Remote Sensing

23 pages, 4156 KiB

Open AccessArticle

Spatiotemporal Drivers of Urban Vegetation Carbon Sequestration in the Yangtze River Delta Urban Agglomeration: A Remote Sensing-Based GWR-RF-SEM Framework Analysis

by Weibo Ma, Yueming Zhu, Depin Ou, Yicong Chen, Yamei Shao, Nannan Wang, Nan Wang and Haidong Li

Remote Sens. 2025, 17(12), 2110; https://doi.org/10.3390/rs17122110 - 19 Jun 2025

Viewed by 485

Abstract

Vegetation carbon sequestration (CS) is critical for mitigating climate change in urban agglomerations, yet its driving mechanisms remain poorly understood in rapidly urbanizing regions. This study introduces an integrated attribution and influence analysis framework, GWR-RF-SEM, to quantitatively assess the driving forces, mechanisms, and [...] Read more.

Vegetation carbon sequestration (CS) is critical for mitigating climate change in urban agglomerations, yet its driving mechanisms remain poorly understood in rapidly urbanizing regions. This study introduces an integrated attribution and influence analysis framework, GWR-RF-SEM, to quantitatively assess the driving forces, mechanisms, and pathways of CS using multi-source remote sensing data at the county scale within the Yangtze River Delta Urban Agglomeration (YRDUA), China, from 2001 to 2020. Our results reveal an overall increase in CS across 70.14% districts in the YRDUA, with municipal districts exhibiting significantly lower CS compared to the outside districts. Photosynthesis and human activities emerged as the dominant drivers, collectively accounting for 73.1% of CS variation, significantly surpassing the influence of climate factors. Although most factors influenced urban vegetation CS either directly or indirectly, photosynthesis, afforestation, and urban green space structure were identified as the primary direct drivers of CS enhancement in both districts. Notably, we found significant spatial heterogeneity in CS drivers between municipal districts and the outside districts, highlighting the need for targeted strategies to enhance CS efficiency. These findings advance our understanding of urban vegetation CS mechanisms, providing essential support for the enhancement of nature-based solutions depending on ecosystem services under urbanization and climate change. Full article

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26 pages, 9416 KiB

Open AccessArticle

Multi-Component Remote Sensing for Mapping Buried Water Pipelines

by John Lioumbas, Thomas Spahos, Aikaterini Christodoulou, Ioannis Mitzias, Panagiota Stournara, Ioannis Kavouras, Alexandros Mentes, Nopi Theodoridou and Agis Papadopoulos

Remote Sens. 2025, 17(12), 2109; https://doi.org/10.3390/rs17122109 - 19 Jun 2025

Viewed by 440

Abstract

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV [...] Read more.

Accurate localization of buried water pipelines in rural areas is crucial for maintenance and leak management but is often hindered by outdated maps and the limitations of traditional geophysical methods. This study aimed to develop and validate a multi-source remote-sensing workflow, integrating UAV (unmanned aerial vehicle)-borne near-infrared (NIR) surveys, multi-temporal Sentinel-2 imagery, and historical Google Earth orthophotos to precisely map pipeline locations and establish a surface baseline for future monitoring. Each dataset was processed within a unified least-squares framework to delineate pipeline axes from surface anomalies (vegetation stress, soil discoloration, and proxies) and rigorously quantify positional uncertainty, with findings validated against RTK-GNSS (Real-Time Kinematic—Global Navigation Satellite System) surveys of an excavated trench. The combined approach yielded sub-meter accuracy (±0.3 m) with UAV data, meter-scale precision (≈±1 m) with Google Earth, and precision up to several meters (±13.0 m) with Sentinel-2, significantly improving upon inaccurate legacy maps (up to a 300 m divergence) and successfully guiding excavation to locate a pipeline segment. The methodology demonstrated seasonal variability in detection capabilities, with optimal UAV-based identification occurring during early-vegetation growth phases (NDVI, Normalized Difference Vegetation Index ≈ 0.30–0.45) and post-harvest periods. A Sentinel-2 analysis of 221 cloud-free scenes revealed persistent soil discoloration patterns spanning 15–30 m in width, while Google Earth historical imagery provided crucial bridging data with intermediate spatial and temporal resolution. Ground-truth validation confirmed the pipeline location within 0.4 m of the Google Earth-derived position. This integrated, cost-effective workflow provides a transferable methodology for enhanced pipeline mapping and establishes a vital baseline of surface signatures, enabling more effective future monitoring and proactive maintenance to detect leaks or structural failures. This methodology is particularly valuable for water utility companies, municipal infrastructure managers, consulting engineers specializing in buried utilities, and remote-sensing practitioners working in pipeline detection and monitoring applications. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Infrastructures)

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24 pages, 3716 KiB

Open AccessArticle

HRRPGraphNet++: Dynamic Graph Neural Network with Meta-Learning for Few-Shot HRRP Radar Target Recognition

by Lingfeng Chen, Zhiliang Pan, Qi Liu and Panhe Hu

Remote Sens. 2025, 17(12), 2108; https://doi.org/10.3390/rs17122108 - 19 Jun 2025

Viewed by 410

Abstract

High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition. Our approach generates graph representations dynamically through multi-head self attention (MSA) mechanisms that [...] Read more.

High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition. Our approach generates graph representations dynamically through multi-head self attention (MSA) mechanisms that adapt to target-specific scattering characteristics, integrated with a specialized meta-learning framework employing layer-wise learning rates. Experiments demonstrate state-of-the-art performance in 1-shot (82.3%), 5-shot (91.8%), and 20-shot (94.7%) settings, with enhanced noise robustness (68.7% accuracy at 0 dB SNR). Our hybrid graph mechanism combines physical priors with learned relationships, significantly outperforming conventional methods in challenging scenarios. Full article

(This article belongs to the Special Issue Advanced AI Technology for Remote Sensing Analysis)

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19 pages, 8609 KiB

Open AccessArticle

A Microwave Vision-Enhanced Environmental Perception Method for the Visual Navigation of UAVs

by Rui Li, Dewei Wu, Peiran Li, Chenhao Zhao, Jingyi Zhang and Jing He

Remote Sens. 2025, 17(12), 2107; https://doi.org/10.3390/rs17122107 - 19 Jun 2025

Viewed by 276

Abstract

Visual navigation technology holds significant potential for applications involving unmanned aerial vehicles (UAVs). However, the inherent spectral limitations of optical-dependent navigation systems prove particularly inadequate for high-altitude long-endurance (HALE) UAV operations, as they are fundamentally constrained in maintaining reliable environment perception under conditions [...] Read more.

