Synthetic Aperture Radar: Imaging, Processing, and Applications in Remote Sensing)
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29 pages, 18966 KiB  
Article
Aerial Biological Target Classification Based on Time–Frequency Multi-Scale Feature Fusion Network
by Lianjun Wang, Rui Wang, Weidong Li, Jiangtao Wang, Yujia Yan and Cheng Hu
Remote Sens. 2025, 17(11), 1942; https://doi.org/10.3390/rs17111942 - 4 Jun 2025
Viewed by 189
Abstract
Migrating insects and birds are the primary biological targets in the aerial ecosystem. Radar is a powerful tool for monitoring and studying aerial animals. However, accurately identifying insects and birds based on radar observations has remained an unsolved problem. To address this research [...] Read more.
Migrating insects and birds are the primary biological targets in the aerial ecosystem. Radar is a powerful tool for monitoring and studying aerial animals. However, accurately identifying insects and birds based on radar observations has remained an unsolved problem. To address this research gap, this paper proposed an intelligent classification method based on a novel multi-scale time–frequency deep feature fusion network (MSTFF-Net). A comprehensive radar dataset of aerial biological targets was established. The analysis revealed that radar cross section (RCS) features are insufficient to support insect and bird classification tasks, as aerial biological targets may be detected in radar sidelobes, leading to uncertainty in RCS values. Additionally, the motion characteristics of insects and birds are complex, with diverse motion patterns observed during limited observation periods. Simple feature extraction and classification algorithms struggle to achieve accurate classification of insects and birds, making aerial biological target classification a challenging task. Based on the analysis of insect and bird features, the designed MSTFF-Net consists of the following three modules. The first module is the amplitude sequence extraction module, which abandons traditional RCS features and instead extracts the dynamic variation features of the echo amplitude. The second module is the time–frequency feature extraction module, which extracts multi-scale time–frequency features to address the complex motion characteristics of biological targets. The third module is the adaptive feature fusion attention module, which captures the correlation between features to adjust feature weights and achieve the fusion of different feature types with varying representations. The reliability of the classification algorithm was finally verified using a manually selected dataset, which includes typical bird, insect, and other unknown targets. The algorithm proposed in this paper achieved a classification accuracy of 94.0% for insect and bird targets. Full article
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16 pages, 9188 KiB  
Technical Note
ensembleDownscaleR: R Package for Bayesian Ensemble Averaging of PM2.5 Geostatistical Downscalers
by Wyatt G. Madden, Meng Qi, Yang Liu and Howard H. Chang
Remote Sens. 2025, 17(11), 1941; https://doi.org/10.3390/rs17111941 - 4 Jun 2025
Viewed by 182
Abstract
Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM2.5) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM2.5 are important for supporting epidemiological and health [...] Read more.
Ambient fine particulate matter of size less than 2.5 μm in aerodynamic diameter (PM2.5) is a key ambient air pollutant that has been linked to numerous adverse health outcomes. Reliable estimates of PM2.5 are important for supporting epidemiological and health impact assessment studies. Precise measurements of PM2.5 are available through networks of monitors; however, these are spatially sparse and temporally incomplete. Chemical transport model (CTM) simulations and satellite-retrieved aerosol optical depth (AOD) measurements are two data sources that have been used to develop prediction models for PM2.5 at fine spatial resolutions with increased spatial coverage. As part of the Multi-Angle Imager for Aerosols (MAIA) project, a geostatistical regression model has been developed to bias-correct AOD, followed by Bayesian ensemble averaging to gap-fill missing AOD values with CTM simulations. Here, we present a suite of statistical software (available in the R package ensembleDownscaleR) to facilitate the adaptation of this modeling approach to other settings and air quality modeling applications. We describe the Bayesian ensemble averaging approach, model specifications, estimation methods, and evaluation via cross-validation that is implemented in the software. We also provide a case study of estimating PM2.5 using 2018 data from the Los Angeles metropolitan area with an accompanying tutorial. All code is fully reproducible and available on GitHub, data are made on Zenodo, and the ensembleDownscaleR package is available for download on GitHub. Full article
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14 pages, 705 KiB  
Technical Note
Sensing Lunar Dust Density Using Radio Science Signals of Opportunity
by Kamal Oudrhiri, Yu-Ming Yang and Daniel Erwin
Remote Sens. 2025, 17(11), 1940; https://doi.org/10.3390/rs17111940 - 4 Jun 2025
Viewed by 164
Abstract
Previous lunar missions, such as Surveyor, Apollo, and the Lunar Atmosphere and Dust Environment Explorer (LADEE), have played a pivotal role in advancing our understanding of the lunar exosphere’s dynamics and its relationship with solar wind flux. The insights gained from these missions [...] Read more.
