Remote Sensing

46 pages, 56646 KiB

Open AccessArticle

A 1.8 m Class Pathfinder Raman LIDAR for the Northern Site of the Cherenkov Telescope Array Observatory—Technical Design

by Otger Ballester, Oscar Blanch, Joan Boix, Paolo G. Calisse, Anna Campoy-Ordaz, Sidika Merve Çolak, Vania Da Deppo, Michele Doro, Lluís Font, Eudald Font-Pladevall, Rafael Garcia, Markus Gaug, Roger Grau, Darko Kolar, Alicia López-Oramas, Camilla Maggio, Manel Martinez, Òscar Martínez, Victor Riu-Molinero, David Roman, Samo Stanič, Júlia Tartera-Barberà, Santiago Ubach, Marko Zavrtanik and Miha Živec Show full author list Hide full author list

Remote Sens. 2025, 17(6), 1074; https://doi.org/10.3390/rs17061074 - 18 Mar 2025

Abstract

This paper presents the technical design of the pathfinder Barcelona Raman LIDAR (pbrl) for the northern site of the Cherenkov Telescope Array Observatory (Northern site of the Cherenkov Telescope Array Observatory (CTAO-N)) located at the Roque de los Muchachos Observatory (Roque de los [...] Read more.

This paper presents the technical design of the pathfinder Barcelona Raman LIDAR (pbrl) for the northern site of the Cherenkov Telescope Array Observatory (Northern site of the Cherenkov Telescope Array Observatory (CTAO-N)) located at the Roque de los Muchachos Observatory (Roque de los Muchachos Observatory (ORM)). The pBRL is developed for continuous atmospheric characterization, essential for correcting high-energy gamma-ray observations captured by Imaging Atmospheric Cherenkov Telescopes (IACTs). The LIDAR consists of a steerable telescope with a 1.8 m parabolic mirror and a pulsed Nd:YAG laser with frequency doubling and tripling. It emits at wavelengths of 355 nm and 532 nm to measure aerosol scattering and extinction through two elastic and Raman channels. Built upon a former Cherenkov Light Ultraviolet Experiment (Cherenkov Light Ultraviolet Experiment (CLUE)) telescope, the pBRL’s design includes a Newtonian mirror configuration, a coaxial laser beam, a near-range system, a liquid light guide and a custom-made polychromator. During a one-year test at the ORM, the stability of the LIDAR and semi-remote-controlled operations were tested. This pathfinder leads the way to designing a final version of a Cherenkov Telescope Array Observatory (CTAO) Raman LIDAR which will provide real-time atmospheric monitoring and, as such, ensure the necessary accuracy of scientific data collected by the CTAO-N telescope array. Full article

(This article belongs to the Special Issue Remote Sensing: 15th Anniversary)

32 pages, 4176 KiB

Open AccessReview

Potential of Earth Observation for the German North Sea Coast—A Review

by Karina Raquel Alvarez, Felix Bachofer and Claudia Kuenzer

Remote Sens. 2025, 17(6), 1073; https://doi.org/10.3390/rs17061073 - 18 Mar 2025

Abstract

Rising sea levels, warming ocean temperatures, and other climate change impacts threaten the German North Sea coast, making monitoring of this system even more critical. This study reviews the potential of remote sensing for the German North Sea coast, analyzing 97 publications from [...] Read more.

Rising sea levels, warming ocean temperatures, and other climate change impacts threaten the German North Sea coast, making monitoring of this system even more critical. This study reviews the potential of remote sensing for the German North Sea coast, analyzing 97 publications from 2000 to 2024. Publications fell into four main research topics: coastal morphology (33), water quality (34), ecology (22), and sediment (8). More than two-thirds of these papers (69%) used satellite platforms, whereas about one third (29%) used aircrafts and very few (4%) used uncrewed aerial vehicles (UAVs). Multispectral data were the most used data type in these studies (59%), followed by synthetic aperture radar data (SAR) (23%). Studies on intertidal topography were the most numerous overall, making up one-fifth (21%) of articles. Research gaps identified in this review include coastal morphology and ecology studies over large areas, especially at scales that align with administrative or management areas such as the German Wadden Sea National Parks. Additionally, few studies utilized free, publicly available high spatial resolution imagery, such as that from Sentinel-2 or newly available very high spatial resolution satellite imagery. This review finds that remote sensing plays a notable role in monitoring the German North Sea coast at local scales, but fewer studies investigated large areas at sub-annual temporal resolution, especially for coastal morphology and ecology topics. Earth Observation, however, has the potential to fill this gap and provide critical information about impacts of coastal hazards on this region. Full article

(This article belongs to the Special Issue Remote Sensing Techniques for Ocean Dynamics: State of the Art, Present and Future Applications)

34 pages, 3497 KiB

Open AccessArticle

An Airborne Lidar-Based 15-Year Study of Dune Reconstruction and Overwash Formation in a Microtidal and Fetch Limited Environment

by Clara Armaroli, Riccardo Brunetta and Paolo Ciavola

Remote Sens. 2025, 17(6), 1072; https://doi.org/10.3390/rs17061072 - 18 Mar 2025

Abstract

This paper examines the long-term morphological evolution of the Bevano River’s former sand spit (Ravenna, Italy) after an artificial intervention carried out in 2006 that shifted the river mouth to enhance the hydraulic efficiency, preventing flooding and reconstructing a local dune system. Using [...] Read more.

