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Volume 17
Issue 9
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Remote Sens., Volume 17, Issue 9 (May-1 2025) – 175 articles

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24 pages, 8013 KiB  
Article
Assessing the Combined Impact of Land Surface Temperature and Droughts to Heatwaves over Europe Between 2003 and 2023
by Foteini Karinou, Ilias Agathangelidis and Constantinos Cartalis
Remote Sens. 2025, 17(9), 1655; https://doi.org/10.3390/rs17091655 - 7 May 2025
Viewed by 202
Abstract
The increasing frequency, intensity, and duration of heatwaves and droughts pose significant societal and environmental challenges across Europe. This study analyzes land surface temperature (LST) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2003 and 2023 to identify thermal anomalies associated with [...] Read more.
The increasing frequency, intensity, and duration of heatwaves and droughts pose significant societal and environmental challenges across Europe. This study analyzes land surface temperature (LST) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) between 2003 and 2023 to identify thermal anomalies associated with heatwaves. Additionally, this study examines the role of different land cover types in modulating heatwave impacts, employing turbulent flux observations from micrometeorological towers. The interaction between heatwaves and droughts is further explored using the Standardized Precipitation Evapotranspiration Index (SPEI) and soil moisture data, highlighting the amplifying role of water stress through land–atmosphere feedbacks. The results reveal a statistically significant upward trend in LST-derived thermal anomalies, with the 2022 heatwave identified as the most extreme event, when approximately 75% of Europe experienced strong positive anomalies. On average, 91% of heatwave episodes identified in reanalysis-based air temperature records coincided with LST-defined anomaly events, confirming LST as a robust proxy for heatwave detection. Flux tower observations show that, during heatwaves, evergreen coniferous and mixed forests predominantly enhance sensible heat fluxes (mean anomalies during midday of 74 W/m2 and 62 W/m2, respectively), while grasslands exhibit increased latent heat flux (89 W/m2). Notably, under extreme compound heat–drought conditions, this pattern reverses for grassed sites due to rapid soil moisture depletion. Overall, the findings underscore the combined influence of surface temperature and drought in driving extreme heat events and introduce a novel, multi-source approach that integrates satellite, reanalysis, and ground-based data to assess heatwave dynamics across scales. Full article
(This article belongs to the Special Issue Remote Sensing of Land Surface Temperature: Retrieval, Modeling, and Applications)
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31 pages, 5067 KiB  
Review
Passive Microwave Imagers, Their Applications, and Benefits: A Review
by Nazak Rouzegari, Mohammad Bolboli Zadeh, Claudia Jimenez Arellano, Vesta Afzali Gorooh, Phu Nguyen, Huan Meng, Ralph R. Ferraro, Satya Kalluri, Soroosh Sorooshian and Kuolin Hsu
Remote Sens. 2025, 17(9), 1654; https://doi.org/10.3390/rs17091654 - 7 May 2025
Viewed by 138
Abstract
Passive Microwave Imagers (PMWIs) aboard meteorological satellites have been instrumental in advancing the understanding of Earth’s atmospheric and surface processes, providing invaluable data for weather forecasting, climate monitoring, and environmental research. This review examines the relevance, applications, and benefits of PMWI data, focusing [...] Read more.
Passive Microwave Imagers (PMWIs) aboard meteorological satellites have been instrumental in advancing the understanding of Earth’s atmospheric and surface processes, providing invaluable data for weather forecasting, climate monitoring, and environmental research. This review examines the relevance, applications, and benefits of PMWI data, focusing on their practical use and benefits to society rather than the specific techniques or algorithms involved in data processing. Specifically, it assesses the impact of PMWI data on Tropical Cyclone (TC) intensity and structure, global precipitation and extreme events, flood prediction, the effectiveness of tropical storm and hurricane watches, fire severity and carbon emissions, weather forecasting, and drought mitigation. Additionally, it highlights the importance of PMWIs in hydrometeorological and real-time applications, emphasizing their current usage and potential for improvement. Key recommendations from users include expanding satellite networks for more frequent global coverage, reducing data latency, and enhancing resolution to improve forecasting accuracy. Despite the notable benefits, challenges remain, such as a lack of direct research linking PMWI data to broader societal outcomes, the time-intensive process of correlating PMWI use with measurable societal impacts, and the indirect links between PMWI and improved weather forecasting and disaster management. This study provides insights into the effectiveness and limitations of PMWI data, stressing the importance of continued research and development to maximize their contribution to disaster preparedness, climate resilience, and global weather forecasting. Full article
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20 pages, 10414 KiB  
Article
MambaMeshSeg-Net: A Large-Scale Urban Mesh Semantic Segmentation Method Using a State Space Model with a Hybrid Scanning Strategy
by Wenjie Zi, Hao Chen, Jun Li and Jiangjiang Wu
Remote Sens. 2025, 17(9), 1653; https://doi.org/10.3390/rs17091653 - 7 May 2025
Viewed by 136
Abstract
Semantic segmentation of urban meshes plays an increasingly crucial role in the analysis and understanding of 3D environments. Most existing large-scale urban mesh semantic segmentation methods focus on integrating multi-scale local features but struggle to model long-range dependencies across facets effectively. Furthermore, owing [...] Read more.
Semantic segmentation of urban meshes plays an increasingly crucial role in the analysis and understanding of 3D environments. Most existing large-scale urban mesh semantic segmentation methods focus on integrating multi-scale local features but struggle to model long-range dependencies across facets effectively. Furthermore, owing to high computational complexity or excessive pre-processing operations, these methods lack the capability for the efficient semantic segmentation of large-scale urban meshes. Inspired by Mamba, we propose MambaMeshSeg-Net, a novel 3D urban mesh semantic segmentation method based on the State Space Model (SSM). The proposed method incorporates a hybrid scanning strategy that adaptively scans 3D urban meshes to extract long-range dependencies across facets, enhancing semantic segmentation performance. Moreover, our model exhibits faster performance in both inference and pre-processing compared to other mainstream models. In comparison with existing state-of-the-art (SOTA) methods, our model demonstrates superior performance on two widely utilized open urban mesh datasets. Full article
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21 pages, 8847 KiB  
Article
Characteristics of Eddy Dissipation Rates in Atmosphere Boundary Layer Using Doppler Lidar
by Yufei Chu, Guo Lin, Min Deng and Zhien Wang
Remote Sens. 2025, 17(9), 1652; https://doi.org/10.3390/rs17091652 - 7 May 2025
Viewed by 78
Abstract
The eddy dissipation rate (EDR, or turbulence dissipation rate) is a crucial parameter in the study of the atmospheric boundary layer (ABL). However, the existing Doppler lidar-based estimates of EDR seldom offer long-term comparisons that span the entire ABL. Building upon prior research [...] Read more.
