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Remote Sens., Volume 17, Issue 15 (August-1 2025) – 211 articles

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19 pages, 3601 KiB  
Article
Study on Correction Methods for GPM Rainfall Rate and Radar Reflectivity Using Ground-Based Raindrop Spectrometer Data
by Lin Chen, Huige Di, Dongdong Chen, Ning Chen, Qinze Chen and Dengxin Hua
Remote Sens. 2025, 17(15), 2747; https://doi.org/10.3390/rs17152747 - 7 Aug 2025
Abstract
The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy [...] Read more.
The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions. Full article
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17 pages, 4471 KiB  
Technical Note
Agronomic Information Extraction from UAV-Based Thermal Photogrammetry Using MATLAB
by Francesco Paciolla, Giovanni Popeo, Alessia Farella and Simone Pascuzzi
Remote Sens. 2025, 17(15), 2746; https://doi.org/10.3390/rs17152746 - 7 Aug 2025
Abstract
Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, [...] Read more.
Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector. Full article
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20 pages, 2798 KiB  
Article
LSTMConvSR: Joint Long–Short-Range Modeling via LSTM-First–CNN-Next Architecture for Remote Sensing Image Super-Resolution
by Qiwei Zhu, Guojing Zhang, Xiaoying Wang and Jianqiang Huang
Remote Sens. 2025, 17(15), 2745; https://doi.org/10.3390/rs17152745 (registering DOI) - 7 Aug 2025
Abstract
The inability of existing super-resolution methods to jointly model short-range and long-range spatial dependencies in remote sensing imagery limits reconstruction efficacy. To address this, we propose LSTMConvSR, a novel framework inspired by top-down neural attention mechanisms. Our approach pioneers an LSTM-first–CNN-next architecture. First, [...] Read more.
The inability of existing super-resolution methods to jointly model short-range and long-range spatial dependencies in remote sensing imagery limits reconstruction efficacy. To address this, we propose LSTMConvSR, a novel framework inspired by top-down neural attention mechanisms. Our approach pioneers an LSTM-first–CNN-next architecture. First, an LSTM-based global modeling stage efficiently captures long-range dependencies via downsampling and spatial attention, achieving 80.3% lower FLOPs and 11× faster speed. Second, a CNN-based local refinement stage, guided by the LSTM’s attention maps, enhances details in critical regions. Third, a top-down fusion stage dynamically integrates global context and local features to generate the output. Extensive experiments on Potsdam, UAVid, and RSSCN7 benchmarks demonstrate state-of-the-art performance, achieving 33.94 dB PSNR on Potsdam with 2.4× faster inference than MambaIRv2. Full article
(This article belongs to the Special Issue Neural Networks and Deep Learning for Satellite Image Processing)
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20 pages, 11125 KiB  
Article
Application of a Bicubic Quasi-Uniform B-Spline Surface Fitting Method for Characterizing Mesoscale Eddies in the Atlantic Ocean
by Chunzheng Kong, Shengyi Jiao, Xuefeng Cao and Xianqing Lv
Remote Sens. 2025, 17(15), 2744; https://doi.org/10.3390/rs17152744 - 7 Aug 2025
Abstract
The direct fitting of sea level anomaly (SLA) using satellite along-track data provides a critical approach for monitoring mesoscale ocean dynamics. While bicubic quasi-uniform B-spline surface fitting has demonstrated feasibility in localized sea areas, its applicability to basin-scale regions remains underexplored. This study [...] Read more.
The direct fitting of sea level anomaly (SLA) using satellite along-track data provides a critical approach for monitoring mesoscale ocean dynamics. While bicubic quasi-uniform B-spline surface fitting has demonstrated feasibility in localized sea areas, its applicability to basin-scale regions remains underexplored. This study focuses on the northern Atlantic Ocean, employing B-spline surface fitting to derive SLA fields from satellite along-track data. The results show strong agreement with in situ measurements, yielding a mean absolute error (MAE) of 1.89 cm and a root mean square error (RMSE) of 3.02 cm. Comparative analysis against the Copernicus Marine Environment Monitoring Service (CMEMS) Level-4 gridded SSH data reveals nearly equivalent accuracy (MAE: 1.95 cm; RMSE: 3.06 cm). The relationship between the order of fitting and the spatial extent of the fitting domain is also examined. Furthermore, the influence of the coastline on the fitting results is investigated in detail. As the coastline area expanded, the MAE and RMSE for the entire region increased. But the maximum increase in MAE was only 1.20 cm, and the maximum increase in RMSE was only 2.49 cm. Notably, there was no upward trend in MAE and RMSE in the mesoscale vortex dense area, which highlights the advantage of B-spline’s local support. Geostrophic flow and vertical component of relative vorticity are computed from the satellite along-track SLA data, with results showing agreement with Level-4 gridded geostrophic flow and vertical component of relative vorticity data. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Ocean Observation (Third Edition))
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19 pages, 4292 KiB  
Article
The Establishment and Verification of a Velocity Doppler Transfer Model for Dual-Beam Squint Airborne SAR
by Jingwei Gu, Baochang Liu, Yijun He and Xiuzhong Li
Remote Sens. 2025, 17(15), 2743; https://doi.org/10.3390/rs17152743 - 7 Aug 2025
Abstract
Measuring ocean currents is essential for oceanographic studies, and dual-beam squint airborne SAR measurements provide significant advantages, including flexibility, cost-effectiveness, and extensive coverage. However, substantial attitude changes in the airborne platform introduce challenges to achieving accurate ocean current measurements. Additionally, existing attitude correction [...] Read more.
Measuring ocean currents is essential for oceanographic studies, and dual-beam squint airborne SAR measurements provide significant advantages, including flexibility, cost-effectiveness, and extensive coverage. However, substantial attitude changes in the airborne platform introduce challenges to achieving accurate ocean current measurements. Additionally, existing attitude correction methods fail to account for the off-nadir angle and squint angle errors of targets located at the edge of the beam’s ground footprint, further impacting measurement precision. To address these limitations, this paper proposes a dual-beam squint airborne velocity Doppler transfer model. The squint antenna view vector is initially defined in the aircraft-centered frame of reference and subsequently described using the flightpath frame of reference. By estimating the Doppler frequency caused by aircraft attitude changes, the velocity Doppler transfer model is established. This model is then applied to invert sea surface currents. An error analysis is conducted, and the Monte Carlo method is employed to validate the model’s accuracy. The results demonstrate that the proposed velocity Doppler transfer model effectively inverts sea surface currents with high accuracy in both velocity and direction. Compared to pre-existing methods, the proposed model shows superior performance, particularly in addressing off-nadir and squint angle errors, thereby enhancing overall measurement precision. Full article
(This article belongs to the Section Ocean Remote Sensing)
24 pages, 7944 KiB  
Article
BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images
by Wei Zhang, Jinsong Li, Shuaipeng Wang and Jianhua Wan
Remote Sens. 2025, 17(15), 2742; https://doi.org/10.3390/rs17152742 - 7 Aug 2025
Abstract
Observing building changes in remote sensing images plays a crucial role in monitoring urban development and promoting sustainable urbanization. Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, [...] Read more.