This paper examines the long-term morphological evolution of the Bevano River’s former sand spit (Ravenna, Italy) after an artificial intervention carried out in 2006 that shifted the river mouth to enhance the hydraulic efficiency, preventing flooding and reconstructing a local dune system. Using multitemporal Lidar data (2004–2019), combined with orthophotos and a storm dataset, this study tracked shoreline changes and morphological shifts, highlighting the role of overwash processes in sediment transfer from dunes to back-barrier areas. Based on the analysis, a set of washover fans was identified that began to form after a storm event in 2008 and rapidly accreted until 2015. These fans, which later coalesced into terraces and were colonised by vegetation, stabilised after 2015. Despite an initial low resilience, due to insufficient nourishment and slow vegetation development, the barrier system eventually stabilised, with dunes growing higher and forming a continuous dune crest. The study emphasises the role of surge levels, waves, and low initial elevation in triggering and shaping overwash processes. Full article

(This article belongs to the Section Ocean Remote Sensing)

25 pages, 2163 KiB

Open AccessArticle

A Dual-Branch Network of Strip Convolution and Swin Transformer for Multimodal Remote Sensing Image Registration

by Kunpeng Mu, Wenqing Wang, Han Liu, Lili Liang and Shuang Zhang

Remote Sens. 2025, 17(6), 1071; https://doi.org/10.3390/rs17061071 - 18 Mar 2025

Abstract

Multimodal remote sensing image registration aims to achieve effective fusion and analysis of information by accurately aligning image data obtained by different sensors, thereby improving the accuracy and application value of remote sensing data in engineering. However, current advanced registration frameworks are unable [...] Read more.

Multimodal remote sensing image registration aims to achieve effective fusion and analysis of information by accurately aligning image data obtained by different sensors, thereby improving the accuracy and application value of remote sensing data in engineering. However, current advanced registration frameworks are unable to accurately register large-scale rigid distortions, such as rotation or scaling, that occur in multi-source remote sensing images. This paper presents a stable and high-precision end-to-end registration network that incorporates dual-branch feature extraction to address the stringent registration requirements encountered in practical engineering applications. The deep neural network consists of three parts: dual-branch feature extraction, affine parameter regression, and spatial transformation network. In the upper branch of the dual-branch feature extraction module, we designed a combination of multi-scale convolution and Swin Transformer to fully extract features of remote sensing images at different scales and levels to better understand the global structure and context information. In the lower branch, we incorporate strip convolution blocks to capture remote contextual information from various directions in multimodal images. Additionally, we introduce an efficient and lightweight ResNet module to enhance global features. At the same time, we developed a strategy to parallelize various convolution kernels in affine parameter regression networks, aiming to enhance the accuracy of transformation parameters and the robustness of the model. We conducted experiments on panchromatic–multispectral, infrared–optical, and SAR–optical image pairs with large-scale rigid transformations. The experimental results show that our method achieves the best registration effect. Full article

(This article belongs to the Special Issue Image Processing and Analysis: Trends in Registration, Data Fusion, 3D Reconstruction, and Change Detection (Third Edition))

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23 pages, 23795 KiB

Open AccessArticle

Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022

by Federico Filipponi, Giulia Colazzo, Erica Vassoney, Claudio Comoglio and Gianluca Filippa

Remote Sens. 2025, 17(6), 1070; https://doi.org/10.3390/rs17061070 - 18 Mar 2025

Abstract

Monitoring inland waters is of critical importance for the effective and sustainable management of water resources, especially under climate change scenarios. This paper introduces a satellite-based approach for river monitoring using optical multispectral data. Time series of percentage water content, derived by the [...] Read more.

Monitoring inland waters is of critical importance for the effective and sustainable management of water resources, especially under climate change scenarios. This paper introduces a satellite-based approach for river monitoring using optical multispectral data. Time series of percentage water content, derived by the normalized difference water index (NDWI) calculated for each satellite acquisition, are aggregated at monthly timesteps to generate monthly water frequencies. Then, the river dynamics are evaluated by comparing each month with the previous one and with the average conditions of the same month in previous years. The ability of the method to investigate hydromorphological processes over time is demonstrated with the case study of the record-breaking Po River shrinking due to the severe 2022 drought in northern Italy, through the analysis of Copernicus Sentinel-2 satellite acquisitions. Earth observation data analysis is complemented with metrics generated from in situ river discharge measurements, including the coefficient of variation and the Streamflow Drought Index (SDI), to provide a more comprehensive understanding of the severity and variability of the hydrological drought throughout the year 2022. The findings demonstrate the satellite-based observation capabilities in monitoring surface waters, thereby stimulating the development of operational services like hydromorphological assessment. Full article

(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)

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22 pages, 5042 KiB

Open AccessArticle

SAAS-Net: Self-Supervised Sparse Synthetic Aperture Radar Imaging Network with Azimuth Ambiguity Suppression

by Zhiyi Jin, Zhouhao Pan, Zhe Zhang and Xiaolan Qiu

Remote Sens. 2025, 17(6), 1069; https://doi.org/10.3390/rs17061069 - 18 Mar 2025

Abstract

Sparse Synthetic Aperture Radar (SAR) imaging has garnered significant attention due to its ability to suppress azimuth ambiguity in under-sampled conditions, making it particularly useful for high-resolution wide-swath (HRWS) SAR systems. Traditional compressed sensing-based sparse SAR imaging algorithms are hindered by range–azimuth coupling [...] Read more.

