Remote Sensing

20 pages, 6591 KiB

Open AccessArticle

UAV Imaging of the Riverbed in Small, Tree-Lined Streams: Importance of the Light Environment

by Richard Hedger and Marie-Pierre Gosselin

Remote Sens. 2025, 17(16), 2775; https://doi.org/10.3390/rs17162775 - 11 Aug 2025

Unmanned aerial vehicles (UAVs) are an ideal platform for the remote sensing of riverbeds in small, tree-lined streams, allowing unobstructed viewing of the channel at high spatial resolution. However, effective UAV surveying of these riverbeds is hindered by a range of phenomena associated [...] Read more.

Unmanned aerial vehicles (UAVs) are an ideal platform for the remote sensing of riverbeds in small, tree-lined streams, allowing unobstructed viewing of the channel at high spatial resolution. However, effective UAV surveying of these riverbeds is hindered by a range of phenomena associated with the complex light environments of rivers, and small tree-lined streams in particular, including reflections of the overlying cloud layer from the water surface, sunglint on the water surface, and shadows from topography and riparian vegetation. We used UAV imagery acquired from small, tree-lined streams under different light conditions to identify the prevalence of the main phenomena—reflections of clouds, sunglint, and shadows—that hinder the ability to discern the riverbed. We characterized how large a constraint these phenomena are on the optimal imaging window. We then examined the degree to which sub-optimal light conditions may restrict this window, both within the year and within the day, across Europe. Our investigations suggest that different regions across Europe will have different priorities with regard to imaging, with surveys in northern rivers emphasizing avoiding low irradiant intensity in winter and those in southern rivers emphasizing avoiding sunglint around midday. We use our findings to suggest a protocol for improved riverbed imaging that is specific to the light environment of the stream under investigation. Full article

(This article belongs to the Special Issue Integration of Multi-sources in Fluvial Hydro-Geomorphology: A Machine Learning, Deep Learning, and Remote Sensing Perspective)

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29 pages, 1827 KiB

Open AccessArticle

One-Step Enhancement Method for Data Registration Based on the Lidargrammetric Approach

by Antoni Rzonca and Mariusz Twardowski

Remote Sens. 2025, 17(16), 2774; https://doi.org/10.3390/rs17162774 - 11 Aug 2025

Abstract

The present paper introduces a novel methodology for LiDAR point transformation and adjustment, grounded in two primary concepts. In the initial phase of the process, LiDAR data are mapped onto synthetic images, known as lidargrams, through the utilization of exterior orientation parameters (EOPs) [...] Read more.

The present paper introduces a novel methodology for LiDAR point transformation and adjustment, grounded in two primary concepts. In the initial phase of the process, LiDAR data are mapped onto synthetic images, known as lidargrams, through the utilization of exterior orientation parameters (EOPs) of a virtual camera. Secondly, unique lidargram point identifiers (ULPIs) are assigned to each LiDAR point, ensuring the preservation of the relationship between specific LiDAR points and their corresponding lidargram projections. This process facilitates the reconstruction of ground points from their respective projections. The integration of these concepts facilitates the alignment and adjustment of blocks of lidargrams, thereby enabling the estimation of novel EOPs. The exchange of arbitrary EOPs and the intersection of the transformed point cloud based on the ULPIs are facilitated by these refined EOPs. The LiDAR data undergo a three-dimensional transformation using photogrammetric algorithms. This is in accordance with the fundamental principles of lidargrammetry. The accuracy of the new approach and its implementation in a research tool were verified on a range of data types, encompassing synthetic, semisynthetic, and real data. By evaluating the approach across a wide range of data sources, the authors were able to assess its effectiveness and reliability in different scenarios. The method’s flexibility is evidenced by its ability to reduce the final 3D root mean square error of discrepancies measured at check points by 30 times in synthetic data tests, 12 times in semisynthetic data tests, and 96 times in real data tests. The quantitative results obtained provide substantial support for the validity of the presented methodology. The efficacy of the proposed method was also evaluated by way of a comparative analysis with a selection of widely utilized LiDAR processing software developed by TerraSolid Ltd. Full article

(This article belongs to the Section Engineering Remote Sensing)

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22 pages, 3460 KiB

Open AccessArticle

Investigating the Earliest Identifiable Timing of Sugarcane at Early Season Based on Optical and SAR Time-Series Data

by Yingpin Yang, Jiajun Zou, Yu Huang, Zhifeng Wu, Ting Fang, Jia Xue, Dakang Wang, Yibo Wang, Jinnian Wang, Xiankun Yang and Qiting Huang

Remote Sens. 2025, 17(16), 2773; https://doi.org/10.3390/rs17162773 - 10 Aug 2025

Abstract

Early-season sugarcane identification plays a pivotal role in precision agriculture, enabling timely yield forecasting and informed policy-making. Compared to post-season crop identification, early-season identification faces unique challenges, including incomplete temporal observations and spectral ambiguity among crop types in early seasons. Previous studies have [...] Read more.