Observing building changes in remote sensing images plays a crucial role in monitoring urban development and promoting sustainable urbanization. Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. Moreover, most existing methods only detect whether changes have occurred but ignore change types, such as new construction and demolition. To address these issues, we present a building change-type detection network (BCTDNet) based on the Segment Anything Model (SAM) to identify newly constructed and demolished buildings. We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. Furthermore, we develop a change-aware attribute decoder that integrates building semantics into the change detection process via an extraction decoding network. Subsequently, an attribute-aware strategy is adopted to explicitly generate distinct maps for newly constructed and demolished buildings, thereby establishing clear temporal relationships among different change types. To evaluate BCTDNet’s performance, we construct the JINAN-MCD dataset, which covers Jinan’s urban core area over a six-year period, capturing diverse change scenarios. Moreover, we adapt the WHU-CD dataset into WHU-MCD to include multiple types of changing. Experimental results on both datasets demonstrate the superiority of BCTDNet. On JINAN-MCD, BCTDNet achieves improvements of 12.64% in IoU and 11.95% in F1 compared to suboptimal methods. Similarly, on WHU-MCD, it outperforms second-best approaches by 2.71% in IoU and 1.62% in F1. BCTDNet’s effectiveness and robustness in complex urban scenarios highlight its potential for applications in land-use analysis and urban planning. Full article
(This article belongs to the Special Issue Deep Learning for Multi-Source Remote Sensing Image Interpretation: Exploring, Rethinking, and Limiting Breakthroughs)
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25 pages, 7063 KiB  
Article
An Improved InTEC Model for Estimating the Carbon Budgets in Eucalyptus Plantations
by Zhipeng Li, Mingxing Zhou, Kunfa Luo, Yunzhong Wu and Dengqiu Li
Remote Sens. 2025, 17(15), 2741; https://doi.org/10.3390/rs17152741 - 7 Aug 2025
Abstract
Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC [...] Read more.
Eucalyptus has become a major plantation crop in southern China, with a carbon sequestration capacity significantly higher than that of other species. However, its long-term carbon sequestration capacity and regional-scale potential remain highly uncertain due to commonly applied short-rotation management practices. The InTEC (Integrated Terrestrial Ecosystem Carbon) model is a process-based biogeochemical model that simulates carbon dynamics in terrestrial ecosystems by integrating physiological processes, environmental drivers, and management practices. In this study, the InTEC model was enhanced with an optimized eucalyptus module (InTECeuc) and a data assimilation module (InTECDA), and driven by multiple remote sensing products (Net Primary Productivity (NPP) and carbon density) to simulate the carbon budgets of eucalyptus plantations from 2003 to 2023. The results indicated notable improvements in the performance of the InTECeuc model when driven by different datasets: carbon density simulation showed improvements in R2 (0.07–0.56), reductions in MAE (5.99–28.51 Mg C ha−1), reductions in RMSE (8.1–31.85 Mg C ha−1), and improvements in rRMSE (12.37–51.82%), excluding NPPLin. The carbon density-driven InTECeuc model outperformed the NPP-driven model, with improvements in R2 (0.28), MAE (−8.15 Mg C ha−1), RMSE (−9.43 Mg C ha−1), and rRMSE (−15.34%). When the InTECDA model was employed, R2 values for carbon density improved by 0–0.03 (excluding ACDYan), with MAE reductions between 0.17 and 7.22 Mg C ha−1, RMSE reductions between 0.33 and 12.94 Mg C ha−1 and rRMSE improvements ranging from 0.51 to 20.22%. The carbon density-driven InTECDA model enabled the production of high-resolution and accurate carbon budget estimates for eucalyptus plantations from 2003 to 2023, with average NPP, Net Ecosystem Productivity (NEP), and Net Biome Productivity (NBP) values of 17.80, 10.09, and 9.32 Mg C ha−1 yr−1, respectively, offering scientific insights and technical support for the management of eucalyptus plantations in alignment with carbon neutrality targets. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Monitoring Forest Carbon and Supporting Nature-Based Carbon Crediting Mechanisms)
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22 pages, 3869 KiB  
Article
On-Orbit Calibration Method for Rotation Axis Misalignment in Rotating Mirror-Based Wide-Field Space Cameras
by Guoxiu Zhang, Yishi Qiao, Zhenyuan Guo, Chen Wang, Yingming Zhao, Yuxin Zhang, Chunyu Liu and Xianren Kong
Remote Sens. 2025, 17(15), 2740; https://doi.org/10.3390/rs17152740 - 7 Aug 2025
Abstract
Rotating mirror-based space cameras are susceptible to mirror misalignment due to the severe vibrations experienced during rocket launch and the harsh, variable conditions of the space environment, which can result in deviations of the camera’s line of sight. To mitigate this risk, this [...] Read more.
Rotating mirror-based space cameras are susceptible to mirror misalignment due to the severe vibrations experienced during rocket launch and the harsh, variable conditions of the space environment, which can result in deviations of the camera’s line of sight. To mitigate this risk, this study proposes a simulation-based on-orbit calibration method for quantifying rotating mirror misalignment using a system of pointing vector equations. The method employs star coordinates as a reference to establish the reference pointing vector for stars, while simultaneously developing a model of the rotating mirror imaging system. By incorporating a misalignment matrix, the actual pointing vector of star points is derived. Subsequently, the reference star pointing vector and the actual star point pointing vector are combined to formulate a system of pointing vector equations. Solving these equations enables precise measurement of the rotating mirror’s rotational misalignment without requiring additional spaceborne equipment. Through simulations, the three-axis misalignment of the rotating mirror is deduced from imaging pixel coordinates, given the known right ascension and declination of reference star points. The influence and patterns of three-axis misalignment on pointing accuracy are analyzed separately. Although validation based on real measurement data remains to be carried out in future work, this simulation-based method provides a theoretical foundation for the calibration of internal orientation elements of space cameras equipped with moving components. Full article
19 pages, 11346 KiB  
Article
Seasonal and Interannual Variations in Hydrological Dynamics of the Amazon Basin: Insights from Geodetic Observations
by Meilin He, Tao Chen, Yuanjin Pan, Lv Zhou, Yifei Lv and Lewen Zhao
Remote Sens. 2025, 17(15), 2739; https://doi.org/10.3390/rs17152739 - 7 Aug 2025
Abstract
The Amazon Basin plays a crucial role in the global hydrological cycle, where seasonal and interannual variations in terrestrial water storage (TWS) are essential for understanding climate–hydrology coupling mechanisms. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission [...] Read more.