The eddy dissipation rate (EDR, or turbulence dissipation rate) is a crucial parameter in the study of the atmospheric boundary layer (ABL). However, the existing Doppler lidar-based estimates of EDR seldom offer long-term comparisons that span the entire ABL. Building upon prior research utilizing Doppler lidar wind-field data, we optimized the EDR retrieval algorithm using a genetic adaptive approach. The newly developed algorithm demonstrates enhanced accuracy in EDR estimation. The daily evolution of EDR reveals a distinct diurnal pattern in its variation. A detailed four consecutive days study of turbulence generated via low-level jets (LLJs) indicated that EDR driven by heat flux (~10−2 m2/s3) is significantly stronger than that produced through wind shear (~10−3 m2/s3). Subsequently, we examined seasonal variations in EDR at different mixing layer heights (MLH, Zi): elevated EDR values in summer (~7 × 10−3 m2/s3 at 0.1Zi) contrasted with reduced levels in winter (~6 × 10−4 m2/s3 at 0.1Zi). In the early morning, EDR decreases with height for 1 magnitude, while in later stages, it remains relatively stable within 0.1 order of magnitude across 0.1Zi to 0.9Zi. Notably, the EDR during DJF exceeds that of MAM and SON in the afternoon. This suggests that ML turbulence is not solely dependent on surface fluxes (SHF + LHF) but may also be influenced by MLH. A lower MLH (smaller volume), even with reduced surface fluxes, could potentially result in a stronger EDR. Finally, we compared the evolution of the EDR and MLH in the boundary layer using Doppler lidar data from ARM sites and the PBL (Planetary Boundary Layer) Moving Active Profiling System (PBLMAPS) Airborne Doppler Lidar (ADL). The results show that the vertical wind data exhibit strong consistency (R = 0.96) when the ADL is positioned near ARM Southern Great Plains (SGP) sites C1 or E37. The ADL’s mobility and flexibility provide significant advantages for future field experiments, particularly in challenging environments such as mountainous or complex terrains. This study not only highlights the potential of utilizing Doppler lidar alone for EDR calculations but also extensively explores the development patterns of EDR within the ABL. Full article
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17 pages, 4824 KiB  
Article
Snow Cover Trends in the Chilean Andes Derived from 39 Years of Landsat Data and a Projection for the Year 2050
by Andreas J. Dietz, Jonas Köhler, Laura Obrecht, Sebastian Rößler, Celia A. Baumhoer, Francisco Cereceda-Balic and Freddy Saavedra
Remote Sens. 2025, 17(9), 1651; https://doi.org/10.3390/rs17091651 - 7 May 2025
Viewed by 137
Abstract
Snow cover is an important freshwater source in many mountain ranges around the world and is heavily affected by climate change, often leading to reduced overall snow cover availability and duration as well as shifts in seasonality. To monitor these changes and long-term [...] Read more.
Snow cover is an important freshwater source in many mountain ranges around the world and is heavily affected by climate change, often leading to reduced overall snow cover availability and duration as well as shifts in seasonality. To monitor these changes and long-term trends, the analysis of remote sensing is a commonly used tool, as data are available consistently and for long time series. In this study we acquired and processed the whole archive of available Landsat data between 1985 and 2024 for two catchments in the Chilean Andes, Aconcagua and Río Maipo, located in the Valparaíso and Santiago de Chile metropolitan regions, respectively. We generated monthly Snow Line Elevation (SLE) time series from the entire archive for both catchments and performed trend analyses on these time series. Strong positive long-term SLE change rates of 11.25 m per year for the Aconcagua catchment and 9.85 m to 15.65 m per year for the Río Maipo catchment were detected, indicating a decrease in snow cover as well as available freshwater from snowmelt. The projection to the year 2050 revealed a potential loss of snow covered area of up to 42% during summer months, with the SLE receding up to 231 m. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in the Monitoring of the Mountain Cryosphere)
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25 pages, 10432 KiB  
Article
PolyReg: Autoregressive Building Outline Regularization via Masked Attention Sequence Generation
by Longfei Cui, Chao Li, Xin Chen, Xiao Wang and Haizhong Qian
Remote Sens. 2025, 17(9), 1650; https://doi.org/10.3390/rs17091650 - 7 May 2025
Viewed by 86
Abstract
High-resolution remote sensing imagery has become the primary data source for obtaining building information. Automatically extracting regularized building outline polygon vectors is crucial for improving vector mapping efficiency and geographic information system applications, but existing deep learning methods struggle to simultaneously achieve accurate [...] Read more.
High-resolution remote sensing imagery has become the primary data source for obtaining building information. Automatically extracting regularized building outline polygon vectors is crucial for improving vector mapping efficiency and geographic information system applications, but existing deep learning methods struggle to simultaneously achieve accurate detection, high pixel-level coverage, and geometric regularity. This paper proposes a novel two-stage building outline extraction method. In the first stage, the SegFormer model is used to extract image features, effectively capturing global context information. In the second stage, a polygon outline regularization model (PolyReg) based on a Masked Attention Encoder is innovatively introduced. The PolyReg model draws on the sequence generation idea from natural language processing, transforming the outline regularization task into a sequence generation problem. Through a cleverly designed self-attention mask matrix, it achieves an autoregressive output of regularized building outline coordinates, eliminating the need for cumbersome post-processing steps. Experimental results show that on the Inria Aerial Image Labeling Dataset, compared with traditional methods and existing deep learning methods, the proposed method demonstrates significant advantages in metrics such as IoU, C-IoU, and Hausdorff distance. It effectively improves the regularity and geometric accuracy of building outlines while maintaining high pixel-level coverage. Full article
(This article belongs to the Section Remote Sensing for Geospatial Science)
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31 pages, 9022 KiB  
Article
An Analysis of Powder, Hard-Packed, and Wet Snow in High Mountain Areas Based on SAR, Optical Data, and In Situ Data
by Andrey Stoyanov, Temenuzhka Spasova and Daniela Avetisyan
Remote Sens. 2025, 17(9), 1649; https://doi.org/10.3390/rs17091649 - 7 May 2025
Viewed by 146
Abstract
The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study [...] Read more.
The following study presents the results obtained from a comparative analysis of dry (powder and hard snow) and wet snow based on satellite data and in situ data methods for monitoring in the high mountain belt of Bulgaria. The aim of the study is to analyze the effectiveness of different spectral indices based on satellite data from Synthetic Aperture Radar (SAR), high-resolution (HR) imagery, and spectrometer data for assessing the state and dynamics of the snow cover. The methods studied and the results obtained were validated by instrument-based field observations, with instruments using thermal imaging cameras, spectrometer measurements, ground control points, and HR imagery. Satellite data offer an ever-widening view of trends in snow distribution over time. All these data combined provide a detailed picture of surface temperature and snow properties, which are crucial for understanding snowmelt processes and the energy balance in the high-altitude belt. The findings suggest that a multi-method approach, utilizing the combined advantages of SAR satellite data, offers the most comprehensive and accurate framework for satellite-based snow cover monitoring in the high mountain regions of Bulgaria, such as Rila Mountain. This integrative strategy not only improves the precision of snow cover estimates but can also support many water resource-related studies, such as snowmelt runoff studies, snow avalanche modeling, and better-informed decisions in the management and maintenance of winter tourism resorts. Full article
(This article belongs to the Special Issue Mapping and Monitoring Glacial and Snow Changes through Remote Sensing)
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25 pages, 11467 KiB  
Article
Assessing Coincidence of Satellite Acquisitions and Flood Events to Predict Suitability for Flood Map Synthesis
by Lyle Prince, Riley C. Hales, Kel N. Markert, E. James Nelson, Gustavious P. Williams, Daniel P. Ames, Hyongki Lee and Amirhossein Rostami
Remote Sens. 2025, 17(9), 1648; https://doi.org/10.3390/rs17091648 - 7 May 2025
Viewed by 99
Abstract
Flooding is a global problem that impacts people, communities, and governments every year. A better understanding of flooding in an area can enable an improved emergency response before a flood hits. Flood maps are a crucial tool to translate what, for most, is [...] Read more.
Flooding is a global problem that impacts people, communities, and governments every year. A better understanding of flooding in an area can enable an improved emergency response before a flood hits. Flood maps are a crucial tool to translate what, for most, is an abstract streamflow into a more understandable and actionable representation of who and what is at risk. Satellite-based flood maps are a useful tool that has potential global applications. We developed methods to determine areas that are suitable for generating satellite-based synthetic flood maps. For our processes, we used Forecasting Inundation Extents using REOF analysis (FIER), a data-driven method of synthesizing flood maps by correlating extracted spatial and temporal patterns from satellite imagery with historical hydrological variables. To overcome the limitation of only using places where gauges are installed, we used large-scale hydrological models, namely the National Water Model (NWM) and the GEOGLOWS Streamflow Model, to provide simulated retrospective streamflow data to train our model. We evaluated locations where both optical and radar imagery would be suitable for creating these models. The procedures we developed and the results that we obtained are potentially transferable to many satellite data sources and methods of model generation. Full article
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14 pages, 9838 KiB  
Technical Note
The Identification of Exposed Beachrocks on South China Sea Islands Based on UAV Images
by Chuang Liu, Wei Gao, Junhui Xing and Wei Gong
Remote Sens. 2025, 17(9), 1647; https://doi.org/10.3390/rs17091647 - 7 May 2025
Viewed by 74
Abstract
Beachrocks are common coastal sedimentary rocks in tropical and subtropical seas. They are widely spread especially in islands and coastal areas. These rocks are important for island geological evolution research. Research on beachrocks aids in protecting island ecosystems and enhances islands’ ability to [...] Read more.