Previous lunar missions, such as Surveyor, Apollo, and the Lunar Atmosphere and Dust Environment Explorer (LADEE), have played a pivotal role in advancing our understanding of the lunar exosphere’s dynamics and its relationship with solar wind flux. The insights gained from these missions have laid a strong foundation for our current knowledge. However, due to insufficient near-surface observations, the scientific community has faced challenges in interpreting the phenomena of lunar dust lofting and levitation. This paper introduces the concept of signals of opportunity (SoOP), which utilizes radio occultation (RO) to retrieve the near-surface dust density profile on the Moon. Gravity Recovery and Interior Laboratory (GRAIL) radio science beacon (RSB) signals are used to demonstrate this method. By mapping the concentration of lunar near-surface dust using RO, we aim to enhance our understanding of how charged lunar dust interacts with surrounding plasma, thereby contributing to future research in this field and supporting human exploration of the Moon. Additionally, the introduced SoOP will be able to provide observational constraints to physical model development related to lunar surface particle sputtering and the reactions of near-surface dust in the presence of solar wind and electrostatically charged dust grains. Full article
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32 pages, 14098 KiB  
Article
Characteristics and Climatic Indications of Ice-Related Landforms at Low Latitudes (0°–±30°) on Mars
by Yan Zhou, Yu-Yan Sara Zhao, Xiaoting Xu and Yiran Wang
Remote Sens. 2025, 17(11), 1939; https://doi.org/10.3390/rs17111939 - 4 Jun 2025
Viewed by 326
Abstract
The deposition and evolution of ice-rich materials on Martian surfaces offer valuable insights into climatic evolution and the potential driving forces behind global climate change. Substantial evidence indicates that the mid-latitudes of Mars played a crucial role in the formation and development of [...] Read more.
The deposition and evolution of ice-rich materials on Martian surfaces offer valuable insights into climatic evolution and the potential driving forces behind global climate change. Substantial evidence indicates that the mid-latitudes of Mars played a crucial role in the formation and development of glacial and periglacial landforms during the Amazonian period. However, few studies have comprehensively examined ice-related landforms in the low-latitude region of Mars. Whether extensive glacial activity has occurred in the equatorial region of Mars and whether there are any potential geological records of such activities remain unclear. In this study, we analyzed remote sensing data from the Martian equatorial region (0°–±30°) and identified existing glacial/periglacial features, as well as remnant landforms of past glaciation. Our findings reveal that glaciation at low latitudes is more widespread than previously thought, with ice-related remnants extending as far equatorward as 13°N in the northern hemisphere and 19°S in the southern hemisphere, highlighting a broader latitudinal range for ice-related landforms. These landforms span multiple episodes of Martian geological history, supporting the hypothesis on the occurrence of repeated glaciation and various high-obliquity events. Evidence of dynamic interactions between ice deposition and sublimation in low-latitude regions demonstrates substantial ice loss over time, leaving ice-related remnants that provide valuable insights into Mars’ climatic evolution. Based on volumetric estimates of the concentric crater fill (CCF), the low-latitude regions of Mars may contain up to 1.05 × 103 km3 of ice. This corresponds to a global equivalent ice layer thickness ranging from 21.7 mm (assuming a pore ice with 30% ice content) to 65.1 mm (assuming glacial ice with 90% ice content), suggesting a potentially greater low-latitude ice reservoir than previously recognized. Full article
(This article belongs to the Special Issue Planetary Geologic Mapping and Remote Sensing (Second Edition))
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23 pages, 996 KiB  
Article
3-D Moving Target Localization in Multistatic HFSWR: Efficient Algorithm and Performance Analysis
by Xun Zhang, Jun Geng, Yunlong Wang and Yijia Guo
Remote Sens. 2025, 17(11), 1938; https://doi.org/10.3390/rs17111938 - 3 Jun 2025
Viewed by 251
Abstract
High-frequency surface wave radar (HFSWR) is unable to measure the target’s altitude information due to its limited antenna aperture in the elevation dimension. This paper focuses on the 3-D localization problem for moving targets within the line of sight (LOS) in multistatic HFSWR. [...] Read more.