The Amazon Basin plays a crucial role in the global hydrological cycle, where seasonal and interannual variations in terrestrial water storage (TWS) are essential for understanding climate–hydrology coupling mechanisms. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission and its follow-on mission (GRACE-FO, collectively referred to as GRACE) to investigate the spatiotemporal dynamics of hydrological mass changes in the Amazon Basin from 2002 to 2021. Results reveal pronounced spatial heterogeneity in the annual amplitude of TWS, exceeding 65 cm near the Amazon River and decreasing to less than 25 cm in peripheral mountainous regions. This distribution likely reflects the interplay between precipitation and topography. Vertical displacement measurements from the Global Navigation Satellite System (GNSS) show strong correlations with GRACE-derived hydrological load deformation (mean Pearson correlation coefficient = 0.72) and reduce its root mean square (RMS) by 35%. Furthermore, the study demonstrates that existing hydrological models, which neglect groundwater dynamics, underestimate hydrological load deformation. Principal component analysis (PCA) of the Amazon GNSS network demonstrates that the first principal component (PC) of GNSS vertical displacement aligns with abrupt interannual TWS fluctuations identified by GRACE during 2010–2011, 2011–2012, 2013–2014, 2015–2016, and 2020–2021. These fluctuations coincide with extreme precipitation events associated with the El Niño–Southern Oscillation (ENSO), confirming that ENSO modulates basin-scale interannual hydrological variability primarily through precipitation anomalies. This study provides new insights for predicting extreme hydrological events under climate warming and offers a methodological framework applicable to other critical global hydrological regions. Full article
(This article belongs to the Special Issue Advancing Hydrological Monitoring and Prediction Through Multisource Geodetic Observations)
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32 pages, 7705 KiB  
Article
Deep Learning Small Water Body Mapping by Transfer Learning from Sentinel-2 to PlanetScope
by Yuyang Li, Pu Zhou, Yalan Wang, Xiang Li, Yihang Zhang and Xiaodong Li
Remote Sens. 2025, 17(15), 2738; https://doi.org/10.3390/rs17152738 - 7 Aug 2025
Abstract
Small water bodies are widely spread and play crucial roles in supporting regional agricultural and aquaculture activities. PlanetScope imagery has a high resolution (3 m) with daily global coverage and has obviously enhanced small water body mapping. Recent studies have demonstrated the effectiveness [...] Read more.
Small water bodies are widely spread and play crucial roles in supporting regional agricultural and aquaculture activities. PlanetScope imagery has a high resolution (3 m) with daily global coverage and has obviously enhanced small water body mapping. Recent studies have demonstrated the effectiveness of deep learning for mapping small water bodies using PlanetScope; however, a persistent challenge remains in the scarcity of high-quality, manually annotated water masks used for model training, which limits the generalization capability of data-driven deep learning models. In this study, we propose a transfer learning framework that leverages Sentinel-2 data to improve PlanetScope-based small water body mapping, capitalizing on the spectral interoperability between PlanetScope and Sentinel-2 bands and the abundance of open-source Sentinel-2 water masks. Eight state-of-the-art segmentation models have been explored. Additionally, this paper presents the first assessment of the VMamba model for small water body mapping, building on its demonstrated success in segmentation tasks. The models were pre-trained using Sentinel-2-derived water masks and subsequently fine-tuned with a limited set (1292 image patches, 256 × 256 pixels in each patch) of manually annotated PlanetScope labels. Experiments were conducted using 5648 image patches and two areas of 9636 km2 and 2745 km2, respectively. Among the evaluated methods, VMamba achieved higher accuracy compared with both CNN- and Transformer-based models. This study highlights the efficacy of combining global Sentinel-2 datasets for pre-training with localized fine-tuning, which not only enhances mapping accuracy but also reduces reliance on labor-intensive manual annotation in regional small water body mapping. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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27 pages, 8056 KiB  
Article
Spatiotemporal Mapping of Soil Profile Moisture in Oases in Arid Areas
by Zihan Zhang, Jinjie Wang, Jianli Ding, Jinming Zhang, Li Li, Liya Shi and Yue Liu
Remote Sens. 2025, 17(15), 2737; https://doi.org/10.3390/rs17152737 - 7 Aug 2025
Abstract
Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers [...] Read more.
Soil moisture is a key factor in the exchange of energy and matter between the soil and atmosphere, playing a vital role in the hydrological cycle and agricultural management. Traditional monitoring methods are limited in achieving large-scale, real-time observations, while deep learning offers new avenues to model the complex nonlinear relationships between spectral features and soil moisture content. This study focuses on the Wei-Ku Oasis in Xinjiang, using multi-source remote sensing data (Landsat series and Sentinel-1) and in situ multi-layer soil moisture measurements. The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. Their performances were systematically compared on both training and testing sets, and the optimal model was used for spatiotemporal mapping. The results show that the CNN-LSTM-based multi-depth soil moisture inversion model achieved superior performance, with the 0–10 cm model showing the highest accuracy and a testing R2 of 0.64, outperforming individual models. The testing R2 values for the soil moisture inversion models at depths of 10–20 cm, 20–40 cm, and 40–60 cm were 0.59, 0.54, and 0.59, respectively. According to the mapping results, soil moisture in the 0–60 cm profile of the Wei-Ku Oasis exhibited a vertical gradient, increasing with depth. Spatially, soil moisture was higher in the central oasis and lower toward the periphery, forming a “center-high, edge-low” pattern. This study provides a high-accuracy method for multi-layer soil moisture remote sensing in arid regions, offering valuable data support for oasis water resource management and precision irrigation planning. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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20 pages, 11966 KiB  
Article
Improved Photosynthetic Accumulation Models for Biomass Estimation of Soybean and Cotton Using Vegetation Indices and Canopy Height
by Jinglong Liu, Jordi J. Mallorqui, Albert Aguasca, Xavier Fàbregas, Antoni Broquetas, Jordi Llop, Mireia Mas, Feng Zhao and Yanan Wang
Remote Sens. 2025, 17(15), 2736; https://doi.org/10.3390/rs17152736 - 7 Aug 2025
Abstract
Most crops accumulate above-ground biomass (AGB) through photosynthesis, inspiring the development of the Photosynthetic Accumulation Model (PAM) and Simplified PAM (SPAM). Both models estimate AGB based on time-series optical vegetation indices (VIs) and canopy height. To further enhance the model performance and evaluate [...] Read more.