Beachrocks are common coastal sedimentary rocks in tropical and subtropical seas. They are widely spread especially in islands and coastal areas. These rocks are important for island geological evolution research. Research on beachrocks aids in protecting island ecosystems and enhances islands’ ability to prevent and mitigate damage from natural disasters. This study uses unmanned aerial vehicle (UAV) images and the U-Net model based on deep learning to identify beachrocks. To enhance identification accuracy, the efficient channel attention (ECA) mechanism was integrated, leading to improvements of 0.49% in overall accuracy, 1.41% in precision, 0.97% in recall, 1.10% in F1-score, and 2.09% in intersection over union (IoU) compared to the baseline U-Net model. The final results demonstrate that the model effectively identified beachrocks, achieving 97.47% accuracy, 93.27% precision, 94.73% recall, 93.95% F1-score, and 88.65% IoU. This study offers a valuable tool for island geological evolution research and supports the development of large-scale island conservation efforts. Full article
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42 pages, 6895 KiB  
Article
IceBench: A Benchmark for Deep-Learning-Based Sea-Ice Type Classification
by Samira Alkaee Taleghan, Andrew P. Barrett, Walter N. Meier and Farnoush Banaei-Kashani
Remote Sens. 2025, 17(9), 1646; https://doi.org/10.3390/rs17091646 - 6 May 2025
Viewed by 153
Abstract
Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are time-consuming, costly, and have inherent biases. Automating sea-ice type classification addresses these challenges by enabling faster, more consistent, [...] Read more.
Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are time-consuming, costly, and have inherent biases. Automating sea-ice type classification addresses these challenges by enabling faster, more consistent, and scalable analysis. While both traditional and deep-learning approaches have been explored, deep-learning models offer a promising direction for improving efficiency and consistency in sea-ice classification. However, the absence of a standardized benchmark and comparative study prevents a clear consensus on the best-performing models. To bridge this gap, we introduce IceBench, a comprehensive benchmarking framework for sea-ice type classification. Our key contributions are three-fold: First, we establish the IceBench benchmarking framework, which leverages the existing AI4Arctic Sea Ice Challenge Dataset as a standardized dataset, incorporates a comprehensive set of evaluation metrics, and includes representative models from the entire spectrum of sea-ice type-classification methods categorized in two distinct groups, namely pixel-based classification methods and patch-based classification methods. IceBench is open-source and allows for convenient integration and evaluation of other sea-ice type-classification methods, hence facilitating comparative evaluation of new methods and improving reproducibility in the field. Second, we conduct an in-depth comparative study on representative models to assess their strengths and limitations, providing insights for both practitioners and researchers. Third, we leverage IceBench for systematic experiments addressing key research questions on model transferability across seasons (time) and locations (space), data downsampling, and preprocessing strategies. By identifying the best-performing models under different conditions, IceBench serves as a valuable reference for future research and a robust benchmarking framework for the field. Full article
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19 pages, 5383 KiB  
Article
An Assembled Feature Attentive Algorithm for Automatic Detection of Waste Water Treatment Plants Based on Multiple Neural Networks
by Cong Li, Zhengchao Chen, Zhuonan Huang, Yue Shuai, Shaohua Wang, Xiangkun Qi and Jiayi Zheng
Remote Sens. 2025, 17(9), 1645; https://doi.org/10.3390/rs17091645 - 6 May 2025
Viewed by 185
Abstract
Wastewater treatment plants (WWTPs) play a vital role in controlling wastewater discharge and promoting recycling. Accurate WWTP identification and spatial analysis are crucial for environmental protection, urban planning, and sustainable development. However, the diverse shapes and scales of WWTPs and their key facilities [...] Read more.
Wastewater treatment plants (WWTPs) play a vital role in controlling wastewater discharge and promoting recycling. Accurate WWTP identification and spatial analysis are crucial for environmental protection, urban planning, and sustainable development. However, the diverse shapes and scales of WWTPs and their key facilities pose challenges for traditional detection methods. This study employs a Multi-Attention Network (MANet) for WWTP extraction, integrating channel and spatial feature attention. Additionally, a Global-Local Feature Modeling Network (GLFMN) is introduced to segment key facilities, specifically sedimentation and secondary sedimentation tanks. The approach is applied to Beijing, utilizing geographic data such as WWTP locations, treatment capacities, and surrounding residential and water distributions. Results indicate that MANet achieves 80.1% accuracy with a 90.4% recall rate, while GLFMN significantly improves the extraction of key facilities compared to traditional methods. The spatial analysis reveals WWTP distribution characteristics, offering insights into treatment capacity and geographic influences. These findings contribute to emission regulation, water quality supervision, and enterprise management of WWTPs in Beijing. This research provides a valuable reference for optimizing wastewater treatment infrastructure and supports decision-making in environmental governance and sustainable urban development. Full article
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28 pages, 5379 KiB  
Article
Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon
by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens and Zamir Libohova
Remote Sens. 2025, 17(9), 1644; https://doi.org/10.3390/rs17091644 - 6 May 2025
Viewed by 194
Abstract
Morro de Seis Lagos, a region in the Brazilian Amazon, contains a small (less than 1%) formation of siderite carbonatites which is considered to be one of the world’s largest niobium reserves. This highly weathered geological and pedological occurrence makes the site ideal [...] Read more.
Morro de Seis Lagos, a region in the Brazilian Amazon, contains a small (less than 1%) formation of siderite carbonatites which is considered to be one of the world’s largest niobium reserves. This highly weathered geological and pedological occurrence makes the site ideal for studying the pedogenetic process of lateralization and the spatial variability of chemical elements. The aim of this study was to investigate the influences of various sampling combinations (scenarios) derived from three sampling designs on the spatial predictions associated with chemical compounds (Al2O3, Fe2O3, MnO, Nb2O5, TiO2, and SiO2), using multiple machine learning algorithms combined with remotely sensed imagery. The dataset comprised 341 samples from the Geological Survey of Brazil (CPRM). Covariates included remotely sensed data collected from Sentinel-2 MSI, Sentinel-1A, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and topographic attributes were calculated from a 20 m digital elevation model derived from hydrologic data (HC-DEM). The machine learning algorithms (Generalized Linear Models with Elastic Net Regularization (GLMNET), Nearest Neighbors (KNN), Neural Network (NNET), Random Forest (RF) and Support Vector Machine (SVMRadial) were used in combination with covariates and measured elements at point locations to spatially map the concentrations of these chemical elements. The optimal covariates for modeling were selected using Recursive Feature Elimination (RFE), processing 10 runs for each chemical element. The RF, SVMRadial, and KNN models performed best, followed by the models from the Neural Network group (NNET). The sampling scenarios were not significantly different, based on root mean square error (F = 1.7; p-value = 0.15) and mean absolute error (F = 0.4; p-value = 0.79); however, significant differences were observed in the coefficient of determination (F = 41.2; p-value < 0.00) across all models. Overall, the models performed poorly for all elements, with R2 ranging from 0.07 to 0.27, regardless of sampling scenario (F = 1.6; p-value = 0.08). Relatively, RF, GLMET, and KNN performed better, compared to other models. The terrain attributes were significantly more successful as to the spatial predictions of the elements contained in laterites than were the remote sensing spectral indices, likely due to the fact that the underlying spatial structures of the two formations (laterite and talus) occur at different elevations. Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition))
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23 pages, 5331 KiB  
Article
Unveiling the Effects of Crop Rotation on Cropland Soil pH Mapping: A Remote Sensing-Based Soil Sample Grouping Strategy
by Yuan Liu, Songchao Chen, Ge Shen, Cheng Chen, Zejiang Cai, Ji Zhu, Xia Zhang, Guofei Shang, Qingbo Zhou, Sonoko Dorothea Bellingrath-Kimura, Qiangyi Yu and Wenbin Wu
Remote Sens. 2025, 17(9), 1643; https://doi.org/10.3390/rs17091643 - 6 May 2025
Viewed by 172
Abstract
Crop rotation affects soil pH by disturbing H+ production and consumption within soil–crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil [...] Read more.