Visual navigation technology holds significant potential for applications involving unmanned aerial vehicles (UAVs). However, the inherent spectral limitations of optical-dependent navigation systems prove particularly inadequate for high-altitude long-endurance (HALE) UAV operations, as they are fundamentally constrained in maintaining reliable environment perception under conditions of fluctuating illumination and persistent cloud cover. To address this challenge, this paper introduces microwave vision to assist optical vision for environmental measurement and proposes a novel microwave vision-enhanced environmental perception method. In particular, the richness of perceived environmental information can be enhanced by SAR and optical image fusion processing in the case of sufficient light and clear weather. In order to simultaneously mitigate inherent SAR speckle noise and address existing fusion algorithms’ inadequate consideration of UAV navigation-specific environmental perception requirements, this paper designs a SAR Target-Augmented Fusion (STAF) algorithm based on the target detection of SAR images. On the basis of image preprocessing, this algorithm utilizes constant false alarm rate (CFAR) detection along with morphological operations to extract critical target information from SAR images. Subsequently, the intensity–hue–saturation (IHS) transform is employed to integrate this extracted information into the optical image. The experimental results show that the proposed microwave vision-enhanced environmental perception method effectively utilizes microwave vision to shape target information perception in the electromagnetic spectrum and enhance the information content of environmental measurement results. The unique information extracted by the STAF algorithm from SAR images can effectively enhance the optical images while retaining their main attributes. This method can effectively enhance the environmental measurement robustness and information acquisition ability of the visual navigation system. Full article

(This article belongs to the Section Remote Sensing Image Processing)

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15 pages, 3297 KiB

Open AccessArticle

Evaluating Leaf Water Potential of Maize Through Multi-Cultivar Dehydration Experiments and Segmentation Thresholding

by Shuanghui Zhao, Yanqun Zhang, Pancen Feng, Xinlong Hu, Yan Mo, Hao Li and Jiusheng Li

Remote Sens. 2025, 17(12), 2106; https://doi.org/10.3390/rs17122106 - 19 Jun 2025

Viewed by 224

Abstract

Estimating leaf water potential (Ψ_leaf) is essential for understanding plant physiological processes’ response to drought. The estimation of Ψ_leaf based on different regression analysis methods with hyperspectral vegetation indices (VIs) has been proven to be a simple and efficient [...] Read more.

Estimating leaf water potential (Ψ_leaf) is essential for understanding plant physiological processes’ response to drought. The estimation of Ψ_leaf based on different regression analysis methods with hyperspectral vegetation indices (VIs) has been proven to be a simple and efficient technique. However, models constructed by existing methods and VIs still face challenges regarding the generalizability and limited ranges of field experiment datasets. In this study, leaf dehydration experiments of three maize cultivars were applied to provide a dataset covering a wide range of Ψ_leaf variations, which is often challenging to obtain in field trials. The analysis screened published VIs highly correlated with Ψ_leaf and constructed a model for Ψ_leaf estimation based on three algorithms—partial least squares regression (PLSR), random forest (RF), and multiple linear stepwise regression (MLR)—for each cultivar and all three cultivars. Models were constructed using PLSR and MLR for each cultivar and PLSR, MLR, and RF for the samples from all three cultivars. The performance of the models developed for each cultivar was compared with the performance of the cross-cultivar model. Simultaneously, the normalized ratio (ND) and double-difference (DDn) were applied to determine the VIs and models. Finally, the relationship between the optimal VIs and Ψ_leaf was analyzed using discontinuous linear segmental fitting. The results showed that leaf spectral reflectance variations in the 350~700 nm bands and 1450~2500 nm bands were significantly sensitive to Ψ_leaf. The RF method achieved the highest prediction accuracy when all three cultivars’ data were used, with a normalized root mean square error (NRMSE) of 9.02%. In contrast, there was little difference in the predictive effectiveness of the models constructed for each cultivar and all three cultivars. Moreover, the simple linear regression model built based on the DDn(2030,45) outperformed the RF method regarding prediction accuracy, with an NRMSE of 7.94%. Ψ_leaf at the breakpoint obtained by discontinuous linear segment fitting was about −1.20 MPa, consistent with the published range of the turgor loss point (Ψ_TLP). This study provides an effective methodology for Ψ_leaf monitoring with significant practical value, particularly in irrigation decision-making and drought prediction. Full article

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24 pages, 29179 KiB

Open AccessArticle

SAR 3D Reconstruction Based on Multi-Prior Collaboration

by Yangyang Wang, Zhenxiao Zhou, Zhiming He, Xu Zhan, Jiapan Yu, Xingcheng Han, Xiaoling Zhang, Zhiliang Yang and Jianping An

Remote Sens. 2025, 17(12), 2105; https://doi.org/10.3390/rs17122105 - 19 Jun 2025

Viewed by 376

Abstract

Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation. SAR image reconstruction remains challenging due to issues such as noise contamination and incomplete echo data. By [...] Read more.

Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation. SAR image reconstruction remains challenging due to issues such as noise contamination and incomplete echo data. By introducing sparse priors such as

L_{1}

regularization functions, image quality can be improved to a certain extent and the impact of noise can be reduced. However, in scenarios involving distributed targets, the aforementioned methods often fail to maintain continuous structural features such as edges and contours, thereby limiting their reconstruction performance and adaptability. Recent studies have introduced geometric regularization functions to preserve the structural continuity of targets, yet these lack multi-prior consensus, resulting in limited reconstruction quality and robustness in complex scenarios. To address the above issues, a novel array SAR 3D reconstruction method based on multi-prior collaboration (ASAR-MPC) is proposed in this article. In this method, firstly, each optimization module in 3D reconstruction based on multi-prior is treated as an independent function module, and these modules are reformulated as parallel operations rather than sequential utilization. During the reconstruction process, the solution is constrained within the solution space of the module, ensuring that the SAR image simultaneously satisfies multiple prior conditions and achieves a coordinated balance among different priors. Then, a collaborative equilibrium framework based on Mann iteration is presented to solve the optimization problem of 3D reconstruction, which can ensure convergence to an equilibrium point and achieve the joint optimization of all modules. Finally, a series of simulation and experimental tests are described to validate the proposed method. The experimental results show that under limited echo and noise conditions, the proposed method outperforms existing methods in reconstructing complex target structures. 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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35 pages, 9804 KiB

Open AccessArticle

LAI-Derived Atmospheric Moisture Condensation Potential for Forest Health and Land Use Management

by Jung-Jun Lin and Ali Nadir Arslan

Remote Sens. 2025, 17(12), 2104; https://doi.org/10.3390/rs17122104 - 19 Jun 2025

Viewed by 332

Abstract

The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, [...] Read more.

The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = T_a − T_d) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Land Use, Land-Use Change and Forestry (LULUCF))

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31 pages, 21378 KiB

Open AccessArticle

PhA-MOE: Enhancing Hyperspectral Retrievals for Phytoplankton Absorption Using Mixture-of-Experts

by Weiwei Wang, Bingqing Liu, Song Gao, Jiang Li, Yueling Zhou, Songyang Zhang and Zhi Ding

Remote Sens. 2025, 17(12), 2103; https://doi.org/10.3390/rs17122103 - 19 Jun 2025

Viewed by 281

Abstract

As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient (

a_{p h y}

), especially in hyperspectral, greatly enhances our understanding of phytoplankton community composition (PCC). The recent launches of NASA’s hyperspectral missions, such [...] Read more.