High-frequency surface wave radar (HFSWR) is unable to measure the target’s altitude information due to its limited antenna aperture in the elevation dimension. This paper focuses on the 3-D localization problem for moving targets within the line of sight (LOS) in multistatic HFSWR. For this purpose, the 1-D space angle (SA) measurement is introduced into multistatic HFSWR to perform 3-D joint localization together with bistatic range (BR) and bistatic range rate (BRR) measurements. The target’s velocity can also be estimated due to the inclusion of BRR. In multistatic HFSWR, commonly used azimuth measurements offer no information about the target’s altitude. Since SA is associated with the target’s 3-D coordinates, combining SA measurements from multiple receivers can effectively enhance localization accuracy, particularly in altitude estimation. In this paper, we develop a two-stage localization algorithm that first derives a weighted least-squares (WLS) coarse estimate and then performs an algebraic error reduction (ER) procedure to enhance accuracy. Both stages yield closed-form results, thus ensuring overall computational efficiency. Theoretical analysis shows that the proposed WLS-ER algorithm can asymptotically attain the Cramér–Rao lower bound (CRLB) as the measurement noise decreases. Simulation results demonstrate the effectiveness of the proposed WLS-ER algorithm and highlight the contribution of SA measurements to altitude estimation in multistatic HFSWR. Full article
(This article belongs to the Special Issue HF Surface Wave Radar: Improving Performance and Extending Capabilities)
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24 pages, 960 KiB  
Article
Design of Constant Modulus Radar Waveform for PSD Matching Based on MM Algorithm
by Hao Zheng, Chaojie Qiu, Chenyu Liang and Junkun Yan
Remote Sens. 2025, 17(11), 1937; https://doi.org/10.3390/rs17111937 - 3 Jun 2025
Viewed by 161
Abstract
The power spectral density (PSD) shape of the transmit waveform plays an important role in some fields of radar, such as electronic counter-countermeasures (ECCM), target detection, and target classification. In addition, radar hardware generally requires the waveform to have constant modulus (CM) characteristics. [...] Read more.