Most crops accumulate above-ground biomass (AGB) through photosynthesis, inspiring the development of the Photosynthetic Accumulation Model (PAM) and Simplified PAM (SPAM). Both models estimate AGB based on time-series optical vegetation indices (VIs) and canopy height. To further enhance the model performance and evaluate its applicability across different crop types, an improved PAM model (IPAM) is proposed with three strategies. They are as follows: (i) using numerical integration to reduce reliance on dense observations, (ii) introduction of Fibonacci sequence-based structural correction to improve model accuracy, and (iii) non-photosynthetic area masking to reduce overestimation. Results from both soybean and cotton demonstrate the strong performance of the PAM-series models. Among them, the proposed IPAM model achieved higher accuracy, with mean R2 and RMSE values of 0.89 and 207 g/m2 for soybean and 0.84 and 251 g/m2 for cotton, respectively. Among the vegetation indices tested, the recently proposed Near-Infrared Reflectance of vegetation (NIRv) and Kernel-based normalized difference vegetation index (Kndvi) yielded the most accurate results. Both Monte Carlo simulations and theoretical error propagation analyses indicate a maximum deviation percentage of approximately 20% for both crops, which is considered acceptable given the expected inter-annual variation in model transferability. In addition, this paper discusses alternatives to height measurements and evaluates the feasibility of incorporating synthetic aperture radar (SAR) VIs, providing practical insights into the model’s adaptability across diverse data conditions. Full article
(This article belongs to the Special Issue Monitoring, Assessment, and Prediction of Agroecosystem Dynamics for Sustainable Agriculture: Applications of Photogrammetry, Remote Sensing, and Geographic Information Systems)
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27 pages, 40090 KiB  
Article
Spatiotemporal Super-Resolution of Satellite Sea Surface Salinity Based on a Progressive Transfer Learning-Enhanced Transformer
by Zhenyu Liang, Senliang Bao, Weimin Zhang, Huizan Wang, Hengqian Yan, Juan Dai and Peikun Xiao
Remote Sens. 2025, 17(15), 2735; https://doi.org/10.3390/rs17152735 - 7 Aug 2025
Abstract
Satellite sea surface salinity (SSS) products suffer from coarse spatiotemporal resolution, limiting their utility for mesoscale ocean monitoring. To address this, we proposed the Transformer-based satellite SSS super-resolution (SR) model (TSR) coupled with a progressive transfer learning (PTL) strategy. TSR improved the resolution [...] Read more.
Satellite sea surface salinity (SSS) products suffer from coarse spatiotemporal resolution, limiting their utility for mesoscale ocean monitoring. To address this, we proposed the Transformer-based satellite SSS super-resolution (SR) model (TSR) coupled with a progressive transfer learning (PTL) strategy. TSR improved the resolution of the salinity satellite SMOS from 1/4° and 10 days to 1/12° and daily. Leveraging Transformer, TSR captured long-range dependencies critical for reconstructing fine-scale structures. PTL effectively balanced structural detail acquisition and local accuracy correction by combining the gridded reanalysis products with scattered in situ observations as training labels. Validated against independent in situ measurements, TSR outperformed existing L3 salinity satellite products, as well as convolutional neural network and generative adversarial network-based SR models, particularly reducing the root mean square error (RMSE) by 33% and the mean bias (MB) by 81% compared to the SMOS input. More importantly, TSR demonstrated an enhanced capability in resolving mesoscale eddies, which were previously obscured by noise in salinity satellite products. Compared to training with a single label type or switching label types non-progressively, PTL achieved a 3%–66% lower RMSE and a 73–92% lower MB. TSR enables higher-resolution satellite monitoring of SSS, contributing to the study of ocean dynamics and climate change. Full article
(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography (2nd Edition))
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23 pages, 14727 KiB  
Article
A Novel Method for Single-Station Lightning Distance Estimation Based on the Physical Time Reversal
by Yingcheng Zhao, Zheng Sun, Yantao Duan, Hailin Chen, Yicheng Liu and Lihua Shi
Remote Sens. 2025, 17(15), 2734; https://doi.org/10.3390/rs17152734 - 7 Aug 2025
Abstract
A single-station lightning location has the obvious advantages of low cost and convenience in lightning monitoring and warning. To address the critical challenge of distance estimation accuracy in this technology, we propose a novel physical time-reversal (PTR) method to utilize the full wave [...] Read more.
A single-station lightning location has the obvious advantages of low cost and convenience in lightning monitoring and warning. To address the critical challenge of distance estimation accuracy in this technology, we propose a novel physical time-reversal (PTR) method to utilize the full wave information of both the ground wave and the sky wave in the detected signal. First, we improved the numerical model for accurately calculating the lightning sferics signals in the complex propagation environment of the Earth–ionosphere waveguide using the measured International Reference Ionosphere 2020. Subsequently, the sferics signal with multipath effect is transformed by time reversal and back propagated in the numerical model. Furthermore, a broadening factor reflecting the waveform dispersion in the back propagation is defined as the single-station focusing criterion to determine the optimal lightning propagation distance, considering the multipath effect and the focus of the PTR process. The experimental results demonstrate that the average root mean square error (RMSE) and the mean relative error (MRE) of the PTR method for the lightning distance estimation in the numerical simulation within the range of 100–1200 km are 5.517 km and 1.21%, respectively, and the average RMSE and the MRE for the natural lightning strikes to the Canton Tower from the measured data in the range of 181.643–1152.834 km are 9.251 km and 2.07%, respectively. Moreover, the correlation coefficients of the detection results are all as high as 0.999. These results indicate that the PTR method significantly outperforms the traditional ionospheric reflection method, demonstrating that it is able to perform a more accurate single-station lightning distance estimation by utilizing the compensation mechanism of the multipath effect on the sferics. The implementation of the proposed method has significant application value for improving the accuracy of single-station lightning location. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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34 pages, 2523 KiB  
Technical Note
A Technical Note on AI-Driven Archaeological Object Detection in Airborne LiDAR Derivative Data, with CNN as the Leading Technique
by Reyhaneh Zeynali, Emanuele Mandanici and Gabriele Bitelli
Remote Sens. 2025, 17(15), 2733; https://doi.org/10.3390/rs17152733 - 7 Aug 2025
Abstract
Archaeological research fundamentally relies on detecting features to uncover hidden historical information. Airborne (aerial) LiDAR technology has significantly advanced this field by providing high-resolution 3D terrain maps that enable the identification of ancient structures and landscapes with improved accuracy and efficiency. This technical [...] Read more.
Archaeological research fundamentally relies on detecting features to uncover hidden historical information. Airborne (aerial) LiDAR technology has significantly advanced this field by providing high-resolution 3D terrain maps that enable the identification of ancient structures and landscapes with improved accuracy and efficiency. This technical note comprehensively reviews 45 recent studies to critically examine the integration of Machine Learning (ML) and Deep Learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), with airborne LiDAR derivatives for automated archaeological feature detection. The review highlights the transformative potential of these approaches, revealing their capability to automate feature detection and classification, thus enhancing efficiency and accuracy in archaeological research. CNN-based methods, employed in 32 of the reviewed studies, consistently demonstrate high accuracy across diverse archaeological features. For example, ancient city walls were delineated with 94.12% precision using U-Net, Maya settlements with 95% accuracy using VGG-19, and with an IoU of around 80% using YOLOv8, and shipwrecks with a 92% F1-score using YOLOv3 aided by transfer learning. Furthermore, traditional ML techniques like random forest proved effective in tasks such as identifying burial mounds with 96% accuracy and ancient canals. Despite these significant advancements, the application of ML/DL in archaeology faces critical challenges, including the scarcity of large, labeled archaeological datasets, the prevalence of false positives due to morphological similarities with natural or modern features, and the lack of standardized evaluation metrics across studies. This note underscores the transformative potential of LiDAR and ML/DL integration and emphasizes the crucial need for continued interdisciplinary collaboration to address these limitations and advance the preservation of cultural heritage. Full article
(This article belongs to the Special Issue Application of Remote Sensing in Cultural Heritage Research II)
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28 pages, 19171 KiB  
Article
Spatiotemporal Evolution of Precipitation Concentration in the Yangtze River Basin (1960–2019): Associations with Extreme Heavy Precipitation and Validation Using GPM IMERG
by Tao Jin, Yuliang Zhou, Ping Zhou, Ziling Zheng, Rongxing Zhou, Yanqi Wei, Yuliang Zhang and Juliang Jin
Remote Sens. 2025, 17(15), 2732; https://doi.org/10.3390/rs17152732 - 7 Aug 2025
Abstract
Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain [...] Read more.