Early-season sugarcane identification plays a pivotal role in precision agriculture, enabling timely yield forecasting and informed policy-making. Compared to post-season crop identification, early-season identification faces unique challenges, including incomplete temporal observations and spectral ambiguity among crop types in early seasons. Previous studies have not systematically investigated the capability of optical and synthetic aperture radar (SAR) data for early-season sugarcane identification, which may result in suboptimal accuracy and delayed identification timelines. Both the timing for reliable identification (≥90% accuracy) and the earliest achievable timepoint matching post-season level remain undetermined, and which features are effective in the early-season identification is still unknown. To address these questions, this study integrated Sentinel-1 and Sentinel-2 data, extracted 10 spectral indices and 8 SAR features, and employed a random forest classifier for early-season sugarcane identification by means of progressive temporal analysis. It was found that LSWI (Land Surface Water Index) performed best among 18 individual features. Through the feature set accumulation, the seven-dimensional feature set (LSWI, IRECI (Inverted Red-Edge Chlorophyll Index), EVI (Enhanced Vegetation Index), PSSRa (Pigment Specific Simple Ratio a), NDVI (Normalized Difference Vegetation Index), VH backscatter coefficient, and REIP (Red-Edge Inflection Point Index)) achieved the earliest attainment of 90% accuracy by 30 June (early-elongation stage), with peak accuracy (92.80% F1-score) comparable to post-season accuracy reached by 19 August (mid-elongation stage). The early-season sugarcane maps demonstrated high agreement with post-season maps. The 30 June map achieved 88.01% field-level and 90.22% area-level consistency, while the 19 August map reached 91.58% and 93.11%, respectively. The results demonstrate that sugarcane can be reliably identified with accuracy comparable to post-season mapping as early as six months prior to harvest through the integration of optical and SAR data. This study develops a robust approach for early-season sugarcane identification, which could fundamentally enhance precision agriculture operations through timely crop status assessment. Full article

(This article belongs to the Special Issue Advances in Remote Sensing for Crop Monitoring and Food Security)

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22 pages, 28581 KiB

Open AccessArticle

Remote Sensing Interpretation of Geological Elements via a Synergistic Neural Framework with Multi-Source Data and Prior Knowledge

by Kang He, Ruyi Feng, Zhijun Zhang and Yusen Dong

Remote Sens. 2025, 17(16), 2772; https://doi.org/10.3390/rs17162772 - 10 Aug 2025

Abstract

Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development. Remote sensing technologies, combined with artificial intelligence algorithms, offer new opportunities for the efficient interpretation [...] Read more.

Geological elements are fundamental components of the Earth’s ecosystem, and accurately identifying their spatial distribution is essential for analyzing environmental processes, guiding land-use planning, and promoting sustainable development. Remote sensing technologies, combined with artificial intelligence algorithms, offer new opportunities for the efficient interpretation of geological features. However, in areas with dense vegetation coverage, the information directly extracted from single-source optical imagery is limited, thereby constraining interpretation accuracy. Supplementary inputs such as synthetic aperture radar (SAR), topographic features, and texture information—collectively referred to as sensitive features and prior knowledge—can improve interpretation, but their effectiveness varies significantly across time and space. This variability often leads to inconsistent performance in general-purpose models, thus limiting their practical applicability. To address these challenges, we construct a geological element interpretation dataset for Northwest China by incorporating multi-source data, including Sentinel-1 SAR imagery, Sentinel-2 multispectral imagery, sensitive features (such as the digital elevation model (DEM), texture features based on the gray-level co-occurrence matrix (GLCM), geological maps (GMs), and the normalized difference vegetation index (NDVI)), as well as prior knowledge (such as base geological maps). Using five mainstream deep learning models, we systematically evaluate the performance improvement brought by various sensitive features and prior knowledge in remote sensing-based geological interpretation. To handle disparities in spatial resolution, temporal acquisition, and noise characteristics across sensors, we further develop a multi-source complement-driven network (MCDNet) that integrates an improved feature rectification module (IFRM) and an attention-enhanced fusion module (AFM) to achieve effective cross-modal alignment and noise suppression. Experimental results demonstrate that the integration of multi-source sensitive features and prior knowledge leads to a 2.32–6.69% improvement in mIoU for geological elements interpretation, with base geological maps and topographic features contributing most significantly to accuracy gains. Full article

(This article belongs to the Special Issue Multimodal Remote Sensing Data Fusion, Analysis and Application)

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22 pages, 7467 KiB

Open AccessArticle

VAWI_log: A Log-Transformed LSWI–EVI Index for Improved Surface Water Mapping in Agricultural Environments

by Alexis Declaro, Zachary Brown and Shinjiro Kanae

Remote Sens. 2025, 17(16), 2771; https://doi.org/10.3390/rs17162771 - 9 Aug 2025

Abstract

Detecting surface water beneath vegetation canopies remains a major challenge for widely used water indices, which often underestimate water obscured by vegetation. This limitation is further compounded by the scarcity of reliable in situ data needed for robust index development and validation. To [...] Read more.