Sparse Synthetic Aperture Radar (SAR) imaging has garnered significant attention due to its ability to suppress azimuth ambiguity in under-sampled conditions, making it particularly useful for high-resolution wide-swath (HRWS) SAR systems. Traditional compressed sensing-based sparse SAR imaging algorithms are hindered by range–azimuth coupling induced by range cell migration (RCM), which results in high computational cost and limits their applicability to large-scale imaging scenarios. To address this challenge, the approximated observation-based sparse SAR imaging algorithm was developed, which decouples the range and azimuth directions, significantly reducing computational and temporal complexities to match the performance of conventional matched filtering algorithms. However, this method requires iterative processing and manual adjustment of parameters. In this paper, we propose a novel deep neural network-based sparse SAR imaging method, namely the Self-supervised Azimuth Ambiguity Suppression Network (SAAS-Net). Unlike traditional iterative algorithms, SAAS-Net directly learns the parameters from data, eliminating the need for manual tuning. This approach not only improves imaging quality but also accelerates the imaging process. Additionally, SAAS-Net retains the core advantage of sparse SAR imaging—azimuth ambiguity suppression in under-sampling conditions. The method introduces self-supervision to achieve orientation ambiguity suppression without altering the hardware architecture. Simulations and real data experiments using Gaofen-3 validate the effectiveness and superiority of the proposed approach. Full article

17 pages, 3801 KiB

Open AccessArticle

Solar Radiation Pressure Modeling and Validation for BDS-3 MEO Satellites

by Qiuli Chen, Xu Zhang, Chen Wang, Haihong Wang, Chen Ren, Fujian Ma and Xinglong Zhao

Remote Sens. 2025, 17(6), 1068; https://doi.org/10.3390/rs17061068 - 18 Mar 2025

Abstract

The solar radiation pressure (SRP) model, as a key factor affecting the precise orbit determination (POD) accuracy of navigation satellites, is related to the state and optical properties of the satellite surface. This study establishes a high-precision SRP model for BDS-3 medium earth [...] Read more.

The solar radiation pressure (SRP) model, as a key factor affecting the precise orbit determination (POD) accuracy of navigation satellites, is related to the state and optical properties of the satellite surface. This study establishes a high-precision SRP model for BDS-3 medium earth orbit (MEO) satellites manufactured by the China Academy of Space Technology based on the satellite engineering parameters, which comprises the satellites’ size and optical properties measured before launch. Then, the physical-based SRP model is re-constructed into the body-fixed coordinate as the function of the Sun elongation angle. The use of the hybrid SRP model, combining the reconstructed SRP model and the 5-parameter ECOM, results in a better POD performance. The orbit results, validated using satellite laser ranging (SLR) observations, show that the radial precision of approximately 3–4 cm can be achieved, with a reduction of the bias by up to 38% and a removal of the systematic error related to the Sun elongation angle in SLR residuals. Considering the possible degradation of the reconstructed SRP model with the engineering parameters, the evolution of SRP accelerations along with orbit quality based on a time series from over 5 years was studied. The results indicate that a variation of the total SRP acceleration for the BDS-3 satellites is minor and there is no apparent degradation in validations of 2019–2023, which proved the reliability and usability of the proposed SRP model for the BDS-3 MEO satellites. Full article

(This article belongs to the Special Issue BDS/GNSS for Earth Observation: Part II)

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21 pages, 66133 KiB

Open AccessArticle

Forecasting and Evaluation of Ecosystem Services Supply-Demand Under SSP-RCP Scenarios in the Henan Segment of the Yellow River Basin, China

by Chaokun Wang, Yujie Chang, Benxin Guo and Pengfei Liu

Remote Sens. 2025, 17(6), 1067; https://doi.org/10.3390/rs17061067 - 18 Mar 2025

Abstract

Equilibrating the supply and demand for ecosystem services (ESs) is essential for sustainable development. Nonetheless, elements like policy modifications, land utilization, and climate change are profoundly transforming the dynamics of ecosystem service supply and demand (ESSD). As a result, there is an imperative [...] Read more.

