Remote Sensing

36 pages, 7526 KB

Open AccessArticle

Versatile FourierTransform Spectrometer Model for Earth Observation Missions Validated with In-Flight Systems Measurements

by Tom Piekarski, Christophe Buisset, Anne Kleinert, Felix Friedl-Vallon, Arnaud Heliere, Julian Hofmann, Ljubiša Babić, Micael Dias Miranda, Tobias Guggenmoser, Daniel Lamarre, Flavio Mariani, Felice Vanin and Ben Veihelmann

Remote Sens. 2025, 17(23), 3903; https://doi.org/10.3390/rs17233903 (registering DOI) - 30 Nov 2025

Abstract

Fourier-transform spectrometers (FTSs) are cornerstone instruments in Earth observation space missions, effectively monitoring atmospheric gases in missions such as Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), and Infrared Atmospheric Sounding Interferometer (IASI). It will also be the core instrument of Meteosat Third Generation—Sounding [...] Read more.

Fourier-transform spectrometers (FTSs) are cornerstone instruments in Earth observation space missions, effectively monitoring atmospheric gases in missions such as Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), and Infrared Atmospheric Sounding Interferometer (IASI). It will also be the core instrument of Meteosat Third Generation—Sounding (MTG-S) and the future Earth Explorer (EE) mission Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM). Building on this legacy, the European Space Agency (ESA) has developed an FTS instrument and an inverse model designed to estimate the radiometric and spectral performance from a set of instrumental parameters. The model and its validation using in-flight measurements of the FTS instrument Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA)-Lite are described in this paper. The results indicate that the difference between the model predictions and the measured signal is less than 2% relative to the average of the measurements. Moreover, we can correctly predict the instrument’s radiometric gain and offset and reconstruct a scientific science spectrum. This model can be utilised effectively to evaluate the radiometric performance of future FTS missions. Full article

19 pages, 5153 KB

Open AccessTechnical Note

Assessment of Instrument Performance of the FY3E/JTSIM/DARA Radiometer Through the Analysis of TSI Observations

by Jean-Philippe Montillet, Wolfgang Finsterle, Ping Zhu, Margit Haberreiter, Silvio Koller, Daniel Pfiffner, Duo Wu, Xin Ye, Dongjun Yang, Wei Fang, Jin Qi and Peng Zhang

Remote Sens. 2025, 17(23), 3902; https://doi.org/10.3390/rs17233902 (registering DOI) - 30 Nov 2025

Abstract

Since the late 1970s, satellite missions have monitored Total Solar Irradiance (TSI), providing a long-term record of solar variability. The Digital Absolute Radiometer (DARA), onboard the Chinese Fengyun-3E (FY3E) spacecraft since 4 July 2021, contributes to extending this record. In this study, we [...] Read more.

Since the late 1970s, satellite missions have monitored Total Solar Irradiance (TSI), providing a long-term record of solar variability. The Digital Absolute Radiometer (DARA), onboard the Chinese Fengyun-3E (FY3E) spacecraft since 4 July 2021, contributes to extending this record. In this study, we evaluate the DARA observations in both World Radiometric Reference (WRR) and International System of Units (SI) scales. We compare these records with those from other instruments on different spacecraft (i.e., VIRGO/PMO6, TSIS-1/TIM) and with the co-located Solar Irradiance Absolute Radiometer (SIAR) on FY3E. A key finding is the identification and correction of an instrumental artifact: an issue in the thermal aperture model, linked to annual satellite maneuvers, repetitively introduced an artificial step of

0.15 \pm 0.05

Wm

^{- 2}

into the TSI measurements.A statistical analysis of the measurements in the SI scale shows that the mean value of the DARA TSI observations is approximately

1359.58

Wm

^{- 2}

(6-hourly rate), which is lower than the ones recorded by VIRGO/PMO6 (

1.82

Wm

^{- 2}

), TSIS-1/TIM (

2.90

Wm

^{- 2}

), and SIAR (

2.54

Wm

^{- 2}

). We estimate a degradation of ∼49 ppm over 46 months due to the exposure of the instrument to the (Extreme) Ultraviolet (UV/EUV) radiations. Finally, the corrected DARA observations are incorporated into the long-term TSI composite time series. Comparison with the PMOD/WRC composite shows only marginal differences (less than

0.015

Wm

^{- 2}

), confirming the consistency and reliability of including the new TSI product (i.e., JTSIM-DARAv1). Full article

(This article belongs to the Section Satellite Missions for Earth and Planetary Exploration)

27 pages, 13224 KB

Open AccessArticle

Spectral Prototype Attention Domain Adaptation for Hyperspectral Image Classification

by Weina Zhang, Runshan Hu, Jierui Wang, Lanlan Zhang and Chenyang Zhu

Remote Sens. 2025, 17(23), 3901; https://doi.org/10.3390/rs17233901 (registering DOI) - 30 Nov 2025

Abstract

Hyperspectral image (HSI) classification is often challenged by cross-scene domain shifts and limited target annotations. Existing approaches relying on class-agnostic moment matching or confidence-based pseudo-labeling tend to blur decision boundaries, propagate noise, and struggle with spectral overlap and class imbalance. We propose Spectral [...] Read more.