Crop rotation affects soil pH by disturbing H+ production and consumption within soil–crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil pH prediction, because the spatial variability in soil pH is lower and the way crop rotation influences pH is different. To quantify the extent to which crop rotation improves soil pH mapping, we introduced the strategy of grouping soil samples by crop rotation and modeling separately. We chose a typical multiple-cropping region suffering soil acidification in Southern China, where the complex crop rotation was mapped by Sentinel-1/2 time series and a legend featuring three main systems (i.e., paddy, vegetable, and orchard) and nine subsystems. This crop rotation map was then combined with other variables to derive multiple combinations and predict soil pH. Based on the best combination, we further assessed the grouping strategy. The results showed that simply incorporating crop rotation in one joint model was useful but could not obtain the expected accuracy, with a root mean squared error (RMSE) of 0.66 and an R2 of 0.36. The individual statistical accuracies were quite low for the vegetable and orchard rotations, with an RMSE of 0.77/0.70 and an R2 of 0.30/−0.04. Grouping soil samples by crop rotation significantly enhanced soil pH predictability with a decrease in the RMSE of 15% and an increase in the R2 of 53%. The results proved that grouping by crop rotation can fit and optimize the sub-models after learning the characteristics of the rotation subsamples, offering a way for improving digital mapping of soil pH over heterogeneous agricultural landscapes. Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition))
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26 pages, 9328 KiB  
Article
Global Optical and SAR Image Registration Method Based on Local Distortion Division
by Bangjie Li, Dongdong Guan, Yuzhen Xie, Xiaolong Zheng, Zhengsheng Chen, Lefei Pan, Weiheng Zhao and Deliang Xiang
Remote Sens. 2025, 17(9), 1642; https://doi.org/10.3390/rs17091642 - 6 May 2025
Viewed by 146
Abstract
Variations in terrain elevation cause images acquired under different imaging modalities to deviate from a linear mapping relationship. This effect is particularly pronounced between optical and SAR images, where the range-based imaging mechanism of SAR sensors leads to significant local geometric distortions, such [...] Read more.
Variations in terrain elevation cause images acquired under different imaging modalities to deviate from a linear mapping relationship. This effect is particularly pronounced between optical and SAR images, where the range-based imaging mechanism of SAR sensors leads to significant local geometric distortions, such as perspective shrinkage and occlusion. As a result, it becomes difficult to represent the spatial correspondence between optical and SAR images using a single geometric model. To address this challenge, we propose a global optical-SAR image registration method that leverages local distortion characteristics. Specifically, we introduce a Superpixel-based Local Distortion Division (SLDD) method, which defines superpixel region features and segments the image into local distortion and normal regions by computing the Mahalanobis distance between superpixel features. We further design a Multi-Feature Fusion Capsule Network (MFFCN) that integrates shallow salient features with deep structural details, reconstructing the dimensions of digital capsules to generate feature descriptors encompassing texture, phase, structure, and amplitude information. This design effectively mitigates the information loss and feature degradation problems caused by pooling operations in conventional convolutional neural networks (CNNs). Additionally, a hard negative mining loss is incorporated to further enhance feature discriminability. Feature descriptors are extracted separately from regions with different distortion levels, and corresponding transformation models are built for local registration. Finally, the local registration results are fused to generate a globally aligned image. Experimental results on public datasets demonstrate that the proposed method achieves superior performance over state-of-the-art (SOTA) approaches in terms of Root Mean Squared Error (RMSE), Correct Match Number (CMN), Distribution of Matched Points (Scat), Edge Fidelity (EF), and overall visual quality. Full article
(This article belongs to the Special Issue Temporal and Spatial Analysis of Multi-Source Remote Sensing Images)
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28 pages, 11617 KiB  
Article
PS-YOLO: A Lighter and Faster Network for UAV Object Detection
by Han Zhong, Yan Zhang, Zhiguang Shi, Yu Zhang and Liang Zhao
Remote Sens. 2025, 17(9), 1641; https://doi.org/10.3390/rs17091641 - 6 May 2025
Viewed by 267
Abstract
The operational environment of UAVs poses unique challenges for object detection compared to conventional methods. When UAVs capture remote sensing images from elevated altitudes, objects often appear minuscule and can be easily obscured by complex backgrounds. This increases the likelihood of false positives [...] Read more.
The operational environment of UAVs poses unique challenges for object detection compared to conventional methods. When UAVs capture remote sensing images from elevated altitudes, objects often appear minuscule and can be easily obscured by complex backgrounds. This increases the likelihood of false positives and missed detections, thereby complicating the detection process. Furthermore, the hardware resources available on UAV platforms are typically highly constrained. To meet deployment requirements, researchers often must compromise some detection accuracy in favor of a more lightweight model. To address these challenges, we propose PS-YOLO, a fast and precise network specifically designed for UAV-based object detection. In the proposed network, we first design a lightweight backbone based on partial convolution. Then, we introduce a more efficient neck network called FasterBIFFPN to replace the original PAFPN, enabling more effective multi-scale feature fusion. Finally, we propose the GSCD head. GSCD employs shared convolutions to enhance the network’s ability to learn common features across objects of different scales and introduces Normalized Gaussian Wasserstein Distance Loss (NWDLoss) to improve detection accuracy. This detection head effectively increases inference speed without significantly increasing parameter counts. The proposed PS-YOLO is validated on the Visdrone2019 dataset, and the results demonstrate that PS-YOLO provides a 2% improvement in precision, 0.5% improvement in recall, 1.3% improvement in mean average precision (mAP), 41.3% reduction in parameter counts, 6.1% reduction in computational cost, and 26.73 FPS improvement in inference speed compared to the benchmark model YOLOv11-s. Full article
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20 pages, 5063 KiB  
Article
Spatiotemporal Changes in China’s Mangroves and Their Possible Impacts on Coastal Water Quality from 1998 to 2018
by Jingwen Ren, Gang Yang, Weiwei Sun, Ke Huang, Chengqi Lu, Wenrui Yu, Xinyi Zhang, Binjie Chen, Weiwei Liu and Tian Feng
Remote Sens. 2025, 17(9), 1640; https://doi.org/10.3390/rs17091640 - 6 May 2025
Viewed by 183
Abstract
Mangroves serve as critical transitional ecosystems between land and sea. However, their large-scale possible impacts on coastal water quality have not been investigated. This study systematically examined the possible impacts of mangrove dynamics on coastal water quality in China over a 20-year period [...] Read more.