Detecting surface water beneath vegetation canopies remains a major challenge for widely used water indices, which often underestimate water obscured by vegetation. This limitation is further compounded by the scarcity of reliable in situ data needed for robust index development and validation. To address this, we introduce a Vegetation-Adjusted Water Index using a logarithmic transformation of the ratio between the Land Surface Water Index (LSWI) and the Enhanced Vegetation Index (EVI), referred to as VAWI_log. This transformation compresses high vegetation values while expanding the range typical of water surfaces, enhancing contrast in mixed land cover areas and improving class separability. The index was developed and validated using in situ water level measurements, providing a strong empirical basis for detecting surface water under variable vegetation conditions. VAWI_log consistently outperformed established indices in detecting surface water beneath vegetation, demonstrating superior detection accuracy and overall performance, with a balanced accuracy (BA) of 0.69 and a producer’s accuracy (PA) of 0.80. This reflects an average improvement of 25% over conventional methods. Benchmarking against the Dynamic World V1 dataset further confirmed the improved capability of the proposed index to detect surface water in vegetated areas, which are often missed by the dataset. This enhanced performance can be attributed to the fact that the proposed index was developed using on-the-ground observations, rather than relying solely on expert-labeled imagery as in the case of Dynamic World. Overall, VAWI_log provides a simple yet effective solution for improved surface water mapping in vegetated landscapes. Its compatibility with open-source optical satellite data enables broader applications, including irrigation monitoring and greenhouse gas assessments. Full article

16 pages, 22555 KiB

Open AccessTechnical Note

A Hybrid RNN-CNN Approach with TPI for High-Precision DEM Reconstruction

by Ruizhe Cao, Chunjing Yao, Hongchao Ma, Bin Guo, Jie Wang and Junhao Xu

Remote Sens. 2025, 17(16), 2770; https://doi.org/10.3390/rs17162770 - 9 Aug 2025

Abstract

Digital elevation models (DEMs), as the fundamental unit of terrain morphology, are crucial for understanding surface processes and for land use planning. However, automated classification faces challenges due to inefficient terrain feature extraction from raw LiDAR point clouds and the limitations of traditional [...] Read more.

Digital elevation models (DEMs), as the fundamental unit of terrain morphology, are crucial for understanding surface processes and for land use planning. However, automated classification faces challenges due to inefficient terrain feature extraction from raw LiDAR point clouds and the limitations of traditional methods in capturing fine-scale topographic variations. To address this, we propose a novel hybrid RNN-CNN framework that integrates multi-scale Topographic Position Index (TPI) features to enhance DEM generation. Our approach first models voxelated LiDAR point clouds as spatially ordered sequences, using Recurrent Neural Networks (RNNs) to encode vertical elevation dependencies and Convolutional Neural Networks (CNNs) to extract planar spatial features. By incorporating TPI as a semantic constraint, the model learns to distinguish terrain structures at multiple scales. Residual connections refine feature representations to preserve micro-topographic details during DEM reconstruction. Extensive experiments in the complex terrains of Jiuzhaigou, China, demonstrate that our lightweight hybrid framework not only achieves excellent DEM reconstruction accuracy in complex terrains, but also improves computational efficiency by more than 20% on average compared to traditional interpolation methods, making it highly suitable for resource-constrained applications. Full article

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26 pages, 7314 KiB

Open AccessArticle

Radiative Transfer Simulation in the Near-Space Region for a Point Source at High Temperature Based on a Monte Carlo Method

by Mingyang Liu, Bingqiang Sun and Rui Lu

Remote Sens. 2025, 17(16), 2769; https://doi.org/10.3390/rs17162769 - 9 Aug 2025

Abstract

When vehicles fly at hypersonic speeds, a high-temperature flow field forms and emits intense radiation across some spectral bands. This strong flow field can be seen as a point source and is transmitted to high altitudes through the radiative transfer process. In this [...] Read more.

When vehicles fly at hypersonic speeds, a high-temperature flow field forms and emits intense radiation across some spectral bands. This strong flow field can be seen as a point source and is transmitted to high altitudes through the radiative transfer process. In this study, the corresponding atmospheric profiles were introduced, the databases in high-temperature conditions were established, and the corresponding gas absorption characteristics were further calculated using the line-by-line method. Correspondingly, the radiative transfer processes were calculated by a Monte Carlo model with surface and point sources. The Monte Carlo model is rigorously validated against the VLIDORT model through detailed comparisons. For radiative simulations in the near-space environment, both high-temperature conditions and 3D scenarios are considered. The simulations reveal that the use of high-temperature databases could introduce an error of approximately 3%, and the radiation field exhibits stronger inhomogeneity in the infrared band. Full article

(This article belongs to the Special Issue Advances in Near-Earth Space and Atmospheric Physics from Ground-Based and Satellite Observations)

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29 pages, 6375 KiB

Open AccessArticle

“Ground–Aerial–Satellite” Atmospheric Correction Method Based on UAV Hyperspectral Data for Coastal Waters

by Xinyuan Su, Jianyong Cui, Jinying Zhang, Jie Guo, Mingming Xu and Wenwen Gao

Remote Sens. 2025, 17(16), 2768; https://doi.org/10.3390/rs17162768 - 9 Aug 2025

Abstract

In ocean color remote sensing, most of the radiative energy received by sensors comes from the atmosphere, requiring highly accurate atmospheric correction. Although atmospheric correction models based on ground measurements—especially the Ground-Aerial-Satellite Atmospheric Correction (GASAC) method that integrates multi-scale synchronous data—are theoretically optimal, [...] Read more.