Hyperspectral image (HSI) classification is often challenged by cross-scene domain shifts and limited target annotations. Existing approaches relying on class-agnostic moment matching or confidence-based pseudo-labeling tend to blur decision boundaries, propagate noise, and struggle with spectral overlap and class imbalance. We propose Spectral Prototype Attention Domain Adaptation (SPADA), a framework that integrates an attention-guided spectral–spatial backbone with dual prototype banks and distance-based posterior modeling. SPADA performs global and class-conditional alignment through source supervision, kernel-based distribution matching, and prototype coupling, followed by diversity-aware active adaptation and confidence-calibrated refinement via prior-adjusted self-training. Across multiple cross-scene benchmarks in urban and inter-city scenarios, SPADA consistently outperforms strong baselines in overall accuracy, average accuracy, and Cohen’s

κ

, achieving clear gains on classes affected by spectral overlap or imbalance and maintaining low variance across runs, demonstrating robust and stable domain transfer. Full article

38 pages, 1800 KB

Open AccessArticle

Critical Factors for the Application of InSAR Monitoring in Ports

by Jaime Sánchez-Fernández, Alfredo Fernández-Landa, Álvaro Hernández Cabezudo and Rafael Molina Sánchez

Remote Sens. 2025, 17(23), 3900; https://doi.org/10.3390/rs17233900 (registering DOI) - 30 Nov 2025

Abstract

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. [...] Read more.

Ports pose distinctive monitoring challenges due to harsh marine conditions, mixed construction typologies, and heterogeneous ground conditions. These factors complicate the routine use of satellite InSAR, especially when medium-resolution scatterers must be reliably attributed to specific assets for risk and asset management decisions. In current practice, persistent and distributed scatterer (PS/DS) points are often interpreted in map view without an explicit positional uncertainty model or systematic linkage to three-dimensional infrastructure geometry. We present an end-to-end Differential InSAR framework tailored to large ports that fuses medium-resolution Sentinel-1 Level 2 Co-registered Single-Look Complex (L2-CSLC) stacks with high-resolution airborne LiDAR at the post-processing stage. For the Port of Bahía de Algeciras (Spain), we process 123 Sentinel-1A/B images (2020–2022) in ascending and descending geometry using PS/DS time-series analysis with ETAD-like timing corrections and RAiDER tropospheric/ionospheric mitigation. LiDAR is then used to (i) derive look-specific shadow/layover masks and (ii) perform a whitening-transformed nearest-neighbor association that assigns PS/DS points to LiDAR points under an explicit range–azimuth–cross-range (RAC) uncertainty ellipsoid. The RAC standard deviations

(σ_{r}, σ_{a}, σ_{c})

are derived from the effective CSLC range/azimuth resolution and from empirical height correction statistics, providing a geometry- and data-informed prior on positional uncertainty. Finally, we render dual-geometry red–green composites (ascending to R, descending to G; shared normalization) on the LiDAR point cloud, enabling consistent inspection in plan and elevation. Across asset types, rigid steel/concrete elements (trestles, quay faces, and dolphins) sustain high coherence, small whitened offsets, and stable backscatter in both looks; cylindrical storage tanks are bright but exhibit look-dependent visibility and larger cross-range residuals due to height and curvature; and container yards and vessels show high amplitude dispersion and lower temporal coherence driven by operations. Overall, LiDAR-assisted whitening-based linking reduces effective positional ambiguity and improves structure-specific attribution for most scatterers across the port. The fusion products, geometry-aware linking plus three-dimensional dual-geometry RGB, enhance the interpretability of medium-resolution SAR and provide a transferable, port-oriented basis for integrating deformation evidence into risk and asset management workflows. Full article

17 pages, 1649 KB

Open AccessArticle

Improvement of Snow Albedo Simulation Considering Water Content

by Fengyu Li and Kun Wu

Remote Sens. 2025, 17(23), 3899; https://doi.org/10.3390/rs17233899 (registering DOI) - 30 Nov 2025

Abstract

By combining the Maxwell–Garnett mixing rule, Mie scattering, and the four-stream discrete ordinates adding method, a snow albedo model with explicit consideration of water content was constructed, and the influence of snow water content on snow albedo simulation was systematically analyzed. The results [...] Read more.