Precipitation concentration reflects the uneven temporal distribution of rainfall. It plays a critical role in water resource management and flood–drought risk under climate change. However, its long-term trends, associations with atmospheric teleconnections as potential drivers, and links to extreme heavy precipitation events remain poorly understood in complex basins like the Yangtze River Basin. This study analyzes these aspects using ground station data from 1960 to 2019 and conducts a comparison using the Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM (GPM IMERG) satellite product. We calculated three indices—Daily Precipitation Concentration Index (PCID), Monthly Precipitation Concentration Index (PCIM), and Seasonal Precipitation Concentration Index (SPCI)—to quantify rainfall unevenness, selected for their ability to capture multi-scale variability and associations with extremes. Key methods include Mann–Kendall trend tests for detecting changes, Hurst exponents for persistence, Pettitt detection for abrupt shifts, random forest modeling to assess atmospheric teleconnections, and hot spot analysis for spatial clustering. Results show a significant basin-wide decrease in PCID, driven by increased frequency of small-to-moderate rainfall events, with strong spatial synchrony to extreme heavy precipitation indices. PCIM is most strongly associated with El Niño-Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). GPM IMERG captures PCIM patterns well but underestimates PCID trends and magnitudes, highlighting limitations in daily-scale resolution. These findings provide a benchmark for satellite product improvement and support adaptive strategies for extreme precipitation risks in changing climates. Full article
(This article belongs to the Special Issue Remote Sensing in Hydrometeorology and Natural Hazards)
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42 pages, 8886 KiB  
Article
Standard Classes for Urban Topographic Mapping with ALS: Classification Scheme and a First Implementation
by Agata Walicka and Norbert Pfeifer
Remote Sens. 2025, 17(15), 2731; https://doi.org/10.3390/rs17152731 - 7 Aug 2025
Abstract
Research regarding airborne laser scanning (ALS) point cloud semantic segmentation typically revolves around supervised machine learning, which requires time-consuming generation of training data. Therefore, the models are usually trained using one of the benchmarking datasets that cover a small area. Recently, many European [...] Read more.
Research regarding airborne laser scanning (ALS) point cloud semantic segmentation typically revolves around supervised machine learning, which requires time-consuming generation of training data. Therefore, the models are usually trained using one of the benchmarking datasets that cover a small area. Recently, many European countries published classified ALS data, which can be potentially used for training models. However, a review of the classification schemes of these datasets revealed that these schemes vary substantially, therefore limiting their applicability. Thus, our goal was three-fold. First, to develop a common classification scheme that can be applied for the semantic segmentation of various ALS datasets. Second, to unify the classification scheme of existing ALS datasets. Third, to employ them for the training of a classifier that will be able to classify data from different sources and will not require additional training. We propose a classification scheme of four classes: ground and water, vegetation, buildings and bridges, and ‘other’. The developed classifier is trained jointly using ALS data from Austria, Switzerland, and Poland. A test on unseen datasets demonstrates that the achieved intersection over union accuracy varies between 90.0–97.3% for ground and water, 68.0–95.9% for vegetation, 77.6–94.8% for buildings and bridges, and 13.5–52.7% for ‘other’. As a result, we conclude that the developed method generalizes well to previously unseen data. Full article
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22 pages, 15367 KiB  
Article
All-Weather Precipitable Water Vapor Retrieval over Land Using Integrated Near-Infrared and Microwave Satellite Observations
by Shipeng Song, Mengyao Zhu, Zexing Tao, Duanyang Xu, Sunxin Jiao, Wanqing Yang, Huaxuan Wang and Guodong Zhao
Remote Sens. 2025, 17(15), 2730; https://doi.org/10.3390/rs17152730 - 7 Aug 2025
Abstract
Precipitable water vapor (PWV) is a critical component of the Earth’s atmosphere, playing a pivotal role in weather systems, climate dynamics, and hydrological cycles. Accurate estimation of PWV is essential for numerical weather prediction, climate modeling, and atmospheric correction in remote sensing. Ground-based [...] Read more.
Precipitable water vapor (PWV) is a critical component of the Earth’s atmosphere, playing a pivotal role in weather systems, climate dynamics, and hydrological cycles. Accurate estimation of PWV is essential for numerical weather prediction, climate modeling, and atmospheric correction in remote sensing. Ground-based observation stations can only provide PWV measurements at discrete points, whereas spaceborne infrared remote sensing enables spatially continuous coverage, but its retrieval algorithm is restricted to clear-sky conditions. This study proposes an innovative approach that uses ensemble learning models to integrate infrared and microwave satellite data and other geographic features to achieve all-weather PWV retrieval. The proposed product shows strong consistency with IGRA radiosonde data, with correlation coefficients (R) of 0.96 for the ascending orbit and 0.95 for the descending orbit, and corresponding RMSE values of 5.65 and 5.68, respectively. Spatiotemporal analysis revealed that the retrieved PWV product exhibits a clear latitudinal gradient and seasonal variability, consistent with physical expectations. Unlike MODIS PWV products, which suffer from cloud-induced data gaps, the proposed method provides seamless spatial coverage, particularly in regions with frequent cloud cover, such as southern China. Temporal consistency was further validated across four east Asian climate zones, with correlation coefficients exceeding 0.88 and low error metrics. This algorithm establishes a novel all-weather approach for atmospheric water vapor retrieval that does not rely on ground-based PWV measurements for model training, thereby offering a new solution for estimating water vapor in regions lacking ground observation stations. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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27 pages, 17353 KiB  
Article
A Framework to Retrieve Water Quality Parameters in Small, Optically Diverse Freshwater Ecosystems Using Sentinel-2 MSI Imagery
by Matheus Henrique Tavares, David Guimarães, Joana Roussillon, Valentin Baute, Julien Cucherousset, Stéphanie Boulêtreau and Jean-Michel Martinez
Remote Sens. 2025, 17(15), 2729; https://doi.org/10.3390/rs17152729 - 7 Aug 2025
Abstract
Small lakes (<10 km2) provide a range of ecosystem services but are often overlooked in both monitoring efforts and limnological studies. Remote sensing has been increasingly used to complement in situ monitoring or to provide water colour data for unmonitored inland [...] Read more.