In ocean color remote sensing, most of the radiative energy received by sensors comes from the atmosphere, requiring highly accurate atmospheric correction. Although atmospheric correction models based on ground measurements—especially the Ground-Aerial-Satellite Atmospheric Correction (GASAC) method that integrates multi-scale synchronous data—are theoretically optimal, their application in nearshore areas is limited by the lack of synchronous samples, pixel mismatches, and nonlinear atmospheric effects. This study focuses on Tangdao Bay in Qingdao, Shandong Province, China, and proposes an innovative GASAC method for nearshore waters using synchronized surface spectrometer data and UAV hyperspectral imagery collected during Sentinel-2 satellite overpasses. The method first resolves pixel mismatch issues in UAV data through Pixel-by-Pixel Matching (MPP) and applies the Empirical Line Model (ELM) for high-accuracy ground-aerial atmospheric correction. Then, based on spectrally unified UAV and satellite data, a large amount of high-quality spatial atmospheric reference data is obtained. Finally, a Transformer model optimized by an Exponential-Trigonometric Optimization (ETO) algorithm is used to fit nonlinear atmospheric effects and perform aerial-to-satellite correction, forming a stepwise GASAC framework. The results show that GASAC achieves high accuracy and good generalization in local areas, with predicted remote sensing reflectance reaching

R^{2} = 0

.962 and

R M S E

= 12.54 × 10⁻⁴ sr⁻¹, improving by 5.2% and 23.5%, respectively, over the latest deep learning baseline. In addition, the corrected data achieved R² = 0.866 in a Chl-a retrieval model based on in situ measurements, demonstrating strong application potential. This study offers a precise and generalizable atmospheric correction method for satellite imagery in nearshore water quality monitoring, with important value for coastal aquatic ecological sensing. Full article

(This article belongs to the Special Issue AI-Empowered Remote Sensing Monitoring and Geospatial Analysis for Ocean and Coastal Environments)

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25 pages, 57425 KiB

Open AccessArticle

Assessment of the Applicability of Hue from In Situ Spectral Measurements to Remote Sensing of Plant Phenology

by Yuki Mizuno, Taiga Sasagawa, Yoshiyuki Takahashi, Reiko Ide, Toshiyuki Kobayashi, Hiroyuki Muraoka, Kentaro Takagi, Keisuke Ono and Kenlo Nishida Nasahara

Remote Sens. 2025, 17(16), 2767; https://doi.org/10.3390/rs17162767 - 9 Aug 2025

Abstract

Climate change is accelerating, and the monitoring of plant phenology is becoming increasingly important. In response to this need, many vegetation indices (VIs) and analytical methods have been developed. However, many VIs are vulnerable to uncertainties caused by snowmelt, making them potentially unsuitable [...] Read more.

Climate change is accelerating, and the monitoring of plant phenology is becoming increasingly important. In response to this need, many vegetation indices (VIs) and analytical methods have been developed. However, many VIs are vulnerable to uncertainties caused by snowmelt, making them potentially unsuitable for monitoring spring phenology in forested regions where leaf flush (start of season, SOS) begins simultaneously with snowmelt. Although several VIs for snowy regions have been proposed, most of them were designed for tundra vegetation, such as grasslands. Currently, no VI or analytical method specifically suited for snowy forested regions has been firmly established. Similarly, there is still no well-established method for continuously monitoring autumn coloration. In this study, we propose the use of hue, one of the components of the HSV model, for remote sensing of plant phenology. Hue quantifies differences in object color and is expected to facilitate clearer distinction of snow influence. It may also enable accurate detection of canopy color transitions, such as autumn coloration. We evaluate the applicability of hue to remote sensing using in situ spectroradiometer observations collected from five sites of the Phenological Eyes Network (PEN), which represent a range of ecosystems—including forests, grasslands, and paddy fields—as well as the relative spectral response (RSR) of the Second-generation Global Imager (SGLI) onboard the GCOM-C satellite operated by JAXA (Japan Aerospace Exploration Agency). The results suggest that hue is more robust to snow-related uncertainties than traditional VIs (NDVI, EVI, CCI, NDGI) and may also be effective for quantifying autumn coloration. Hue is calculated solely from blue, green, and red reflectance, without relying on near-infrared (NIR) or shortwave infrared (SWIR) channels. Since blue, green and red channels are available on almost all optical satellite sensors, hue may offer broader applicability than many traditional VIs. Full article

(This article belongs to the Section Ecological Remote Sensing)

20 pages, 3551 KiB

Open AccessArticle

Integrity Monitoring for BDS/INS Real-Time Kinematic Positioning Between Two Moving Platforms

by Yangyang Li, Weiming Tang, Chenlong Deng, Xuan Zou, Siyu Zhang, Zhiyuan Li and Yipeng Wang

Remote Sens. 2025, 17(16), 2766; https://doi.org/10.3390/rs17162766 - 9 Aug 2025

Abstract

In recent years, the rapid development of moving platforms, especially unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), has promoted their widespread applications in various fields such as precision agriculture and formation flight. In these applications, for accurate real-time kinematic positioning between [...] Read more.