Mangroves serve as critical transitional ecosystems between land and sea. However, their large-scale possible impacts on coastal water quality have not been investigated. This study systematically examined the possible impacts of mangrove dynamics on coastal water quality in China over a 20-year period (1998–2018). Theil–Sen trend analysis and Mann-Kendall tests were employed to assess long-term trends of mangrove area and four water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), particulate attenuation coefficient at 660 nm (Cp660), and seawater transparency (Secchi disk depth, SDD). Partial correlation analysis and convergent cross-mapping (CCM) techniques were applied to evaluate the relationships between mangroves and water quality parameters, while a factor detector was used to quantify the specific contribution of mangroves to water quality improvement. The results revealed the following: (1) a significant nationwide expansion of mangroves, particularly after 2005, accompanied by accelerated recovery rates; (2) notable variations in water quality indicators, with SDD and CDOM experiencing degradation, while Chl-a and Cp660 showed varying degrees of improvement; (3) statistical evidence indicating that mangrove expansion was negatively partially correlated with Chl-a concentrations, and had moderate effects on CDOM, Cp660, and SDD. These findings highlight the measurable role of mangroves in improving coastal water quality at a national scale, provide a robust scientific basis for integrated coastal zone management, and underscore the need for further investigation into the underlying mechanisms, with comprehensive consideration of the dynamic impacts of climate change and anthropogenic activities. Full article
(This article belongs to the Section Remote Sensing for Geospatial Science)
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24 pages, 9257 KiB  
Article
Mapping of Monodominant Gilbertiodendron dewevrei Forest Across the Western Congo Basin Using Sentinel-2 Imagery
by Ellen Heimpel, David J. Harris, Josérald Mamboueni, David Morgan, Crickette Sanz and Antje Ahrends
Remote Sens. 2025, 17(9), 1639; https://doi.org/10.3390/rs17091639 - 6 May 2025
Viewed by 199
Abstract
Tropical rainforests are complex mosaics of different forests types, each with its own biodiversity and structure. Efforts to characterize and map diversity and composition of tropical forests are vital at both local and larger scales in order to improve conservation strategies and accurately [...] Read more.
Tropical rainforests are complex mosaics of different forests types, each with its own biodiversity and structure. Efforts to characterize and map diversity and composition of tropical forests are vital at both local and larger scales in order to improve conservation strategies and accurately monitor anthropogenic threats. However, despite advances in remote sensing, classifying and mapping forest types remains a significant challenge and remotely sensed classifications in the tropics often treat forests as a single category. Here, we used Sentinel-2 data, and a high-quality ground reference dataset, to map monodominant Gilbertiodendron dewevrei forest, a unique forest type in central Africa. We used a random forest classifier, and spectral, vegetation, and textural indices, to map G. dewevrei forest across the Sangha Trinational, a network of national parks in central Africa. The overall accuracy of our classification was 83% when evaluated against an independently sampled reference test dataset, successfully distinguishing this monodominant forest from the spectrally similar terre firme mixed forest present throughout much of the study area. The gray level co-occurrence matrix (GLCM) textural metrics proved the most important factors for distinguishing G. dewevrei forest, due to the homogenous canopy texture created by this monodominant species. In conclusion, our study illustrates that freely available Sentinel-2 data hold promise for mapping distinct forest types in tropical forests, particularly when they exhibit structural and textural differences, as seen in monodominant and mixed forests, and provided that high-quality ground reference data are available. Full article
(This article belongs to the Special Issue New Methods and Applications in Remote Sensing of Tropical Forests)
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16 pages, 5610 KiB  
Article
Influence of Digital Elevation Model Resolution on the Normalized Stream Length–Gradient Index in Intraplate Regions: A Case Study of the Yangsan Fault, Korea
by Hyunjee Lim, Sangmin Ha, Sohee Kim, Hee-Cheol Kang and Moon Son
Remote Sens. 2025, 17(9), 1638; https://doi.org/10.3390/rs17091638 - 6 May 2025
Viewed by 299
Abstract
The spatial variability of input parameters plays a crucial role in the interpretation of geomorphic indices, with digital elevation models (DEMs) being the primary data source. However, the influence of DEM resolution on these indices has rarely been investigated. This study investigated the [...] Read more.
The spatial variability of input parameters plays a crucial role in the interpretation of geomorphic indices, with digital elevation models (DEMs) being the primary data source. However, the influence of DEM resolution on these indices has rarely been investigated. This study investigated the influence of DEM resolution on the assessment of tectonic activity using the normalized stream length–gradient (SLk) index, which reflects variations along river profiles. The SLk index is sensitive to changes in river gradients that may result from active faulting or differential uplift, making it a valuable tool for identifying zones of active tectonic deformation. Therefore, understanding the impact of DEM resolution on SLk analysis is critical for accurately detecting and interpreting subtle tectonic signals, particularly in intraplate regions where deformation is slow and geomorphic expressions are faint and discontinuous. By comparing high-resolution LiDAR-derived DEMs (L-DEMs) and low-resolution topographic map-derived DEMs (T-DEMs), we analyzed the SLk index distributions along the Yangsan Fault, Korean Peninsula, an intraplate setting with Quaternary activity. According to the results, SLk anomalies derived from L-DEMs had a continuous distribution along the fault, closely aligning with known surface ruptures and indicating active tectonic deformation. In contrast, SLk anomalies derived from T-DEMs were sporadic and less continuous, especially in low-relief landscapes such as alluvial fans and floodplains, highlighting the limitations of T-DEMs in detecting fault-related features. High-resolution DEMs were better able to capture finer-scale geomorphic features, such as fault scarps, deflected streams, and lineaments associated with active tectonics, providing a more comprehensive view of fault-related deformation. This discrepancy highlights the importance of resolution choice in tectonic assessments, as low-resolution DEMs may underestimate the tectonic activities of intraplate faults by missing subtle topographic variations. While the choice of DEM resolution may depend on study area, scope, and data availability, high-resolution DEMs are critical for identifying tectonic activity in intraplate regions where geomorphic features of faulting due to slow deformation are subtle and dispersed. Full article
(This article belongs to the Special Issue Remote Sensing Monitoring for Earthquakes, Tectonics and Seismic Hazards)
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28 pages, 7770 KiB  
Article
Synergistic Semantic Segmentation and Height Estimation for Monocular Remote Sensing Images via Cross-Task Interaction
by Xuanang Peng, Shixin Wang, Futao Wang, Jinfeng Zhu, Suju Li, Longfei Liu and Zhenqing Wang
Remote Sens. 2025, 17(9), 1637; https://doi.org/10.3390/rs17091637 - 5 May 2025
Viewed by 201
Abstract
Semantic segmentation and height estimation in remote sensing imagery are two pivotal tasks for scene understanding, and they are highly interrelated. Although deep learning methods have achieved remarkable progress in these tasks in recent years, several challenges remain. Recent studies have shown that [...] Read more.
Semantic segmentation and height estimation in remote sensing imagery are two pivotal tasks for scene understanding, and they are highly interrelated. Although deep learning methods have achieved remarkable progress in these tasks in recent years, several challenges remain. Recent studies have shown that multi-task learning methods can enhance the complementarity of task-related features, thus maximizing the prediction accuracy of multiple tasks at a low computational cost. However, due to factors such as complex semantic categories and the inconsistent spatial scales of remotely sensed images, existing multi-task learning methods often fail to achieve better results on these two tasks. To address this issue, we propose CTME-Net, a novel architecture termed the Cross-Task Mutual Enhancement Network, designed to jointly perform height estimation and semantic segmentation tasks on remote sensing imagery. Firstly, to generate discriminative initial features for each task branch and activate dedicated pathways for cross-task feature disentanglement, we design a universal initial feature embedding module for each downstream task. Secondly, to address the impact of redundancy in general features during global–local fusion, we develop an Adaptive Task-specific Feature Distillation Module that enhances the model’s ability to acquire task-specific features. Finally, we propose a task feature interaction module to optimize features across tasks through mutual optimization, maximizing task-specific feature expression. We conduct extensive experiments on the ISPRS Vaihingen and Potsdam datasets to validate the effectiveness of our approach. The results demonstrate that our proposed method outperforms existing methods in both height estimation and semantic segmentation. Full article
(This article belongs to the Special Issue Advances in High-Resolution Satellite Remote Sensing Image Processing and Classification)
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20 pages, 4733 KiB  
Article
Significant Improvement in Short-Term Green-Tide Transport Predictions Using the XGBoost Model
by Menghao Ji and Chengyi Zhao
Remote Sens. 2025, 17(9), 1636; https://doi.org/10.3390/rs17091636 - 5 May 2025
Viewed by 236
Abstract
Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data [...] Read more.