The power spectral density (PSD) shape of the transmit waveform plays an important role in some fields of radar, such as electronic counter-countermeasures (ECCM), target detection, and target classification. In addition, radar hardware generally requires the waveform to have constant modulus (CM) characteristics. Therefore, it is a significant problem to synthesize the discrete-time CM waveform from a given PSD. To address this problem, some algorithms have been proposed in the existing literature. In this paper, based on the majorization–minimization (MM) framework, a novel algorithm is proposed to solve this problem. The proposed algorithm can be proved to converge to the stationary point, and the error reduction property can be obtained without the unitary requirements on the discrete Fourier transform (DFT) matrix. To accelerate the convergence rate of the proposed algorithm, three acceleration schemes are developed for the proposed algorithm. Considering a specific algorithm stopping condition, one of the proposed acceleration schemes shows better computation efficiency than the existing algorithms and is more robust to the initial points. Besides, when the DFT matrix is not unitary, the numerical results show that the proposed acceleration scheme has better matching performance compared with the existing algorithms. Full article
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21 pages, 8914 KiB  
Article
Impacts of Extreme Flood and Drought Events on Dish-Shaped Lake Habitats in Poyang Lake Under Altered Hydrological Regimes
by Yifan Xu, Tengfei Hu, Lian-Gang Chen, Hao Lu, Li-Ming Chen, Zhenyu Luan, Qiu Jin and Yong Shi
Remote Sens. 2025, 17(11), 1936; https://doi.org/10.3390/rs17111936 - 3 Jun 2025
Viewed by 255
Abstract
In recent years, the altered hydrological regimes and frequent extreme hydrological events in its watershed have significantly affected the stability and biodiversity of the dish-shaped lakes (DSLs) ecosystem in Poyang Lake. This study uses long-term water level records from the Xingzi hydrological station, [...] Read more.
In recent years, the altered hydrological regimes and frequent extreme hydrological events in its watershed have significantly affected the stability and biodiversity of the dish-shaped lakes (DSLs) ecosystem in Poyang Lake. This study uses long-term water level records from the Xingzi hydrological station, multi-source remote sensing imagery, and field surveys to assess how altered hydrological regimes and frequent extreme hydrological events influence the coupled hydro-ecological evolution of DSLs under different gate-controlled conditions. The results reveal the following: (1) After 2003, average monthly water levels declined by 0.84 m, shifting prolonged inundation depths from the 10.0 to 14.0 m range into the 5.5 to 9.5 m range. Extreme hydrological events disrupted the hydrological regimes, triggering a clear “collapse–recovery” succession in submerged plants and major shifts in shoal wetland vegetation. (2) Gate-controlled DSLs (GC DSLs) mitigated many of these impacts by reducing the autumnal drawdown in the water area change rate to 0.324 km2/d, curbing the upward expansion of emergent and hygrophytic vegetation during high-water-level years, and stabilizing habitats during low-water-level years, although different management strategies and substrate characteristics may still lead to divergent habitat trajectories. (3) The habitat heterogeneity exhibited by the DSLs’ vegetation communities along the elevation gradient had differential effects on migratory birds, and GC DSLs can offer migratory birds relatively stable resting habitats and food resources during extreme hydrological events. The study recommends that DSL management should adopt a hierarchical dynamic regulation strategy to balance natural hydrological fluctuations with human interventions, thereby strengthening the resilience of DSL wetland habitats to extreme hydrological events. Full article
(This article belongs to the Section Ecological Remote Sensing)
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24 pages, 14937 KiB  
Article
AECA-FBMamba: A Framework with Adaptive Environment Channel Alignment and Mamba Bridging Semantics and Details
by Xin Chai, Wenrong Zhang, Zhaoxin Li, Ning Zhang and Xiujuan Chai
Remote Sens. 2025, 17(11), 1935; https://doi.org/10.3390/rs17111935 - 3 Jun 2025
Viewed by 147
Abstract
Large-scale high-resolution (HR) land cover mapping is essential in monitoring the Earth’s surface and addressing critical challenges facing humanity. While weakly supervised methods help to mitigate the scarcity of HR annotations across wide geographic areas, existing approaches struggle with feature extraction instability. To [...] Read more.