In recent years, the rapid development of moving platforms, especially unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), has promoted their widespread applications in various fields such as precision agriculture and formation flight. In these applications, for accurate real-time kinematic positioning between two moving platforms, receiver autonomous integrity monitoring (RAIM) is necessary to assure the reliability of the obtained relative positioning. However, the existing carrier phase-based RAIM (CRAIM) algorithms are mainly a direct extension of pseudorange-based RAIM (PRAIM), whose availability is also a major challenge in signal-harsh environments. Learning from the integrated system between Global Navigation Satellite System (GNSS) and INS and based on a multiple hypothesis solution separation (MHSS) algorithm, we have developed an improved CRAIM algorithm, which combines Beidou Navigation Satellite System (BDS) and INS to offer integrity information for real-time kinematic relative positioning between two moving platforms in challenging environments. To achieve more robust and efficient fault detection and exclusion (FDE) results, an algorithm of observation-domain outlier detection combined with MHSS (OOD-MHSS) is also proposed. In this algorithm, the kinematic relative positioning method with INS addition is performed first, then, based on double-difference (DD) phase observations with known integer ambiguities and the OOD-MHSS method, the integrity monitoring information can be provided for the kinematic relative positioning between two moving platforms. To assess the performance of the OOD-MHSS and the improved CRAIM algorithm, a series of kinematic experiments between different platforms was analyzed and discussed. The results show that the improved CRAIM algorithm can perform effective FDE and provide reliable integrity information, which offers centimeter-level relative position solutions with decimeter-level protection levels (PLs) (integrity budget: 1 × 10⁻⁵/h). Both observation outlier detection and INS improve the continuity and availability of kinematic relative positioning and the PLs in horizontal and vertical directions. The PL values have been improved by up to 24.3%, and availability has reached 96.67% in harsh urban areas. This is of great significance for applications requiring higher precision and integrity in kinematic relative positioning. Full article

(This article belongs to the Section Earth Observation Data)

28 pages, 16358 KiB

Open AccessArticle

GRACE/GFO and Swarm Observation Analysis of the 2023–2024 Extreme Drought in the Amazon River Basin

by Jun Zhou, Lilu Cui, Yu Li, Chaolong Yao, Jiacheng Meng, Zhengbo Zou and Yuheng Lu

Remote Sens. 2025, 17(16), 2765; https://doi.org/10.3390/rs17162765 - 9 Aug 2025

Abstract

The Amazon River Basin (ARB) experienced an extreme drought from summer 2023 to spring 2024, driven by complex interactions among multiple climatic and environmental factors. A detailed investigation into this drought is crucial in understanding the entire process of the drought. Here, we [...] Read more.

The Amazon River Basin (ARB) experienced an extreme drought from summer 2023 to spring 2024, driven by complex interactions among multiple climatic and environmental factors. A detailed investigation into this drought is crucial in understanding the entire process of the drought. Here, we employ drought indices derived from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GFO), and Swarm missions to reconstruct the drought’s progression, combined with reanalysis datasets and extreme-climate indices to analyze atmospheric and hydrological mechanisms. Our findings reveal a six-month drought from September 2023, reaching a drought peak of −1.29 and a drought severity of −5.62, with its epicenter migrating systematically from the northwestern to southeastern basin, spatially mirroring the 2015–2016 extreme drought pattern. Reduced precipitation and abnormal warming were the direct causes, which were closely linked to the 2023 El Niño event. This event disrupted atmospheric vertical movements. These changes led to abnormally strong sinking motions over the basin, which interacted synergistically with anomalies in land cover types caused by deforestation, triggering this extreme drought. This study provides spatiotemporal drought diagnostics valuable for hydrological forecasting and climate adaptation planning. Full article

(This article belongs to the Special Issue New Advances of Space Gravimetry in Climate and Hydrology Studies)

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25 pages, 28729 KiB

Open AccessArticle

A Random Forest-Based CO₂ Profile Emulator for Real-Time Prior Profile Generation in TanSat XCO₂ Retrieval

by Shaojie Wu, Yang Wang, Likun Zhang, Heng Jia, Xianmei Zhang, Linglin Xu and Yunxiao Dai

Remote Sens. 2025, 17(16), 2764; https://doi.org/10.3390/rs17162764 (registering DOI) - 9 Aug 2025

Abstract

Greenhouse gas monitoring satellites provide extensive observational data for the global remote sensing of atmospheric carbon dioxide (CO₂), yet a critical limitation in utilizing these data is the dependence of the full physics retrieval accuracy on a priori CO₂ profiles. [...] Read more.

Greenhouse gas monitoring satellites provide extensive observational data for the global remote sensing of atmospheric carbon dioxide (CO₂), yet a critical limitation in utilizing these data is the dependence of the full physics retrieval accuracy on a priori CO₂ profiles. This challenge is pronounced due to the significant time delay inherent in data assimilation products of high quality, whose latency prevents their use for retrieval in real time. The resulting temporal mismatch between the a priori constraint and the actual atmospheric state is a primary source of systematic bias in the retrieved CO₂. To address this issue, this paper develops a random forest-based CO₂ profile emulator (RF-CPE) with the core novelty of emulating the high-quality Carbon Tracker CO₂ profiles in real time. By learning the complex relationships between multisource features and the corresponding Carbon Tracker profiles, the emulator generates a dynamic profile specific to each observation. The application of this emulator-based approach to TanSat observations from 2017 to 2018 demonstrates significant performance gains, reducing the mean retrieval bias by 44.11% (from 2.63 ppm to 1.47 ppm) compared to using a static prior. The emulator itself exhibits high performance, with an R² of 0.71 and an RMSE of 2.13 ppm, in agreement with the Carbon Tracker data. Ultimately, this work presents a robust and computationally efficient solution that resolves the conflict between the accuracy and timeliness of a priori constraints, effectively translating the performance of a delayed assimilation system into a real-time retrieval framework to significantly enhance the reliability of satellite CO₂ monitoring. Full article