Small lakes (<10 km2) provide a range of ecosystem services but are often overlooked in both monitoring efforts and limnological studies. Remote sensing has been increasingly used to complement in situ monitoring or to provide water colour data for unmonitored inland water bodies. However, due to spatial, radiometric, and spectral constraints, it has been heavily focused on large lakes. Sentinel-2 MSI is the first sensor with the capability to consistently retrieve a wide range of essential water quality variables, such as chlorophyll-a concentration (chl-a) and water transparency, in small water bodies, and to provide long time series. Here, we provide and validate a framework for retrieving two variables, chl-a and turbidity, over lakes with diverse optical characteristics using Sentinel-2 imagery. It is based on GRS for atmospheric and sun glint correction, WaterDetect for water detection, and inversion models that were automatically selected based on two different sets of optical water types (OWTs)—one for each variable; for chl-a, we produced a blended product for improved spatial representation. To validate the approach, we compared the products with more than 600 in situ data from 108 lakes located in the Adour–Garonne river basins, ranging from 3 to ∼5000 ha, as well as remote sensing reflectance (Rrs) data collected during 10 field campaigns during the summer and spring seasons. Rrs retrieval (n = 65) was robust for bands 2 to 5, with MAPE varying from 15 to 32% and achieving correlation from 0.74 up to 0.92. For bands 6 to 8A, the Rrs retrieval was much less accurate, being influenced by adjacency effects. Glint removal significantly enhanced Rrs accuracy, with RMSE improving from 0.0067 to 0.0021 sr−1 for band 4, for example. Water quality retrieval showed consistent results, with an MAPE of 56%, an RMSE of 11.4 mg m−3, and an r of 0.76 for chl-a, and an MAPE of 47%, an RMSE of 9.7 NTU, and an r of 0.87 for turbidity, and no significant effect of lake area or lake depth on retrieval errors. The temporal and spatial representations of the selected parameters were also shown to be consistent, demonstrating that the framework is robust and can be applied over lakes as small as 3 ha. The validated methods can be applied to retrieve time series of chl-a and turbidity starting from 2016 and with a frequency of up to 5 days, largely expanding the database collected by water agencies. This dataset will be extremely useful for studying the dynamics of these small freshwater ecosystems. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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28 pages, 15025 KiB  
Article
Freezing Fog Microphysics and Visibility Based on CFACT Feb 19 Case
by Onur Durmus, Ismail Gultepe, Orhan Sen, Zhaoxia Pu, Eric R. Pardyjak, Sebastian W. Hoch, Alexei Perelet, Anna G. Hallar, Gerardo Carrillo-Cardenas and Simla Durmus
Remote Sens. 2025, 17(15), 2728; https://doi.org/10.3390/rs17152728 - 7 Aug 2025
Abstract
The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations [...] Read more.
The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations are collected using visibility, droplet spectra, ice crystal spectra, and aerosol spectral instruments, as well as in-situ meteorological instruments. Particle phase is determined from relative humidity with respect to water (RHw) as well as ground cloud imaging probe (GCIP), ceilometer (CL61) depolarization ratio, and icing accumulation on the platforms. Results showed that freezing droplet density can affect visibility (Vis) up to 100 m during Vis less than 1 km. In addition, increasing volume can lead to up to a 2 μm increase in droplet radius due to a change in the chemical composition of aerosols from Sodium Chloride (NaCl) to Ammonium Nitrate (NH4NO3). Overall, comparisons suggested that Vis parameterizations are highly variable, and freezing fog conditions resulted in lower Vis values compared to warm fog microphysical parameterizations. Furthermore, riming of freezing fog conditions can lead to more than 50% uncertainty in Vis. It is concluded that changing aerosol composition and freezing fog droplet density and riming can play a major role in Vis simulations. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: Recent Progress in Atmospheric Remote Sensing)
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44 pages, 5169 KiB  
Review
Neural Architecture Search for Hyperspectral Image Classification: A Comprehensive Review and Future Perspectives
by Aili Wang, Xinyu Liu, Kang Zhang, Haoran Lv, Haibin Wu, Xing Chen and Manman Yao
Remote Sens. 2025, 17(15), 2727; https://doi.org/10.3390/rs17152727 - 7 Aug 2025
Abstract
Hyperspectral image classification (HSIC) is a key task in the field of remote sensing, but the complex nature of hyperspectral data poses a serious challenge to traditional methods. Although deep learning significantly improves classification performance through automatic feature extraction, manually designed network architectures [...] Read more.
Hyperspectral image classification (HSIC) is a key task in the field of remote sensing, but the complex nature of hyperspectral data poses a serious challenge to traditional methods. Although deep learning significantly improves classification performance through automatic feature extraction, manually designed network architectures suffer from issues such as dependence on expert experience and lack of flexibility. Neural architecture search (NAS) provides new ideas for HSIC through automated network structure optimization. This article systematically reviews the application progress of NAS in HSIC: firstly, the core components of NAS are analyzed, and the characteristics of various methods are compared from three aspects: search space, search strategy, and performance evaluation. Furthermore, the focus is on exploring NAS technology based on convolutional neural networks, covering 1D, 2D, and 3D convolutional architectures and their innovative integration with various technologies, revealing the advantages of NAS in HSIC. However, NAS still faces challenges such as high computing resource requirements and insufficient interpretability. This article systematically reviews the application of NAS in the field of HSIC for the first time, facilitating readers to quickly understand the development process of NAS in HSIC and the advantages and disadvantages of various technologies, proposing possible future research directions. Full article
(This article belongs to the Special Issue Integrating Deep Learning with Image Perception for Advanced Remote Sensing Applications)
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20 pages, 4791 KiB  
Article
Satellite-Measured Suspended Particulate Matter Flux and Freshwater Flux in the Yellow Sea and East China Sea
by Wei Shi and Menghua Wang
Remote Sens. 2025, 17(15), 2726; https://doi.org/10.3390/rs17152726 - 6 Aug 2025
Abstract
Traditionally, the surface suspended particulate matter (SPM) and freshwater fluxes have been computed using in situ SPM, salinity, and current measurements or through the numerical modeling. In this study, satellite-derived SPM concentration, ocean current, and sea surface salinity (SSS) are used to demonstrate [...] Read more.
Traditionally, the surface suspended particulate matter (SPM) and freshwater fluxes have been computed using in situ SPM, salinity, and current measurements or through the numerical modeling. In this study, satellite-derived SPM concentration, ocean current, and sea surface salinity (SSS) are used to demonstrate the capability to characterize and quantify the surface SPM flux and freshwater flux in the Yellow Sea (YS) and East China Sea (ECS). The different routes for SPM and freshwater to transport from the coastal region to the interior ECS are identified. The seasonal and interannual SPM and freshwater fluxes from the coastal region of the ECS are further characterized and quantified. The average SPM flux reaches ~0.3–0.4 g m−2 s−1 along the route. The SPM and the freshwater fluxes in the region show different seasonality. The intensified SPM flux from the ECS coast to the offshore in winter is one order higher than the SPM flux in summer, while the offshore freshwater flux peaks in summer and weakens significantly in winter. Particularly, we found that the SPM and SSS features in the ECS changed in response to the 2020 summer Yangtze River flood event. These spatial and temporal changes for SPM and SSS in the ECS in the 2020 summer and early autumn were attributed to the anomalous surface SPM and freshwater fluxes in the same period. Full article
(This article belongs to the Special Issue Remote Sensing for Ocean-Atmosphere Interaction Studies)
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28 pages, 9378 KiB  
Article
A Semantic Segmentation-Based GNSS Signal Occlusion Detection and Optimization Method
by Zhe Yue, Chenchen Sun, Xuerong Zhang, Chengkai Tang, Yuting Gao and Kezhao Li
Remote Sens. 2025, 17(15), 2725; https://doi.org/10.3390/rs17152725 - 6 Aug 2025
Abstract
Existing research fails to effectively address the problem of increased GNSS positioning errors caused by non-line-of-sight (NLOS) and line-of-sight (LOS) signal attenuation due to obstructions such as buildings and trees in complex urban environments. To address this issue, we dig into the environmental [...] Read more.