By combining the Maxwell–Garnett mixing rule, Mie scattering, and the four-stream discrete ordinates adding method, a snow albedo model with explicit consideration of water content was constructed, and the influence of snow water content on snow albedo simulation was systematically analyzed. The results indicate that liquid water content is the key factor contributing to significant changes in albedo in the near-infrared band. The albedo of snow with small particle sizes is more sensitive to water content. The water content in the surface layer of snow has a more pronounced effect on reducing albedo. The actual measurement cases at the stations on the Tibetan Plateau, Xinjiang, and Northeast China show that the model established here provides a good simulation of albedo accuracy, with a bias of −0.0069 and a Root Mean Square Error (RMSE) of 0.0583 compared to the observations. This indicates that the model has a strong ability to express physical mechanisms and performs stably in complex environments, thereby demonstrating good regional applicability. This model can also be applied to wet snow containing impurities in the future. Full article

(This article belongs to the Special Issue Remote Sensing Modelling and Measuring Snow Cover and Snow Albedo)

21 pages, 9861 KB

Open AccessArticle

Accounting for 10 m Resolution Mapping for Above-Ground Biomass of Urban Trees in C40 Cities Across Eurasia Continent

by Ge Yan, Zhifang Shi, Gaomin Lian, Kailong Cui, Nan Li, Ying Luo, Shuyuan Zhou, Mengmeng Cao and Yaoping Cui

Remote Sens. 2025, 17(23), 3898; https://doi.org/10.3390/rs17233898 (registering DOI) - 30 Nov 2025

Abstract

High-resolution above-ground biomass (AGB) data play a critical role in advancing low-carbon development strategies across cities. However, research on urban trees’ AGB largely relies on high-accuracy field measurements, which limits the feasibility of conducting cross-regional studies. In contrast, existing remote-sensing-based AGB products provide [...] Read more.

High-resolution above-ground biomass (AGB) data play a critical role in advancing low-carbon development strategies across cities. However, research on urban trees’ AGB largely relies on high-accuracy field measurements, which limits the feasibility of conducting cross-regional studies. In contrast, existing remote-sensing-based AGB products provide extensive coverage while lacking the spatial resolution required for precise city-scale analysis. To address the dilemma of achieving both high spatial resolution and broad coverage, this study integrated 149 feature variables derived from multi-source datasets and implemented quality-control procedures to select high-quality samples from two globally representative AGB products (GEDI AGB and CCI AGB). This strategy substantially improved the performance of the random forest model and generated 10 m resolution urban trees’ AGB maps for 51 C40 cities across Eurasia continent. The results indicate that: (1) after applying quality control to the target variables, the mean R² of ten-fold cross validation improved from 0.37 to 0.75, and the MAE decreased substantially from 47.02 Mg/ha to 17.48 Mg/ha; (2) by enhancing the spatial resolution of AGB maps to 10 m, the resulting products exhibit superior spatial detail, better capture local variations, and maintain greater spatial continuity compared with the CCI AGB and GEDI AGB datasets; (3) the mean AGB density across the Eurasian continent was 39.44 Mg/ha, with total urban tree s’ AGB reaching 83.83 × 10⁶ t. Comparison with previous single-city C40 studies shows that our estimated AGB density and total AGB closely align with previously reported values. The above data implies that cities carry an undeniable amount of carbon storage, both in terms of carbon density and total amount. This study provides a robust foundation for accurately assessing the potential of urban carbon sinks and optimizing the path to achieving carbon neutrality. Full article

(This article belongs to the Special Issue Multisource Remote Sensing Data Fusion and Applications in Vegetation Monitoring (Second Edition))

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19 pages, 16891 KB

Open AccessArticle

A Novel U-Shaped Network Combined with a Hierarchical Sparse Attention Mechanism for Coastal Aquaculture Area Extraction in a Complex Environment

by Chengyi Wang, Yuyang Zhao, Lu Li and Tianyi Liu

Remote Sens. 2025, 17(23), 3897; https://doi.org/10.3390/rs17233897 (registering DOI) - 30 Nov 2025

Abstract

Aquaculture pond extraction based on remote sensing (RS) plays a pivotal role in coastal resource utilization and production management. However, most existing studies have focused on limited coastal aquaculture pond extraction and neglected the extraction around saltpans. There are two key challenges in [...] Read more.

Aquaculture pond extraction based on remote sensing (RS) plays a pivotal role in coastal resource utilization and production management. However, most existing studies have focused on limited coastal aquaculture pond extraction and neglected the extraction around saltpans. There are two key challenges in aquaculture pond extraction. Firstly, aquaculture ponds are difficult to accurately extract owing to the spectral and spatial similarities with evaporation ponds and brine concentration ponds within saltpans. Secondly, refining and delineating the boundaries of aquaculture ponds remains challenging. To address these issues, we propose a novel deep learning neural network, namely the U-shaped Network with Hierarchical Sparse Attention (HSAUNet), for coastal aquaculture pond extraction. We proposed the Dycross Sample Module to dynamically generate learnable offsets, which empower our model to accurately capture edge-specific information under the guidance of lower-level feature maps, thus improving the precise perceptiveness of aquaculture boundaries. The Sparse Attention Module with rolling mechanism is proposed to effectively capture global semantic relationships and contextual information in different directions, achieving clear differentiation between aquaculture ponds and evaporation or brine ponds within saltpans. Our datasets are derived from the multispectral Sentinel-2 imagery satellite data including aquaculture ponds around saltpans such as the Changlu Hangu, Huaibei, and Yinggehai salt fields and also some other coastal aquaculture areas such as Shanwei Changsha Bay (Guangdong province) and Dalian Biliuhe Bay (Liaoning province). Experimental results demonstrate that HSAUNet outperforms other state-of-the-art methods on test datasets, achieving an intersection over union (IoU) of 93.42%, which exceeds the highest scores of Deeplabv3+ with a IoU of 92.97%. Our proposed method greatly facilitates and serves as a valuable reference for resource management authorities in monitoring aquaculture ponds. Full article