Large-scale high-resolution (HR) land cover mapping is essential in monitoring the Earth’s surface and addressing critical challenges facing humanity. While weakly supervised methods help to mitigate the scarcity of HR annotations across wide geographic areas, existing approaches struggle with feature extraction instability. To address this issue, this study proposes AECA-FBMamba, an efficient weakly supervised framework that enhances model perception by stabilizing feature transitions during encoding. Specifically, this work introduces the Adaptive Environment Channel Alignment (AECA) module at the input stage, processing independently grouped color channels to enhance robust channel-wise feature extraction. Additionally, we incorporate the Feature Bridging Mamba (FBMamba) module, which enables smooth receptive field reduction, effectively addressing feature alignment issues when integrating local contexts into global representations. The proposed AECA-FBMamba achieved a 65.27% mIoU on the Chesapeake Bay dataset and a 56.96% mIoU on the Poland dataset. Experiments conducted on these two large-scale datasets demonstrate the method’s effectiveness in automatically updating high-resolution (HR) land cover maps using low-resolution (LR) historical annotations. This framework advances weakly supervised learning in remote sensing and offers solutions for large-scale land cover mapping applications. Full article
(This article belongs to the Section AI Remote Sensing)
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28 pages, 4298 KiB  
Article
Leveraging Open-Source Tools to Analyse Ground-Based Forest LiDAR Data in South Australian Forests
by Spencer O’Keeffe, Bruce H. Thomas, Jim O’Hehir, Jan Rombouts, Michelle Balasso and Andrew Cunningham
Remote Sens. 2025, 17(11), 1934; https://doi.org/10.3390/rs17111934 - 3 Jun 2025
Viewed by 328
Abstract
This paper investigates the application of open-source software and methods for forest LiDAR analysis, with a focus on enhancing forest inventory metrics in the radiata pine forests of South Australia’s Green Triangle region. A semi-systematic survey identified 22 relevant open-source tools, evaluated for [...] Read more.
This paper investigates the application of open-source software and methods for forest LiDAR analysis, with a focus on enhancing forest inventory metrics in the radiata pine forests of South Australia’s Green Triangle region. A semi-systematic survey identified 22 relevant open-source tools, evaluated for their capabilities in inventory metric extraction and practicality for implementation in industrial workflows. Ground truth data from radiata pine forests across multiple development stages provided the basis for validating the tools’ precision, accuracy, and practicality. Results showed that stratified tool selection, optimized for each forest development stage, achieved high accuracy for inventory, achieving stem detection rates up to 99.1% and errors as low as 0.94 m for height and 1.18 cm for diameter at breast height (DBH) in specific cases. Additionally, we provide scripts to support future research, discuss the limitations of our approach, and propose solutions to address these gaps in future implementations. Our findings highlight the utility of open-source tools to optimize forest inventory workflows through stratified, modular approaches. Full article
(This article belongs to the Special Issue Lidar for Forest Parameters Retrieval)
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24 pages, 6654 KiB  
Article
The Capabilities of Optical and C-Band Radar Satellite Data to Detect and Understand Faba Bean Phenology over a 6-Year Period
by Frédéric Baup, Rémy Fieuzal, Clément Battista, Herivanona Ramiakatrarivony, Louis Tournier, Serigne-Fallou Diarra, Serge Riazanoff and Frédéric Frappart
Remote Sens. 2025, 17(11), 1933; https://doi.org/10.3390/rs17111933 - 3 Jun 2025
Viewed by 323
Abstract
This study analyzes the potential of optical and radar satellite data to monitor faba bean (Vicia faba L.) phenology over six years (2016–2021) in southwestern France. Using Sentinel-1, Sentinel-2, and Landsat-8 data, temporal variations in NDVI and radar backscatter coefficients (γ0 [...] Read more.