Accurately predicting the drift trajectory of green tides is crucial for assessing potential risks and implementing effective countermeasures. This paper proposes a short-term green-tide drift prediction method that combines green-tide patch characteristics, 1 h interval drift distances from GOCI-II images, and driving-factor data using the XGBoost machine learning model to enhance prediction accuracy. The results demonstrate that the proposed method outperforms the traditional OpenDrift model in short-term predictions. Specifically, at time intervals of 3, 5, and 7 h, the root mean square errors (RMSEs) of the OpenDrift model in the zonal direction are 1.81 km, 2.89 km, and 3.55 km, respectively, whereas the RMSEs of the proposed method are 0.80 km, 0.98 km, and 1.20 km, respectively; in the meridional direction, the RMSEs of the OpenDrift model are 1.77 km, 2.67 km, and 3.10 km, while the RMSEs for the proposed method are 0.82 km, 1.10 km, and 1.25 km, respectively. Furthermore, the proposed XGBoost method more-accurately tracks the actual positions of green-tide patches compared to the OpenDrift model. Specifically, at the 25 h interval, the proposed method continues to accurately predict patch positions, while the OpenDrift model exhibits significant deviations. This study demonstrates that the proposed method, by learning drift patterns from historical data, effectively predicts the short-term drift process of green tides. It provides valuable support for early warning systems, thereby helping to mitigate the ecological and economic impacts of green-tide disasters. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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22 pages, 10584 KiB  
Article
Assimilation of Moderate-Resolution Imaging Spectroradiometer Level Two Cloud Products for Typhoon Analysis and Prediction
by Haomeng Zhang, Yubao Liu, Yu Qin, Zheng Xiang, Yueqin Shi and Zhaoyang Huo
Remote Sens. 2025, 17(9), 1635; https://doi.org/10.3390/rs17091635 - 5 May 2025
Viewed by 228
Abstract
A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and [...] Read more.
A novel data assimilation technique is developed to assimilate MODIS (Moderate Resolution Imaging Spectroradiometer) level two (L2) cloud products, including cloud optical thickness (COT), cloud particle effective radius (Re), cloud water path (CWP), and cloud top pressure (CTP), into the Weather Research and Forecast (WRF) model. Its impact on the analysis and forecast of Typhoon Talim in 2023 at its initial developing stage is demonstrated. First, the conditional generative adversarial networks–bidirectional ensemble binned probability fusion (CGAN-BEBPF) model ) is applied to retrieve three-dimensional (3D) CloudSat CPR (cloud profiling radar) equivalent W-band (94 Ghz) radar reflectivity factor for the typhoons Talim and Chaba using the MODIS L2 data. Next, a W-band to S-band radar reflectivity factor mapping algorithm (W2S) is developed based on the collocated measurements of the retrieved W-band radar and ground-based S-band (4 Ghz) radar data for Typhoon Chaba at its landfall time. Then, W2S is utilized to project the MODIS-retrieved 3D W-band radar reflectivity factor of Typhoon Talim to equivalent ground-based S-band reflectivity factors. Finally, data assimilation and forecast experiments are conducted by using the WRF Hydrometeor and Latent Heat Nudging (HLHN) radar data assimilation technique. Verification of the simulation results shows that assimilating the MODIS L2 cloud products dramatically improves the initialization and forecast of the cloud and precipitation fields of Typhoon Talim. In comparison to the experiment without assimilation of the MODIS data, the Threat Score (TS) for general cloud areas and major precipitation areas is increased by 0.17 (from 0.46 to 0.63) and 0.28 (from 0.14 to 0.42), respectively. The fraction skill score (FSS) for the 5 mm precipitation threshold is increased by 0.43. This study provides an unprecedented data assimilation method to initialize 3D cloud and precipitation hydrometeor fields with the MODIS imagery payloads for numerical weather prediction models. Full article
(This article belongs to the Section Atmospheric Remote Sensing)
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24 pages, 34699 KiB  
Article
The Study on Landslide Hazards Based on Multi-Source Data and GMLCM Approach
by Zhifang Zhao, Zhengyu Li, Penghui Lv, Fei Zhao and Lei Niu
Remote Sens. 2025, 17(9), 1634; https://doi.org/10.3390/rs17091634 - 5 May 2025
Viewed by 287
Abstract
The southwest region of China is characterized by numerous rugged mountains and valleys, which create favorable conditions for landslide disasters. The landslide-influencing factors show different sensitivities regionally, which induces the occurrence of disasters to different degrees, especially in small sample areas. This study [...] Read more.
The southwest region of China is characterized by numerous rugged mountains and valleys, which create favorable conditions for landslide disasters. The landslide-influencing factors show different sensitivities regionally, which induces the occurrence of disasters to different degrees, especially in small sample areas. This study constructs a framework for the identification, analysis, and evaluation of landslide hazards in complex mountainous regions within small sample areas. This study utilizes small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology and high-resolution optical imagery for a comprehensive interpretation to identify landslide hazards. A geodetector is employed to analyze disaster-inducing factors, and machine-learning models such as random forest (RF), gradient boosting decision tree (GBDT), categorical boosting (CatBoost), logistic regression (LR), and stacking ensemble strategies (Stacking) are applied for landslide sensitivity evaluation. GMLCM stands for geodetector–machine-learning-coupled modeling. The results indicate the following: (1) 172 landslide hazards were identified, primarily concentrated along the banks of the Lancang River. (2) A geodetector analysis shows that the key disaster-inducing factors for landslides include a digital elevation model (DEM) (1321–1857 m), rainfall (1181–1290 mm/a), the distance from roads (0–1285 m), and geological rock formation (soft rock formation). (3) Based on the application of the K-means clustering algorithm and the Bayesian optimization algorithm, the GD-CatBoost model shows excellent performance. High-sensitivity zones were predominantly concentrated along the Lancang River, accounting for 24.2% in the study area. The method for identifying landslide hazards and small-sample sensitivity evaluation can provide guidance and insights for landslide monitoring and harnessing in similar geological environments. Full article
(This article belongs to the Special Issue Geological Hazard Monitoring, Identify, Predict, and Risk Assessment Using Geographic Information Science and Remote Sensing)
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18 pages, 3381 KiB  
Article
Sea Breeze-Driven Variations in Planetary Boundary Layer Height over Barrow: Insights from Meteorological and Lidar Observations
by Hui Li, Wei Gong, Boming Liu, Yingying Ma, Shikuan Jin, Weiyan Wang, Ruonan Fan, Shuailong Jiang, Yujie Wang and Zhe Tong
Remote Sens. 2025, 17(9), 1633; https://doi.org/10.3390/rs17091633 - 5 May 2025
Viewed by 307
Abstract
The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from [...] Read more.
The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from 2014 to 2021 to investigate the annual and polar day PBLH evolution driven by sea breezes in the Barrow region of Alaska, as well as the specific mechanisms. The results show that sea breeze events significantly suppress PBLH, especially during the polar day, when prolonged solar radiation intensifies the thermal contrast between land and ocean. The cold, moist sea breeze stabilizes the atmospheric conditions, reducing net radiation and sensible heat flux. All these factors inhibit turbulent mixing and PBLH development. Lidar and sounding analyses further reveal that PBLH is lower during sea breeze events compared to non-sea-breeze conditions, with the peak of its probability density distribution occurring at a lower PBLH range. The variable importance in projection (VIP) analysis identifies relative humidity (VIP = 1.95) and temperature (VIP = 1.1) as the primary factors controlling PBLH, highlighting the influence of atmospheric stability in regulating PBLH. These findings emphasize the crucial role of sea breeze in modulating PBL dynamics in the Arctic, with significant implications for improving climate models and studies on pollutant dispersion in polar regions. Full article
(This article belongs to the Special Issue Advanced Lidar Remote Sensing for Atmosphere, Vegetation, and Ocean Observations)
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21 pages, 13372 KiB  
Article
Long-Term (2015–2024) Daily PM2.5 Estimation in China by Using XGBoost Combining Empirical Orthogonal Function Decomposition
by Jiacheng Jiang, Jiaxin Dong, Yu Ding, Wenjia Ni, Jie Yang and Siwei Li
Remote Sens. 2025, 17(9), 1632; https://doi.org/10.3390/rs17091632 - 4 May 2025
Viewed by 286
Abstract
Fine particulate matter (PM2.5) has garnered significant scientific and public health concern owing to its capacity for deep penetration into the human respiratory system, presenting significant health risks. Despite the implementation of strict environmental policies in China over the past decade [...] Read more.