24 pages, 13139 KB

Open AccessArticle

Photovoltaic Power Station Identification Based on High-Resolution Network and Google Earth Engine: A Case Study of Qinghai Province, Northwest China

by Hongling Chen, Li Zhang, Yang Yu, Chuandong Wu, Ting Hua and Chunlian Gao

Remote Sens. 2025, 17(23), 3896; https://doi.org/10.3390/rs17233896 (registering DOI) - 30 Nov 2025

Abstract

The precise identification of photovoltaic power stations is essential for advancing the assessment of energy infrastructure and for the efficient management of land resources. To address the need for spatially explicit data on photovoltaic (PV) development in arid and semi-arid regions amid green [...] Read more.

The precise identification of photovoltaic power stations is essential for advancing the assessment of energy infrastructure and for the efficient management of land resources. To address the need for spatially explicit data on photovoltaic (PV) development in arid and semi-arid regions amid green energy transitions, particularly in the context of identification challenges induced by the widespread distribution of bare ground, this study optimized a remote sensing-based identification method integrating Principal Component Analysis (PCA), automated sampling via Google Earth Engine (GEE), and deep learning models, and applied it to Qinghai Province, one of China’s largest PV regions. The results showed that HRNetv2 (validation Dice = 0.9463) outperformed UNet (0.9328), Attention UNet (0.9399), and HRNet + OCR (0.9184) in small-sample (1871 training samples) PV segmentation; the PV installed area during 2020–2024 accounted for 63.5% of the total pre-2024 area (~607 km²), exceeding the cumulative area before 2019, with projects predominantly distributed in areas with elevation less than 2500 m and slope less than 2°; bare land dominated PV land use (88.7%), followed by grassland (6.9%) and shrubland (3.9%), and PV construction contributed to desert greening by modifying microclimates. The study concludes that its optimized method effectively supports PV spatial identification, and the revealed PV distribution and land use patterns provide scientific guidance for synergistic PV development and ecological conservation in arid regions, while acknowledging limitations in generalizability to other regions due to Qinghai-specific data, suggesting future algorithm refinement and expanded research scales. Full article

(This article belongs to the Section Ecological Remote Sensing)

19 pages, 17064 KB

Open AccessArticle

Optimization of Spatial Sampling in Satellite–UAV Integrated Remote Sensing: Rationale and Applications in Crop Monitoring

by Zhen Zhao, Hang Xiong, Yawen Yu, Baodong Xu and Jian Zhang

Remote Sens. 2025, 17(23), 3895; https://doi.org/10.3390/rs17233895 (registering DOI) - 30 Nov 2025

Abstract

Satellite and UAV-based remote sensing have been widely used for agricultural systems monitoring jointly. How to quantitatively optimize the efficiency of integrating these two techniques remains largely understudied. To address this gap, we, for the first time, formulate the configuration of satellite–UAV integrated [...] Read more.

Satellite and UAV-based remote sensing have been widely used for agricultural systems monitoring jointly. How to quantitatively optimize the efficiency of integrating these two techniques remains largely understudied. To address this gap, we, for the first time, formulate the configuration of satellite–UAV integrated system as a spatial sampling optimization problem and propose an SSO (spatial sampling optimization) model that jointly optimizes the spatial locations and flight paths of UAV sampling within the satellite monitoring area. The SSO model enables maximizing the accuracy of monitoring under a given cost constraint. We obtained comprehensive data in rapeseed fields and conducted experiments based on the SSO model. We compared the sampling effectiveness of the SSO model with that of simple random sampling, systematic sampling, equal stratified sampling and Neyman stratified sampling. The results showed that the SSO-optimized plan had the highest sampling efficiency, which was at least 38.7% higher than that of the best-performing conventional method (Neyman stratified sampling). Under the same cost constraint, the SSO-optimized sampling scheme can have 11.1% more sampling points than the conventional sampling scheme. The Elite Genetic Algorithm (EGA) performed well in solving the SSO model. The error of the SSO-optimized scheme was reduced by 27.3% and the sampling distance was reduced by 7000 to 8000 m on average. In conclusion, the proposed SSO model helps to optimize the configuration of satellite–UAV integrated remote sensing, thereby improving the cost-effectiveness of agricultural monitoring systems. We call for considering cost constraints and increasing efficiency in agricultural system monitoring and government censuses in the future. Full article

(This article belongs to the Special Issue Advancing UAV-Based Remote Sensing: Innovations, Techniques and Applications)

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25 pages, 8066 KB

Open AccessArticle

Estimation of All-Weather Daily Surface Net Radiation over the Tibetan Plateau Using an Optimized CNN Model

by Bin Ma, Yaoming Ma and Weiqiang Ma

Remote Sens. 2025, 17(23), 3894; https://doi.org/10.3390/rs17233894 (registering DOI) - 30 Nov 2025

Abstract

Accurate daily surface net radiation (R_n) estimation over the Tibetan Plateau’s complex and highly heterogeneous terrain is essential for advancing the understanding of land–atmosphere exchanges and regional climate processes. This study developed an optimized deep learning framework that systematically evaluates 19 [...] Read more.