As a key component of inherent optical properties (IOPs) in ocean color remote sensing, phytoplankton absorption coefficient (

a_{p h y}

), especially in hyperspectral, greatly enhances our understanding of phytoplankton community composition (PCC). The recent launches of NASA’s hyperspectral missions, such as EMIT and PACE, have generated an urgent need for hyperspectral algorithms for studying phytoplankton. Retrieving

a_{p h y}

from ocean color remote sensing in coastal waters has been extremely challenging due to complex optical properties. Traditional methods often fail under these circumstances, while improved machine-learning approaches are hindered by data scarcity, heterogeneity, and noise from data collection. In response, this study introduces a novel machine learning framework for hyperspectral retrievals of

a_{p h y}

based on the mixture-of-experts (MOEs), named PhA-MOE. Various preprocessing methods for hyperspectral training data are explored, with the combination of robust and logarithmic scalers identified as optimal. The proposed PhA-MOE for

a_{p h y}

prediction is tailored to both past and current hyperspectral missions, including EMIT and PACE. Extensive experiments reveal the importance of data preprocessing and improved performance of PhA-MOE in estimating

a_{p h y}

as well as in handling data heterogeneity. Notably, this study marks the first application of a machine learning–based MOE model to real PACE-OCI hyperspectral imagery, validated using match-up field data. This application enables the exploration of spatiotemporal variations in

a_{p h y}

within an optically complex estuarine environment. Full article

(This article belongs to the Special Issue Artificial Intelligence for Ocean Remote Sensing (Second Edition))

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18 pages, 3896 KiB

Open AccessArticle

The Contribution of Meteosat Third Generation–Flexible Combined Imager (MTG-FCI) Observations to the Monitoring of Thermal Volcanic Activity: The Mount Etna (Italy) February–March 2025 Eruption

by Carolina Filizzola, Giuseppe Mazzeo, Francesco Marchese, Carla Pietrapertosa and Nicola Pergola

Remote Sens. 2025, 17(12), 2102; https://doi.org/10.3390/rs17122102 - 19 Jun 2025

Viewed by 432

Abstract

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 [...] Read more.

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article

(This article belongs to the Special Issue Multi-Sensor Detecting, Monitoring, and Modelling of Volcanic Activity)

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23 pages, 1208 KiB

Open AccessArticle

UCrack-DA: A Multi-Scale Unsupervised Domain Adaptation Method for Surface Crack Segmentation

by Fei Deng, Shaohui Yang, Bin Wang, Xiujun Dong and Siyuan Tian

Remote Sens. 2025, 17(12), 2101; https://doi.org/10.3390/rs17122101 - 19 Jun 2025

Viewed by 440

Abstract

Surface cracks serve as early warning signals for potential geological hazards, and their precise segmentation is crucial for disaster risk assessment. Due to differences in acquisition conditions and the diversity of crack morphology, scale, and surface texture, there is a significant domain shift [...] Read more.

Surface cracks serve as early warning signals for potential geological hazards, and their precise segmentation is crucial for disaster risk assessment. Due to differences in acquisition conditions and the diversity of crack morphology, scale, and surface texture, there is a significant domain shift between different crack datasets, necessitating transfer training. However, in real work areas, the sparse distribution of cracks results in a limited number of samples, and the difficulty of crack annotation makes it highly inefficient to use a high proportion of annotated samples for transfer training to predict the remaining samples. Domain adaptation methods can achieve transfer training without relying on manual annotation, but traditional domain adaptation methods struggle to effectively address the characteristics of cracks. To address this issue, we propose an unsupervised domain adaptation method for crack segmentation. By employing a hierarchical adversarial mechanism and a prediction entropy minimization constraint, we extract domain-invariant features in a multi-scale feature space and sharpen decision boundaries. Additionally, by integrating a Mix-Transformer encoder, a multi-scale dilated attention module, and a mixed convolutional attention decoder, we effectively solve the challenges of cross-domain data distribution differences and complex scene crack segmentation. Experimental results show that UCrack-DA achieves superior performance compared to existing methods on both the Roboflow-Crack and UAV-Crack datasets, with significant improvements in metrics such as mIoU, mPA, and Accuracy. In UAV images captured in field scenarios, the model demonstrates excellent segmentation Accuracy for multi-scale and multi-morphology cracks, validating its practical application value in geological hazard monitoring. Full article

(This article belongs to the Section AI Remote Sensing)

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21 pages, 6504 KiB

Open AccessArticle

Drought Amplifies the Suppressive Effect of Afforestation on Net Primary Productivity in Semi-Arid Ecosystems: A Case Study of the Yellow River Basin

by Futao Wang, Ziqi Zhang, Mingxuan Du, Jianzhong Lu and Xiaoling Chen

Remote Sens. 2025, 17(12), 2100; https://doi.org/10.3390/rs17122100 - 19 Jun 2025

Viewed by 391

Abstract

As a critical ecologicalbarrier in the semi-arid to semi-humid transition zone of northern China, the interaction between afforestation and climatic stressors in the Yellow River Basin constitutes a pivotal scientific challenge for regional sustainable development. However, the synthesis effects of afforestation and climate [...] Read more.

As a critical ecologicalbarrier in the semi-arid to semi-humid transition zone of northern China, the interaction between afforestation and climatic stressors in the Yellow River Basin constitutes a pivotal scientific challenge for regional sustainable development. However, the synthesis effects of afforestation and climate on primary productivity require further investigation. Integrating multi-source remote sensing data (2000–2020), meteorological observations with the Standardized Precipitation Evapotranspiration Index (SPEI) and an improved CASA model, this study systematically investigates spatiotemporal patterns of vegetation net primary productivity (NPP) responses to extreme drought events while quantifying vegetation coverage’s regulatory effects on ecosystem drought sensitivity. Among drought events identified using a three-dimensional clustering algorithm, high-intensity droughts caused an average NPP loss of 23.2 gC·m⁻² across the basin. Notably, artificial irrigation practices in the Hetao irrigation district significantly mitigated NPP reduction to −9.03 gC·m⁻². Large-scale afforestation projects increased the NDVI at a rate of 3.45 × 10⁻⁴ month⁻¹, with a contribution rate of 78%, but soil moisture competition from high-density vegetation reduced carbon-sink benefits. However, mixed forest structural optimization in the Three-North Shelterbelt Forest Program core area achieved local carbon-sink gains, demonstrating that vegetation configuration alleviates water competition pressure. Drought amplified the suppressive effect of afforestation through stomatal conductance-photosynthesis coupling mechanisms, causing additional NPP losses of 7.45–31.00 gC·m⁻², yet the April–July 2008 event exhibited reversed suppression effects due to immature artificial communities during the 2000–2004 baseline period. Our work elucidates nonlinear vegetation-climate interactions affecting carbon sequestration in semi-arid ecosystems, providing critical insights for optimizing ecological restoration strategies and climate-adaptive management in the Yellow River Basin. Full article

(This article belongs to the Special Issue Supporting Earth Observation and Human–Environment Interaction with Global Geospatial Information)

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20 pages, 3305 KiB

Open AccessArticle

SRM-YOLO for Small Object Detection in Remote Sensing Images

by Bin Yao, Chengkun Zhang, Qingxiang Meng, Xiandong Sun, Xuyang Hu, Lu Wang and Xilai Li

Remote Sens. 2025, 17(12), 2099; https://doi.org/10.3390/rs17122099 - 19 Jun 2025

Viewed by 758

Abstract

Small object detection presents significant challenges in computer vision, often affected by factors such as low resolution, dense object distribution, and complex backgrounds, which can lead to false positives or missed detections. In this paper, we introduce SRM-YOLO, a novel small object detection [...] Read more.