(This article belongs to the Section Atmospheric Remote Sensing)

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21 pages, 29283 KiB

Open AccessArticle

WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data

by Yao Gao, Qiangyu Zeng, Yin Liu, Fugui Zhang, Hao Wang and Zhicheng Ren

Remote Sens. 2025, 17(16), 2763; https://doi.org/10.3390/rs17162763 (registering DOI) - 9 Aug 2025

Abstract

With the rapid expansion of Wind Parks (WPs), Wind Turbine Clutter (WTC) has become a significant challenge due to the interference it causes with data from next-generation Doppler weather radars. Traditional clutter detection methods struggle to strike a balance between detection accuracy and [...] Read more.

With the rapid expansion of Wind Parks (WPs), Wind Turbine Clutter (WTC) has become a significant challenge due to the interference it causes with data from next-generation Doppler weather radars. Traditional clutter detection methods struggle to strike a balance between detection accuracy and efficiency. This study proposes a deep learning model named WTC-MobResNet, which integrates the architectures of MobileNet and ResNet and is specifically designed for WTC detection tasks. The model combines the lightweight characteristics of MobileNet with the residual learning capabilities of ResNet, enabling efficient extraction of WTC features from weather radar echo data and achieving precise identification of WTC. The experimental results demonstrate that the proposed model achieves an ACC of 98.21%, a PRE of 97.52%, a POD of 98.99%, and an F1 score of 98.25%, outperforming several existing deep learning models in both detection accuracy and false alarm control. These results confirm the potential of WTC-MobResNet for real-world operational applications. Full article

(This article belongs to the Section AI Remote Sensing)

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19 pages, 3430 KiB

Open AccessArticle

Multi-Scale Wind Shear at a Plateau Airport: Insights from Lidar and Radiosonde Observations

by Jianfeng Chen, Chenbo Xie, Jie Ji and Jie Lu

Remote Sens. 2025, 17(16), 2762; https://doi.org/10.3390/rs17162762 - 9 Aug 2025

Abstract

Low-level wind shear poses a significant hazard to aviation, especially at airports located on high plateaus and surrounded by complex terrain. In this study, we present a comprehensive analysis integrating Doppler Lidar and radiosonde measurements collected at the Xining Caojiapu Airport, situated on [...] Read more.

Low-level wind shear poses a significant hazard to aviation, especially at airports located on high plateaus and surrounded by complex terrain. In this study, we present a comprehensive analysis integrating Doppler Lidar and radiosonde measurements collected at the Xining Caojiapu Airport, situated on the northeastern Tibetan Plateau, during June 2022. The results indicate a remarkably high frequency of severe wind shear events (|Δv| ≥ 6 m/s), with an overall occurrence rate of 34% during the observation period. These events are predominantly confined to two distinct atmospheric layers: just above the surface and near the top of the convective boundary layer. The diurnal cycle of wind shear is closely associated with boundary-layer dynamics, exhibiting sharp increases after sunrise and pronounced peaks around midday, coinciding with enhanced turbulent mixing and surface heating. Case analyses further reveal that the most intense shear episodes occur at strong thermal inversions, where momentum decoupling produces thin, critical interfaces conducive to turbulence generation. In contrast, well-mixed convective conditions result in more distributed but persistent shear throughout the lower atmosphere. Diagnostic profiles of atmospheric stratification and dynamic instability, characterized by the Brunt–Väisälä frequency and Richardson number, elucidate the intricate interplay between thermal structure and vertical wind gradients. Collectively, these findings provide a robust quantitative basis for improving wind shear risk assessments and early warning systems at airports in mountainous regions, while offering new insights into the complex interactions between turbulence and atmospheric stratification. Full article

(This article belongs to the Section Environmental Remote Sensing)

18 pages, 7103 KiB

Open AccessArticle

Multiscale Precipitating Characteristics of Categorized Extremely Persistent Flash Heavy Rainfalls over the Sichuan Basin in China Based on SOM and Multi-Source Datasets

by Changqing Liu, Jie Cao, Chengzhi Deng and Furong Qian

Remote Sens. 2025, 17(16), 2761; https://doi.org/10.3390/rs17162761 - 9 Aug 2025

Abstract

Extremely persistent flash heavy rainfalls (EPHRs) over the Sichuan Basin in China are influenced by both multiscale weather systems and complex underlying surfaces, making it difficult to understand the favorable dynamic mechanisms and to further improve operational numerical forecasting skills. In this study, [...] Read more.