Accurate daily surface net radiation (R_n) estimation over the Tibetan Plateau’s complex and highly heterogeneous terrain is essential for advancing the understanding of land–atmosphere exchanges and regional climate processes. This study developed an optimized deep learning framework that systematically evaluates 19 CNN architectures using a per-pixel multivariate regression design (1 × 1 × 21). The channel-rich representation incorporates engineered neighborhood descriptors to statistically embed spatial context while fully avoiding the mosaic and boundary artifacts common in patch-based approaches. Among all tested networks, Xception delivered the best combination of accuracy (R² > 0.94), computational efficiency, and physical consistency. Its depthwise separable convolutions and skip connections enable hierarchical nonlinear cross-channel feature learning, effectively capturing the complex dependencies between surface variables and

R_{n}

. Independent validation confirmed stable performance under diverse weather conditions and substantially better skill than GLASS, especially across rugged terrain and high-albedo surfaces. SHAP analysis further highlights physically meaningful behavior, with astronomical and topographic factors contributing ~70% and surface properties ~25% to predictions. Remaining challenges include dependence on continuous high-quality multi-source inputs and scale effects from mixed pixels. Future work will enhance operational deployment through automated daily preprocessing, improved sub-diurnal characterization via multi-scale data fusion, and stronger physical constraints to increase reliability. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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45 pages, 54716 KB

Open AccessArticle

A Deep Learning Approach to Downscaling Microwave Land Surface Temperatures for a Clear-Sky Merged Infrared-Microwave Product

by Abigail Marie Waring, Darren Ghent, David Moffat, Carlos Jimenez and John Remedios

Remote Sens. 2025, 17(23), 3893; https://doi.org/10.3390/rs17233893 (registering DOI) - 30 Nov 2025

Abstract

Reliable land surface temperature (LST) data are required for monitoring climate variability, hydrological processes, and land–atmosphere interactions. Yet existing satellite-derived LST products, such as those from thermal infrared (TIR) sensors, are limited by gaps due to clouds, while passive microwave (PMW) observations, though [...] Read more.

Reliable land surface temperature (LST) data are required for monitoring climate variability, hydrological processes, and land–atmosphere interactions. Yet existing satellite-derived LST products, such as those from thermal infrared (TIR) sensors, are limited by gaps due to clouds, while passive microwave (PMW) observations, though less affected by atmospheric interference, suffer from coarse resolution and larger uncertainty. This study presents the first validated clear-sky merged LST product for the USA and combines downscaled PMW data from AMSR-E and AMSR2 with MODIS TIR observations, using a modified U-Net deep learning network. The merged dataset covers 2004–2021 at 5 km resolution, providing a compromise between spatial detail and robustness. The model performs well, with low mean squared errors and

R^{2}

values of 0.80 (day) and 0.75 (night). The merged time series captures seasonal trends and shows a marked reduction in cloud-contamination artefacts compared to MODIS and AMSR signals. Spatially, the product is consistent across sensor transitions and reduces artefacts from TIR cloud contamination. Validation against ground stations shows results between those of TIR and PMW, with better accuracy at night and moderate positive biases influenced by land cover and terrain. Although the merged product does not match the fine resolution of TIR data by choice, it enhances spatial coverage over AMSR alone and temporal completeness over MODIS alone, where single-sensor products are limited. Residual temporal and seasonal biases are moderate, with systematic warm and cold deviations linked to land cover, propagation of emissivity errors, and sampling differences. Strong positive biases remain over terrain with complex surface properties as the downscaled AMSR is closer to MODIS temperatures. Results demonstrate the combined benefits of PMW’s broader coverage and cloud tolerance with TIR’s spatial detail. Overall, results demonstrate the potential of sensor fusion for producing spatially consistent LST records suitable for long-term environmental and climate monitoring. Full article

(This article belongs to the Section Earth Observation Data)

27 pages, 6722 KB

Open AccessArticle

Oil Spill Detection and Identification on Coastal Sandy Beaches: Application of Field Spectroscopy and CMOS Sensor Imagery

by Qian Yan, Mengqi Yin, Yongchao Hou, Chunxiao Mu, Tianyu Wang and Haokun Chi

Remote Sens. 2025, 17(23), 3892; https://doi.org/10.3390/rs17233892 (registering DOI) - 30 Nov 2025

Abstract

Monitoring oil spills on coastal beaches using satellite imagery has received limited attention, primarily due to the lack of characteristic spectral data as well as constraints in spatial or temporal resolution. In this study, we employ both reflectance spectroscopy and CMOS-sensing imagery to [...] Read more.