Small object detection presents significant challenges in computer vision, often affected by factors such as low resolution, dense object distribution, and complex backgrounds, which can lead to false positives or missed detections. In this paper, we introduce SRM-YOLO, a novel small object detection algorithm based on the YOLOv8 framework. The model incorporates the following key innovations: Reuse Fusion Structure (RFS), which enhances feature fusion; SPD-Conv, which enables effective downsampling while preserving critical information; and a specialized detection head designed for small objects. Additionally, the MPDIoU loss function is employed to improve detection accuracy. Experimental results on the VisDrone2019 dataset show that SRM-YOLO significantly enhances detection accuracy, achieving a 5.2% improvement in mAP50 over YOLOv8n. Additionally, its superior performance on the SSDD and NWPU VHR-10 datasets further validates its effectiveness in small object detection tasks. Full article

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6 pages, 146 KiB

Open AccessEditorial

SAR Image Object Detection and Information Extraction: Methods and Applications

by Zhongzhen Sun, Xiangguang Leng, Mingjin Zhang, Haohao Ren and Kefeng Ji

Remote Sens. 2025, 17(12), 2098; https://doi.org/10.3390/rs17122098 - 19 Jun 2025

Viewed by 376

Abstract

Synthetic aperture radar (SAR) is now recognized as a critical source of observational data in domains such as military reconnaissance, maritime monitoring, and disaster response, owing to its ability to deliver fine spatial resolution and broad-area imaging irrespective of weather or daylight conditions [...] Read more.

Synthetic aperture radar (SAR) is now recognized as a critical source of observational data in domains such as military reconnaissance, maritime monitoring, and disaster response, owing to its ability to deliver fine spatial resolution and broad-area imaging irrespective of weather or daylight conditions [...] Full article

(This article belongs to the Special Issue SAR Image Object Detection and Information Extraction: Methods and Applications)

23 pages, 3792 KiB

Open AccessArticle

Investigating the Mechanisms of Hyperspectral Remote Sensing for Belowground Yield Traits in Potato Plants

by Wenqian Chen, Yurong Huang, Wei Tan, Yujia Deng, Cuihong Yang, Xiguang Zhu, Jian Shen and Nanfeng Liu

Remote Sens. 2025, 17(12), 2097; https://doi.org/10.3390/rs17122097 - 19 Jun 2025

Viewed by 373

Abstract

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has [...] Read more.

Potatoes, as the world’s fourth-largest staple crop, are vital for global food security. Efficient methods for assessing yield and quality are essential for policy-making and optimizing production. Traditional yield assessment techniques remain destructive, labor-intensive, and unsuitable for large-scale monitoring. While remote sensing has offered a promising alternative, current approaches largely depend on empirical correlations rather than physiological mechanisms. This limitation arises because potato tubers grow underground, rendering their traits invisible to aboveground sensors. This study investigated the mechanisms underlying hyperspectral remote sensing for assessing belowground yield traits in potatoes. Field experiments with four cultivars and five nitrogen treatments were conducted to collect foliar biochemistries (chlorophyll, nitrogen, and water and dry matter content), yield traits (tuber yield, fresh/dry weight, starch, protein, and water content), and leaf spectra. Two approaches were developed for predicting belowground yield traits: (1) a direct method linking leaf spectra to yield via statistical models and (2) an indirect method using structural equation modeling (SEM) to link foliar biochemistry to yield. The SEM analysis revealed that foliar nitrogen exhibited negative effects on tuber fresh weight (path coefficient b = −0.57), yield (−0.37), and starch content (−0.30). Similarly, leaf water content negatively influenced tuber water content (0.52), protein (−0.27), and dry weight (−0.42). Conversely, chlorophyll content showed positive associations with both tuber protein (0.59) and dry weight (0.56). Direct models (PLSR, SVR, and RFR) achieved higher accuracy for yield (R² = 0.58–0.84) than indirect approaches (R² = 0.16–0.45), though the latter provided physiological insights. The reduced accuracy in indirect methods primarily stemmed from error propagation within the SEM framework. Future research should scale these leaf-level mechanisms to canopy observations and integrate crop growth models to improve robustness across environments. This work advances precision agriculture by clarifying spectral–yield linkages in potato systems, offering a framework for hyperspectral-based yield prediction. Full article

(This article belongs to the Special Issue Real-Time Agricultural Monitoring from Remotely Sensed Data (Second Edition))

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26 pages, 2362 KiB

Open AccessArticle

ELNet: An Efficient and Lightweight Network for Small Object Detection in UAV Imagery

by Hui Li, Jianbo Ma and Jianlin Zhang

Remote Sens. 2025, 17(12), 2096; https://doi.org/10.3390/rs17122096 - 18 Jun 2025

Viewed by 487

Abstract

Real-time object detection is critical for unmanned aerial vehicles (UAVs) performing various tasks. However, efficiently deploying detection models on UAV platforms with limited storage and computational resources remains a significant challenge. To address this issue, we propose ELNet, an efficient and lightweight object [...] Read more.

Real-time object detection is critical for unmanned aerial vehicles (UAVs) performing various tasks. However, efficiently deploying detection models on UAV platforms with limited storage and computational resources remains a significant challenge. To address this issue, we propose ELNet, an efficient and lightweight object detection model based on YOLOv12n. First, based on an analysis of UAV image characteristics, we strategically remove two A2C2f modules from YOLOv12n and adjust the size and number of detection heads. Second, we propose a novel lightweight detection head, EPGHead, to alleviate the computational burden introduced by adding the large-scale detection head. In addition, since YOLOv12n employs standard convolution for downsampling, which is inefficient for extracting UAV image features, we design a novel downsampling module, EDown, to further reduce model size and enable more efficient feature extraction. Finally, to improve detection in UAV imagery with dense, small, and scale-varying objects, we propose DIMB-C3k2, an enhanced module built upon C3k2, which boosts feature extraction under complex conditions. Compared with YOLOv12n, ELNet achieves an 88.5% reduction in parameter count and a 52.3% decrease in FLOPs, while increasing mAP₅₀ by 1.2% on the VisDrone dataset and 0.8% on the HIT-UAV dataset, reaching 94.7% mAP₅₀ on HIT-UAV. Furthermore, the model achieves a frame rate of 682 FPS, highlighting its superior computational efficiency without sacrificing detection accuracy. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence (AI) and Deep Learning (DL) in UAV-Based Remote Sensing)

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17 pages, 3392 KiB

Open AccessArticle

Crop Classification Using Time-Series Sentinel-1 SAR Data: A Comparison of LSTM, GRU, and TCN with Attention

by Yuta Tsuchiya and Rei Sonobe

Remote Sens. 2025, 17(12), 2095; https://doi.org/10.3390/rs17122095 - 18 Jun 2025

Viewed by 434

Abstract

This study investigates the performance of temporal deep learning models with attention mechanisms for crop classification using Sentinel-1 C-band synthetic aperture radar (C-SAR) data. A time series of 16 scenes, acquired at 12-day intervals from 25 April to 22 October 2024, was used [...] Read more.