Extremely persistent flash heavy rainfalls (EPHRs) over the Sichuan Basin in China are influenced by both multiscale weather systems and complex underlying surfaces, making it difficult to understand the favorable dynamic mechanisms and to further improve operational numerical forecasting skills. In this study, EPHRs from 2010 to 2024 are objectively identified and then classified into three categories based on the SOM method. Precipitating characteristics for each category are further investigated from the perspective of the diurnal cycle and spatial features with the use of rain-gauge-based observations. Evaluations of the ERA5 reanalysis dataset, MSWX bias-corrected meteorological product, and CMORPH satellite-based precipitation product are performed to determine their capabilities in representing precipitating characteristics of different EPHR categories at different stages. The following results are obtained. During EPHR events, CMORPH outperforms MSWX and ERA5 in capturing heavy precipitation distribution, diurnal cycles, and evolution over the central basin. Both MSWX and ERA5 miss the central precipitation core, with MSWX showing premature peaks and ERA5 generating secondary evening peaks while overestimating precipitation duration. During events influenced by small-scale weather systems, all three products exhibit minimal false alarms but show the largest errors in intensity and diurnal variation. Under certain circulation types, MSWX and ERA5 significantly underestimate precipitation development with comparable metrics, while CMORPH achieves superior accuracy in precipitation intensity and correlations, yet it underestimates nighttime precipitation occurrences in steep western terrain. This study may help to facilitate not only theoretical studies but also numerical model developments for precipitation extremes. Full article

(This article belongs to the Special Issue Application of Remote Sensing to Flood and Drought Analysis, Monitoring and Risk Management)

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28 pages, 9566 KiB

Open AccessArticle

CSBBNet: A Specialized Detection Method for Corner Reflector Targets via a Cross-Shaped Bounding Box Network

by Wangshuo Tang, Yuexin Gao, Mengdao Xing, Min Xue, Huitao Liu and Guangcai Sun

Remote Sens. 2025, 17(16), 2760; https://doi.org/10.3390/rs17162760 - 8 Aug 2025

Abstract

In synthetic aperture radar (SAR) maritime target detection tasks, corner reflector targets (CRTs) and their arrays can easily interfere with the accurate detection of ship targets, significantly increasing the misdetection rate and false alarm rate of detectors. Current deep learning-based research on SAR [...] Read more.

In synthetic aperture radar (SAR) maritime target detection tasks, corner reflector targets (CRTs) and their arrays can easily interfere with the accurate detection of ship targets, significantly increasing the misdetection rate and false alarm rate of detectors. Current deep learning-based research on SAR maritime target detection primarily focuses on ship targets, while dedicated detection methods addressing corner reflector interference have not yet established a comprehensive research framework. There remains a lack of theoretical innovation in detection principles for such targets. To address these issues, utilizing the prior knowledge of cross-shaped structures exhibited by marine CRTs in SAR images, we propose an innovative cross-shaped bounding box (CSBB) annotation strategy and design a novel dedicated detection network CSBBNet. The proposed method is constructed through three innovative component modules, namely the cross-shaped spatial feature perception (CSSFP) module, the wavelet cross-shaped attention downsampling (WCSAD) module, and the cross-shaped attention detection head (CSAD-Head). Additionally, to ensure effective training, we propose a cross-shaped intersection over union (CS-IoU) loss function. Comparative experiments with state-of-the-art methods demonstrate that our approach exhibits efficient detection capabilities for CRTs. Ablation experiment results validate the effectiveness of the proposed component architectures. Full article

(This article belongs to the Special Issue Advances in Imaging Radar Signal Processing, Target Feature Extraction and Recognition)

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25 pages, 4671 KiB

Open AccessArticle

OSSMDNet: An Omni-Selective Scanning Mechanism for a Remote Sensing Image Denoising Network Based on the State-Space Model

by Na Deng, Jie Han, Haiyong Ding, Dongsheng Liu, Zhichao Zhang, Wenping Song and Xudong Tong

Remote Sens. 2025, 17(16), 2759; https://doi.org/10.3390/rs17162759 - 8 Aug 2025

Abstract

Remote sensing images often degrade during acquisition due to various environmental factors, leading to noise contamination and loss of texture details. Existing methods based on convolutional neural networks (CNNs) are limited by their local receptive fields, making it difficult to effectively model long-range [...] Read more.

Remote sensing images often degrade during acquisition due to various environmental factors, leading to noise contamination and loss of texture details. Existing methods based on convolutional neural networks (CNNs) are limited by their local receptive fields, making it difficult to effectively model long-range dependencies. Although Transformers possess global modeling capabilities, they face high computational costs and poor scalability in high-resolution remote sensing images. To address these challenges, this paper proposes an efficient remote sensing image denoising network—OSSMDNet—based on the Mamba network and incorporating an omni-directional selective scanning mechanism (OSSM). Its advantages include (1) introducing a multi-directional state-space modeling mechanism to enhance spatial structure perception capabilities and mitigate the limitations of traditional unidirectional modeling; (2) OSSMDNet is designed based on the Mamba architecture, achieving efficient fusion of global context and local details while maintaining linear computational complexity. On multiple remote sensing and natural image denoising datasets such as CBSD68 and DOTA, OSSMDNet significantly outperforms existing CNN-, Transformer-, and Mamba-based methods in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics, with PSNR and SSIM values 0.14 dB and 0.0033 higher than the most iconic Mamba baseline method, respectively. This demonstrates that the proposed OSSMDNet achieves an excellent balance between accuracy and efficiency. Full article