Monitoring oil spills on coastal beaches using satellite imagery has received limited attention, primarily due to the lack of characteristic spectral data as well as constraints in spatial or temporal resolution. In this study, we employ both reflectance spectroscopy and CMOS-sensing imagery to detect and characterize different species of oil contaminants on sandy beaches and investigate their behavior throughout the weathering process. Laboratory and field measurements were conducted on oil-contaminated and clean beach samples with a high-resolution portable spectrometer and a highly sensitive CMOS camera. Predictive modeling of the reflectance spectra using LW-PLS, SVR, and SVM yielded R² values of 0.86 for oil concentration and 0.89 for weathering time, and achieved an oil species classification accuracy of 0.86. Furthermore, beach oil spills in the image dataset were detected using a DeepLabV3+ segmentation model with a ResNet-50 backbone, achieving a mean prediction accuracy of 98.73%. Finally, the segmentation model was successfully applied to accurately detect oil spill pollution on the beaches of Goa, India, confirming its field effectiveness. These reflectance spectroscopy and CMOS-sensing imagery technologies can provide critical data for calibrating remote sensing satellites, thereby offering direct technical support for targeted oil spill cleanup operations on beaches. Full article

(This article belongs to the Section Environmental Remote Sensing)

21 pages, 25898 KB

Open AccessArticle

A Hybrid Strategy Combining Maritime Physical Data to the OpenSARShip RCS Statistics for Fast and Effective Vessel Detection in SAR Imagery

by Ocione Dias do Nascimento Filho, João Antônio Lorenzzetti, Douglas Francisco Marcolino Gherardi, Diego Xavier Bezerra and Rafael Lemos Paes

Remote Sens. 2025, 17(23), 3891; https://doi.org/10.3390/rs17233891 (registering DOI) - 30 Nov 2025

Abstract

Maritime surveillance has become increasingly relevant due to the growth of shipping, illegal fishing, and the need to monitor remote oceanic regions. Synthetic Aperture Radar (SAR) imagery supports this task under day-and-night and almost all-weather conditions. However, automatic ship detection in heterogeneous ocean [...] Read more.

Maritime surveillance has become increasingly relevant due to the growth of shipping, illegal fishing, and the need to monitor remote oceanic regions. Synthetic Aperture Radar (SAR) imagery supports this task under day-and-night and almost all-weather conditions. However, automatic ship detection in heterogeneous ocean environments still faces challenges, especially regarding computational cost. This study develops and compares approaches for detecting vessels in SAR imagery using radar backscatter statistics (

σ^{0}

) to identify and characterize maritime targets. The OpenSARShip 2.0 dataset, which provides ship samples with AIS-based validation and reliable

σ^{0}

estimates by type and size, was combined with maritime physical parameters such as wave age (from ERA5 reanalysis). The objective is to combine fast processing, robustness to sea variability, and inference capability regarding target size for operational applications. Four algorithms were evaluated: Rapid Thresholding (RT), based on OpenSARShip

σ^{0}

values by ship length; Adjusted Rapid Thresholding (ART), with clutter-adapted thresholds; CFAR G

Γ

D, based on Gamma pdf modeling of ocean clutter; and a Hybrid Strategy combining RT with CFAR G

Γ

D. Results showed that CFAR G

Γ

D achieved the highest recall (87.4%) but at high computational cost, while the Hybrid Strategy (HS) offered comparable performance (Recall: 86.6%; F1-score: 74.8%) with 18× faster execution time. RT and ART were faster but less sensitive. These findings highlight the HS as an efficient compromise, supporting scalable, near-real-time vessel detection systems. Full article

(This article belongs to the Section Ocean Remote Sensing)

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23 pages, 3630 KB

Open AccessArticle

Characterizing the Surface Grain Size Distribution in a Gravel-Bed River Using UAV Optical Imagery and SfM Photogrammetry

by Chyan-Deng Jan, Tung-Yang Lai and Kuan-Chung Lai

Remote Sens. 2025, 17(23), 3890; https://doi.org/10.3390/rs17233890 (registering DOI) - 30 Nov 2025

Abstract

Understanding the sediment grain size distribution in riverbeds is essential for analyzing sediment transport, riverbed morphology, and ecological habitats. Previous studies have shown that riverbed grain size can be inferred from surface roughness using linear relations between manually sampled grain sizes and percentile [...] Read more.