Fine particulate matter (PM2.5) has garnered significant scientific and public health concern owing to its capacity for deep penetration into the human respiratory system, presenting significant health risks. Despite the implementation of strict environmental policies in China over the past decade to reduce PM2.5 levels, long-term public health concerns remain a serious issue. Our study aims to provide a high-quality, seamless daily PM2.5 dataset for China covering the years 2015 to 2024. A two-step PM2.5 estimation model is established based on a machine learning algorithm and a spatio-temporal decomposition method. First, we utilize the machine learning algorithm XGBoost (EXtreme Gradient Boosting) to address gaps in the daily MAIAC (Multi-Angle Implementation of Atmospheric Correction) AOD (Aerosol Optical Depth), with R2/RMSE (coefficient of determination/Root Mean Square Error) of 0.67/0.2678 compared to AERONET (Aerosol Robotic Network) AOD. Then, a novel approach by integrating XGBoost with EOF (Empirical Orthogonal Function) decomposition is introduced for PM2.5 estimation. The integration of EOF allows for the incorporation of entire meteorological field information into the PM2.5 estimation model, significantly enhancing its accuracy: spatial CV (cross-validation)-R2 improved from 0.8340 to 0.8935, and spatial CV-RMSE reduced from 13.8177 to 11.0668. Leveraging the newly produced dataset, we analyze the spatio-temporal variations of PM2.5 across China with EOF decomposition, particularly noting that PM2.5 levels in the eastern anthropogenic intensive regions continuously declined from 2015 to 2020, and fluctuated steadily during 2020–2024. This research underscores the critical need for sustained and effective air quality management strategies in China. Full article
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications (Second Edition))
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21 pages, 7179 KiB  
Article
Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent
by Karim Malik and Colin Robertson
Remote Sens. 2025, 17(9), 1631; https://doi.org/10.3390/rs17091631 - 4 May 2025
Viewed by 266
Abstract
Snow water equivalent (SWE), the amount of water generated when a snowpack melts, has been used to study the impacts of climate change on the cryosphere processes and snow cover dynamics during the winter season. In most analyses, high-temporal-resolution SWE and SD data [...] Read more.
Snow water equivalent (SWE), the amount of water generated when a snowpack melts, has been used to study the impacts of climate change on the cryosphere processes and snow cover dynamics during the winter season. In most analyses, high-temporal-resolution SWE and SD data are aggregated into monthly and yearly averages to detect and characterize changes. Aggregating snow measurements, however, can magnify the modifiable aerial unit problem, resulting in differing snow trends at different temporal resolutions. Time series analysis of gridded SWE data holds the potential to unravel the impacts of climate change and global warming on daily, weekly, and monthly changes in snow during the winter season. Consequently, this research presents a high-temporal-resolution analysis of changes in the SWE across the cold regions of Canada. A Siamese UNet (Si-UNet) was developed by modifying the model’s last layer to incorporate the structural similarity (SSIM) index. The similarity values from the SSIM index are passed to a contrastive loss function, where the optimization process maximizes SSIM index values for pairs of similar SWE images and minimizes the values for pairs of dissimilar SWE images. A comparison of different model architectures, loss functions, and similarity metrics revealed that the SSIM index and the contrastive loss improved the Si-UNet’s accuracy by 16%. Using our Si-UNet, we found that interannual SWE declined steadily from 1979 to 2018, with March being the month in which the most significant changes occurred (R2 = 0.1, p-value < 0.05). We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks. Full article
(This article belongs to the Special Issue Recent Progress on Remote Sensing Change Detection and Application Driven by AI)
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25 pages, 2740 KiB  
Article
Research on Monitoring Oceanic Precipitable Water Vapor and Short-Term Rainfall Forecasting Using Low-Cost Global Navigation Satellite System Buoy
by Maosheng Zhou, Pengcheng Wang, Zelu Ji, Yunzhou Li, Dingfeng Yu, Zengzhou Hao, Min Li and Delu Pan
Remote Sens. 2025, 17(9), 1630; https://doi.org/10.3390/rs17091630 - 4 May 2025
Viewed by 222
Abstract
This study utilizes a low-cost Global Navigation Satellite System (GNSS) buoy platform, combined with multi-system GNSS data, to investigate the impact of GNSS signal quality and multipath effects on the accuracy of atmospheric precipitable water vapor (PWV) retrievals. It also explores the methods [...] Read more.
This study utilizes a low-cost Global Navigation Satellite System (GNSS) buoy platform, combined with multi-system GNSS data, to investigate the impact of GNSS signal quality and multipath effects on the accuracy of atmospheric precipitable water vapor (PWV) retrievals. It also explores the methods for oceanic rainfall event forecasting and precipitation prediction based on GNSS-PWV. By analyzing the data quality from various GNSS systems and using the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset as a reference, the study assesses the accuracy of PWV retrievals in dynamic marine environments. The results show that the GNSS-derived PWV from the buoy platform is highly consistent with ERA5 data in both trend and characteristics, with an RMSE of 3.8 mm for the difference between GNSS-derived PWV and ERA5 PWV. To enhance rainfall forecasting accuracy, a balanced threshold selection (BTS) method is proposed, significantly improving the balance between the probability of detection (POD) and false alarm rate (FAR). Furthermore, a Random Forest model based on multiple meteorological parameters optimizes precipitation forecasting, especially in reducing false alarms. Additionally, a particle swarm optimization (PSO)-based BP Neural Network model for rainfall prediction achieves high precision, with an R2 of 97.8%, an average absolute error of 0.08 mm, and an RMSE of 0.1 mm. The findings demonstrate the potential of low-cost GNSS buoy for monitoring atmospheric water vapor and short-term rainfall forecasting in dynamic marine environments. Full article
(This article belongs to the Special Issue Remote Sensing in Space Geodesy and Cartography Methods (Third Edition))
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18 pages, 9085 KiB  
Article
Analysis of Ionospheric Disturbances in China During the December 2023 Geomagnetic Storm Using Multi-Instrument Data
by Jun Tang, Sheng Wang, Jintao Wang, Mingxian Hu and Chaoqian Xu
Remote Sens. 2025, 17(9), 1629; https://doi.org/10.3390/rs17091629 - 4 May 2025
Viewed by 217
Abstract
This study investigates the ionospheric response over China during the geomagnetic storm that occurred on 1–2 December 2023. The data used include GPS measurements from the Crustal Movement Observation Network of China, BDS-GEO satellite data from IGS MEGX stations, [O]/[N2] ratio [...] Read more.