Equilibrating the supply and demand for ecosystem services (ESs) is essential for sustainable development. Nonetheless, elements like policy modifications, land utilization, and climate change are profoundly transforming the dynamics of ecosystem service supply and demand (ESSD). As a result, there is an imperative necessity to methodically evaluate and predict these alterations by including both social and environmental elements. This study utilized the Henan region of the Yellow River Basin (HYRB) as a case study to forecast alterations in the supply and demand for three ESs—water production (WY), carbon storage (CS), and food production (FP)—under three scenarios for 2030 and 2050, grounded in the SSP-RCP framework. We further evaluated the supply–demand equilibrium at both grid and county degrees. The results indicate the following key findings: (1) From 2020 to 2050, there are significant spatial differences in the supply and demand of these services. While the supply of CS and FP exceeds demand, the supply of WY falls short. (2) The supply–demand ratios for WY and CS are projected to decline under all scenarios, whereas FP is expected to continue growing. Surplus areas for WY and CS are aggregated in the northwest, southwest, and central areas, while FP surpluses are found in the eastern and northern plains. Deficits for all three services are primarily located in urban areas. (3) The dominant spatial patterns of supply–demand matching also vary. WY and CS exhibit high–low agglomeration patterns, particularly in the northwest and southwest mountain regions, while FP shows low–low agglomeration, mainly in the southwest and northwest mountain areas. These findings enhance comprehension of the dynamics of ESSD, serving as a foundation for environmental preservation and sustainable advancement in the Yellow River Basin, China. Full article

(This article belongs to the Special Issue Remote Sensing Based Monitoring of Terrestrial Ecosystem Service Bundles, Trade-Offs and Synergies (Second Edition))

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35 pages, 8703 KiB

Open AccessArticle

Vision Transformer-Based Unhealthy Tree Crown Detection in Mixed Northeastern US Forests and Evaluation of Annotation Uncertainty

by Durga Joshi and Chandi Witharana

Remote Sens. 2025, 17(6), 1066; https://doi.org/10.3390/rs17061066 - 18 Mar 2025

Abstract

Forest health monitoring at scale requires high-spatial-resolution remote sensing images coupled with deep learning image analysis methods. However, high-quality large-scale datasets are costly to acquire. To address this challenge, we explored the potential of freely available National Agricultural Imagery Program (NAIP) imagery. By [...] Read more.

Forest health monitoring at scale requires high-spatial-resolution remote sensing images coupled with deep learning image analysis methods. However, high-quality large-scale datasets are costly to acquire. To address this challenge, we explored the potential of freely available National Agricultural Imagery Program (NAIP) imagery. By comparing the performance of traditional convolutional neural network (CNN) models (U-Net and DeepLabv3+) with a state-of-the-art Vision Transformer (SegFormer), we aimed to determine the optimal approach for detecting unhealthy tree crowns (UTC) using a publicly available data source. Additionally, we investigated the impact of different spectral band combinations on model performance to identify the most effective configuration without incurring additional data acquisition costs. We explored various band combinations, including RGB, color infrared (CIR), vegetation indices (VIs), principal components (PC) of texture features (PCA), and spectral band with PC (RGBPC). Furthermore, we analyzed the uncertainty associated with potential subjective crown annotation and its impact on model evaluation. Our results demonstrated that the Vision Transformer-based model, SegFormer, outperforms traditional CNN-based models, particularly when trained on RGB images yielding an F1-score of 0.85. In contrast, DeepLabv3+ achieved F1-score of 0.82. Notably, PCA-based inputs yield reduced performance across all models, with U-Net producing particularly poor results (F1-score as low as 0.03). The uncertainty analysis indicated that the Intersection over Union (IoU) could fluctuate between 14.81% and 57.41%, while F1-scores ranged from 8.57% to 47.14%, reflecting the significant sensitivity of model performance to inconsistencies in ground truth annotations. In summary, this study demonstrates the feasibility of using publicly available NAIP imagery and advanced deep learning techniques to accurately detect unhealthy tree canopies. These findings highlight SegFormer’s superior ability to capture complex spatial patterns, even in relatively low-resolution (60 cm) datasets. Our findings underline the considerable influence of human annotation errors on model performance, emphasizing the need for standardized annotation guidelines and quality control measures. Full article

(This article belongs to the Special Issue Computer Vision-Based Methods and Tools in Remote Sensing)

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18 pages, 5898 KiB

Open AccessTechnical Note

Spatial Regionalization of the Arctic Ocean Based on Ocean Physical Property

by Joo-Eun Yoon, Jinku Park and Hyun-Cheol Kim

Remote Sens. 2025, 17(6), 1065; https://doi.org/10.3390/rs17061065 - 18 Mar 2025

Abstract

The Arctic Ocean has a uniquely complex system associated with tightly coupled ocean–ice–atmosphere–land interactions. The Arctic Ocean is considered to be highly susceptible to global climate change, with the potential for dramatic environmental impacts at both regional and global scales, and its spatial [...] Read more.