Understanding the sediment grain size distribution in riverbeds is essential for analyzing sediment transport, riverbed morphology, and ecological habitats. Previous studies have shown that riverbed grain size can be inferred from surface roughness using linear relations between manually sampled grain sizes and percentile roughness derived from point-cloud data. However, these relations are often established within narrow grain-size ranges, causing regression coefficients to vary across percentiles and limiting their applicability to broader grain-size variability. This study conducted field investigations and UAV (Unmanned Aerial Vehicle) surveys to examine grain size–roughness relations across four coarse-grained mountainous river reaches in Taiwan, characterized by a wide grain-size distribution (D₁₆–D₈₄: 2.3–525 mm). High-resolution 3D point clouds were generated using UAV-SfM (Structure-from-Motion) techniques for roughness metric computation. Linear relations between grain size D_i (i = 16, 25, 50, 75, and 84) and their corresponding percentile roughness RH_i were developed and evaluated. Results indicate that D_i-RH_i relations exhibit moderate to strong correlations (R² = 0.60–0.94), and the regression slope increases exponentially with grain size. To address cross-percentile variability, an integrated power-law relation was proposed by pooling all paired D_i-RH_i data from Reach R1, yielding a single, continuous reach-scale grain size–roughness correlation. Applicability tests using data from the remaining three reaches show that the integrated relation performs better for coarser grains (D₅₀–D₈₄) than for finer grains. Future work incorporating more sampling sites across diverse river types will help further refine the integrated relation and improve its cross-reach applicability. Full article

(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)

28 pages, 30533 KB

Open AccessArticle

A Two-Dimensional InSAR-Based Framework for Landslide Identification and Movement Pattern Classification

by Xuhao Li, Qianyou Fan, Yufen Niu, Shuangcheng Zhang, Jinqi Zhao, Jinzhao Si, Zixuan Wang, Ziheng Ju and Zhong Lu

Remote Sens. 2025, 17(23), 3889; https://doi.org/10.3390/rs17233889 (registering DOI) - 30 Nov 2025

Abstract

Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. [...] Read more.

Frequent extreme climate events have intensified landslide hazards in mountainous regions, necessitating efficient identification and classification to understand movement mechanisms and mitigate risks. This study develops a novel, non-contact InSAR framework that seamlessly integrates three key steps—Identification, Inversion, and Classification—to address this challenge. By applying this framework to ascending and descending Sentinel-1 data in the complex terrain of the Jishi Mountain region, we first introduce geometric distortion masking and a C-Index deformation consistency check, which enables the reliable identification of 530 active landslides, with 154 detected in both orbits. Second, we employ a local parallel flow model to invert the landslide movement geometry without relying on DEM-derived prior assumptions, successfully retrieving the two-dimensional (sliding and normal direction) deformation fields for all 154 consistent landslides. Finally, by synthesizing these 2D deformation patterns with geomorphological features, we achieve a systematic classification of movement types, categorizing them into retrogressive translational (31), progressive translational (66), rotational (19), composite (24), and earthflows (14). This integrated methodology provides a validated, transferable solution for deciphering landslide mechanisms and assessing risks in remote, complex mountainous areas. Full article

(This article belongs to the Topic Remote Sensing and Geological Disasters)

41 pages, 7642 KB

Open AccessReview

Fine-Grained Interpretation of Remote Sensing Image: A Review

by Dongbo Wang, Zedong Yan and Peng Liu

Remote Sens. 2025, 17(23), 3887; https://doi.org/10.3390/rs17233887 (registering DOI) - 30 Nov 2025

Abstract

This article conducts a systematic review on the fine-grained interpretation of remote sensing images, delving deeply into its background, current situation, datasets, methodology, and future trends, aiming to provide a comprehensive reference framework for research in this field. In terms of fine-grained interpretation [...] Read more.

This article conducts a systematic review on the fine-grained interpretation of remote sensing images, delving deeply into its background, current situation, datasets, methodology, and future trends, aiming to provide a comprehensive reference framework for research in this field. In terms of fine-grained interpretation datasets, we focus on introducing representative datasets and analyze their key characteristics such as the number of categories, sample size, and resolution, as well as their benchmarking role in research. For methodologies, by classifying the core methods according to the interpretation level system, this paper systematically summarizes the methods, models, and architectures for implementing fine-grained remote sensing image interpretation based on deep learning at different levels such as pixel-level classification and segmentation, object-level detection, and scene-level recognition. Finally, the review concluded that although deep learning has driven substantial advances in accuracy and applicability, fine-grained interpretation remains an inherently challenging problem due to issues such as the distinction of highly similar categories, cross-sensor domain migration, and high annotation costs. We also look forward to future directions, emphasizing the need to enhance the generalization, support open-world recognition further, and adapt to actual complex scenarios, etc. This review aims to promote the application of fine-grained interpretation technology for remote sensing images across a broader range of fields. Full article

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34 pages, 10774 KB

Open AccessArticle

A Comparative Analysis of Low-Cost Devices for High-Precision Diameter at Breast Height Estimation

by Jozef Výbošťok, Juliána Chudá, Daniel Tomčík, Julián Tomaštík, Roman Kadlečík and Martin Mokroš

Remote Sens. 2025, 17(23), 3888; https://doi.org/10.3390/rs17233888 (registering DOI) - 29 Nov 2025

Abstract

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and [...] Read more.