This study analyzes the potential of optical and radar satellite data to monitor faba bean (Vicia faba L.) phenology over six years (2016–2021) in southwestern France. Using Sentinel-1, Sentinel-2, and Landsat-8 data, temporal variations in NDVI and radar backscatter coefficients (γ0VV, γ0VH, and γ0VH/VV) are examined to assess crop growth, detect anomalies, and evaluate the impact of climatic conditions and sowing strategies. The results show that NDVI and the radar ratio (γ0VH/VV) were suited to monitor faba bean phenology, with distinct growth phases observed annually. NDVI provides a clear seasonal pattern but is affected by cloud cover, while radar backscatter offers continuous monitoring, making their combination highly beneficial. The signal γ0VH/VV exhibits well-marked correlations with NDVI (r = 0.81) and LAI (r = 0.83), particularly in orbit 30, which provides greater sensitivity to vegetation changes. The analysis of individual fields (inter-field approach) reveals variations in sowing strategies, with both autumn and spring plantings detected. Fields sown in autumn show early NDVI (and γ0VH/VV) increases, while spring-sown fields display delayed growth patterns. This study also highlights the impact of climatic factors, such as precipitation and temperature, on inter-annual variability. Moreover, faba beans used as an intercropping species exhibit a shorter and more intense growth cycle, with a rapid NDVI (and γ0VH/VV) increase and an earlier end of the vegetative cycle compared to standard rotations. Double logistic modeling successfully reconstructs temporal trends, achieving high accuracy (r > 0.95 and rRMSE < 9% for γ0VH/VV signals and r > 0.89 and rRMSE < 15% for NDVI). These double logistic functions are capable of reproducing the differences in phenological development observed between fields and years, providing a reference set of functions that can be used to monitor the phenological development of faba beans in real time. Future applications could extend this methodology to other crops and explore alternative radar systems for improved monitoring (such as TerraSAR-X, Cosmos-SkyMed, ALOS-2/PALSAR, NISAR, ROSE-L…). Full article
(This article belongs to the Special Issue Advances in Detecting and Understanding Land Surface Phenology)
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26 pages, 2906 KiB  
Article
Street-Scale Urban Air Temperatures Predicted by Simple High-Resolution Cover- and Shade-Weighted Surface Temperature Mosaics in a Variety of Residential Neighborhoods
by Katarina Kubiniec, Kevan B. Moffett and Kyle Blount
Remote Sens. 2025, 17(11), 1932; https://doi.org/10.3390/rs17111932 - 3 Jun 2025
Viewed by 494
Abstract
A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is [...] Read more.
A simple statistical model capturing the degree to which different patterns of urban development intensify urban heat islands (UHIs) and stress human health would be useful but has remained elusive. Accurately predicting street-level urban air temperatures from land cover and thermal data is difficult due to (1) the coarse scale of common remote sensing data, which do not observe the key environments beneath urban tree canopies, and, (2) conversely, the immense labor of intense, location-specific, ground-based survey campaigns. This work tested whether remotely sensed urban heat merged with land cover heterogeneity and shade/sun fractions, if combined at a sufficiently fine scale so as to be linearly additive, would enable simple and accurate statistical modeling of street-scale urban air temperatures with minimal empirical fitting. We used ground-based thermography of a sample of 12 residential streetscapes in Portland, Oregon, to characterize the land surface temperatures (LSTg) of eleven common urban surface cover types when sun-exposed and in shade. Surfaces were cooler in shade than sun, but with surface-specific differences not explained by greenery nor (im)perviousness. Also, surfaces on streetscapes with more canopy cover, even when sun-exposed at midday, remained significantly cooler than comparable sun-exposed surfaces on streets with less canopy cover, indicating the key significance of partial diurnal shading, not typically accounted for in urban thermal statistical models. We used high-resolution orthoimagery to quantify the area of each surface cover type within each streetscape and computed an area-weighted average surface temperature (Ts), accounting for sun/shade heterogeneity. The data revealed a significant, nearly 1:1 relationship between calculated Ts values and sun-shielded air temperatures (Ta). In contrast, relationships of Ta to tree coverage, impervious area, or the LSTg of dominant surface cover types were all statistically insignificant. These results suggest that statistical models may more reliably bridge the gap between remote sensing urban surface temperatures and reliable predictions of street-scale air temperatures if (1) analysis is at a sufficiently high resolution (e.g., <10 m) to avoid some of the known scale-dependence of urban thermal environments and enable simple weighted linear models, and (2) distinctions between thermal contributions of sunlit and shaded surfaces are included along with the influence of diurnal shading. Such models may provide effective and low-cost predictions of local UHIs and help inform effective street-level approaches to mitigating urban heat. Full article
(This article belongs to the Special Issue Monitoring Urban Environment and Temperature Change Using Remote Sensing)
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20 pages, 6516 KiB  
Article
On Flood Detection Using Dual-Polarimetric SAR Observation
by Su-Young Kim, Yeji Lee and Sang-Eun Park
Remote Sens. 2025, 17(11), 1931; https://doi.org/10.3390/rs17111931 - 2 Jun 2025
Viewed by 241
Abstract
This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. For an effective comparison of the flood detection performance between dual-pol parameters, we presented a simple fuzzy-based flood detection algorithm. Scattering characteristics of water surface and non-water [...] Read more.