The Arctic Ocean has a uniquely complex system associated with tightly coupled ocean–ice–atmosphere–land interactions. The Arctic Ocean is considered to be highly susceptible to global climate change, with the potential for dramatic environmental impacts at both regional and global scales, and its spatial differences particularly have been exacerbated. A comprehensive understanding of Arctic Ocean environmental responses to climate change thus requires classifying the Arctic Ocean into subregions that describe spatial homogeneity of the clusters and heterogeneity between clusters based on ocean physical properties and implementing the regional-scale analysis. In this study, utilizing the long-term optimum interpolation sea surface temperature (SST) datasets for the period 1982–2023, which is one of the essential indicators of physical processes, we applied the K-means clustering algorithm to generate subregions of the Arctic Ocean, reflecting distinct physical characteristics. Using the variance ratio criterion, the optimal number of subregions for spatial clustering was 12. Employing methods such as information mapping and pairwise multi-comparison analysis, we found that the 12 subregions of the Arctic Ocean well represent spatial heterogeneity and homogeneity of physical properties, including sea ice concentration, surface ocean currents, SST, and sea surface salinity. Spatial patterns in SST changes also matched well with the boundaries of clustered subregions. The newly identified physical subregions of the Arctic Ocean will contribute to a more comprehensive understanding of the Arctic Ocean’s environmental response to accelerating climate change. Full article

(This article belongs to the Section Ocean Remote Sensing)

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26 pages, 15844 KiB

Open AccessArticle

The Spatial Distribution and Transition of Meteorological and Ecological Droughts in the Shendong Mining Area

by He Qin, Zhichao Chen, Hao Li, Xufei Zhang, Chengyuan Hao, Shidong Wang, Hebing Zhang and Youfeng Zou

Remote Sens. 2025, 17(6), 1064; https://doi.org/10.3390/rs17061064 - 18 Mar 2025

Abstract

Arid and semi-arid regions are highly sensitive and vulnerable to climate change and human activities. Clarifying their spatial distribution is of great significance for understanding regional drought dynamics. This research examines the Shendong mining region, employing time series data of vegetation growth anomalies [...] Read more.

Arid and semi-arid regions are highly sensitive and vulnerable to climate change and human activities. Clarifying their spatial distribution is of great significance for understanding regional drought dynamics. This research examines the Shendong mining region, employing time series data of vegetation growth anomalies derived from total primary productivity data to delineate ecological drought. The SPI dataset, representing meteorological drought, is utilized to identify drought frequency, duration, and intensity for both types of droughts based on the run theory. The drought characteristics of different land use patterns are analyzed, and the center of gravity of meteorological and ecological droughts in the study area are calculated. The results show the following: (1) The frequency, duration, and intensity of meteorological drought in the Shendong mining area are 0.74 times per year, 9.2 months, and 0.91, respectively. The frequency, duration, and intensity of ecological drought are 0.33 times per year, 18.2 months, and 0.09, respectively. (2) The intensity of meteorological and ecological droughts is generally consistent across different land use types. The frequency of meteorological drought is minimal for croplands and high-coverage grasslands. The duration of meteorological drought is shortest for high-coverage grasslands. High- and medium-to-high-coverage grasslands and cultivated lands have lower ecological drought frequencies. Low- and medium-to-low-coverage grasslands have relatively shorter ecological drought durations. (3) In regions where land use alterations are evident, the frequency and duration of meteorological drought in areas where cropland has been converted to grassland are relatively low. The frequency, duration, and intensity of ecological drought for croplands converted to grasslands and grasslands converted to croplands are similar. (4) The average incidence of meteorological drought transitioning into ecological drought in the study area is roughly 55%, with areas of stable land use demonstrating a more robust correlation between meteorological and ecological drought in croplands. In the converted areas, the correlation between meteorological drought and ecological drought is higher for croplands converted to grasslands. (5) The transition frequency from meteorological drought to ecological drought exceeds 60% in mining areas. Compared to other mining areas, the meteorological drought intensity near Jitu well and Daliuta well is notably higher. The research findings reveal the spatial distribution attributes and transition dynamics of meteorological and ecological droughts in the Shendong coal mining region, providing reference for the implementation of ecological restoration projects and protection measures in the area. Full article

(This article belongs to the Special Issue Exploring the Variations and Interplay of Drought and Heatwaves: Uncovering the Associated Mechanisms and Impacts (2nd Edition))

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26 pages, 11704 KiB

Open AccessArticle

Forest Aboveground Biomass Estimation in Küre Mountains National Park Using Multifrequency SAR and Multispectral Optical Data with Machine-Learning Regression Models

by Eren Gursoy Ozdemir and Saygin Abdikan

Remote Sens. 2025, 17(6), 1063; https://doi.org/10.3390/rs17061063 - 18 Mar 2025

Abstract

Aboveground biomass (AGB) is crucial in forest ecosystems and is intricately linked to the carbon cycle and global climate change dynamics. This study investigates the efficacy of synthetic aperture radar (SAR) data from the X, C, and L bands, combined with Sentinel-2 optical [...] Read more.