This study investigates the performance of temporal deep learning models with attention mechanisms for crop classification using Sentinel-1 C-band synthetic aperture radar (C-SAR) data. A time series of 16 scenes, acquired at 12-day intervals from 25 April to 22 October 2024, was used to classify six crop types: beans, beetroot, grassland, maize, potato, and winter wheat. Three temporal models—long short-term memory (LSTM), bidirectional gated recurrent unit (Bi-GRU), and temporal convolutional network (TCN)—were evaluated with and without an attention mechanism. All model configurations achieved accuracies above 83%, demonstrating the potential of Sentinel-1 SAR data for reliable, weather-independent crop classification. The TCN with attention model achieved the highest accuracy of 85.7%, significantly outperforming the baseline. LSTM also showed improved accuracy when combined with attention, whereas Bi-GRU did not benefit from the attention mechanism. These results highlight the effectiveness of combining temporal deep learning models with attention mechanisms to enhance crop classification using Sentinel-1 SAR time-series data. This study further confirms that freely available, regularly acquired Sentinel-1 observations are well-suited for robust crop mapping under diverse environmental conditions. Full article

(This article belongs to the Special Issue Crop Classification Using Synthetic Aperture Radar and Optical Imagery)

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20 pages, 18798 KiB

Open AccessArticle

Assessing Intraspecific Variation of Tree Species Based on Sentinel-2 Vegetation Indices Across Space and Time

by Tiziana L. Koch, Martina L. Hobi, Felix Morsdorf, Alexander Damm, Dominique Weber, Marius Rüetschi, Jan D. Wegner and Lars T. Waser

Remote Sens. 2025, 17(12), 2094; https://doi.org/10.3390/rs17122094 - 18 Jun 2025

Viewed by 523

Abstract

Forest ecosystems are vital for biodiversity, climate regulation, and ecosystem services. Their resilience depends not only on species diversity but also on intraspecific variation—the genetic and phenotypic differences within species—which underpins adaptive capacity to environmental change. However, large-scale, continuous monitoring of intraspecific variation [...] Read more.

Forest ecosystems are vital for biodiversity, climate regulation, and ecosystem services. Their resilience depends not only on species diversity but also on intraspecific variation—the genetic and phenotypic differences within species—which underpins adaptive capacity to environmental change. However, large-scale, continuous monitoring of intraspecific variation remains challenging. Here, we present a remote sensing approach using Sentinel-2 time series of five vegetation indices as proxies for pigment content, canopy structure, and water content to detect intraspecific variation in seven tree species across a broad environmental gradient in Switzerland. Using pure-species plot data from the Swiss National Forest Inventory, we decomposed variation into spatial, temporal, and spatiotemporal components. We found that spatial variation dominated in evergreen species (48–86%), while temporal variation was more pronounced in deciduous species (56–82%), reflecting their stronger seasonality. These findings demonstrate that species-specific Sentinel-2 time series can effectively track intraspecific variation, providing a scalable method for forest monitoring. This approach opens new pathways for studying forest adaptation, informing management strategies, and guiding species selection for conservation under changing climate conditions. Full article

(This article belongs to the Section Forest Remote Sensing)

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20 pages, 1857 KiB

Open AccessArticle

Multi-Information-Assisted Joint Detection and Tracking of Ground Moving Target for Airborne Radar

by Ran Liu, Xiangqian Li, Jinping Sun and Tao Shan

Remote Sens. 2025, 17(12), 2093; https://doi.org/10.3390/rs17122093 - 18 Jun 2025

Viewed by 266

Abstract

Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a [...] Read more.

Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, enabling bidirectional data exchange between the detector and tracker for multiple ground targets. An adaptive threshold detector is developed by incorporating a priori information and tracker feedback. Additionally, we innovatively propose an improved Variable Structure Interacting Multiple Model (VS-IMM) filter that leverages road network constraints and detector outputs for tracking, featuring an enhanced model probability calculation to significantly reduce computational time. Simulation results demonstrate that the proposed method significantly improves data association accuracy and tracking precision. Full article

(This article belongs to the Special Issue Radar Data Processing and Analysis)

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20 pages, 14971 KiB

Open AccessArticle

The Influence of Australian Bushfire on the Upper Tropospheric CO and Hydrocarbon Distribution in the South Pacific

by Donghee Lee, Jin-Soo Kim, Kaley Walker, Patrick Sheese, Sang Seo Park, Taejin Choi, Minju Park, Hwan-Jin Song and Ja-Ho Koo

Remote Sens. 2025, 17(12), 2092; https://doi.org/10.3390/rs17122092 - 18 Jun 2025

Viewed by 377

Abstract

To determine the long-term effect of Australian bushfires on the upper tropospheric composition in the South Pacific, we investigated the variation in CO and hydrocarbon species in the South Pacific according to the extent of Australian bushfires (2004–2020). We conducted analyses using satellite [...] Read more.

To determine the long-term effect of Australian bushfires on the upper tropospheric composition in the South Pacific, we investigated the variation in CO and hydrocarbon species in the South Pacific according to the extent of Australian bushfires (2004–2020). We conducted analyses using satellite data on hydrocarbon and CO from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS), and on fire (fire count, burned area, and fire radiative power) from the Moderate Resolution Imaging Spectroradiometer (MODIS). Additionally, we compared the effects of bushfires between Northern and Southeastern Australia (N_Aus and SE_Aus, respectively). Our analyses show that Australian bushfires in austral spring (September to November) result in the largest increase in CO and hydrocarbon species in the South Pacific and even in the west of South America, indicating the trans-Pacific transport of smoke plumes. In addition to HCN (a well-known wildfire indicator), CO and other hydrocarbon species (C₂H₂, C₂H₆, CH₃OH, HCOOH) are also considerably increased by Australian bushfires. A unique finding in this study is that the hydrocarbon increase in the South Pacific mostly relates to the bushfires in N_Aus, implying that we need to be more vigilant of bushfires in N_Aus, although the severe Australian bushfire in 2019–2020 occurred in SE_Aus. Due to the surface conditions in springtime, bushfires on grassland in N_Aus during this time account for most Australian bushfires. All results show that satellite data enables us to assess the long-term effect of bushfires on the air composition over remote areas not having surface monitoring platforms. Full article

(This article belongs to the Special Issue Satellite Observations for Particulate Matter and Gaseous Pollutants Research)

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17 pages, 6547 KiB

Open AccessArticle

Direct Estimation of Forest Aboveground Biomass from UAV LiDAR and RGB Observations in Forest Stands with Various Tree Densities

by Kangyu So, Jenny Chau, Sean Rudd, Derek T. Robinson, Jiaxin Chen, Dominic Cyr and Alemu Gonsamo

Remote Sens. 2025, 17(12), 2091; https://doi.org/10.3390/rs17122091 - 18 Jun 2025

Viewed by 631

Abstract

Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observations onboard airborne or unoccupied aerial vehicles (UAVs) may [...] Read more.