Existing research fails to effectively address the problem of increased GNSS positioning errors caused by non-line-of-sight (NLOS) and line-of-sight (LOS) signal attenuation due to obstructions such as buildings and trees in complex urban environments. To address this issue, we dig into the environmental perception perspective to propose a semantic segmentation-based GNSS signal occlusion detection and optimization method. The approach distinguishes between building and tree occlusions and adjusts signal weights accordingly to enhance positioning accuracy. First, a fisheye camera captures environmental imagery above the vehicle, which is then processed using deep learning to segment sky, tree, and building regions. Subsequently, satellite projections are mapped onto the segmented sky image to classify signal occlusions. Then, based on the type of obstruction, a dynamic weight optimization model is constructed to adjust the contribution of each satellite in the positioning solution, thereby enhancing the positioning accuracy of vehicle-navigation in urban environments. Finally, we construct a vehicle-mounted navigation system for experimentation. The experimental results demonstrate that the proposed method enhances accuracy by 16% and 10% compared to the existing GNSS/INS/Canny and GNSS/INS/Flood Fill methods, respectively, confirming its effectiveness in complex urban environments. Full article
(This article belongs to the Special Issue GNSS and Multi-Sensor Integrated Precise Positioning and Applications)
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20 pages, 11969 KiB  
Article
Spatiotemporal Variability of Cloud Parameters and Their Climatic Impacts over Central Asia Based on Multi-Source Satellite and ERA5 Data
by Xinrui Xie, Liyun Ma, Junqiang Yao and Weiyi Mao
Remote Sens. 2025, 17(15), 2724; https://doi.org/10.3390/rs17152724 - 6 Aug 2025
Abstract
As key components of the climate system, clouds exert a significant influence on the Earth’s radiation budget and hydrological cycle. However, studies focusing on cloud properties over Central Asia are still limited, and the impacts of cloud variability on regional temperature and precipitation [...] Read more.
As key components of the climate system, clouds exert a significant influence on the Earth’s radiation budget and hydrological cycle. However, studies focusing on cloud properties over Central Asia are still limited, and the impacts of cloud variability on regional temperature and precipitation remain poorly understood. This study uses reanalysis and multi-source remote sensing datasets to investigate the spatiotemporal characteristics of clouds and their influence on regional climate. The cloud cover increases from the southwest to the northeast, with mid and low-level clouds predominating in high-altitude regions. All clouds have shown a declining trend during 1981–2020. According to satellite data, the sharpest decline in total cloud cover occurs in summer, while reanalysis data show a more significant reduction in spring. In addition, cloud cover changes influence the local climate through radiative forcing mechanisms. Specifically, the weakening of shortwave reflective cooling and the enhancement of longwave heating of clouds collectively exacerbate surface warming. Meanwhile, precipitation is positively correlated with cloud cover, and its spatial distribution aligns with the cloud water path. The cloud phase composition in Central Asia is dominated by liquid water, accounting for over 40%, a microphysical characteristic that further impacts the regional hydrological cycle. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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29 pages, 55752 KiB  
Article
PolSAR-SFCGN: An End-to-End PolSAR Superpixel Fully Convolutional Generation Network
by Mengxuan Zhang, Jingyuan Shi, Long Liu, Wenbo Zhang, Jie Feng, Jin Zhu and Boce Chu
Remote Sens. 2025, 17(15), 2723; https://doi.org/10.3390/rs17152723 - 6 Aug 2025
Abstract
Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain the influence of the speckle noise to an extent. Most [...] Read more.
Polarimetric Synthetic Aperture Radar (PolSAR) image classification is one of the most important applications in remote sensing. The impressive superpixel generation approaches can improve the efficiency of the subsequent classification task and restrain the influence of the speckle noise to an extent. Most of the classical PolSAR superpixel generation approaches use the features extracted manually and even only consider the pseudocolor images. They do not make full use of polarimetric information and do not necessarily lead to good enough superpixels. The deep learning methods can extract effective deep features but they are difficult to combine with superpixel generation to achieve true end-to-end training. Addressing the above issues, this study proposes an end-to-end fully convolutional superpixel generation network for PolSAR images. It integrates the extraction of polarization information features and the generation of PolSAR superpixels into one step. PolSAR superpixels can be generated based on deep polarization feature extraction and need no traditional clustering process. Both the performance and efficiency of generations of PolSAR superpixels can be enhanced effectively. The experimental results on various PolSAR datasets show that the proposed method can achieve impressive superpixel segmentation by fitting the real boundaries of different types of ground objects effectively and efficiently. It can achieve excellent classification performance by connecting a very simple classification network, which is helpful to improve the efficiency of the subsequent PolSAR image classification tasks. Full article
(This article belongs to the Special Issue Object Detection and Information Extraction Based on Remote Sensing Imagery (Second Edition))
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26 pages, 14813 KiB  
Article
Application and Comparison of Satellite-Derived Sea Surface Temperature Gradients to Identify Seasonal and Interannual Variability off the California Coast: Preliminary Results and Future Perspectives
by Jorge Vazquez-Cuervo, Marisol García-Reyes, David S. Wethey, Daniele Ciani and Jose Gomez-Valdes
Remote Sens. 2025, 17(15), 2722; https://doi.org/10.3390/rs17152722 - 6 Aug 2025
Abstract
The application of satellite-derived sea surface temperature in coastal regions is critical for resolving the dynamics of frontal features and coastal upwelling. Here, we examine and compare sea surface temperature (SST) gradients derived from two satellite products, the Multi-Scale Ultra-High Resolution SST Product [...] Read more.