Aboveground biomass (AGB) is crucial in forest ecosystems and is intricately linked to the carbon cycle and global climate change dynamics. This study investigates the efficacy of synthetic aperture radar (SAR) data from the X, C, and L bands, combined with Sentinel-2 optical imagery, vegetation indices, gray-level co-occurrence matrix (GLCM) texture metrics, and topographical variables in estimating AGB in the Küre Mountains National Park, Türkiye. Four machine-learning regression models were employed: partial least squares (PLS), least absolute shrinkage and selection operator (LASSO), multivariate linear, and ridge regression. Among these, the PLS regression (PLSR) model demonstrated the highest accuracy in AGB estimation, achieving an R² of 0.74, a mean absolute error (MAE) of 28.22 t/ha, and a root mean square error (RMSE) of 30.77 t/ha. An analysis across twelve models revealed that integrating ALOS-2 PALSAR-2 and SAOCOM L-band satellite data, particularly the SAOCOM HV and ALOS-2 PALSAR-2 HH polarizations with optical imagery, significantly enhances the precision and reliability of AGB estimations. Full article

(This article belongs to the Special Issue SAR for Forest Mapping III)

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22 pages, 2652 KiB

Open AccessArticle

Millimeter-Wave OFDM-FMCW Radar-Communication Integration System Design

by Jiangtao Liu, Wenyuan Feng, Tao Su, Jianzhong Chen and Shaohong Xue

Remote Sens. 2025, 17(6), 1062; https://doi.org/10.3390/rs17061062 - 18 Mar 2025

Abstract

Frequency-modulated continuous wave (FMCW) and orthogonal frequency-division multiplexing (OFDM) technologies play significant roles in millimeter-wave radar and communication. Their combinations, however, are understudied in the literature. This paper introduces a novel OFDM-FMCW dual-functional radar-communications (DFRC) system that takes advantage of the merits of [...] Read more.

Frequency-modulated continuous wave (FMCW) and orthogonal frequency-division multiplexing (OFDM) technologies play significant roles in millimeter-wave radar and communication. Their combinations, however, are understudied in the literature. This paper introduces a novel OFDM-FMCW dual-functional radar-communications (DFRC) system that takes advantage of the merits of both technologies. Specifically, we introduce a baseband modulation to the traditional FMCW radar system architecture. This integration combines the advantages of both waveforms, enhancing the diversity of radar transmission waveforms without compromising high-resolution distance detection and enjoying the communication capabilities of OFDM in the meantime. We establish the system and signal models for the proposed DFRC and develop holistic methods for both sensing and communications to accommodate the integration. For radar, we develop an efficient radar sensing scheme, with the impacts of adding OFDM also being analyzed. A communication scheme is also proposed, utilizing the undersampling theory to recover the OFDM baseband signals modulated by FMCW. The theoretical model of the communication receive signal is analyzed, and a coarse estimation combined with a fine estimation method for Carrier Frequency Offset (CFO) estimation is proposed. System simulations validate the feasibility of radar detection and communication demodulation. Full article

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43 pages, 20516 KiB

Open AccessArticle

Ground-Truth-Free 3D Seismic Denoising Based on Diffusion Models: Achieving Effective Constraints Through Embedded Self-Supervised Noise Modeling

by Zhonghan Zhang, Guihe Qin, Yanhua Liang, Minghui Sun, Yingqing Wang and Jiaru Song

Remote Sens. 2025, 17(6), 1061; https://doi.org/10.3390/rs17061061 - 17 Mar 2025

Abstract

Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution. Existing denoising research faces great challenges due to the scarcity of ground truth and the difficulty in obtaining [...] Read more.

Three-dimensional (3D) seismic data, essential for revealing subsurface structures and exploring oil and gas resources, are often contaminated by noise with an unknown prior distribution. Existing denoising research faces great challenges due to the scarcity of ground truth and the difficulty in obtaining prior knowledge of noise distributions. Moreover, few algorithms are specifically designed to leverage the unique spatial structural information inherent in 3D seismic data, leading to inefficient utilization of this valuable information during denoising. To address these issues, we propose Self-Supervised Seismic Denoising using the Denoising Diffusion Probabilistic Model (SSDn-DDPM), an algorithm specifically tailored for 3D seismic data that utilizes diffusion generative models for self-supervised blind denoising. The algorithm begins with self-supervised modeling of seismic noise to estimate its distribution. Subsequently, spatial structural information of 3D seismic data is leveraged to improve the accuracy of noise distribution estimation. Furthermore, the algorithm integrates the noise distribution estimation network into the diffusion model to further guide and refine the sampling process, thereby optimizing computational complexity and improving detail representation. Finally, it performs self-supervised 3D seismic noise suppression using the diffusion probabilistic model. In the experimental section, we comprehensively compare the proposed algorithm with six different types of seismic denoising methods. Various comparative experiments demonstrate that the proposed algorithm achieves exceptional denoising performance on 3D seismic data, even without ground truth or any prior knowledge about the noise distribution. Full article

(This article belongs to the Special Issue New Technologies, Methods and Studies for Seismic and Radar Subsurface Exploration)

29 pages, 28978 KiB

Open AccessArticle

Static Early Fusion Techniques for Visible and Thermal Images to Enhance Convolutional Neural Network Detection: A Performance Analysis

by Enrique Heredia-Aguado, Juan José Cabrera, Luis Miguel Jiménez, David Valiente and Arturo Gil

Remote Sens. 2025, 17(6), 1060; https://doi.org/10.3390/rs17061060 - 17 Mar 2025

Abstract

This paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). While object detection with RGB images is a well-developed area, it is still greatly limited [...] Read more.