Canada’s vast forests play a substantial role in the global carbon balance but require laborious and expensive forest inventory campaigns to monitor changes in aboveground biomass (AGB). Light detection and ranging (LiDAR) or reflectance observations onboard airborne or unoccupied aerial vehicles (UAVs) may address scalability limitations associated with traditional forest inventory but require simple forest structures or large sets of manually delineated crowns. Here, we introduce a deep learning approach for crown delineation and AGB estimation reproducible for complex forest structures without relying on hand annotations for training. Firstly, we detect treetops and delineate crowns with a LiDAR point cloud using marker-controlled watershed segmentation (MCWS). Then we train a deep learning model on annotations derived from MCWS to make crown predictions on UAV red, blue, and green (RGB) tiles. Finally, we estimate AGB metrics from tree height- and crown diameter-based allometric equations, all derived from UAV data. We validate our approach using 14 ha mixed forest stands with various experimental tree densities in Southern Ontario, Canada. Our results show that using an unsupervised LiDAR-only algorithm for tree crown delineation alongside a self-supervised RGB deep learning model trained on LiDAR-derived annotations leads to an 18% improvement in AGB estimation accuracy. In unharvested stands, the self-supervised RGB model performs well for height (adjusted R², R_a² = 0.79) and AGB (R_a² = 0.80) estimation. In thinned stands, the performance of both unsupervised and self-supervised methods varied with stand density, crown clumping, canopy height variation, and species diversity. These findings suggest that MCWS can be supplemented with self-supervised deep learning to directly estimate biomass components in complex forest structures as well as atypical forest conditions where stand density and spatial patterns are manipulated. Full article

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18 pages, 2585 KiB

Open AccessArticle

Incremental SAR Automatic Target Recognition with Divergence-Constrained Class-Specific Dictionary Learning

by Xiaojie Ma, Xusong Bu, Dezhao Zhang, Zhaohui Wang and Jing Li

Remote Sens. 2025, 17(12), 2090; https://doi.org/10.3390/rs17122090 - 18 Jun 2025

Viewed by 235

Abstract

Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained by the sequential arrival of training data and high retraining costs. To overcome [...] Read more.

Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained by the sequential arrival of training data and high retraining costs. To overcome this challenge, this paper introduces a divergence-constrained incremental dictionary learning framework that enables progressive model updates without full data reprocessing. Specifically, firstly, this method learns class-specific dictionaries for each target category via sub-dictionary learning, where the learning process for a specific class does not involve data from other classes. Secondly, the intra-class divergence constraint is incorporated during sub-dictionary learning to address the challenges of significant intra-class variations and minor inter-class differences in SAR targets. Thirdly, the sparse representation coefficients of the target to be classified are solved across all sub-dictionaries, followed by the computation of corresponding reconstruction errors and intra-class divergence metrics to achieve classification. Finally, when the targets of new categories are obtained, the corresponding class-specific dictionaries are calculated and added to the learned dictionary set. In this way, the incremental update of the SAR ATR system is completed. Experimental results on the MSTAR dataset indicate that our method attains >96.62% accuracy across various incremental scenarios. Compared with other state-of-the-art methods, it demonstrates better recognition performance and robustness. 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, 44010 KiB

Open AccessArticle

SMM-POD: Panoramic 3D Object Detection via Spherical Multi-Stage Multi-Modal Fusion

by Jinghan Zhang, Yusheng Yang, Zhiyuan Gao, Hang Shi and Yangmin Xie

Remote Sens. 2025, 17(12), 2089; https://doi.org/10.3390/rs17122089 - 18 Jun 2025

Viewed by 449

Abstract

Panoramic 3D object detection is a challenging task due to image distortion, sensor heterogeneity, and the difficulty of combining information from multiple modalities over a wide field-of-view (FoV). To address these issues, we propose SMM-POD, a novel framework that introduces a spherical multi-stage [...] Read more.

Panoramic 3D object detection is a challenging task due to image distortion, sensor heterogeneity, and the difficulty of combining information from multiple modalities over a wide field-of-view (FoV). To address these issues, we propose SMM-POD, a novel framework that introduces a spherical multi-stage fusion strategy for panoramic 3D detection. Our approach creates a five-channel spherical image aligned with LiDAR data and uses a quasi-uniform Voronoi sphere (UVS) model to reduce projection distortion. A cross-attention-based feature extraction module and a transformer encoder–decoder with spherical positional encoding enable the accurate and efficient fusion of image and point cloud features. For precise 3D localization, we adopt a Frustum PointNet module. Experiments on the DAIR-V2X-I benchmark and our self-collected SHU-3DPOD dataset show that SMM-POD achieves a state-of-the-art performance across all object categories. It significantly improves the detection of small objects like cyclists and pedestrians and maintains stable results under various environmental conditions. These results demonstrate the effectiveness of SMM-POD in panoramic multi-modal 3D perception and establish it as a strong baseline for wide FoV object detection. Full article

(This article belongs to the Section Urban Remote Sensing)

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13 pages, 12190 KiB

Open AccessArticle

Mapping the Mineralogical Footprints of Petroleum Microseepage Systems in Redbeds of the Qom Region (Iran) Using EnMAP Hyperspectral Data

by Yasmin Elhaei and Saeid Asadzadeh

Remote Sens. 2025, 17(12), 2088; https://doi.org/10.3390/rs17122088 - 18 Jun 2025

Viewed by 226

Abstract

This study utilizes EnMAP hyperspectral satellite data to map the mineralogical footprints of hydrocarbon microseepage systems induced in the Upper-Red Formation (URF), a clastic Upper Miocene sedimentary sequence in the Qom region (Iran) affected by petroleum leakage from the underlying Alborz reservoir. The [...] Read more.