The application of satellite-derived sea surface temperature in coastal regions is critical for resolving the dynamics of frontal features and coastal upwelling. Here, we examine and compare sea surface temperature (SST) gradients derived from two satellite products, the Multi-Scale Ultra-High Resolution SST Product (MUR, 0.01° grid scale) and the Operational SST and Ice Analysis (OSTIA, 0.05° grid scale), available through the Group for High Resolution SST (GHRSST). Both products show similar seasonal variability, with maxima occurring in the summer time frame. Additionally, both products show an increasing trend of SST gradients near the coast. However, differences exist between the two products (maximum gradient intensities were around 0.11 and 0.06 °C/km for OSTIA and MUR, respectively). The potential contributions of both cloud cover and the collocation of the MUR SST onto the OSTIA SST grid product to these differences were examined. Spectra and coherences were examined at two specific latitudes along the coast where upwelling can occur. A major conclusion is that future work needs to focus on cloud cover and its impact on the derivation of SST in coastal regions. Future comparisons also need to apply collocation methodologies that maintain, as much as possible, the spatial variability of the high-resolution product. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Oceanography: Prospects and Challenges (3rd Edition))
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20 pages, 2104 KiB  
Article
Landscape Heterogeneity and Transition Drive Wildfire Frequency in the Central Zone of Chile
by Mariam Valladares-Castellanos, Guofan Shao and Douglass F. Jacobs
Remote Sens. 2025, 17(15), 2721; https://doi.org/10.3390/rs17152721 - 6 Aug 2025
Abstract
Wildfire regimes are closely linked to changes in landscape structure, yet the influence of accelerated land use transitions on fire activity remains poorly understood, particularly in rapidly transforming regions like central Chile. Although land use change has been extensively documented in the country, [...] Read more.
Wildfire regimes are closely linked to changes in landscape structure, yet the influence of accelerated land use transitions on fire activity remains poorly understood, particularly in rapidly transforming regions like central Chile. Although land use change has been extensively documented in the country, the specific role of the speed, extent, and spatial configuration of these transitions in shaping fire dynamics requires further investigation. To address this gap, we examined how landscape transitions influence fire frequency in central Chile, a region experiencing rapid land use change and heightened fire activity. Using multi-temporal remote sensing data, we quantified land use transitions, calculated landscape metrics to describe their spatial characteristics, and applied intensity analysis to assess their relationship with fire frequency changes. Our results show that accelerated landscape transitions significantly increased fire frequency, particularly in areas affected by forest plantation rotations, new forest establishment, and urban expansion, with changes exceeding uniform intensity expectations. Regional variations were evident: In the more densely populated northern areas, increased fire frequency was primarily linked to urban development and deforestation, while in the more rural southern regions, forest plantation cycles played a dominant role. Areas with a high number of large forest patches were especially prone to fire frequency increases. These findings demonstrate that both the speed and spatial configuration of landscape transitions are critical drivers of wildfire activity. By identifying the specific land use changes and landscape characteristics that amplify fire risks, this study provides valuable knowledge to inform fire risk reduction, landscape management, and urban planning in Chile and other fire-prone regions undergoing rapid transformation. Full article
(This article belongs to the Special Issue Remote Sensing for Digital Twins in Forestry: Applications and Future Perspectives)
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21 pages, 4909 KiB  
Article
Rapid 3D Camera Calibration for Large-Scale Structural Monitoring
by Fabio Bottalico, Nicholas A. Valente, Christopher Niezrecki, Kshitij Jerath, Yan Luo and Alessandro Sabato
Remote Sens. 2025, 17(15), 2720; https://doi.org/10.3390/rs17152720 - 6 Aug 2025
Abstract
Computer vision techniques such as three-dimensional digital image correlation (3D-DIC) and three-dimensional point tracking (3D-PT) have demonstrated broad applicability for monitoring the conditions of large-scale engineering systems by reconstructing and tracking dynamic point clouds corresponding to the surface of a structure. Accurate stereophotogrammetry [...] Read more.
Computer vision techniques such as three-dimensional digital image correlation (3D-DIC) and three-dimensional point tracking (3D-PT) have demonstrated broad applicability for monitoring the conditions of large-scale engineering systems by reconstructing and tracking dynamic point clouds corresponding to the surface of a structure. Accurate stereophotogrammetry measurements require the stereo cameras to be calibrated to determine their intrinsic and extrinsic parameters by capturing multiple images of a calibration object. This image-based approach becomes cumbersome and time-consuming as the size of the tested object increases. To streamline the calibration and make it scale-insensitive, a multi-sensor system embedding inertial measurement units and a laser sensor is developed to compute the extrinsic parameters of the stereo cameras. In this research, the accuracy of the proposed sensor-based calibration method in performing stereophotogrammetry is validated experimentally and compared with traditional approaches. Tests conducted at various scales reveal that the proposed sensor-based calibration enables reconstructing both static and dynamic point clouds, measuring displacements with an accuracy higher than 95% compared to image-based traditional calibration, while being up to an order of magnitude faster and easier to deploy. The novel approach has broad applications for making static, dynamic, and deformation measurements to transform how large-scale structural health monitoring can be performed. Full article
(This article belongs to the Special Issue New Perspectives on 3D Point Cloud (Third Edition))
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15 pages, 7923 KiB  
Technical Note
Recent Active Wildland Fires Related to Rossby Wave Breaking (RWB) in Alaska
by Hiroshi Hayasaka
Remote Sens. 2025, 17(15), 2719; https://doi.org/10.3390/rs17152719 - 6 Aug 2025
Abstract
Wildland fires are a common and destructive natural disaster in Alaska. Recent active fires in Alaska were assessed and analysed for their associated synoptic-scale climatic conditions in this study. Hotspot (HS) data from satellite observations over the past 20 years since 2004 (total [...] Read more.
Wildland fires are a common and destructive natural disaster in Alaska. Recent active fires in Alaska were assessed and analysed for their associated synoptic-scale climatic conditions in this study. Hotspot (HS) data from satellite observations over the past 20 years since 2004 (total number of HS = 300,988) were used to identify active fire-periods, and the occurrence of Rossby wave breaking (RWB) was examined using various weather maps. Analysis results show that there are 13 active fire-periods of which 7 active fire-periods are related to RWB. The total number of HSs during the seven RWB-related fire-periods was 164,422, indicating that about half (54.6%) of the recent fires in Alaska occurred under fire weather conditions related to RWB. During the RWB-related fire-periods, two hotspot peaks with different wind directions occurred. At the first hotspot peak, southwesterly wind blew from high-pressure systems in the Gulf of Alaska. In the second hotspot peak, the Beaufort Sea High (BSH) supplied strong easterly wind into Interior Alaska. It was suggested that changes in wind direction during active fire-period and continuously blowing winds from BSH may affect fire propagation. It is hoped that this study will stimulate further research into active fires related to RWBs in Alaska. Full article
(This article belongs to the Special Issue Feature Papers of Section “Atmospheric Remote Sensing” (Second Edition))
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21 pages, 7718 KiB  
Article
Monitoring the Early Growth of Pinus and Eucalyptus Plantations Using a Planet NICFI-Based Canopy Height Model: A Case Study in Riqueza, Brazil
by Fabien H. Wagner, Fábio Marcelo Breunig, Rafaelo Balbinot, Emanuel Araújo Silva, Messias Carneiro Soares, Marco Antonio Kramm, Mayumi C. M. Hirye, Griffin Carter, Ricardo Dalagnol, Stephen C. Hagen and Sassan Saatchi
Remote Sens. 2025, 17(15), 2718; https://doi.org/10.3390/rs17152718 - 6 Aug 2025
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Abstract
Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address [...] Read more.
Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article
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