This paper presents a comparison of different image fusion methods for matching visible-spectrum images with thermal-spectrum (far-infrared) images, aimed at enhancing person detection using convolutional neural networks (CNNs). While object detection with RGB images is a well-developed area, it is still greatly limited by lighting conditions. This limitation poses a significant challenge in image detection playing a larger role in everyday technology, where illumination cannot always be controlled. Far-infrared images (which are partially invariant to lighting conditions) can serve as a valuable complement to RGB images in environments where illumination cannot be controlled and robust object detection is needed. In this work, various early and middle fusion techniques are presented and compared using different multispectral datasets, with the aim of addressing these limitations and improving detection performance. Full article

(This article belongs to the Special Issue Recent Advances in Multispectral and Hyperspectral Image Analysis and Classification)

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22 pages, 7397 KiB

Open AccessArticle

Integrated GNSS and InSAR Analysis for Monitoring the Shoulder Structures of the MOSE System in Venice, Italy

by Massimo Fabris and Mario Floris

Remote Sens. 2025, 17(6), 1059; https://doi.org/10.3390/rs17061059 - 17 Mar 2025

Abstract

Ground-based global navigation satellite system (GNSS) and remote sensing interferometric synthetic aperture radar (InSAR) techniques have proven to be very useful for deformation monitoring. GNSS provides high-precision data but only at a limited number of points, whereas InSAR allows for a much denser [...] Read more.

Ground-based global navigation satellite system (GNSS) and remote sensing interferometric synthetic aperture radar (InSAR) techniques have proven to be very useful for deformation monitoring. GNSS provides high-precision data but only at a limited number of points, whereas InSAR allows for a much denser distribution of measurement points, though only in areas with high and consistent signal backscattering. This study aims to integrate these two techniques to overcome their respective limitations and explore their potential for effective monitoring of critical infrastructure, ensuring the protection of people and the environment. The proposed approach was applied to monitor deformations of the shoulder structures of the MOSE (MOdulo Sperimentale Elettromeccanico) system, the civil infrastructure designed to protect Venice and its lagoon from high tides. GNSS data were collected from 36 continuous GNSS (CGNSS) stations located at the corners of the emerged shoulder structures in the Treporti, San Nicolò, Malamocco, and Chioggia barriers. Velocities from February 2021/November 2022 to June 2023 were obtained using daily RINEX data and Bernese software. Three different processing strategies were applied, utilizing networks composed of the 36 MOSE stations and eight other continuous GNSS stations from the surrounding area (Padova, Venezia, Treviso, San Donà, Rovigo, Taglio di Po, Porto Garibaldi, and Porec). InSAR data were sourced from the European ground motion service (EGMS) of the Copernicus program and the Veneto Region database. Both services provide open data related to the line of sight (LOS) velocities derived from Sentinel-1 satellite imagery using the persistent scatterers interferometric synthetic aperture radar (PS-InSAR) approach. InSAR velocities were calibrated using a reference CGNSS station (Venezia) and validated with the available CGNSS data from the external network. Subsequently, the velocities were compared along the LOS at the 36 CGNSS stations of the MOSE system. The results showed a strong agreement between the velocities, with approximately 70% of the comparisons displaying differences of less than 1.5 mm/year. These findings highlight the great potential of satellite-based monitoring and the effectiveness of combining GNSS and InSAR techniques for infrastructure deformation analysis. Full article

(This article belongs to the Special Issue Structural Health Monitoring and Damage Assessment by Advanced Remote Sensing Techniques and Methods)

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25 pages, 4128 KiB

Open AccessArticle

Enhancing the Communication Bandwidth of FH-MIMO DFRC Systems Through Constellation Rotation Modulation

by Jiangtao Liu, Weibin Jiang, Wentie Yang, Tao Su and Jianzhong Chen

Remote Sens. 2025, 17(6), 1058; https://doi.org/10.3390/rs17061058 - 17 Mar 2025

Abstract

This paper presents a technique based on Constellation Rotation Modulation (CRM) to enhance the communication bandwidth of Frequency-Hopping Multiple-Input Multiple-Output Dual-Function Radar and Communication (FH-MIMO DFRC) systems. The technique introduces the dimension of constellation diagram rotation without increasing the system bandwidth or power [...] Read more.