This study utilizes EnMAP hyperspectral satellite data to map the mineralogical footprints of hydrocarbon microseepage systems induced in the Upper-Red Formation (URF), a clastic Upper Miocene sedimentary sequence in the Qom region (Iran) affected by petroleum leakage from the underlying Alborz reservoir. The Level 2A surface reflectance product of EnMAP was processed using spectral matching and polynomial fitting techniques to characterize diagnostic absorption features associated with microseepage-induced alteration minerals. The identified mineralogical changes include partial to complete bleaching of hematite from redbeds, the formation of secondary goethite, and the development of montmorillonite, calcite, and Fe²⁺-bearing chlorite across the affected zones. Compared to previous studies conducted using ASTER and Sentinel-2 multispectral data, EnMAP demonstrated superior performance in identifying mineralogy and delineating petroleum-affected zones, with results aligning closely with field observations and laboratory spectroscopy. This study highlights the advantages of EnMAP hyperspectral data for mapping diagenetic mineralogical alterations induced in sedimentary strata, facilitating remote sensing-based detection of microseepage, and advancing petroleum exploration in exposed terrains. Full article

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21 pages, 6325 KiB

Open AccessArticle

Estimating Flood-Affected Houses as an SDG Indicator to Enhance the Flood Resilience of Sahel Communities Using Geospatial Data

by Miguel A. Belenguer-Plomer, Inês Mendes, Michele Lazzarini, Omar Barrilero, Paula Saameño and Sergio Albani

Remote Sens. 2025, 17(12), 2087; https://doi.org/10.3390/rs17122087 - 18 Jun 2025

Viewed by 281

Abstract

The United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural disasters [...] Read more.

The United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural disasters in all countries. However, there is a lack of official data providers and well-established methodologies for assessing the resilience of populated areas to natural disasters. Earth observation (EO), geospatial technologies, and local data may support the estimation of this indicator and, as such, enhance the resilience of specific communities against hazards. Thus, the present study aims to enhance the capacity to monitor Sustainable Development Goals (SDGs) using the abovementioned technologies. In this context, a methodology that integrates ecoregion-specific model training and flood potential related geospatial datasets has been developed to estimate the number of houses affected by floods. This methodology relies on disaster-related databases, such as the UN’s DesInventar, and flood- and exposure-related data, including precipitation and soil moisture products combined with hydro-modelling based on digital elevation models, infrastructure datasets, and population products. By integrating these data sources, different machine learning regression models were trained and stratified by ecoregions to predict the number of affected houses and, as such, provide a more comprehensive understanding of community resilience to floods in the Sahel region. This effort is particularly crucial as the frequency and intensity of floods significantly increase in many areas due to climate change. Full article

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29 pages, 38483 KiB

Open AccessReview

A Review of Image- and LiDAR-Based Mapping of Shallow Water Scenarios

by Paulina Kujawa and Fabio Remondino

Remote Sens. 2025, 17(12), 2086; https://doi.org/10.3390/rs17122086 - 18 Jun 2025

Viewed by 588

Abstract

There is a growing need for accurate bathymetric mapping in many water-related scientific disciplines. Accurate and up-to-date data are essential for both shallow and deep areas. In this article, methods and techniques for shallow water mapping have been collected and described based on [...] Read more.

There is a growing need for accurate bathymetric mapping in many water-related scientific disciplines. Accurate and up-to-date data are essential for both shallow and deep areas. In this article, methods and techniques for shallow water mapping have been collected and described based on the available scientific literature. The paper focuses on three survey technologies, Unmanned Aerial Systems (UASs), Airborne Bathymetry (AB), and Satellite-Derived Bathymetry (SDB), with multimedia photogrammetry and LiDAR-based approaches as processing methods. The most popular and/or state-of-the-art image and LiDAR data correction techniques are characterized. To develop good practice in shallow water mapping, the authors present examples of data acquired by all the mentioned technologies with selected correction methods. Full article

(This article belongs to the Special Issue Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds III)

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20 pages, 3842 KiB

Open AccessArticle

Altitudinal Shifts as a Climate Resilience Strategy for Angelica sinensis Production in Its Primary Cultivation Region

by Zhengdong Li, Dajing Li, Hongxia Peng, Ruixuan Xu and Zaichun Zhu

Remote Sens. 2025, 17(12), 2085; https://doi.org/10.3390/rs17122085 - 18 Jun 2025

Viewed by 337

Abstract

Angelica sinensis, a highly valued Chinese herb renowned for its medicinal and nutritional properties, occupies a distinctive position in montane agriculture. The remote sensing monitoring of grain crops and their driving factors has been extensively studied, yet research on medicinal cash crops, [...] Read more.

Angelica sinensis, a highly valued Chinese herb renowned for its medicinal and nutritional properties, occupies a distinctive position in montane agriculture. The remote sensing monitoring of grain crops and their driving factors has been extensively studied, yet research on medicinal cash crops, particularly Angelica sinensis, remains limited. This study employed Landsat imagery and a two-step supervised classification method to map Angelica sinensis cultivation areas in southern Gansu Province while also assessing and projecting climate change impacts on its spatial distribution and yield based on the MaxEnt model and CMIP6 models. The results revealed a pronounced upward altitudinal shift in Angelica sinensis cultivation between 1990 and 2020, with the proportion of cultivation areas above 2400 m increasing from 28.75% to 67.80%. Climate factors explained 59.07% of the spatial distribution of Angelica sinensis, with precipitation, temperature, and altitude identified as the key environmental factors influencing its spatial distribution, yield, and growth. Projections for 2020 to 2060 indicate that Angelica sinensis cultivation areas will continue to shift to higher altitudes, accompanied by overall declines in both suitable area and yield. Under the SSP5-8.5 scenario, nearly all suitable areas are expected to be confined to altitudes above 2400 m by 2060, with 41.46% occurring above 2800 m. By 2060, the yield is expected to decrease to 361–421 kg/mu (down 20–31% from 2020) while the suitable area is projected to shrink to 0.98–1.80 million mu (40–60% smaller than 2040) under different scenarios. This study provides new insights into the protection and sustainable management of Angelica sinensis under changing climatic conditions, offering a scientific basis for the sustainable utilization of this valuable medicinal plant. Full article

(This article belongs to the Special Issue Integrating Remote Sensing, Machine Learning, and Process-Based Modelling for Monitoring Environmental and Agricultural Landscapes Under Climate Change)

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26 pages, 4304 KiB

Open AccessArticle

A Hybrid Regression–Kriging–Machine Learning Framework for Imputing Missing TROPOMI NO₂ Data over Taiwan

by Alyssa Valerio, Yi-Chun Chen, Chian-Yi Liu, Yi-Ying Chen and Chuan-Yao Lin

Remote Sens. 2025, 17(12), 2084; https://doi.org/10.3390/rs17122084 - 17 Jun 2025

Viewed by 542

Abstract

This study presents a novel application of a hybrid regression–kriging (RK) and machine learning (ML) framework to impute missing tropospheric NO₂ data from the TROPOMI satellite over Taiwan during the winter months of January, February, and December 2022. The proposed approach combines [...] Read more.