This study aims to elucidate the optimal exploitation of polarimetric scattering information in dual-pol SAR data. For an effective comparison of the flood detection performance between dual-pol parameters, we presented a simple fuzzy-based flood detection algorithm. Scattering characteristics of water surface and non-water land can vary depending on the region and flood conditions. Therefore, the flood detection performance of the dual-pol parameters was evaluated across three datasets with different geographic, climatic, and land cover conditions. The results demonstrated that accurate and stable performance in the detection of inundated areas under different surface conditions can be achieved by combining water body information from dual-pol channels in a disjunctive way. It also suggests that synergy in flood detection can be expected when using polarization observation data by considering each polarimetric channel as an independent information source and combining them rather than deriving the most relevant polarization parameter. Furthermore, combining common information from two dual-pol channels in a conjunctive way could provide the most reliable SAR flood detection results with minimum false alarms from the user’s perspective. Based on these experimental results, a two-class flood classification scheme was proposed for improving the applicability of SAR remote sensing in identifying flooded areas. Full article
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17 pages, 18128 KiB  
Communication
Modified Spherical Geometry Algorithm for Spaceborne SAR Data Processing in Sliding Spotlight Mode
by Jixia Fan, Manyi Tao and Xinhua Mao
Remote Sens. 2025, 17(11), 1930; https://doi.org/10.3390/rs17111930 - 2 Jun 2025
Viewed by 194
Abstract
Spaceborne high-resolution wide-area SAR image formation processing faces critical challenges induced by orbital curvature, Earth rotation, and spherical ground surfaces. The Spherical Geometry Algorithm (SGA) offers an effective solution to these problems. However, the standard SGA is inherently limited to spotlight mode SAR [...] Read more.
Spaceborne high-resolution wide-area SAR image formation processing faces critical challenges induced by orbital curvature, Earth rotation, and spherical ground surfaces. The Spherical Geometry Algorithm (SGA) offers an effective solution to these problems. However, the standard SGA is inherently limited to spotlight mode SAR data processing and cannot be directly extended to other operational modes. To overcome this constraint, this paper proposes an enhanced SGA framework tailored for sliding spotlight mode SAR data processing. Firstly, this paper presents a rigorous analysis of time–frequency relationship variations during the classical SGA processing under sliding spotlight mode, and gives the reasons why the classical SGA can not be directly applied to the data processing in sliding spotlight mode. Then, a modified SGA processing framework is proposed to address the signal sampling ambiguity problem faced by the SGA in processing sliding spotlight mode data. The improved algorithm avoids the sampling ambiguity problem during azimuthal resampling and azimuthal IFFT by introducing an instantaneous Doppler central frequency correction processing before azimuthal resampling and a suitable amount of oversampling during azimuthal resampling. Finally, the effectiveness of the algorithm is verified by measured real data processing. Full article
(This article belongs to the Special Issue Advanced HRWS Spaceborne SAR: System Design and Signal Processing)
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