This paper presents a technique based on Constellation Rotation Modulation (CRM) to enhance the communication bandwidth of Frequency-Hopping Multiple-Input Multiple-Output Dual-Function Radar and Communication (FH-MIMO DFRC) systems. The technique introduces the dimension of constellation diagram rotation without increasing the system bandwidth or power consumption, significantly improving communication efficiency. Specifically, CRM, by rotating the constellation diagram, combines with traditional Frequency-Hopping Code Selection (FHCS) and Quadrature Amplitude Modulation (QAM) to achieve higher data transmission rates. Through theoretical analysis and experimental verification, we demonstrate the specific modulation and demodulation principles of CRM, and we compare the differences between the minimum Euclidean distance-based and constellation diagram folding projection fast demodulation methods. The impact of the proposed modulation on radar detection range and detection performance was analyzed in conjunction with radar equations and ambiguity functions. Finally, achieved through simulation analysis of radar and communication systems, as well as actual system testing on an SDR platform, the simulation and experimental results indicate that CRM modulation can significantly enhance communication bandwidth while maintaining radar detection performance, thereby validating the accuracy and reliability of the theory. Full article

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25 pages, 16833 KiB

Open AccessArticle

R2SCAT-LPR: Rotation-Robust Network with Self- and Cross-Attention Transformers for LiDAR-Based Place Recognition

by Weizhong Jiang, Hanzhang Xue, Shubin Si, Liang Xiao, Dawei Zhao, Qi Zhu, Yiming Nie and Bin Dai

Remote Sens. 2025, 17(6), 1057; https://doi.org/10.3390/rs17061057 - 17 Mar 2025

Abstract

LiDAR-based place recognition (LPR) is crucial for the navigation and localization of autonomous vehicles and mobile robots in large-scale outdoor environments and plays a critical role in loop closure detection for simultaneous localization and mapping (SLAM). Existing LPR methods, which utilize 2D bird’s-eye [...] Read more.

LiDAR-based place recognition (LPR) is crucial for the navigation and localization of autonomous vehicles and mobile robots in large-scale outdoor environments and plays a critical role in loop closure detection for simultaneous localization and mapping (SLAM). Existing LPR methods, which utilize 2D bird’s-eye view (BEV) projections of 3D point clouds, achieve competitive performance in efficiency and recognition accuracy. However, these methods often struggle with capturing global contextual information and maintaining robustness to viewpoint variations. To address these challenges, we propose R2SCAT-LPR, a novel, transformer-based model that leverages self-attention and cross-attention mechanisms to extract rotation-robust place feature descriptors from BEV images. R2SCAT-LPR consists of three core modules: (1) R2MPFE, which employs weight-shared cascaded multi-head self-attention (MHSA) to extract multi-level spatial contextual patch features from both the original BEV image and its randomly rotated counterpart; (2) DSCA, which integrates dual-branch self-attention and multi-head cross-attention (MHCA) to capture intrinsic correspondences between multi-level patch features before and after rotation, enhancing the extraction of rotation-robust local features; and (3) a combined NetVLAD module, which aggregates patch features from both the original feature space and the rotated interaction space into a compact and viewpoint-robust global descriptor. Extensive experiments conducted on the KITTI and NCLT datasets validate the effectiveness of the proposed model, demonstrating its robustness to rotation variations and its generalization ability across diverse scenes and LiDAR sensors types. Furthermore, we evaluate the generalization performance and computational efficiency of R2SCAT-LPR on our self-constructed OffRoad-LPR dataset for off-road autonomous driving, verifying its deployability on resource-constrained platforms. Full article

(This article belongs to the Special Issue 3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing (Second Edition))

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19 pages, 14693 KiB

Open AccessArticle

Community-Level Urban Vitality Intensity and Diversity Analysis Supported by Multisource Remote Sensing Data

by Zhiran Zhang, Jiping Liu, Yangyang Zhao, Qing Zhou, Lijun Song and Shenghua Xu

Remote Sens. 2025, 17(6), 1056; https://doi.org/10.3390/rs17061056 - 17 Mar 2025

Abstract

Urban vitality serves as a crucial metric for evaluating sustainable urban development and the well-being of residents. Existing studies have predominantly focused on analyzing the direct effects of urban vitality intensity (VI) and its influencing factors, while paying less attention to the urban [...] Read more.

Urban vitality serves as a crucial metric for evaluating sustainable urban development and the well-being of residents. Existing studies have predominantly focused on analyzing the direct effects of urban vitality intensity (VI) and its influencing factors, while paying less attention to the urban vitality diversity (VD) and its indirect impact mechanisms. Supported by multisource remote sensing data, this study establishes a five-dimensional urban vitality evaluation system and employs the Partial Least Squares Structural Equation Model (PLS-SEM) to quantify direct and indirect interrelationships between these multidimensional factors and VI/VD. The findings are as follows: (1) Spatial divergence between VI and VD: VI exhibited stronger clustering (I = 1.12), predominantly aggregating in central urban areas, whereas VD demonstrated moderate autocorrelation (I = 0.45) concentrated in mixed-use central or suburban zones. (2) Drivers of vitality intensity: VI are strongly associated with commercial density (β = 0.344) and transportation accessibility (β = 0.253), but negatively correlated with natural environment quality (r = −0.166). (3) Mechanisms of vitality diversity: VD is closely linked to public service (β = 0.228). This research provides valuable insights for city development and decision-making, particularly in strengthening urban vitality and optimizing urban functional layouts. Full article

(This article belongs to the Section Urban Remote Sensing)

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