(This article belongs to the Special Issue Deep Learning for Remote Sensing Image Enhancement)

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24 pages, 3961 KiB

Open AccessArticle

Quantifying the Rate and Extent of Urbanization Effects on Vegetation Phenology in Mainland China

by Yiming Qu, Josep Peñuelas, Zhizhi Yu, Xiang Zeng, Ye Zhang, Yanjin He, Youtu Wu and Jing Wang

Remote Sens. 2025, 17(16), 2758; https://doi.org/10.3390/rs17162758 - 8 Aug 2025

Abstract

Urbanization profoundly alters environmental conditions (e.g., temperature, artificial light at night (ALAN), and precipitation) that strongly influence vegetation phenology. However, the rate and extent of vegetation phenological responses to urbanization, as well as their underlying mechanisms, remain underexplored, particularly the roles of CO [...] Read more.

Urbanization profoundly alters environmental conditions (e.g., temperature, artificial light at night (ALAN), and precipitation) that strongly influence vegetation phenology. However, the rate and extent of vegetation phenological responses to urbanization, as well as their underlying mechanisms, remain underexplored, particularly the roles of CO₂ emissions and PM₂.₅ concentrations, as well as the interactions among environmental conditions. We first used road network density (RND) to represent urbanization effects and quantified the phenological response rate and extent across 31 cities in China (2014–2022) using slope and range metrics derived from linear regressions of phenostages (start of season (SOS), end of season (EOS), length of season (LOS)) against RND. Partial least squares structural equation modeling was applied to assess the direct and indirect effects of RND on phenology via all five key environmental factors. Our results identified substantial differences in the urban phenological responses across latitudinal, hydrothermal, and land−cover gradients. And the impact of urbanization on phenology was most pronounced during early expansion (at a RND threshold of 2.02 ± 0.41 km/km²) but diminished with continued growth. Environmental factors distinctly affected phenological response rate and extent through RND; temperature, ALAN, and CO₂ emissions were the dominant drivers of slope, negatively affecting SOS (β = −0.37 to −0.69) but positively affecting EOS and LOS (β = 0.31 to 0.68). PM₂.₅ played a crucial role in determining the range of SOS (β = −0.31), and precipitation had the largest impact on the range of EOS (β = −0.37). Our study innovatively uses RND to quantify urbanization intensity and improve understanding of the combined effects of multiple drivers, especially PM₂.₅ and CO₂, on phenological responses, which may offer a useful reference for future urban planning strategies that aim to balance development with ecosystem functioning. Full article

(This article belongs to the Special Issue Remote Sensing Applications in Urban Environment and Climate)

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31 pages, 5985 KiB

Open AccessArticle

Comparing Terrestrial and Mobile Laser Scanning Approaches for Multi-Layer Fuel Load Prediction in the Western United States

by Eugênia Kelly Luciano Batista, Andrew T. Hudak, Jeff W. Atkins, Eben North Broadbent, Kody Melissa Brock, Michael J. Campbell, Nuria Sánchez-López, Monique Bohora Schlickmann, Francisco Mauro, Andres Susaeta, Eric Rowell, Caio Hamamura, Ana Paula Dalla Corte, Inga La Puma, Russell A. Parsons, Benjamin C. Bright, Jason Vogel, Inacio Thomaz Bueno, Gabriel Maximo da Silva, Carine Klauberg, Jinyi Xia, Jessie F. Eastburn, Kleydson Diego Rocha and Carlos Alberto Silva Show full author list Hide full author list

Remote Sens. 2025, 17(16), 2757; https://doi.org/10.3390/rs17162757 - 8 Aug 2025

Abstract

Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North [...] Read more.

Effective estimation of fuel load is critical for mitigating wildfire risks. Here, we evaluate the performance of mobile laser scanning (MLS) and terrestrial laser scanning (TLS) to estimate fuel loads across multiple vegetation layers. Data were collected in two forest regions: the North Kaibab (NK) Plateau in Arizona and Monroe Mountain (MM) in Utah. We used random forest models to predict vegetation attributes, evaluating the performance of full models and transferred models using R², RMSE, and bias. The MLS consistently outperformed the TLS system, particularly for canopy-related attributes and woody biomass components. However, the TLS system showed potential for capturing canopy structure attributes, while offering advantages like operational simplicity, low equipment demands, and ease of deployment in the field, making it a cost-effective alternative for managers without access to more complex and expensive mobile or airborne systems. Our results show that model transferability between NK and MM is highly variable depending on the fuel attributes. Attributes related to canopy biomass showed better transferability, with small losses in predictive accuracy when models were transferred between the two sites. Conversely, surface fuel attributes showed more significant challenges for model transferability, given the difficulty of laser penetration in the lower vegetation layers. In general, models trained in NK and validated in MM consistently outperformed those trained in MM and transferred to NK. This may suggest that the NK plots captured a broader complexity of vegetation structure and environmental conditions from which models learned better and were able to generalize to MM. This study highlights the potential of ground-based LiDAR technologies in providing detailed information and important insights into fire risk and forest structure. Full article

(This article belongs to the Section Forest Remote Sensing)

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