This study presents a novel application of a hybrid regression–kriging (RK) and machine learning (ML) framework to impute missing tropospheric NO₂ data from the TROPOMI satellite over Taiwan during the winter months of January, February, and December 2022. The proposed approach combines geostatistical interpolation with nonlinear modeling by integrating RK with ML models—specifically comparing gradient boosting regression (GBR), random forest (RF), and K-nearest neighbors (KNN)—to determine the most suitable auxiliary predictor. This structure enables the framework to capture both spatial autocorrelation and complex relationships between NO₂ concentrations and environmental drivers. Model performance was evaluated using the coefficient of determination (r²), computed against observed TROPOMI NO₂ column values filtered by quality assurance criteria. GBR achieved the highest validation r² values of 0.83 for January and February, while RF yielded 0.82 and 0.79 in January and December, respectively. These results demonstrate the model’s robustness in capturing intra-seasonal patterns and nonlinear trends in NO₂ distribution. In contrast, models using only static land cover inputs performed poorly (r² < 0.58), emphasizing the limited predictive capacity of such variables in isolation. Interpretability analysis using the SHapley Additive exPlanations (SHAP) method revealed temperature as the most influential meteorological driver of NO₂ variation, particularly during winter, while forest cover consistently emerged as a key land-use factor mitigating NO₂ levels through dry deposition. By integrating dynamic meteorological variables and static land cover features, the hybrid RK–ML framework enhances the spatial and temporal completeness of satellite-derived air quality datasets. As the first RK–ML application for TROPOMI data in Taiwan, this study establishes a regional benchmark and offers a transferable methodology for satellite data imputation. Future research should explore ensemble-based RK variants, incorporate real-time auxiliary data, and assess transferability across diverse geographic and climatological contexts. Full article

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26 pages, 11264 KiB

Open AccessArticle

Assessing the Influence of Environmental Factors on Landslide Frequency and Intensity in Northwestern Sichuan, SW China, Using Multi-Temporal Satellite Imagery

by Yu Zhu, Huajin Li, Ran Tang, Zhanfeng Fan, Lixuan Mao, Yifei Lu, Chuanhao Pu and Yusen He

Remote Sens. 2025, 17(12), 2083; https://doi.org/10.3390/rs17122083 - 17 Jun 2025

Viewed by 385

Abstract

Landslides are a significant geological hazard with substantial socio-economic and environmental consequences, particularly in northwestern Sichuan, SW China, where complex geological and climatic conditions contribute to their occurrence. This study examines 1629 recorded landslide events, including 240 active landslides that have undergone substantial [...] Read more.

Landslides are a significant geological hazard with substantial socio-economic and environmental consequences, particularly in northwestern Sichuan, SW China, where complex geological and climatic conditions contribute to their occurrence. This study examines 1629 recorded landslide events, including 240 active landslides that have undergone substantial changes over the past two decades. By analyzing multi-temporal satellite imagery, this research investigates the relationship between landslide occurrence and key environmental factors such as annual rainfall and the Normalized Difference Vegetation Index (NDVI). The results reveal that landslides are most frequent on southwest-, south-, east-, and southeast-facing slopes, where the Föhn effect interacts with rainfall and vegetation patterns, thereby increasing landslide susceptibility. Rainfall intensity is identified as a critical factor, with landslide areas expanding significantly when annual rainfall exceeds 650 mm, while minimal changes are observed when rainfall is below 550 mm. The relationship between the NDVI and landslide occurrence is non-linear; higher vegetation cover does not necessarily correlate with reduced landslide frequency. Notably, landslide expansion is more pronounced when NDVI values are below 0.82, with a suppression effect occurring beyond this threshold. A threshold model based on the interaction between the NDVI and rainfall provides valuable insights into landslide dynamics, offering a framework for improved risk management. Slope characteristics are crucial in landslide evolution, with steeper slopes leading to greater vertical drops and more frequent events, making slope zone identification key for predicting future expansion. Full article

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20 pages, 838 KiB

Open AccessArticle

Energy-Efficient Target Area Imaging for UAV-SAR-Based ISAC: Beamforming Design and Trajectory Optimization

by Jiayi Zhou, Xiangyin Zhang, Kaiyu Qin, Feng Yang, Libo Wang and Deyu Song

Remote Sens. 2025, 17(12), 2082; https://doi.org/10.3390/rs17122082 - 17 Jun 2025

Viewed by 339

Abstract

Integrated Sensing and Communication (ISAC) has been enhanced to serve as a pivotal enabler for next-generation communication systems. In the context of target area detection, a UAV-SAR (Unmanned Aerial Vehicle–Synthetic Aperture Radar) based ISAC system, which shares both physical infrastructure and spectrum, can [...] Read more.

Integrated Sensing and Communication (ISAC) has been enhanced to serve as a pivotal enabler for next-generation communication systems. In the context of target area detection, a UAV-SAR (Unmanned Aerial Vehicle–Synthetic Aperture Radar) based ISAC system, which shares both physical infrastructure and spectrum, can enhance the utilization of spectrum and hardware resources. However, existing studies on UAV-SAR-based ISAC systems for target imaging remain limited. In this study, we first established an ISAC mechanism to enable SAR imaging and communication. Then, we analyzed the energy consumption model, which includes both UAV propulsion and ISAC energy consumption. To maximize system energy efficiency, we propose an optimization method based on sequential convex optimization with linear state-space approximation. Furthermore, we propose a plan with general constraints, including the initial and final positions, the signal-to-noise ratio (SNR) constraint for SAR imaging, the data transmission rate constraint, and the total power limitation of the UAV. To achieve maximum energy efficiency, we jointly optimized the UAV’s trajectory, velocity, communication beamforming, sensing beamforming, and power allocation. Numerical results demonstrate that compared to existing benchmarks and PSO algorithms, the proposed method significantly improves the energy efficiency of UAV-SAR-based ISAC systems through optimized trajectory design. Full article

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25 pages, 7295 KiB

Open AccessArticle

Non-Exemplar Incremental ISAR Target Classification via Mix-Mamba Feature Adjustment Network

by Ruihang Xue, Caipin Li, Wencan Peng, Xueru Bai and Feng Zhou

Remote Sens. 2025, 17(12), 2081; https://doi.org/10.3390/rs17122081 - 17 Jun 2025

Viewed by 408

Abstract

To tackle the challenges of unknown image distortion and catastrophic forgetting in incremental inverse synthetic aperture radar (ISAR) target classification, this article introduces a deformation-robust non-exemplar incremental ISAR target classification method based on the Mix-Mamba feature adjustment network (MMFAN). The Mix-Mamba backbone employs [...] Read more.

To tackle the challenges of unknown image distortion and catastrophic forgetting in incremental inverse synthetic aperture radar (ISAR) target classification, this article introduces a deformation-robust non-exemplar incremental ISAR target classification method based on the Mix-Mamba feature adjustment network (MMFAN). The Mix-Mamba backbone employs channel-wise spatial transformations across multi-scale feature maps to inherently resist deformation distortions while generating compact global embedding through Mamba vision blocks. Then, the feature adjustment network facilitates knowledge transfer between base and incremental classes by dynamically maintaining a prototype for each target class. Finally, the loss bar synergizes supervised classification, unsupervised adaptation, and prototype consistency enforcement, enabling stable incremental training. Extensive experiments on ISAR datasets demonstrate the performance improvements of incremental learning and classification robustness under scaled, rotated, and mixed deformation test scenarios. Full article

(This article belongs to the Special Issue Target Recognition and Detection Based on High Resolution Radar Images)

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Remote Sens., Volume 17, Issue 12 (June-2 2025) – 151 articles

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