This study investigates the ionospheric response over China during the geomagnetic storm that occurred on 1–2 December 2023. The data used include GPS measurements from the Crustal Movement Observation Network of China, BDS-GEO satellite data from IGS MEGX stations, [O]/[N2] ratio information obtained by the TIMED/GUVI, and electron density (Ne) observations from Swarm satellites. The Prophet time series forecasting model is employed to detect ionospheric anomalies. VTEC variations reveal significant daytime increases in GNSS stations such as GAMG, URUM, and CMUM after the onset of the geomagnetic storm on 1 December, indicating a dayside positive ionospheric response primarily driven by prompt penetration electric fields (PPEF). In contrast, the stations JFNG and CKSV show negative responses, reflecting regional differences. The [O]/[N2] ratio increased significantly in the southern region between 25°N and 40°N, suggesting that atmospheric gravity waves (AGWs) induced thermospheric compositional changes, which played a crucial role in the ionospheric disturbances. Ne observations from Swarm A and C satellites further confirmed that the intense ionospheric perturbations were consistent with changes in VTEC and [O]/[N2], indicating the medium-scale traveling ionospheric disturbance was driven by atmospheric gravity waves. Precise point positioning (PPP) analysis reveals that ionospheric variations during the geomagnetic storm significantly impact GNSS positioning precision, with various effects across different stations. Station GAMG experienced disturbances in the U direction (vertical positioning error) at the onset of the storm but quickly stabilized; station JFNG showed significant fluctuations in the U direction around 13:00 UT; and station CKSV experienced similar fluctuations during the same period; station CMUM suffered minor disturbances in the U direction; while station URUM maintained relatively stable positioning throughout the storm, corresponding to steady VTEC variations. These findings demonstrate the substantial impact of ionospheric disturbances on GNSS positioning accuracy in southern and central China during the geomagnetic storm. This study reveals the complex and dynamic processes of ionospheric disturbances over China during the 1–2 December 2023 storm, highlighting the importance of ionospheric monitoring and high-precision positioning corrections during geomagnetic storms. The results provide scientific implications for improving GNSS positioning stability in mid- and low-latitude regions. Full article
(This article belongs to the Special Issue BDS/GNSS for Earth Observation: Part II)
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19 pages, 4025 KiB  
Article
Study on Class Imbalance in Land Use Classification for Soil Erosion in Dry–Hot Valley Regions
by Yuzhuang Deng, Guokun Chen, Bohui Tang, Xingwu Duan, Lijun Zuo and Haijuan Zhao
Remote Sens. 2025, 17(9), 1628; https://doi.org/10.3390/rs17091628 - 4 May 2025
Viewed by 219
Abstract
The inherent spatial heterogeneity of land types often leads to a class imbalance in remote sensing-based classification, reducing the accuracy of minority class detection. Consequently, current land use datasets are often inadequate for the specific needs of soil erosion studies. In response to [...] Read more.
The inherent spatial heterogeneity of land types often leads to a class imbalance in remote sensing-based classification, reducing the accuracy of minority class detection. Consequently, current land use datasets are often inadequate for the specific needs of soil erosion studies. In response to the need for soil conservation in dry–hot valley regions, this study integrated multi-source remote sensing imagery and constructed three high-precision imbalanced sample datasets on the Google Earth Engine (GEE) platform to perform land use classification. The degree of class imbalance was quantified using the imbalance ratio (IR), and the impact of sample imbalance on the classification accuracy of different land use types in a typical dry–hot valley was analyzed. The results show that (1) Feature selection significantly improved both classification accuracy and computational efficiency. The period from February to April each year, between 2018 and 2023, was identified as the optimal time window for land use classification in dry–hot valleys. (2) Constructing composite images over longer time scales enhanced classification performance: using a 2020 annual composite image combined with a Gradient Tree Boosting classifier yielded the highest accuracy, indicating that longer temporal synthesis improves classification results. (3) The effect of class imbalance on classification accuracy varied by land type: woodland (the majority class) was least affected by imbalance, whereas minority classes such as cultivated land, garden plantations, and grassland were highly sensitive to imbalance. In imbalanced scenarios, minority classes are prone to omission errors, leading to notable accuracy declines; producer’s accuracy (PA) decreased by 46%, 42%, and 25% for cultivated land, garden plantations, and grassland, respectively, as IR increased (with PA dropping faster than user’s accuracy, UA). Cultivated land was especially sensitive and frequently overlooked under high imbalance conditions compared to gardens and grasslands. Despite overall accuracy improving with higher IR, the accuracy of these minority classes dropped significantly, underscoring the importance of addressing the class imbalance in land use classification for erosion-prone areas. Full article
(This article belongs to the Special Issue Remote Sensing Measurements of Land Use and Land Cover)
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28 pages, 18246 KiB  
Article
Forecasting Cumulonimbus Clouds: Evaluation of New Operational Convective Index Using Lightning and Precipitation Data
by Margarida Belo-Pereira
Remote Sens. 2025, 17(9), 1627; https://doi.org/10.3390/rs17091627 - 3 May 2025
Viewed by 295
Abstract
Deep convective clouds, such as towering cumulus and Cumulonimbus, can endanger lives and property, also being a major hazard to aviation. This study presents the convective index (IndexCON) used operationally at the Portuguese Meteorological Watch Office. Moreover, IndexCON is evaluated against [...] Read more.
Deep convective clouds, such as towering cumulus and Cumulonimbus, can endanger lives and property, also being a major hazard to aviation. This study presents the convective index (IndexCON) used operationally at the Portuguese Meteorological Watch Office. Moreover, IndexCON is evaluated against lightning and precipitation data for two years, between January 2022 and December 2023, over mainland Portugal and its surrounding areas. This index combines several European Center for Medium-Range Weather Forecasts (ECMWF) prognostic variables, such as stability indices, cloud water content, relative humidity and vertical velocity, using a fuzzy-logic approach. IndexCON performs well in the warm season (May–October), with a probability of detection (POD) of 70%, a false alarm ratio (FAR) of 30% and a probability of false detection (POFD) less than 5%, leading to a Critical Success Index (CSI) above 0.55. However, IndexCON performs worse in the cold season (November–April), when dynamical drivers are more relevant, mainly due to overestimating the convective activity, resulting in CSI and Heidke Skill Score (HSS) values below 0.3. Optimizing the membership functions partially reduces this overestimation. Finally, the added value of IndexCON was illustrated in detail for a thunderstorm episode, using satellite products, lightning and precipitation data. Full article
(This article belongs to the Special Issue Cloud Remote Sensing: Current Status and Perspective)
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23 pages, 9985 KiB  
Article
One-Dimensional Convolutional Neural Network for Automated Kimchi Cabbage Downy Mildew Detection Using Aerial Hyperspectral Images
by Yang Lyu, Lukas Wiku Kuswidiyanto, Pingan Wang, Hyun-Ho Noh, Hee-Young Jung and Xiongzhe Han
Remote Sens. 2025, 17(9), 1626; https://doi.org/10.3390/rs17091626 - 3 May 2025
Viewed by 280
Abstract
Downy mildew poses a significant threat to kimchi cabbage, a vital agricultural product in Korea, adversely affecting its yield and quality. Traditional disease detection methods based on visual inspection are labor intensive and time consuming. This study proposes a non-destructive, field-scale disease detection [...] Read more.
Downy mildew poses a significant threat to kimchi cabbage, a vital agricultural product in Korea, adversely affecting its yield and quality. Traditional disease detection methods based on visual inspection are labor intensive and time consuming. This study proposes a non-destructive, field-scale disease detection approach using unmanned aerial vehicle (UAV)-based hyperspectral imaging. Hyperspectral images of the kimchi cabbage field were preprocessed, segmented at the pixel level, and classified into four categories: background, healthy, early-stage disease, and late-stage disease. Spectral analysis of the late and early stages of downy mildew infection revealed notable differences in the red-edge band, with infected plants exhibiting increased red-edge reflectance. To automate disease detection, various machine learning models, including Random Forest (RF), 1D Convolutional Neural Network (1D-CNN), 1D Residual Network (1D-ResNet), and 1D Inception Network (1D-InceptionNet), were developed. These models were trained based on a 0.2 sampling dataset, achieving overall accuracy scores of 0.907, 0.901, 0.909, and 0.914, along with F1 scores of 0.876, 0.845, 0.897, and 0.899, respectively. Overall, the results of this study revealed that the red-edge band reliably signaled the presence of downy mildew, and the 1D-InceptionNet model demonstrated the most effective performance for automatic disease detection. Full article
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management II)
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