Forestry is essential for environmental sustainability, biodiversity conservation, carbon sequestration, and renewable resource management. Traditional methods for forest inventory, particularly the manual measurement of diameter at breast height (DBH), are labor-intensive and prone to error. Recent advancements in proximal sensing, including lidar and photogrammetry, have paved the way for more efficient approaches, yet high costs remain a barrier to widespread adoption. This study investigates the potential of close-range photogrammetry (CRP) using low-cost devices, such as smartphones, cameras, and specialized handheld laser scanners (Stonex and LIVOX prototype), to generate 3D point clouds for accurate DBH estimation. We compared these devices by assessing their agreement and efficiency when compared to conventional methods in diverse forest conditions across multiple tree species. Additionally, we analyze factors influencing measurement errors and propose a comprehensive decision-making framework to guide technology selection in forest inventory. The results show that the lowest-cost devices and photogrammetric methods achieved the highest agreement with the conventional (caliper-based) measurements, while mobile applications were the fastest and least expensive but also the least accurate. Photogrammetry provided the most accurate DBH estimates (error ≈ 0.7 cm) but required the highest effort; handheld laser scanners achieved an average accuracy of about 1.5 cm at substantially higher cost, while mobile applications were the fastest and least expensive but also the least accurate (3–3.5 cm error). The outcomes of this research aim to facilitate more accessible, reliable, and sustainable forest management practices. Full article

27 pages, 1926 KB

Open AccessReview

Toward Resilience in Broadacre Agriculture: A Methodological Review of Remote Sensing in Crop Productivity, Phenology, and Environmental Stress Detection

by Jianxiu Shen, Hai Wang and Hasnein Tareque

Remote Sens. 2025, 17(23), 3886; https://doi.org/10.3390/rs17233886 (registering DOI) - 29 Nov 2025

Abstract

Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims [...] Read more.

Large-scale rainfed cropping systems (broadacre agriculture) face intensifying climate and resource stresses that undermine yield stability and farm livelihoods. Remote sensing (RS) offers critical tools for improving resilience by monitoring crop performance—productivity, phenology, and environmental stress—across large areas and timeframes. This review aims to synthesize methodological advances over the past two decades in applying RS for broadacre crop monitoring and to identify key challenges and integration opportunities. Peer-reviewed studies across diverse crops and regions were systematically examined to evaluate the strengths, limitations, and emerging trends across the three RS application themes. The review finds that (1) RS enables spatially explicit yield estimation from regional to paddock scales, with vegetation indices (VIs) and phenology-adjusted metrics closely correlated with yield. (2) Time-series analyses of RS data effectively capture phenological transitions critical for forecasting, supported by advances in curve fitting, sensor fusion, and machine learning. (3) Thermal and multispectral indices support the early detection of abiotic (drought, heat, salinity) and biotic (pests, disease) stresses, though specificity remains limited. Across themes, methodological silos and sensor integration barriers hinder holistic application. Emerging approaches, such as multi-sensor/scale fusion, RS–crop model data assimilation, and operational and big data integration, provide promising pathways toward resilience-focused decision support. Future research should define quantifiable resilience metrics, cross-theme predictive integration, and accessible tools to guide climate adaptation. Full article

(This article belongs to the Special Issue Advances in the Remote Sensing of Crop Phenology and Production Monitoring Under Environmental Constraints)

23 pages, 3319 KB

Open AccessArticle

A 2D-CFAR Target Detection Method in Sea Clutter Based on Copula Theory Using Dual-Observation Channels

by Xingyu Jiang, Jiyuan Tan, Yunlong Dong, Juan Li, Jian Guan, Guoqing Wang and Ningbo Liu

Remote Sens. 2025, 17(23), 3885; https://doi.org/10.3390/rs17233885 (registering DOI) - 29 Nov 2025

Abstract

The target detection method based on a constant false alarm rate (CFAR) and feature space is commonly used in remote sensing for detecting maritime targets within sea clutter. However, the performance of traditional CFAR techniques heavily relies on the signal-to-clutter ratio (SCR) in [...] Read more.

The target detection method based on a constant false alarm rate (CFAR) and feature space is commonly used in remote sensing for detecting maritime targets within sea clutter. However, the performance of traditional CFAR techniques heavily relies on the signal-to-clutter ratio (SCR) in a single observational channel, while feature space methods are overly sensitive to the number of pulse accumulations and rigidly apply outlier classifiers to define detection regions, without theoretical derivation. To address these limitations, this paper proposes a two-dimensional (2D) CFAR target detection method based on echo data from dual-polarization observational channels. First, statistical models of the amplitude distribution for horizontal–horizontal (HH) and vertical–vertical (VV) polarization sea clutter radar echoes are validated under identical observation conditions using measured data, and their correlations are analyzed. Then, the Copula function is introduced as a theoretical foundation to rigorously derive and extend the cell-averaging CFAR detector through strict mathematical formulations, transitioning from single statistics to 2D detection statistics. This leads to the proposed target detection method. Testing with measured data from publicly available datasets demonstrates that the proposed method effectively achieves adaptive false alarm control and significantly improves the detection performance compared to existing single-pulse one-dimensional CFAR detection methods. Full article

(This article belongs to the Special Issue Technical Developments in Radar—Processing and Application (2nd Edition))

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